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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.11.4.1 ML model information storage request
| Table 8.11.4.1-1 describes the information flow from the AIMLE server to the ML repository or from the AIMLE client/VAL server to the AIMLE server as a request for the ML model information storage.
Table 8.11.4.1-1: ML model information storage request
Information element
Status
Description
Requestor Identity
M
The identity of the requestor performing the request.
Security Credentials
O
(NOTE 1)
Security credentials of the requestor performing the request.
ML model
O
(NOTE 2)
The ML model to be stored in the ML repository.
ML model address
O
(NOTE 2)
The address (e.g., a URL or an FQDN) of the ML model file.
ML model information
M
Provides information of the ML model, as described in Table 8.11.4.1-2.
NOTE 1: This information is needed if the requestor is at the VAL service provider domain.
NOTE 2: At least one of these information elements shall be provided.
Table 8.11.4.1-2: ML model information
Information element
Status
Description
ML model identifier
M
An identifier for the ML model
ADAE Analytics ID
O
Represents ADAE analytics ID for which the model can be used.
ML model size
O
Indicates the size of the ML model.
ML model source identifier
O
The identifier of ML model source (e.g., VAL server ID, VAL client ID) that stored the model in the ML repository.
VAL service ID(s)
O
Identify the VAL service ID(s).
Domain
O
Specifies domain for which the model can be used (e.g., for speech recognition, image recognition, video processing, location prediction, etc.).
List of allowed vendors
O
(NOTE 1)
Indicates which vendors that are allowed to use the ML model and thereby also are interoperable to the model.
ML model interoperability information
O
(NOTE 1)
Represents the vendor-specific information that conveys, e.g., requested model file format, model execution environment, input/output parameters of the ML model, etc. The encoding, format, and value of ML Model Interoperable Information is not specified since it is vendor specific information, and is agreed between vendors, if necessary for sharing purposes.
ML Model phase
O
(NOTE 1)
Represents the ML model phase, e.g., in training, trained, re-training, deployed.
> Observed performance
O
(NOTE 2)
Provides information on the performance of the model e.g. accuracy, or application-specific performance metrics (if ML model is in trained or deployment phase).
> Training information
O
(NOTE 2)
If the ML model is in trained or deployed phase: Information on the data that has been used to train the model (e.g. data sources, volume, freshness), and the base model ID in case of Transfer Learning.
> Indication of continuous model training
O
Indicates whether the model can be continuously trained or not.
> Continuous model training parameter
O
Parameters required for continuous model training.
ML model storage and discovery requirements
O
(NOTE 1)
Represents the requirements for the ML repository for the ML model storage and discovery.
> Storage duration
O
Represents the ML model storage duration time. When the storage duration time is expired, the stored ML model and the related information shall be deleted.
> Security and access requirements
O
Represents the information on security requirements for storing the ML model information and the ML model access requirements (e.g., publicly available, private use only, or available for the list of VAL server IDs or VAL client IDs, time period and location access). If the access requirement is private use only, then the model is not discoverable by other consumers.
ML model usage requirements
O
Represents the requirements for using the ML model (e.g. for inference or for training). The requirements are used by the AIMLE server to determine whether an AIMLE client is capable of using the model based on comparing the requirements with information in the AIMLE client profile in Table 8.7.3.1-2.
Energy consumption information
O
Energy consumption information associated with the ML model.
> List of AI/ML operations
M
The AI/ML operations (e.g., ML model training, model transfer, model inference, model offload and model split).
>> Expected energy budget
M
The expected energy budget (e.g., watt-hour, joules) associated with performing an AI/ML operation. The expected energy budget relates energy consumption values to a VAL UE power profile as described in Table 8.7.3.2-2.
NOTE 1: At least one of these information elements shall be provided.
NOTE 2: This IE is included only if trained ML model is available.
6.x4.1.2 Impact to AIMLE client registration
The AIMLE client registration procedure and information flows in 3GPP TS 23.482 [r2348210] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.4.1.2 Impact to AIMLE client registration
| The AIMLE client registration procedure and information flows in 3GPP TS 23.482 [10] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.7.2.2 AIMLE client registration
| Pre-conditions:
1. The AIMLE client has been pre-configured or has discovered the address (e.g., URI) of the AIMLE server.
2. The AIMLE client has been pre-configured with an AIMLE client profile.
Figure 8.7.2.2-1: AIMLE client registration
1. The AIMLE client sends an AIMLE client registration request to the AIMLE server, the registration request includes information as described in Table 8.7.3.2-1. The AIMLE client indicates in the registration request its AI/ML capabilities such as supported ML model types and supported AI/ML operations, supported AIMLE client task capabilities with compute and task performance capabilities, and energy consumption capabilities to assist with performing AIMLE client discovery and AIMLE client selection.
2. The AIMLE server validates the registration request and performs an authentication and authorization check to determine if the AIMLE client is permitted to register to the AIMLE server and participate in AI/ML operations. Upon successful authorization, the AIMLE server saves the context of the AIMLE client registration in the ML repository.
3. The AIMLE server returns an AIMLE client registration response to the AIMLE client with the status of the request.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.7.3.2 AIMLE client registration request
| Table 8.7.3.2-1 shows the request sent by an AIMLE client to an AIMLE server for the AIMLE client registration request.
Table 8.7.3.2-1: AIMLE client registration request
Information element
Status
Description
Requestor identifier
M
The identifier of the requestor.
List of supported profiles
M
Supported AIML client profile(s). For each client profile provided in the list, the supported service information is provided.
> AIMLE client profile
M
Information about the capability of the AIMLE client to support AI/ML operations for the VAL service ID as detailed in Table 8.7.3.2-2.
> List of supported services
M
List of VAL service IDs with corresponding permissions.
> VAL service ID
M
The identifier of the VAL service.
> Service permission level
O
Service permission level (e.g., premium resource usage, standard resource usage, limited resource usage).
Table 8.7.3.2-2: AIMLE client profile
Information element
Status
Description
Supported AI/ML model types
O
AI/ML model types supported by the AIMLE client (e.g., decision trees, linear regression, neural networks).
Supported AI/ML operations
M
AI/ML operations supported by the AIMLE client such as ML model training, model transfer, model inference, model offload, model split, continue perform intermediate AI/ML operation/task).
AIMLE client time schedule configurations
O
Indicates the availability schedule of the AIMLE client for the AIML service, e.g., the AIMLE client is (not) available to participate in the AIML operations (e.g. ML model training) in the given time slot(s) and/or day(s) of the week.
AIMLE client location configurations
O
Indicates the location-based configurations of the AIMLE client for the AIML service, e.g., the AI/ML member is (not) available to participate in the AI/ML operations in the given locations represented by coordinates, civic addresses, network areas, or VAL service area ID.
AIMLE client capabilities
M
AIMLE client capability information (e.g. ML application type, allowed resource usage level).
Dataset availability
O
Dataset availability such as dataset size, age, list of dataset features, and dataset identifiers.
Data capabilities
O
A list of data capabilities such as the type of data that can be collected (e.g. raw data), supported data processing capabilities (e.g. processed data), and supported exploratory data analysis functions.
AIMLE client task capability
O
(NOTE 1)
Indicates the AIML task performing capabilities. It includes compute capabilities (e.g., high, low), task performance preference capabilities (e.g., Green task, energy-efficient, low costs)
Energy consumption capabilities
O
The energy consumption capabilities of the AIMLE client.
> Maximum energy budget
O
(NOTE 2)
The maximum energy budget (e.g., watt-hour, joules) permitted for performing AI/ML operations.
> Power profile
O
(NOTE 2)
The power rating of the VAL UE that represents the VAL UE’s processing capabilities (e.g., entry-level, mid-tier, premium, high performance, high efficiency). The power rating is used to determine the usage rate of energy per unit time that factors into the determination of energy consumption and is static.
NOTE 1: The Green and Energy-efficient task performance may not be applicable to a UE.
NOTE 2: At least one of these information elements shall be present.
6.x4.1.3 Impact to AIMLE client discovery
The AIMLE client selection procedure and information flows in 3GPP TS 23.482 [r2348210] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.4.1.3 Impact to AIMLE client discovery
| The AIMLE client selection procedure and information flows in 3GPP TS 23.482 [10] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.8.3.1 AIMLE client discovery request
| Table 8.8.3.1-1 shows the request sent by a VAL server to an AIMLE server for the AIMLE client discovery procedure.
Table 8.8.3.1-1: AIMLE client discovery request
Information element
Status
Description
Requestor identity
M
The identifier of the requestor (e.g., VAL server).
AIMLE client discovery criteria
M
Discovery criteria for finding suitable AIMLE clients for AI/ML operations as detailed in Table 8.8.3.1-2.
Number of required AIMLE clients
O
Indicates the requested number of AIMLE clients to be discovered based on the discovery criteria.
Table 8.8.3.1-2: AIMLE client discovery criteria
Information element
Status
Description
Service requirement
M
Information about the required service
> VAL Service ID
M
VAL Service ID that the client is required to support. This identifies the service associated with the requester.
> Service permission level
O
Required corresponding service permission level (e.g., premium resource usage standard resource usage, limited resource usage).
Requested ML model types
O
Requested ML model types (decision trees, linear regression, neural networks).
Requested AIML operations
M
Requested role for AI/ML operations such as model training, model transfer, model inference, model offload, model split.
Application layer AIMLE client capabilities
M
Application layer AIMLE client capability information (e.g., ML application type like FL/TL/SL, client availability to support AIML operations at the UE, AIMLE drop off rate).
Dataset requirements
O
Information about dataset.
> Dataset availability
O
Dataset availability, including dataset identifiers, dataset size, age, list of dataset features.
> Data capabilities
O
A list of data capabilities such as the type of data that can be collected (e.g., raw data), supported data processing capabilities (e.g., processed data) and supported exploratory data analysis functions.
AIML client task capability requirements
O
Indicates the AIML task requirements to discover the AIML clients for performing AIML tasks.
It includes compute capabilities (e.g., high, low), task performance preference capabilities (e.g. Green task, Energy-efficient, low costs)
AIMLE client velocity
O
Indicates the AIMLE client velocity. It includes mobile (e.g., high, low), static.
Location information
O
Indicates the location information (e.g., Cell Identity, Tracking Area Identity, GPS Coordinates or civic addresses, VAL service area ID) to discover the AIMLE clients.
AIMLE client QoS requirements
O
Indicates the AIMLE client QoS information (like PLR, bandwidth, latency jitter) with the corresponding threshold(s) and threshold matching direction(s) (e.g., above or below) to discover the AIMLE clients.
Energy consumption requirements
O
(NOTE 1)
The energy consumption requirements of the requestor.
> Maximum energy budget
M
The maximum energy budget (e.g., watt-hour, joules) that is acceptable to the requestor.
NOTE 1: This information element applies to AIMLE client selection.
6.x4.1.4 Impact to AIMLE client selection
The AIMLE client selection procedure and information flows in 3GPP TS 23.482 [r2348210] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.4.1.4 Impact to AIMLE client selection
| The AIMLE client selection procedure and information flows in 3GPP TS 23.482 [10] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.9.2.1 AIMLE client selection
| Pre-conditions:
1. AIMLE clients that support AI/ML operations have registered with the AIMLE server and included their AIMLE client profiles and optionally a list of supported services.
2. The AIMLE server can access a ML repository to obtain AIMLE client profiles and supported services associated with the AIMLE clients.
Figure 8.9.2.1-1: AIMLE client selection
1. A VAL server sends an AIMLE client selection request to an AIMLE server to select AIMLE clients available for participation in AI/ML operations (e.g., is available and have required data to train an ML model). The AIMLE client selection request includes information as described in Table 8.9.3.1-1.
2. The AIMLE server performs authentication and authorization checks to determine if the requestor is able to select AIMLE clients.
3. If the requestor is authorized, the AIMLE server performs AIMLE client selection based on the provided inputs as described below.
For VAL server selection, the AIMLE server receives a list of AIMLE client IDs in the request and selects the AIMLE clients in the list as candidate AIMLE clients.
For AIMLE server selection, the AIMLE server retrieves a list of clients with AI/ML capabilities from the ML repository. From the list of clients, the AIMLE server selects a list of candidate AIMLE clients, whose client profiles fulfil the AIMLE client selection criteria. The AIMLE server may use SEAL (LM service), NEF (e.g. MonitoringEvent API), and NWDAF (e.g. UE mobility analytics as defined in clause 6.7.2 of 3GPP TS 23.288 [2]) capabilities to assist the AIMLE client selection.
The AIMLE server determines UEs by using their identifiers to determine their location and then selects only those that fulfil the location requirements specified in the AIMLE client selection criteria. The AIMLE server may use SEAL-LMS (as in 3GPP TS 23.434 [5] clause 9.3.9, or 3GPP TS 23.434 [5] clause 9.3.10) or 3GPP 5G Core Network Services (such as GMLC as in 3GPP TS 23.273 [13] and NEF as in 3GPP TS 23.273 [13] or 3GPP TS 23.502 [9]) to determine the UEs which fulfil the location requirement.
The AIMLE server may then determine the QoS parameters for the AIML traffic session between the requestor and the candidate AIMLE client(s) and configure the AI/ML traffic session(s) via SEALDD (Sdd_RegularTransmission API) or NEF services (AfSessionWithQoS API). The AIMLE server determines the application QoS parameters based on the VAL Service ID.
NOTE 1: To determine the application QoS parameters based on the VAL service ID, the AIMLE server can use the VAL service ID associated AIMLE client QoS requirements in the AIMLE client selection criteria provided in step 1.
If the AIMLE client selection criteria include energy consumption requirements, the AIMLE server retrieves a list of ML model information from the ML repository using the ML model information discovery procedure as described in clause 8.11.3. The AIMLE server uses information from the AIMLE client selection criteria as filtering criteria for ML model information discovery.
If energy consumption requirements are included, the AIMLE server selects the candidate AIMLE clients that fulfil the expected energy consumption needs for the retrieved ML models. The expected energy consumption needs are based on the requested AI/ML operations and on the AIMLE client’s power profile.
If the energy consumption requirements include a maximum energy budget, the AIMLE server identifies and selects the ML models and candidate AIMLE clients that can perform the requested AI/ML operations without exceeding the maximum energy budget.
NOTE 2: If the available AIMLE clients (as determined by the AIMLE server) that fulfil discovery criteria are less than the required number of AIMLE clients for AI/ML operation (e.g., split AI/ML), then the AIMLE server can discover the remaining required AIMLE clients from other AIMLE servers over AIML-E reference point and include them in the AIMLE client selection response message.
NOTE 3: The AIMLE server can reuse SEAL group management for any necessary group management.
4. The AIMLE server performs AIMLE client participation procedure with each candidate AIMLE client as described in clause 8.10.
For all candidate AIMLE clients that agreed to participate in AI/ML operations, the AIMLE server selects the AIMLE clients and assigns an AIMLE client set identifier for the selected clients. The AIMLE client set may then be used for training a ML model.
NOTE 4: If a required minimum number of AIMLE clients for AI/ML operation is provided in the AIMLE client selection request and the number of AIMLE clients that agreed to participate in AI/ML operations is less than this number, an AIMLE client set identifier will not be assigned and the status will be set to fail in the AIMLE client selection response in step 5.
5. The AIMLE server sends an AIMLE client selection response that includes information in Table 8.9.3.2-1.
6.x4.1.5 Impact to AIMLE client selection subscription
The AIMLE client selection subscription procedure and information flows in 3GPP TS 23.482 [r2348210] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.4.1.5 Impact to AIMLE client selection subscription
| The AIMLE client selection subscription procedure and information flows in 3GPP TS 23.482 [10] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.13.2.2 AIMLE client selection subscription and notification
|
Figure 8.13.2.2-1: AIMLE client selection subscription and notification
1. A VAL server sends an AIMLE client selection subscription request to the AIMLE Server. The AIMLE client selection subscription request includes information as described in Table 8.13.3-1 which includes selection criteria.
2. The AIMLE server validates the AIMLE client selection subscription request. The AIMLE server further performs authentication and authorization checks to determine if the requestor is able to subscribe to the selected AIMLE client selection subscription request.
3. The AIMLE server sends the AIMLE client selection subscription response to the VAL server.
4. The AIMLE server monitors AIMLE clients whether they fulfil the selection criteria as provided in step 1. The AIMLE server interacts with the NEF and/or SEAL services (including SEALDD) to establish monitoring. The AIMLE server utilizes SEAL-LMS (as in 3GPP TS 23.434 [5] clause 9.3.11, or 3GPP TS 23.434 [5] clause 9.3.12) or 3GPP 5G Core Network Services (such as GMLC as in 3GPP TS 23.273 [13] and NEF as in 3GPP TS 23.273 [13] or 3GPP TS 23.502 [9]) to establish monitoring of UEs entering or present in the target location provided in the location information in the selection criteria.
5. The AIMLE Server obtains the identifiers of the AIMLE clients from the monitoring and selects the clients that fulfil the selection criteria and remove the AIMLE clients which do not fulfil the selection criteria. The AIMLE server uses the location monitoring for selecting UEs that fulfil the location criteria and removing UEs which cease to fulfil the location criteria as provided in the location information in the AIMLE client selection criteria. The AIMLE Server may determine the application QoS parameters (e.g. bandwidth, latency, jitter) for the AIML traffic session between the VAL server and the selected AIMLE client and configure the AIML traffic session(s) via SEALDD (Sdd_RegularTransmission API) or NEF services (AfSessionWithQoS API). When the AIMLE clients no more meet the criteria, the QoS adjustment is reversed.
The AIMLE server may determine the application QoS parameters based on the VAL Service ID.
If the AIMLE client selection criteria include energy consumption requirements, the AIMLE server retrieves a list of ML model information from the ML repository using the ML model information discovery procedure as described in clause 8.11.3. The AIMLE server uses information from the AIMLE client selection criteria as filtering criteria for ML model information discovery.
If energy consumption requirements are included, the AIMLE server selects the candidate AIMLE clients that can fulfil the expected energy consumption needs of the ML model. The expected energy consumption needs are based on the requested AI/ML operations and on the AIMLE client’s power profile.
If the energy consumption requirements include a maximum energy budget, the AIMLE server identifies and selects the ML models and candidate AIMLE clients that can perform the requested AI/ML operations without exceeding the maximum energy budget.
If a desired service in the selection criteria is ceased to be provided by the client or its profile change so that it no longer meets the selection criteria, the AIMLE server removes the AIMLE clients which ceases to fulfil the criteria and selects other clients that fulfil selection criteria.
NOTE 1: To determine the application QoS parameters based on the VAL service ID, the AIMLE server can use the VAL service ID associated AIMLE client QoS requirements in the AIMLE client selection criteria provided in step 1.
NOTE 2: In case of AIMLE server determining the application QoS parameters based on the VAL service ID, how it does this is up to implementation.
6. The AIMLE Server notifies the VAL server about the selected and re-selected AIMLE clients e.g., the AIMLE Client A is re-selected and replaced by AIMLE Client B.
6.x4.1.6 Impact to AIMLE client participation
The AIMLE client participation procedure and information flows in 3GPP TS 23.482 [r2348210] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.4.1.6 Impact to AIMLE client participation
| The AIMLE client participation procedure and information flows in 3GPP TS 23.482 [10] are enhanced (highlighted in bold italics) as follows.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.10.3.1 AIMLE client participation request
| Table 8.10.3.1-1 shows the request sent by the AIMLE server to each AIMLE client selected for AIMLE client participation procedure.
Table 8.10.3.1-1: AIMLE client participation request
Information element
Status
Description
Requestor identity
M
The identifier of the requestor.
AIMLE client set identifier
M
An identifier for the AIMLE client set.
Add/remove indicator
M
Indicator for adding/removing the AIMLE client to/from the AIMLE client set.
ML model ID
M
The ML model ID to use for the AI/ML operation.
AI/ML operations
M
A list of AI/ML operations (e.g., training) required to be performed.
Expected energy budget
O
The expected energy budget (e.g., watt-hour, joules) associated with performing the AI/ML operations.
Operational schedule
O
Schedule for the AI/ML operations.
Dataset requirement
M
Dataset requirements for the AI/ML operations. Requirements includes dataset identifier, dataset size and age, and/or dataset features.
Service requirement
M
Information about the required Service including its VAL service ID and its service permission level (e.g., premium resource usage standard resource usage, limited resource usage)
6.x4.2 Architecture impacts
This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
6.x4.3 Solution evaluation
This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.x5 Solution #x5: AIMLE Enhancement of Considering Renewable Energy
6.x5.1 Solution Description
The proposed solution solve the problem listed in KI#1 and KI#4, and proposes to consider renewable energy in AIMLE.
As indicated in 3GPP TS 28.310 [4], the renewable energy and renewable energy factor have the following definitions:
Renewable energy: energy from renewable non-fossil sources.
NOTE 15: This definition is taken from 3GPP TS 22.261 [31].
NOTE 16: Examples of renewable energy sources include wind, solar, aerothermal, geothermal, hydrothermal and ocean energy, hydropower, biomass, landfill gas, sewage treatment plant gas and biogases
NOTE 17: A renewable energy source is an energy source not depleted by extraction as it is naturally replenished at a rate faster than it is extracted ISO/IEC 30134-3:2016 [33].
NOTE 18: Criteria to categorize an energy as renewable can differ among jurisdictions, based on local environmental or other reasons ISO/IEC 30134-3:2016 [33].
Renewable energy factor: ratio of the renewable energy to the total energy.
NOTE 19: This definition is taken from ISO/IEC 30134-3:2016 [33].
Energy can be classified into two major types: renewable energy and non-renewable energy. Renewable energy sources include solar energy, hydropower, wind energy, biomass energy, wave energy, tidal energy, ocean thermal energy, geothermal energy, etc. They can be recycled and regenerated in nature. It is an inexhaustible and endless energy source that regenerates automatically without human intervention.
In order to save the energy consumption and reduce the carbon emission, the operators may consider using renewable energy or green energy to replace traditional energy. So, in the current AIMLE procedure, the following enhancements can be added:
For AIMLE related procedure:
- The FL member capabilities can include whether renewable energy support or not. It is more preferred to select the FL members that support renewable energy to perform such as AI model training. This capability can be indicated during the registration procedure.
- Also, the capability that support renewable energy can be set as the Member selection criteria. During the ML training procedure, the member that supports the renewable energy can have the priority selection.
6.x5.2 Architecture impacts
In this solution, it doesn’t change the overall architecture of Application enablement architecture of AIMLE in section 5 of 3GPP TS 23.482 [x10]. But the following enhancements are added:
- The FL member can register itself of the new capability that support renewable energy supply.
- The subscriber can be notified with that new FL member is added to the ML repository with the energy supply of renewable energy.
- And during member selection procedure, the Member selection criteria can be enhanced that whether renewable energy support or not.
Editor’s nNote: How FL members know whether renewable energy is supported or not is FFS.
6.x5.3 Procedure
6.x5.3.1 Procedure of FL member registration procedure
This procedure is the same as the procedure in section 8.4.2 of 3GPP TS 23.482 [x10] with the following enhancement:
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.4.2 Architecture impacts
| This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.4.3 Solution evaluation
| This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.5 Solution #5: AIMLE Enhancement of Considering Renewable Energy
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.5.1 Solution Description
| The proposed solution solve the problem listed in KI#1 and KI#4, and proposes to consider renewable energy in AIMLE.
As indicated in 3GPP TS 28.310 [4], the renewable energy and renewable energy factor have the following definitions:
Renewable energy: energy from renewable non-fossil sources.
NOTE 15: This definition is taken from 3GPP TS 22.261 [31].
NOTE 16: Examples of renewable energy sources include wind, solar, aerothermal, geothermal, hydrothermal and ocean energy, hydropower, biomass, landfill gas, sewage treatment plant gas and biogases
NOTE 17: A renewable energy source is an energy source not depleted by extraction as it is naturally replenished at a rate faster than it is extracted ISO/IEC 30134-3:2016 [33].
NOTE 18: Criteria to categorize an energy as renewable can differ among jurisdictions, based on local environmental or other reasons ISO/IEC 30134-3:2016 [33].
Renewable energy factor: ratio of the renewable energy to the total energy.
NOTE 19: This definition is taken from ISO/IEC 30134-3:2016 [33].
Energy can be classified into two major types: renewable energy and non-renewable energy. Renewable energy sources include solar energy, hydropower, wind energy, biomass energy, wave energy, tidal energy, ocean thermal energy, geothermal energy, etc. They can be recycled and regenerated in nature. It is an inexhaustible and endless energy source that regenerates automatically without human intervention.
In order to save the energy consumption and reduce the carbon emission, the operators may consider using renewable energy or green energy to replace traditional energy. So, in the current AIMLE procedure, the following enhancements can be added:
For AIMLE related procedure:
- The FL member capabilities can include whether renewable energy support or not. It is more preferred to select the FL members that support renewable energy to perform such as AI model training. This capability can be indicated during the registration procedure.
- Also, the capability that support renewable energy can be set as the Member selection criteria. During the ML training procedure, the member that supports the renewable energy can have the priority selection.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.5.2 Architecture impacts
| In this solution, it doesn’t change the overall architecture of Application enablement architecture of AIMLE in section 5 of 3GPP TS 23.482 [10]. But the following enhancements are added:
- The FL member can register itself of the new capability that support renewable energy supply.
- The subscriber can be notified with that new FL member is added to the ML repository with the energy supply of renewable energy.
- And during member selection procedure, the Member selection criteria can be enhanced that whether renewable energy support or not.
Editor’s Note: How FL members know whether renewable energy is supported or not is FFS.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.5.3 Procedure
| |
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.5.3.1 Procedure of FL member registration procedure
| This procedure is the same as the procedure in section 8.4.2 of 3GPP TS 23.482 [10] with the following enhancement:
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.4.2 Procedure on FL member registration
| Figure 8.4.2-1 illustrates the procedure where the registration of a candidate FL member happens via the ML repository, serving as AIML service registry.
Figure 8.4.2-1: Procedure for registration on FL member registry
1. The candidate FL member (e.g., VAL server via AIMLE server or AIMLE server) sends an FL member registration request to the ML repository for registering to the ML repository which acts as the AIML service registry. The request may include capability whether this FL member supports the renewable energy as energy supply or not.
Editor’s Nnote: How does FL member knows the capability of renewable energy supporting is FFS.
2. The ML repository validates the received request and generates the identity and other security related information for all the FL members listed in the registration request.
3. The ML repository sends the generated information in the FL member registration response message to the candidate FL member.
The procedure for AIMLE client to register to ML repository as FL member is introduced in clause 8.7.2.2.
6.x5.3.2 Procedure of FL-related event subscription of renewable energy
This procedure is the same as the procedure in section 8.5.2 of 3GPP TS 23.482 [x10] with the following enhancement:
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.5.3.2 Procedure of FL-related event subscription of renewable energy
| This procedure is the same as the procedure in section 8.5.2 of 3GPP TS 23.482 [10] with the following enhancement:
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.5.2 Procedure on subscription for FL related events
| This procedure, as illustrated in Figure 8.5.2-1, describes the subscription for events related to FL member availability.
Pre-conditions:
1. The AIMLE server has the authorization to subscribe for the FL-related events (events described in clause 8.5.4).
Figure 8.5.2-1: Procedure for FL-related event subscription
1. The subscriber (AIMLE server or VAL server (via AIMLE server)) sends an FL-related event subscription request to the ML repository to receive notification of FL related events and in particular the availability of FL member for a target area and time. The subscription includes indicator whether the FL member supports the capability of renewable energy supporting or not.
2. Upon receiving the event subscription request from the AIMLE server, the ML repository checks for the relevant authorization for the event subscription. If the authorization is successful, the ML repository stores the subscription information.
3. The ML repository sends an FL-related event subscription response to the subscriber indicating successful operation.
6.x5.3.3 Procedure of member selection during ML model training considering renewable energy
This procedure is the same as the procedure in section 8.3.2 of 3GPP TS 23.482 [x10] with the following enhancement:
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.5.3.3 Procedure of member selection during ML model training considering renewable energy
| This procedure is the same as the procedure in section 8.3.2 of 3GPP TS 23.482 [10] with the following enhancement:
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.3.2 Procedure for ML model training
| Figure 8.3.2-1 illustrates the procedure for AIMLE server to support ML model training based on the request from the VAL server.
Figure 8.3.2-1: ML model training
1. The VAL server sends an ML model training request to AIMLE server, requesting to assist in its ML model training. This request consists of ML model information or ML model requirement information, capability of renewable energy supporting or not, etc.
2. The AIMLE server checks whether the VAL server is authorized to perform the ML model training request. If no model information is provided but only the model requirement information is provided in step 1, the AIMLE server identifies and selects the appropriate ML model for training based on the ML model requirement information using the procedure as specified in clause 8.11.3.
3. If the VAL server is authorized, AIMLE server returns the success response, otherwise a failure response indication the reason for failure.
4. The AIMLE server notifies the VAL server to update the list FL/ML clients selected or de-selected for the ML model training or to share the training output or any errors during the training process.
6.x5.4 Solution evaluation
Editor’s Nnote: This part is for further update in the future.
6.x6 Solution #x6: Support of ADAE analytics for AI/ML energy consumption
6.x6.1 Solution Description
This solution addresses Key Issue #4 – open issue #1 on how to collect and use energy consumption data. The solution proposes a new ADAE service procedure in 3GPP TS 23.436 [r2343611] to support AI/ML energy consumption analytics.
6.x6.1.1 Procedure
Figure 6.x6.1.1-1 illustrates the procedure for a consumer (e.g., VAL server, AIMLE server) to subscribe for AI/ML energy consumption analytics generated from data collected from VAL UEs while the VAL UEs are performing AI/ML operations.
Pre-conditions:
1. ADAE Client (ADAEC) is connected to the ADAE Server (ADAES).
2. Data producers (e.g., VAL Clients) may be pre-configured with data producer profiles for the data they can provide. ADAES and ADAEC have discovered available data producers and their data producer profiles.
Figure 6.x6.1.1-1: ADAES support for AI/ML energy consumption analytics
1. An analytics consumer (e.g. VAL Server, AIMLE Server) sends an AI/ML energy consumption analytics subscription request to an ADAES. The subscription request includes an Analytics ID “AI/ML energy consumption analytics”, ML model identifier, AIML operation, energy consumption metrics, and other information as defined in Table 6.x6.1.2.1-1.
2. The ADAES validates the subscription request and performs authentication and authorization checks to determine if the consumer is authorized to subscribe for the requested analytics. If authorization is successful, the ADAES stores the subscription request information, assigns a subscription ID, and sends a subscription response indicating successful subscription and includes the subscription ID.
3. The ADAES maps the analytics ID to a list of data collection event identifiers and determines a list of data producer IDs for the data collection event identifiers. The determination of data producer IDs is based on data producer profiles.
4. The ADAES sends an AI/ML energy consumption data collection subscription request to the ADAECs determined in step 3. The subscription request includes information as defined in Table 6.x6.1.2.4-1. Data collection at the VAL UE(s) reuses the EVEX mechanism defined in 3GPP TS 26.531 [r2653112].
5. Each ADAEC configures the data producers (i.e., VAL client or AIMLE client) for collecting data (e.g., duration of AI/ML operation, % resource utilization for AI/ML operation) for AI/ML energy consumption analytics requested in the data collection subscription request.
6. The ADAEC sends an AI/ML energy consumption data collection subscription response to acknowledge the ADAES. The response includes a status and a subscription ID for the data collection.
7. The data producers perform AI/ML operations when triggered (e.g., ML model training) using the indicated ML model and collect data for AI/ML energy consumption analytics based on the configured data collection requirements.
8. At the completion of AI/ML operation, the ADAEC sends an AI/ML energy consumption data collection notification to the ADAES. The data notification includes the data collection subscription ID and the information as defined in Table 6.x6.1.2.6-1.
9. The ADAES performs analytics relevant operations to generate the AI/ML energy consumption analytics based on the data received from the ADAECs. The ADAES uses the data collected by the ADAECs to calculate the AI/ML energy consumption analytics.
NOTE: How the collected data are used to calculate energy consumption is up to implementation.
10. The ADAES sends an AI/ML energy consumption analytics notification to the consumer. The analytics notification includes the analytics subscription ID and the information as defined in Table 6.x6.1.2.3-1.
6.x6.1.2 Information flows
6.x6.1.2.1 AI/ML energy consumption analytics subscription request
Table 6.x6.1.2.1-1 describes the information flow from the consumer (e.g. VAL server, AIMLE server) as a request or update request for AI/ML energy consumption analytics.
Table 6.x6.1.2.1-1: AI/ML energy consumption analytics subscription request
Information element
Status
Description
Requestor ID
M
The identifier of the consumer (e.g., VAL server, AIMLE client).
Analytics ID
M
The identifier of the analytics event. This ID can be for example "AI/ML energy consumption analytics".
Analytics type
M
The type of analytics for the event (e.g., statistics or predictions).
ML model identifier
M
The identifier of the ML model for which analytics are determined.
AIML operation
M
The desired AIML operations (e.g., ML model training, inference) for which analytics are determined.
Energy consumption metrics
M
The formula and necessary metrics for calculating the energy consumption based on factors that impact energy consumption for an AIML operation using a particular ML model.
Target VAL UE IDs
O
The identifier(s) of VAL UE(s) for which the analytics subscription applies.
Target data producer IDs
O
The identifier(s) of VAL clients, AIMLE clients or FL members to target for collecting data for energy consumption analytics.
Target data producer profile criteria
O
Characteristics of the data producers to be used.
Reporting requirements
O
It describes the requirements for analytics reporting. This requirement may include e.g. the type and frequency of reporting (periodic or event triggered), the reporting periodicity in case of periodic, and reporting thresholds.
Area of Interest
O
The geographical or service area for which the subscription request applies.
Preferred confidence level
O
The level of accuracy for the analytics service (in case of prediction).
Time validity
O
The time validity of the subscription request.
6.x6.1.2.2 AI/ML energy consumption analytics subscription response
Table 6.x6.1.2.2-1 describes the information elements for the AI/ML energy consumption analytics subscription response from the ADAES to the consumer.
Table 6.x6.1.2.2-1: AI/ML energy consumption analytics subscription response
Information element
Status
Description
Result
M
The result of the analytics subscription request (positive or negative acknowledgement).
Subscription ID
M
An identifier for the subscription.
6.x6.1.2.3 AI/ML energy consumption analytics notification
Table 6.x6.1.2.3-1 describes the information flow from the ADAES to the consumer (e.g. VAL Server, AIMLE Server) as a notification for the AI/ML energy consumption analytics.
Table 6.x6.1.2.3-1: AI/ML energy consumption analytics notification
Information element
Status
Description
Subscription ID
M
The identifier for the subscription.
Analytics ID
M
The identifier of the analytics event. This ID can be for example " AI/ML energy consumption analytics".
ML model identifier
M
The ML model identifier for which analytics are determined.
Energy consumption information
M
The range of energy consumption values for a particular ML model with a certain AIML operation. The output can be an average, minimum, and/or maximum value for the energy consumption. The output may be a prediction or a statistical value.
List of AIML operations
M
The AIML operations for which the analytics are determined.
> Energy consumption
M
The range of energy consumption values for performing an AI/ML operation.
>> Maximum consumption
O
The maximum energy consumption value.
>> Average consumption
O
The average energy consumption value.
>> Minimum consumption
O
The minimum energy consumption value.
Target analytics period
O
The time validity of the analytics.
Confidence level
O
The level of accuracy for the analytics output (e.g. prediction).
6.x6.1.2.4 AI/ML energy consumption data collection subscription request
Table 6.x6.1.2.4-1 describes information elements for the AI/ML energy consumption data collection subscription request from the ADAES to the ADAEC.
Table 6.x6.1.2.4-1: AI/ML energy consumption data collection subscription request
Information element
Status
Description
Requestor ID
M
The identifier of the consumer.
Data Collection Event ID
M
The identifier of the data collection event.
Data collection requirements
M
The requirements for data collection and for information to include in the data notification.
> ML model identifier
M
The identifier of the ML model for which data is collected.
> AIML operations
M
The desired AI/ML operations (e.g., ML model training, inference) for which data is collected.
> Time period
M
The time period of data collection.
> Location or area information
O
The location or area of the data collection.
6.x6.1.2.5 AI/ML energy consumption data collection subscription response
Table 6.x6.1.2.5-1 describes information elements for the AI/ML energy consumption data collection subscription response from the ADAEC to the ADAES.
Table 6.x6.1.2.5-1: AI/ML energy consumption data collection subscription response
Information element
Status
Description
Result
M
The result of the AI/ML energy consumption data collection subscription request (positive or negative acknowledgement).
Subscription ID
M
An identifier for the subscription.
6.x6.1.2.6 AI/ML energy consumption data notification
Table 6.x6.1.2.6-1 describes information elements for the AI/ML energy consumption data notification from the ADAEC to the ADAES.
Table 6.x6.1.2.6-1: AI/ML energy consumption data collection notification
Information element
Status
Description
Subscription ID
M
The identifier for the subscription.
Data Collection Event ID
M
The identifier of the data collection event.
Data Producer ID
M
The identity of Data Producer.
Collected data
M
The collected data.
> ML model identifier
M
The identifier of the ML model for which data was collected.
> AIML operation
M
The AI/ML operation (e.g., ML model training, inference) for which data was collected.
> Power profile
M
The power rating of the VAL UE that represents the VAL UE’s processing capabilities (e.g., entry-level, mid-tier, premium, high performance, high efficiency). The power rating is used to determine the usage rate of energy per unit time that factors into the determination of energy consumption and is static.
> Elapse time for AI/ML operation
M
The time duration for performing the AI/ML operation.
> % resource utilization for AI/ML operation
M
The percentage utilization of hardware resources (e.g., compute, memory, storage) determined for performing the AI/ML operation.
6.x6.2 Architecture impacts
This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
6.x6.3 Solution evaluation
This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.x7 Solution #x7: ADAES Support for AIMLE client energy sustainability analytics
6.x7.1 Solution Description
This solution introduces a new analytics event for “AIMLE client energy sustainability analytics” as requested from the Consumer who can be the VAL Server or AIMLE server to the ADAES. These analytics may be used for predicting whether an AI/ML client at the VAL UE can be considered a candidate in a ML training or inference task, or FL process based on its expected energy status/capability. Such energy analytics can be used for identifying whether the energy consumption is sustainable for a given AIMLE service, or a recommendation on whether the AIMLE client is applicable or suitable for the AI/ML task based on the energy prediction or sustainability indicator.
This clause describes the procedure for supporting the AIMLE client energy analytics in ADAES as new analytics service.
•
Figure 6.x7.1-1: ADAES support for AIMLE client energy analytics
1. The Consumer (AIMLE or VAL server) sends an AIMLE client energy sustainability analytics request to ADAES to perform analytics on the AIMLE client Energy Consumption who is expected to undertake an AIML task, Event ID= “AIMLE client energy sustainability analytics”, for a given service area and a given time window. This request includes a VAL service ID or AIMLE service ID for which the analytics apply, as well as the consumer identifier and the analytics KPI. Such request can include a predicted or expected route of the VAL UE hosting the AIMLE client, and the type of analytics requested. The analytics type can be requesting whether the energy consumption is sustainable for a given AIMLE service.
Such request can also include an AI/ML task or operation or process (ML training or inference or FL task) for which the prediction/stats apply.
2. The ADAES authorizes the consumer’s request.
3. The ADAES maps the AIMLE client energy analytics event ID to a list of data collection event identifiers, and a list of data producer IDs. Such mapping may be preconfigured by OAM or may be determined by ADAES based on the analytics event type/vertical type and/or data producer profile.
ADAES determines and maps the analytics event to the energy data to acquire and the data producers which can be functionalities in the target VAL UE(s) and/or data stored in A-ADRF.
Optionally, the ADAES (or A-ADRF) keeps per AIMLE client. This energy profile keeps stats or energy rating of the AIMLE client or the data source in the UE (e.g. AI app) which can be used as a metric to identify whether the AIMLE client at the VAL UE is energy demanding source; hence applicable or not to be considered as capable of undertaking an AI/ML task.
NOTE: If Energy Profile is used, this information may either be provided by the VAL UE (or the VAL server) as part of the energy data or can be quantified at the ADAES.
4a. The ADAES requests and receives from the VAL UE(s) of interest energy data using the AIMLE client Energy Data Collection API. Such Energy data collection can include measurements or stats on the energy consumption per AIMLE client.
Editor’s Note: Further details on the energy data collection from the VAL UE is FFS.
4b. In addition, or complementary to AIMLE client energy data collection, the ADAES may also request and receive from the ADAEC of the UE, performance statistics related to the application layer or enabler layer sessions (based on existing ADAE capabilities on VAL session analytics).
5. The ADAES calculates the expected AIMLE client energy consumption based on the received energy/ performance data in step 4, based on the request.
6. The ADAES obtains the corresponding trained ML model based on procedure in 3GPP TS 23.482 [10] clause 8.3.2 and performs analytics to derive the predicted UE energy consumption sustainability at the target area and time horizon.
7. The ADAES sends the analytics output via the AIMLE client energy sustainability analytics API to the consumer (VAL or AIMLE server) as analytics response with the analytics output data (based on the type of analytics in the request). The analytics output may include:
• identification on whether the energy consumption is sustainable for a given VAL or AIMLE service, where this service can be an AI/ML service
• a recommendation on whether the AIMLE client is applicable or suitable for the AI/ML task based on the energy prediction or sustainability indicator.
The ADAES and/or the AIMLE (as consumer) may also store the energy data and/or analytics to a repository being A-ADRF or ML repository (if this applies to an ML task).
Based on step 7, the consumer (AIMLE or VAL server) can use these analytics as input to trigger the pro-active selection or re-selection or removal of the AIMLE client from the list of UEs which are capable of undertaking an AI/ML process/task/operation, given the energy factor.
6.x7.2 Architecture impacts
This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
6.x7.3 Solution evaluation
This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.Y18 Solution #8: Network Slice Energy Optimization based on Energy Saving VAL Server Policy
6.Y18.1 Solution Description
6.Y18.1.0 General
This solution does not add new procedure. Instead, it proposes introducing a new VAL server policy in clause 9.5, 'Network slice optimization based on VAL server policy', 3GPP TS 23.435 [zz13]. The new policies enable NSCE server performs slice modifications based on monitoring energy-related information, such as energy consumption and the renewable energy ratio, at the slice level.
The high-level principles are as follows:
1. The VAL server provisions energy saving policies to the NSCE server.
• Based on the monitored energy consumption of a network slice - categorized by energy source (renewable or non-renewable) - over a specific duration (such as one hour) or as accumulated over a longer period (for example, a calendar month), slice modification is triggered when the measured value exceeds or falls below a defined threshold. This modification may involve reducing the slice's maximum throughput, slightly increasing allowable latency, or decreasing redundancy levels (such as removing a redundant UPF instance or redundant RAN cell).
• Based on the monitored renewable energy ratio of a network slice, a slice modification with predefined parameters is triggered when the energy ratio or crosses a specified threshold. Such modifications may include increasing the slice’s maximum throughput, raising allowable latency, expanding the maximum number of supported UEs, or increasing the maximum number of PDU sessions.
2. The NSCE server monitors a slice's energy usage by gathering energy-related data from the slice's OAM system.
3. Based on the defined triggers, the NSCE server carries out the actions outlined in energy-saving policies, aiming to either reduce the slice’s energy consumption or encourage the use of renewable energy sources.
The following clauses specify network slice energy optimization based on energy saving VAL server policy.
6.Y18.1.1 Procedure
Figure 6.Y18.1.1-1 outlines the procedure, where Steps 1-4 represent existing operations. Step 5 introduces a new function in which the NSCE server monitors energy-related information for a network slice. Step 6 involves the NSCE server taking action to adjust the slice, either to conserve energy or to encourage the use of renewable energy sources.
Figure 6.Y18.1.1-1: VAL server policy provision and enforcment for energy optimization
1. The consumer (VAL, SEAL Server) sends a VAL server policy provisioning request to the NSCE server by following clause 9.5 of 3GPP TS 23.435 [zz13]. New energy saving related policies (see examples in clause 6.Y18.1.2) are added:
a. Based on the monitored energy consumption of a network slice - categorized by energy source (renewable or non-renewable) - over a specific duration (such as one hour) or as accumulated over a longer period (for example, a calendar month), slice modification is triggered when the measured value exceeds or falls below a defined threshold. This modification may involve reducing the slice's maximum throughput, slightly increasing allowable latency, or decreasing redundancy levels (such as removing a redundant UPF instance or redundant RAN cell).
b. Based on the monitored renewable energy ratio of a network slice, a slice modification with predefined parameters is triggered when the energy ratio or crosses a specified threshold. Such modifications may include increasing the slice’s maximum throughput, raising allowable latency, expanding the maximum number of supported UEs, or increasing the maximum number of PDU sessions.
The action of the above policy may include charging policy update when slice SLA is modified to save energy to attract the VAL server to use the policies.
2. The NSCE server performs a VAL server policy check. This is the same as step 2 in clause 9.5.2.1, 3GPP TS 23.435 [zz13].
3. The NSCE server performs Slice Harmonization. This step is the same as clause 9.5.2.1.4 Policy harmonization, 3GPP TS 23.435 [zz13].
4. The NSCE server sends back a VAL server policy provisioning response to the Consumer (VAL/ASP), confirming policy provision. This is the same as step 2 in clause 9.5.2, 3GPP TS 23.435 [zz13].
5. The NSCE server initiates the retrieval of energy consumption data from the slice OAM (3GPP TS 28.554 [6]) to monitor energy-related information, including but not limited to the following parameters:
a. slice accumulated energy consumption refers to summing the average energy usage recorded during each slice OAM measurement interval (for example, every 15 minutes) by the OAM system and aggregating it into the total energy consumption over an extended period, such as a calendar month
b. slice average energy consumption: e.g. Energy-Consumption/hour
c. slice renewable energy ratio: e.g. 30%
Editor’s Note: Whether the NSCE collect the energy data directly or by using existing enabler service (e.g. A-DCCF) is FFS.
6. The NSCE server carries out slice modifications in accordance with the policy action defined in Step 1, through its interaction with the Slice OAM and the 5GC.
6.Y18.1.2 Information Flows
6.Y18.1.2.1 General
The following information flows are specified for VAL server policy.
6.Y18.1.2.2 Policy of Reducing Energy Consumption
Table 6.Y18.1.2.2-1 shows the request sent by a consumer to an NSCE server for VAL server policy provisioning procedure.
Table 6.Y18.1.2.2-1: Policy of reducing energy consumption
Information element
Status
Description
Policy
O
Reduce Energy Consumption
>Area of interest
M
The service area for which the policy profile applies, which can be expressed as a geographical area (e.g. geographical coordinates), or a topological area (e.g. a list of TA).
>Trigger event
M
Threshold information, i.e. slices accumulate energy consumption, slices average energy consumption reached the threshold
>Recommended action
M
Examples:
• Modification of PDU sessions / max number of UEs (step for decrease in %).
• Decrease slice capacity (step for decrease in %).
• Decrease packet delay, and increase packet error rate etc.
• Update the energy efficiency parameter.
• Decrease GBR requirements.
• Apply better charging offering for the slice after adjustment.
Lifetime or number of events
M
Time duration or number of times the policy can take action.
Priority
O
Indicates the priority of the policy.
Scheduling period
O
Indicates the scheduling of policy in terms of time.
>Start time
M
Indicates the scheduled start time.
>End time
M
Indicates the scheduled end time.
Preemption
O
Indicates the pre-empt capability of the policy.
6.Y18.1.2.3 Policy of Promoting renewable energy consumption
Table 6.Y18.1.2.3-1 shows the request sent by a consumer to an NSCE server for VAL server policy provisioning procedure.
Table 6.Y18.1.2.3-1: Policy of Promoting renewable energy consumption
Information element
Status
Description
Policy
O
Promote renewable energy usage
>Area of interest
M
The service area for which the policy profile applies, which can be expressed as a geographical area (e.g. geographical coordinates), or a topological area (e.g. a list of TA).
>Trigger event
M
Threshold information, i.e. when renewable energy reached a threshold %
>Recommended action
M
Examples:
• Modification of PDU sessions/max number of UEs (step for increase in %).
• Increase slice capacity.
Lifetime or number of events
M
Time duration or number of times the policy can take action.
Priority
O
Indicates the priority of the policy.
Scheduling period
O
Indicates the scheduling of policy in terms of time.
>Start time
M
Indicates the scheduled start time.
>End time
M
Indicates the scheduled end time.
Preemption
O
Indicates the pre-empt capability of the policy.
6.Y18.2 Architecture impacts
Editor’s Note: The architecture impacts of the solution is FFS.
6.Y18.3 Solution evaluation
Editor’s Note: The evaluation of the solution is FFS.
6.Y9 Solution #Y9: Enhancements to ADAE DN Energy Efficiency Analytics
6.Y9.1 Solution Description
In 3GPP TS 23.436 [yy11], the DN Energy Efficiency analytics, ADAES provides analytics on the energy consumption /efficiency of an edge platform (including the EESs / EASs). The DN energy analytics is performed per DNN/ DNAI and may be used to trigger the application server migration to different cloud. The analytics are based on NWDAF analytics and UPF/DN measurements on user plane load as well as edge/app side measurements on the energy consumption. Information/analytics on renewable energy used for providing application services is needed to support flexibility adjustment of the execution of workloads with GHG emissions. However, analytics relevant to renewable energy used for providing application services is missing. Enhancements to the DN Energy Efficiency analytics is required.
The DN Energy Efficiency analytics as specified in clause 8.18 of 3GPP TS 23.436 [yy11] is enhanced as follows (new text in bold italics):
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.5.4 Solution evaluation
| Editor’s Note: This part is for further update in the future.
6.6 Solution #6: Support of ADAE analytics for AI/ML energy consumption
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1 Solution Description
| This solution addresses Key Issue #4 – open issue #1 on how to collect and use energy consumption data. The solution proposes a new ADAE service procedure in 3GPP TS 23.436 [11] to support AI/ML energy consumption analytics.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.1 Procedure
| Figure 6.6.1.1-1 illustrates the procedure for a consumer (e.g., VAL server, AIMLE server) to subscribe for AI/ML energy consumption analytics generated from data collected from VAL UEs while the VAL UEs are performing AI/ML operations.
Pre-conditions:
1. ADAE Client (ADAEC) is connected to the ADAE Server (ADAES).
2. Data producers (e.g., VAL Clients) may be pre-configured with data producer profiles for the data they can provide. ADAES and ADAEC have discovered available data producers and their data producer profiles.
Figure 6.6.1.1-1: ADAES support for AI/ML energy consumption analytics
1. An analytics consumer (e.g. VAL Server, AIMLE Server) sends an AI/ML energy consumption analytics subscription request to an ADAES. The subscription request includes an Analytics ID “AI/ML energy consumption analytics”, ML model identifier, AIML operation, energy consumption metrics, and other information as defined in Table 6.6.1.2.1-1.
2. The ADAES validates the subscription request and performs authentication and authorization checks to determine if the consumer is authorized to subscribe for the requested analytics. If authorization is successful, the ADAES stores the subscription request information, assigns a subscription ID, and sends a subscription response indicating successful subscription and includes the subscription ID.
3. The ADAES maps the analytics ID to a list of data collection event identifiers and determines a list of data producer IDs for the data collection event identifiers. The determination of data producer IDs is based on data producer profiles.
4. The ADAES sends an AI/ML energy consumption data collection subscription request to the ADAECs determined in step 3. The subscription request includes information as defined in Table 6.6.1.2.4-1. Data collection at the VAL UE(s) reuses the EVEX mechanism defined in 3GPP TS 26.531 [12].
5. Each ADAEC configures the data producers (i.e., VAL client or AIMLE client) for collecting data (e.g., duration of AI/ML operation, % resource utilization for AI/ML operation) for AI/ML energy consumption analytics requested in the data collection subscription request.
6. The ADAEC sends an AI/ML energy consumption data collection subscription response to acknowledge the ADAES. The response includes a status and a subscription ID for the data collection.
7. The data producers perform AI/ML operations when triggered (e.g., ML model training) using the indicated ML model and collect data for AI/ML energy consumption analytics based on the configured data collection requirements.
8. At the completion of AI/ML operation, the ADAEC sends an AI/ML energy consumption data collection notification to the ADAES. The data notification includes the data collection subscription ID and the information as defined in Table 6.6.1.2.6-1.
9. The ADAES performs analytics relevant operations to generate the AI/ML energy consumption analytics based on the data received from the ADAECs. The ADAES uses the data collected by the ADAECs to calculate the AI/ML energy consumption analytics.
NOTE: How the collected data are used to calculate energy consumption is up to implementation.
10. The ADAES sends an AI/ML energy consumption analytics notification to the consumer. The analytics notification includes the analytics subscription ID and the information as defined in Table 6.6.1.2.3-1.
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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.2 Information flows
| |
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.2.1 AI/ML energy consumption analytics subscription request
| Table 6.6.1.2.1-1 describes the information flow from the consumer (e.g. VAL server, AIMLE server) as a request or update request for AI/ML energy consumption analytics.
Table 6.6.1.2.1-1: AI/ML energy consumption analytics subscription request
Information element
Status
Description
Requestor ID
M
The identifier of the consumer (e.g., VAL server, AIMLE client).
Analytics ID
M
The identifier of the analytics event. This ID can be for example "AI/ML energy consumption analytics".
Analytics type
M
The type of analytics for the event (e.g., statistics or predictions).
ML model identifier
M
The identifier of the ML model for which analytics are determined.
AIML operation
M
The desired AIML operations (e.g., ML model training, inference) for which analytics are determined.
Energy consumption metrics
M
The formula and necessary metrics for calculating the energy consumption based on factors that impact energy consumption for an AIML operation using a particular ML model.
Target VAL UE IDs
O
The identifier(s) of VAL UE(s) for which the analytics subscription applies.
Target data producer IDs
O
The identifier(s) of VAL clients, AIMLE clients or FL members to target for collecting data for energy consumption analytics.
Target data producer profile criteria
O
Characteristics of the data producers to be used.
Reporting requirements
O
It describes the requirements for analytics reporting. This requirement may include e.g. the type and frequency of reporting (periodic or event triggered), the reporting periodicity in case of periodic, and reporting thresholds.
Area of Interest
O
The geographical or service area for which the subscription request applies.
Preferred confidence level
O
The level of accuracy for the analytics service (in case of prediction).
Time validity
O
The time validity of the subscription request.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.2.2 AI/ML energy consumption analytics subscription response
| Table 6.6.1.2.2-1 describes the information elements for the AI/ML energy consumption analytics subscription response from the ADAES to the consumer.
Table 6.6.1.2.2-1: AI/ML energy consumption analytics subscription response
Information element
Status
Description
Result
M
The result of the analytics subscription request (positive or negative acknowledgement).
Subscription ID
M
An identifier for the subscription.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.2.3 AI/ML energy consumption analytics notification
| Table 6.6.1.2.3-1 describes the information flow from the ADAES to the consumer (e.g. VAL Server, AIMLE Server) as a notification for the AI/ML energy consumption analytics.
Table 6.6.1.2.3-1: AI/ML energy consumption analytics notification
Information element
Status
Description
Subscription ID
M
The identifier for the subscription.
Analytics ID
M
The identifier of the analytics event. This ID can be for example " AI/ML energy consumption analytics".
ML model identifier
M
The ML model identifier for which analytics are determined.
Energy consumption information
M
The range of energy consumption values for a particular ML model with a certain AIML operation. The output can be an average, minimum, and/or maximum value for the energy consumption. The output may be a prediction or a statistical value.
List of AIML operations
M
The AIML operations for which the analytics are determined.
> Energy consumption
M
The range of energy consumption values for performing an AI/ML operation.
>> Maximum consumption
O
The maximum energy consumption value.
>> Average consumption
O
The average energy consumption value.
>> Minimum consumption
O
The minimum energy consumption value.
Target analytics period
O
The time validity of the analytics.
Confidence level
O
The level of accuracy for the analytics output (e.g. prediction).
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.2.4 AI/ML energy consumption data collection subscription request
| Table 6.6.1.2.4-1 describes information elements for the AI/ML energy consumption data collection subscription request from the ADAES to the ADAEC.
Table 6.6.1.2.4-1: AI/ML energy consumption data collection subscription request
Information element
Status
Description
Requestor ID
M
The identifier of the consumer.
Data Collection Event ID
M
The identifier of the data collection event.
Data collection requirements
M
The requirements for data collection and for information to include in the data notification.
> ML model identifier
M
The identifier of the ML model for which data is collected.
> AIML operations
M
The desired AI/ML operations (e.g., ML model training, inference) for which data is collected.
> Time period
M
The time period of data collection.
> Location or area information
O
The location or area of the data collection.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.2.5 AI/ML energy consumption data collection subscription response
| Table 6.6.1.2.5-1 describes information elements for the AI/ML energy consumption data collection subscription response from the ADAEC to the ADAES.
Table 6.6.1.2.5-1: AI/ML energy consumption data collection subscription response
Information element
Status
Description
Result
M
The result of the AI/ML energy consumption data collection subscription request (positive or negative acknowledgement).
Subscription ID
M
An identifier for the subscription.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.1.2.6 AI/ML energy consumption data notification
| Table 6.6.1.2.6-1 describes information elements for the AI/ML energy consumption data notification from the ADAEC to the ADAES.
Table 6.6.1.2.6-1: AI/ML energy consumption data collection notification
Information element
Status
Description
Subscription ID
M
The identifier for the subscription.
Data Collection Event ID
M
The identifier of the data collection event.
Data Producer ID
M
The identity of Data Producer.
Collected data
M
The collected data.
> ML model identifier
M
The identifier of the ML model for which data was collected.
> AIML operation
M
The AI/ML operation (e.g., ML model training, inference) for which data was collected.
> Power profile
M
The power rating of the VAL UE that represents the VAL UE’s processing capabilities (e.g., entry-level, mid-tier, premium, high performance, high efficiency). The power rating is used to determine the usage rate of energy per unit time that factors into the determination of energy consumption and is static.
> Elapse time for AI/ML operation
M
The time duration for performing the AI/ML operation.
> % resource utilization for AI/ML operation
M
The percentage utilization of hardware resources (e.g., compute, memory, storage) determined for performing the AI/ML operation.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.2 Architecture impacts
| This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.6.3 Solution evaluation
| This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.7 Solution #7: ADAES Support for AIMLE client energy sustainability analytics
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.7.1 Solution Description
| This solution introduces a new analytics event for “AIMLE client energy sustainability analytics” as requested from the Consumer who can be the VAL Server or AIMLE server to the ADAES. These analytics may be used for predicting whether an AI/ML client at the VAL UE can be considered a candidate in a ML training or inference task, or FL process based on its expected energy status/capability. Such energy analytics can be used for identifying whether the energy consumption is sustainable for a given AIMLE service, or a recommendation on whether the AIMLE client is applicable or suitable for the AI/ML task based on the energy prediction or sustainability indicator.
This clause describes the procedure for supporting the AIMLE client energy analytics in ADAES as new analytics service.
•
Figure 6.7.1-1: ADAES support for AIMLE client energy analytics
1. The Consumer (AIMLE or VAL server) sends an AIMLE client energy sustainability analytics request to ADAES to perform analytics on the AIMLE client Energy Consumption who is expected to undertake an AIML task, Event ID= “AIMLE client energy sustainability analytics”, for a given service area and a given time window. This request includes a VAL service ID or AIMLE service ID for which the analytics apply, as well as the consumer identifier and the analytics KPI. Such request can include a predicted or expected route of the VAL UE hosting the AIMLE client, and the type of analytics requested. The analytics type can be requesting whether the energy consumption is sustainable for a given AIMLE service.
Such request can also include an AI/ML task or operation or process (ML training or inference or FL task) for which the prediction/stats apply.
2. The ADAES authorizes the consumer’s request.
3. The ADAES maps the AIMLE client energy analytics event ID to a list of data collection event identifiers, and a list of data producer IDs. Such mapping may be preconfigured by OAM or may be determined by ADAES based on the analytics event type/vertical type and/or data producer profile.
ADAES determines and maps the analytics event to the energy data to acquire and the data producers which can be functionalities in the target VAL UE(s) and/or data stored in A-ADRF.
Optionally, the ADAES (or A-ADRF) keeps per AIMLE client. This energy profile keeps stats or energy rating of the AIMLE client or the data source in the UE (e.g. AI app) which can be used as a metric to identify whether the AIMLE client at the VAL UE is energy demanding source; hence applicable or not to be considered as capable of undertaking an AI/ML task.
NOTE: If Energy Profile is used, this information may either be provided by the VAL UE (or the VAL server) as part of the energy data or can be quantified at the ADAES.
4a. The ADAES requests and receives from the VAL UE(s) of interest energy data using the AIMLE client Energy Data Collection API. Such Energy data collection can include measurements or stats on the energy consumption per AIMLE client.
Editor’s Note: Further details on the energy data collection from the VAL UE is FFS.
4b. In addition, or complementary to AIMLE client energy data collection, the ADAES may also request and receive from the ADAEC of the UE, performance statistics related to the application layer or enabler layer sessions (based on existing ADAE capabilities on VAL session analytics).
5. The ADAES calculates the expected AIMLE client energy consumption based on the received energy/ performance data in step 4, based on the request.
6. The ADAES obtains the corresponding trained ML model based on procedure in 3GPP TS 23.482 [10] clause 8.3.2 and performs analytics to derive the predicted UE energy consumption sustainability at the target area and time horizon.
7. The ADAES sends the analytics output via the AIMLE client energy sustainability analytics API to the consumer (VAL or AIMLE server) as analytics response with the analytics output data (based on the type of analytics in the request). The analytics output may include:
• identification on whether the energy consumption is sustainable for a given VAL or AIMLE service, where this service can be an AI/ML service
• a recommendation on whether the AIMLE client is applicable or suitable for the AI/ML task based on the energy prediction or sustainability indicator.
The ADAES and/or the AIMLE (as consumer) may also store the energy data and/or analytics to a repository being A-ADRF or ML repository (if this applies to an ML task).
Based on step 7, the consumer (AIMLE or VAL server) can use these analytics as input to trigger the pro-active selection or re-selection or removal of the AIMLE client from the list of UEs which are capable of undertaking an AI/ML process/task/operation, given the energy factor.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.7.2 Architecture impacts
| This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.7.3 Solution evaluation
| This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.8 Solution #8: Network Slice Energy Optimization based on Energy Saving VAL Server Policy
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.1 Solution Description
| |
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.1.0 General
| This solution does not add new procedure. Instead, it proposes introducing a new VAL server policy in clause 9.5, 'Network slice optimization based on VAL server policy', 3GPP TS 23.435 [13]. The new policies enable NSCE server performs slice modifications based on monitoring energy-related information, such as energy consumption and the renewable energy ratio, at the slice level.
The high-level principles are as follows:
1. The VAL server provisions energy saving policies to the NSCE server.
• Based on the monitored energy consumption of a network slice - categorized by energy source (renewable or non-renewable) - over a specific duration (such as one hour) or as accumulated over a longer period (for example, a calendar month), slice modification is triggered when the measured value exceeds or falls below a defined threshold. This modification may involve reducing the slice's maximum throughput, slightly increasing allowable latency, or decreasing redundancy levels (such as removing a redundant UPF instance or redundant RAN cell).
• Based on the monitored renewable energy ratio of a network slice, a slice modification with predefined parameters is triggered when the energy ratio or crosses a specified threshold. Such modifications may include increasing the slice’s maximum throughput, raising allowable latency, expanding the maximum number of supported UEs, or increasing the maximum number of PDU sessions.
2. The NSCE server monitors a slice's energy usage by gathering energy-related data from the slice's OAM system.
3. Based on the defined triggers, the NSCE server carries out the actions outlined in energy-saving policies, aiming to either reduce the slice’s energy consumption or encourage the use of renewable energy sources.
The following clauses specify network slice energy optimization based on energy saving VAL server policy.
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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.1.1 Procedure
| Figure 6.8.1.1-1 outlines the procedure, where Steps 1-4 represent existing operations. Step 5 introduces a new function in which the NSCE server monitors energy-related information for a network slice. Step 6 involves the NSCE server taking action to adjust the slice, either to conserve energy or to encourage the use of renewable energy sources.
Figure 6.8.1.1-1: VAL server policy provision and enforcment for energy optimization
1. The consumer (VAL, SEAL Server) sends a VAL server policy provisioning request to the NSCE server by following clause 9.5 of 3GPP TS 23.435 [13]. New energy saving related policies (see examples in clause 6.8.1.2) are added:
a. Based on the monitored energy consumption of a network slice - categorized by energy source (renewable or non-renewable) - over a specific duration (such as one hour) or as accumulated over a longer period (for example, a calendar month), slice modification is triggered when the measured value exceeds or falls below a defined threshold. This modification may involve reducing the slice's maximum throughput, slightly increasing allowable latency, or decreasing redundancy levels (such as removing a redundant UPF instance or redundant RAN cell).
b. Based on the monitored renewable energy ratio of a network slice, a slice modification with predefined parameters is triggered when the energy ratio or crosses a specified threshold. Such modifications may include increasing the slice’s maximum throughput, raising allowable latency, expanding the maximum number of supported UEs, or increasing the maximum number of PDU sessions.
The action of the above policy may include charging policy update when slice SLA is modified to save energy to attract the VAL server to use the policies.
2. The NSCE server performs a VAL server policy check. This is the same as step 2 in clause 9.5.2.1, 3GPP TS 23.435 [13].
3. The NSCE server performs Slice Harmonization. This step is the same as clause 9.5.2.1.4 Policy harmonization, 3GPP TS 23.435 [13].
4. The NSCE server sends back a VAL server policy provisioning response to the Consumer (VAL/ASP), confirming policy provision. This is the same as step 2 in clause 9.5.2, 3GPP TS 23.435 [13].
5. The NSCE server initiates the retrieval of energy consumption data from the slice OAM (3GPP TS 28.554 [6]) to monitor energy-related information, including but not limited to the following parameters:
a. slice accumulated energy consumption refers to summing the average energy usage recorded during each slice OAM measurement interval (for example, every 15 minutes) by the OAM system and aggregating it into the total energy consumption over an extended period, such as a calendar month
b. slice average energy consumption: e.g. Energy-Consumption/hour
c. slice renewable energy ratio: e.g. 30%
Editor’s Note: Whether the NSCE collect the energy data directly or by using existing enabler service (e.g. A-DCCF) is FFS.
6. The NSCE server carries out slice modifications in accordance with the policy action defined in Step 1, through its interaction with the Slice OAM and the 5GC.
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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.1.2 Information Flows
| |
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.1.2.1 General
| The following information flows are specified for VAL server policy.
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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.1.2.2 Policy of Reducing Energy Consumption
| Table 6.8.1.2.2-1 shows the request sent by a consumer to an NSCE server for VAL server policy provisioning procedure.
Table 6.8.1.2.2-1: Policy of reducing energy consumption
Information element
Status
Description
Policy
O
Reduce Energy Consumption
>Area of interest
M
The service area for which the policy profile applies, which can be expressed as a geographical area (e.g. geographical coordinates), or a topological area (e.g. a list of TA).
>Trigger event
M
Threshold information, i.e. slices accumulate energy consumption, slices average energy consumption reached the threshold
>Recommended action
M
Examples:
• Modification of PDU sessions / max number of UEs (step for decrease in %).
• Decrease slice capacity (step for decrease in %).
• Decrease packet delay, and increase packet error rate etc.
• Update the energy efficiency parameter.
• Decrease GBR requirements.
• Apply better charging offering for the slice after adjustment.
Lifetime or number of events
M
Time duration or number of times the policy can take action.
Priority
O
Indicates the priority of the policy.
Scheduling period
O
Indicates the scheduling of policy in terms of time.
>Start time
M
Indicates the scheduled start time.
>End time
M
Indicates the scheduled end time.
Preemption
O
Indicates the pre-empt capability of the policy.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.1.2.3 Policy of Promoting renewable energy consumption
| Table 6.8.1.2.3-1 shows the request sent by a consumer to an NSCE server for VAL server policy provisioning procedure.
Table 6.8.1.2.3-1: Policy of Promoting renewable energy consumption
Information element
Status
Description
Policy
O
Promote renewable energy usage
>Area of interest
M
The service area for which the policy profile applies, which can be expressed as a geographical area (e.g. geographical coordinates), or a topological area (e.g. a list of TA).
>Trigger event
M
Threshold information, i.e. when renewable energy reached a threshold %
>Recommended action
M
Examples:
• Modification of PDU sessions/max number of UEs (step for increase in %).
• Increase slice capacity.
Lifetime or number of events
M
Time duration or number of times the policy can take action.
Priority
O
Indicates the priority of the policy.
Scheduling period
O
Indicates the scheduling of policy in terms of time.
>Start time
M
Indicates the scheduled start time.
>End time
M
Indicates the scheduled end time.
Preemption
O
Indicates the pre-empt capability of the policy.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.2 Architecture impacts
| Editor’s Note: The architecture impacts of the solution is FFS.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.8.3 Solution evaluation
| Editor’s Note: The evaluation of the solution is FFS.
6.9 Solution #9: Enhancements to ADAE DN Energy Efficiency Analytics
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.9.1 Solution Description
| In 3GPP TS 23.436 [11], the DN Energy Efficiency analytics, ADAES provides analytics on the energy consumption /efficiency of an edge platform (including the EESs / EASs). The DN energy analytics is performed per DNN/ DNAI and may be used to trigger the application server migration to different cloud. The analytics are based on NWDAF analytics and UPF/DN measurements on user plane load as well as edge/app side measurements on the energy consumption. Information/analytics on renewable energy used for providing application services is needed to support flexibility adjustment of the execution of workloads with GHG emissions. However, analytics relevant to renewable energy used for providing application services is missing. Enhancements to the DN Energy Efficiency analytics is required.
The DN Energy Efficiency analytics as specified in clause 8.18 of 3GPP TS 23.436 [11] is enhanced as follows (new text in bold italics):
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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18 Procedure for supporting DN Energy Efficiency analytics
| |
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18.1 General
| This clause describes the procedure for DN energy consumption/efficiency analytics, where the analytics are performed based on data collected from one or more DNs and A-ADRF.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18.2 Procedure
| Figure 8.18.2-1 illustrates the procedure for DN energy efficiency analytics enablement solution.
Pre-conditions:
1. Data producers (e.g. A-ADRF, EAS, EES) may be pre-configured with data producer profiles (as in Table 8.2.4.8-1) for the data they can provide. ADAES has discovered available data producers and their data producer profiles.
Figure 8.8.2.1-1: ADAES support for DN energy analytics
1. The VAL server sends a DN energy analytics request/subscription request to ADAES to perform analytics on the DN Energy Consumption/Efficiency for one or more DNs/EDNs, Event ID= “DN energy analytics”, “Energy analytics per application service”, or “Ratio of renewable energy analytics”, for a given DN service area (or subarea) and a given time window. The message in the request/subscription request is as described in Table 8.18.3.2-1.
2. The ADAES authorizes the VAL request. The ADAES responses to the analytics request. The message in the response is as described in Table 8.18.3.4-1.
3. The ADAES requests and receives from the EAS /VAL servers hosted at the serving and target DNs (within the VAL service area).
3a. (If Event ID= “DN energy analytics” in step 1) expected application service load and traffic schedules for the ongoing or future sessions within the area. Such data include traffic schedule report for the VAL Server, and this step re-uses the step 3 to 10 of clause 8.8.2.1.
3b. (If Event ID= “Energy analytics per application service”, or “Ratio of renewable energy analytics” in step 1) energy usage data at the DN per application, and/or in a time period, and/or the type of energy resources used.
4. The ADAES calculates the expected energy consumption or efficiency based on the received traffic and load data for the given DNN/DNAI based on the request.
NOTE: How the collected data are used to calculate energy efficiency metric is up to implementation.
5. (Optional) The ADAES obtains the corresponding trained ML model based on procedure in 3GPP TS 23.482 clause 8.3.2 and performs analytics to derive the predicted energy consumption at the target area and time horizon. The analytics outputs can be the statistics or predicted energy consumption / efficiency for the given DNN/DNAI.
6. The ADAES sends a DN energy analytics response/notifications with the energy consumption/efficiency analytics output data to the VAL server. The message in the analytics response/notifications is as described in Table 8.18.3.3-1.
Based on 6, the VAL server can use these analytics as input to trigger pro-actively:
- an application server migration to a different edge cloud or to a centralized cloud as a way of reducing the energy consumption for the edge (if consumption is expected to be very high (e.g. higher than a pre-configured threshold)).
- an application server offboarding and the instantiation of a new server at the target edge/centralized cloud to minimize energy consumption of the edge platform (taking into account the system wide energy efficiency).
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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18.3 Information flows
| |
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18.3.1 General
| The following information flows are specified for DN energy analytics based on clause 8.18.2.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18.3.2 DN energy analytics request/subscription request
| Table 8.18.3.2-1 describes information elements for the DN energy analytics request/subscription request from the VAL server to the ADAE server.
Table 8.18.3.2-1: DN energy analytics request/subscription request
Information element
Status
Description
Analytics Consumer ID
M
The identifier of the analytics consumer (VAL server, EAS).
Analytics ID
M
The identifier of the analytics event. This ID can be for example “DN energy analytics”, “Energy analytics per application service”, “Ratio of renewable energy analytics”.
Analytics type
M
The type of analytics for the event, e.g. statistics or predictions.
VAL service ID
O
The identifier of the VAL service for which the analytics is requested.
This IE shall be provided when the Analytics ID is “Energy analytics per application service”.
DNN/DNAI
M
DNN or DNAIs information for which the subscription applies.
Energy Efficiency/Consumption metrics
O
The formula and necessary metrics for calculating the EE based on load and traffic information per DN.
Target data producer profile criteria
O
Characteristics of the data producers to be used.
Renewable energy type
O
Indicate the type of renewable energy for which the analytics applies (e.g., solar, wind).
This IE may be provided when the Analytics ID is “Ratio of renewable energy analytics”. If omit, it indicates all types of renewable energy.
Preferred confidence level
O
The level of accuracy for the analytics service (in case of prediction).
Area of Interest
O
The geographical or service area for which the subscription request applies.
Time period of Interest
O
The time period for which the analytics applies.
This IE shall be provided when the Analytics ID is “Ratio of renewable energy analytics”.
Time validity
O
The time validity of the subscription request.
Reporting requirements
O
It describes the requirements for the energy analytics reporting. This requirement may include e.g. the type and frequency of reporting (periodic or event triggered), the reporting periodicity in case of periodic, the event in case of event triggered (e.g. the energy consumption/efficiency crosses a threshold, the ratio of renewable energy crosses a threshold), and reporting thresholds.
Notification endpoints ID or address
O
The identifier or address of the notification endpoints (e.g. VAL server, VAL client, or other network entities).
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eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18.3.3 DN energy analytics response/notification
| Table 8.18.3.3-1 describes information elements for the DN energy analytics response/notification request from the VAL server to the ADAE server to the VAL server (or to the notification endpoints provided in the request/subscription request).
Table 8.18.3.3-1: DN energy analytics response/notification
Information element
Status
Description
Result
M
The result of the analytics request (positive or negative acknowledgement).
Analytics ID
O
The identifier of the analytics event.
Analytics Output
O
The predictive or statistical parameter, which can be stats or prediction related to the energy consumption or efficiency for the edge platform for a given area/time and based on the event type.
> DNN
M
Identifies the data network name for which analytics information is provided.
> DNAI
M
Identifier of a user plane access to one or more DN(s) of the DN.
> Energy metrics
O
The predicted energy metrics.
>> Energy Consumption (NOTE)
O
The predicted energy consumption per DNAI based on network and edge resource usage
>> Energy Efficiency (NOTE)
O
The energy efficiency per DNAI based on network and edge resource usage (given a certain optimal energy consumption metric, which can be pre-configured).
>> DN Data Volume (NOTE)
O
The predicted data volume per DNAI.
>> Energy per Application service (NOTE)
O
The statistics or predicted energy consumption or energy efficiency per application service based on network and edge resource usage.
>>> VAL service ID
M
The identifier of the VAL service for which the analytics applied.
>> Ratio of renewable energy (NOTE)
O
The statistics or predicted ratio of renewable energy analytics per DNAI or per application service based on network and edge resource usage.
>>> Renewable energy type
O
Indicate the type of renewable energy used (e.g., solar, wind).
If omit, it indicates all types of renewable energy.
>>>> Renewable energy ratio
O
Ratio of the renewable energy.
> Area of Interest
O
The area (topological or geographical or edge area) where the analytics apply.
> Applicable time period
O
The time period that the analytics applies to.
>Confidence level
O
For predictive analytics, the achieved confidence level can be provided.
NOTE: At least one of these shall be present.
Editor’s Note: How to obtain the renewable energy information is FFS.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.18.3.4 Response to DN energy analytics request
| Table 8.18.3.4-1 describes information elements for the ADAES responses to the analytics request to the consumer (e.g. VAL server).
Table 8.18.3.4-1: Response to DN energy analytics request
Information element
Status
Description
Successful response (NOTE)
O
(NOTE)
Indicates that the request was successful.
> Subscription Identifier
O
The unique identifier for the event subscription. This IE shall be provided when it’s subscription request.
Failure response (NOTE)
O
(NOTE)
Indicates that the request failed.
> Cause
O
Indicates the cause of request failure (e.g. the energy usage data is not available).
NOTE: One of these IEs shall be present in the message.
6.Y9.2 Architecture impacts
Editor’s Note: The architecture impacts of the solution is FFS.
6.Y9.3 Solution evaluation
Editor’s Note: The evaluation of the solution is FFS.
6.x10 Solution #x10: Support of energy saving for location services
6.x10.1 Solution description
This solution addresses the KI#6.
The following are the procedures to introduce how LMS subscribes the energy related information analysis from ADAES (clause 6.x10.1.1) and how LMS utilizes the obtained energy related information analysis to reduce the energy consumption for the location services (clause 6.x10.1.2).
6.x10.1.1 Procedure of LMS subscription for the energy related information analysis
Figure 6.x10.1.1-1 illustrates the high-level procedure of the LMS subscription for the energy related information analysis.
The LMS may subscribe to the ADAES for the analysis of energy related information.
Figure 6.x10.1.1-1: Procedure of LMS subscription for the energy related information analysis
1. LMS may subscribe the energy information analysis related to location services from ADAES, including the analysis ID (e.g., energy consumption information), the service ID, the target area, the energy type (e.g., energy consumption), the energy information granularity (e.g., per VAL UE per location service), the filtering information (e.g., maximum energy consumption per VAL UE), the target UE ID, the trigger conditions, the time duration, etc.
2. The ADAES check if the LMS is authorized to request the energy information analysis. If the request is authorized, the ADAES responds to the LMS.
3. The ADAES sends the energy information analysis response to the LMS.
4. To obtain the analysis for the energy information, the ADAES may subscribe the energy related information (e.g., energy consumption information) from 3GPP Core network as described in clause 5.51.2.4 of 3GPP TS 23.501 [235013], with the parameters received in service request in step 1.
5. When the trigger condition is met, the 3GPP Core network provides the energy related information (e.g., energy consumption information) to the ADAES per requested granularity (e.g., per VAL UE per location service).
6. Upon receiving the energy related information in step 5, the ADAES utilizes and generates the analysis for energy related information per VAL UE per location service, including the energy information statistic (e.g., maximum/average/minimum energy consumption information over a certain period of time, etc.), historical energy information, energy information predication (e.g., the time point of peak energy consumption), etc.
Editor’s nNote: How the ADAES generates the analysis for energy related information and which information is FFS.
7. The ADAES notifies the energy information analysis to the LMS as service requested, including the analysis results generated in step 6.
6.x10.1.2 Procedure of LMS adjusting the location reporting configuration to save the energy
Figure 6.x10.1.2-1 illustrates the high-level procedure of LMS adjusting the location reporting configuration to save the energy consumption.
Figure 6.x10.1.2-1: Procedure of LMS adjusting the location reporting configuration to save the energy
1. Similar with step 1 of clause 9.3.5 in 3GPP TS 23.434 [2343414], the VAL server sends a location reporting trigger to the LMS to activate a location reporting procedure for obtaining the location information of LMC with the parameters defined in clause 9.3.2.4 of 3GPP TS 23.434 [2343414]. The location reporting trigger is periodical and the request also includes the energy saving indicator which means the LMS should consider saving the energy when performing the location services for the target UE.
2. Upon receiving the service request, the LMS decides to subscribe the energy information analysis from the ADAES for the target UE, following the procedure as described in clause 6.x10.1.1.
The ADAES replies the energy related information analysis for the target UE to the LMS, and may include the energy information statistic (e.g., maximum/average/minimum energy consumption information over a certain period of time), energy information predication (e.g., the time point of peak energy consumption), etc.
3. Based on the energy related information analysis received from ADAES and the service request from VAL server, the LMS determines the UE location reporting configuration. For example, the LMS may decrease the reporting frequency when the UE’s energy consumption almost reaches its daily maximum, and vice versa. Or the LMS may pend the location reporting when the time point of peak energy consumption is coming based on the UE's energy information predication.
4. The LMS may interact with the VAL server regarding the determined UE location reporting configuration and sends the adaptive location reporting configuration provisioning messages as defined in clause 9.3.20 of 3GPP TS 23.434 [2343414].
5. If the VAL server approves the suggested location reporting configuration, the LMS sends such configurations to the LMC as defined in clause 9.3.3 of 3GPP TS 23.434 [2343414]. If the VAL server rejects the suggested location configuration, the LMS may discard the configuration.
6. Considering the received location reporting configuration, the LMC reports the location information to the LMS dynamically when the location reporting trigger is met.
7. The LMS reports the received location information for the target UE to the VAL server.
6.x10.2 Architecture Impacts
This solution has no impact on the existing architecture.
6.x10.3 Solution evaluation
This clause provides an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.x Solution #x: <Title>
Provide a suitable title for the solution.
6.x.1 Solution Description
This clause will provide the description of the solution. Each solution should clearly describe which of the key issues it covers and how.
6.x.2 Architecture impacts
This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
6.x.3 Solution evaluation
This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.9.2 Architecture impacts
| Editor’s Note: The architecture impacts of the solution is FFS.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.9.3 Solution evaluation
| Editor’s Note: The evaluation of the solution is FFS.
6.10 Solution #10: Support of energy saving for location services
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.10.1 Solution description
| This solution addresses the KI#6.
The following are the procedures to introduce how LMS subscribes the energy related information analysis from ADAES (clause 6.10.1.1) and how LMS utilizes the obtained energy related information analysis to reduce the energy consumption for the location services (clause 6.10.1.2).
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.10.1.1 Procedure of LMS subscription for the energy related information analysis
| Figure 6.10.1.1-1 illustrates the high-level procedure of the LMS subscription for the energy related information analysis.
The LMS may subscribe to the ADAES for the analysis of energy related information.
Figure 6.10.1.1-1: Procedure of LMS subscription for the energy related information analysis
1. LMS may subscribe the energy information analysis related to location services from ADAES, including the analysis ID (e.g., energy consumption information), the service ID, the target area, the energy type (e.g., energy consumption), the energy information granularity (e.g., per VAL UE per location service), the filtering information (e.g., maximum energy consumption per VAL UE), the target UE ID, the trigger conditions, the time duration, etc.
2. The ADAES check if the LMS is authorized to request the energy information analysis. If the request is authorized, the ADAES responds to the LMS.
3. The ADAES sends the energy information analysis response to the LMS.
4. To obtain the analysis for the energy information, the ADAES may subscribe the energy related information (e.g., energy consumption information) from 3GPP Core network as described in clause 5.51.2.4 of 3GPP TS 23.501 [3], with the parameters received in service request in step 1.
5. When the trigger condition is met, the 3GPP Core network provides the energy related information (e.g., energy consumption information) to the ADAES per requested granularity (e.g., per VAL UE per location service).
6. Upon receiving the energy related information in step 5, the ADAES utilizes and generates the analysis for energy related information per VAL UE per location service, including the energy information statistic (e.g., maximum/average/minimum energy consumption information over a certain period of time, etc.), historical energy information, energy information predication (e.g., the time point of peak energy consumption), etc.
Editor’s Note: How the ADAES generates the analysis for energy related information and which information is FFS.
7. The ADAES notifies the energy information analysis to the LMS as service requested, including the analysis results generated in step 6.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.10.1.2 Procedure of LMS adjusting the location reporting configuration to save the energy
| Figure 6.10.1.2-1 illustrates the high-level procedure of LMS adjusting the location reporting configuration to save the energy consumption.
Figure 6.10.1.2-1: Procedure of LMS adjusting the location reporting configuration to save the energy
1. Similar with step 1 of clause 9.3.5 in 3GPP TS 23.434 [14], the VAL server sends a location reporting trigger to the LMS to activate a location reporting procedure for obtaining the location information of LMC with the parameters defined in clause 9.3.2.4 of 3GPP TS 23.434 [14]. The location reporting trigger is periodical and the request also includes the energy saving indicator which means the LMS should consider saving the energy when performing the location services for the target UE.
2. Upon receiving the service request, the LMS decides to subscribe the energy information analysis from the ADAES for the target UE, following the procedure as described in clause 6.10.1.1.
The ADAES replies the energy related information analysis for the target UE to the LMS, and may include the energy information statistic (e.g., maximum/average/minimum energy consumption information over a certain period of time), energy information predication (e.g., the time point of peak energy consumption), etc.
3. Based on the energy related information analysis received from ADAES and the service request from VAL server, the LMS determines the UE location reporting configuration. For example, the LMS may decrease the reporting frequency when the UE’s energy consumption almost reaches its daily maximum, and vice versa. Or the LMS may pend the location reporting when the time point of peak energy consumption is coming based on the UE's energy information predication.
4. The LMS may interact with the VAL server regarding the determined UE location reporting configuration and sends the adaptive location reporting configuration provisioning messages as defined in clause 9.3.20 of 3GPP TS 23.434 [14].
5. If the VAL server approves the suggested location reporting configuration, the LMS sends such configurations to the LMC as defined in clause 9.3.3 of 3GPP TS 23.434 [14]. If the VAL server rejects the suggested location configuration, the LMS may discard the configuration.
6. Considering the received location reporting configuration, the LMC reports the location information to the LMS dynamically when the location reporting trigger is met.
7. The LMS reports the received location information for the target UE to the VAL server.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.10.2 Architecture Impacts
| This solution has no impact on the existing architecture.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 6.10.3 Solution evaluation
| This clause provides an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
6.x Solution #x: <Title>
Provide a suitable title for the solution.
6.x.1 Solution Description
This clause will provide the description of the solution. Each solution should clearly describe which of the key issues it covers and how.
6.x.2 Architecture impacts
This clause will provide the architecture impacts of the solution and possible new SA6 capabilities and interfaces.
6.x.3 Solution evaluation
This clause will provide an evaluation of the solution. The evaluation should include the descriptions of the impacts to existing architectures.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 7 Overall Evaluation
| This clause will provide evaluation of different solutions.
|
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8 Conclusions
| |
eb1343f2cdb3d270d65fac7ddb0d0638 | 23.700-44 | 8.1 General conclusions
| This clause will provide general conclusions for the study.
8.2 Conclusions of key issue #x
This clause will provide conclusions for the specific key issue.
Annex A (informative):
Change history
Change history
Date
Meeting
TDoc
CR
Rev
Cat
Subject/Comment
New version
2025-08
SA6#68
TR skeleton as approved by SA6 in S6-253267.
0.0.0
2025-08
SA6#68
Implementation of the following pCRs approved by SA6:
S6-253641, S6-253642, S6-253698, S6-253715, S6-253716, S6-253753, S6-253754, S6-253791.
0.1.0
2025-10
SA6#69
Implementation of the following pCRs approved by SA6:
S6-254687, S6-254689, S6-254706, S6-254754, S6-254771, S6-254772, S6-254774, S6-254775, S6-254776, S6-254791.
0.2.0
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 1 Scope
| The present document investigates further enhancements to the capabilities of IMS data channel (DC), specifically in the areas of ADC establishment in alerting phase, IMS capability exposure and dynamic configuration of MDC2 endpoints.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 2 References
| The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
- References are either specific (identified by date of publication, edition number, version number, etc.) or non‑specific.
- For a specific reference, subsequent revisions do not apply.
- For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1] 3GPP TR 21.905: "Vocabulary for 3GPP Specifications".
[2] 3GPP TS 23.228: "IP Multimedia Subsystem (IMS); Stage 2".
[3] 3GPP TS 26.114: "Media handling and interaction".
[4] 3GPP TS 23.203: "Policy and charging control architecture".
[5] 3GPP TS 23.503: "Policy and charging control framework for the 5G System (5GS); Stage 2".
[6] 3GPP TS 24.229: "IP multimedia call control protocol based on Session Initiation Protocol (SIP) and Session Description Protocol (SDP); Stage 3".
[7] 3GPP TS 23.392: "Application enablement aspects for MMTel".
[8] 3GPP TS 23.502: "Procedures for the 5G System (5GS)".
[9] 3GPP TS 29.175: "IP Multimedia Subsystem (IMS) Application Server (AS) Services Stage 3".
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 3 Definitions of terms, symbols and abbreviations
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 3.1 Terms
| For the purposes of the present document, the terms given in TR 21.905 [1] and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in TR 21.905 [1].
Definition format (Normal)
<defined term>: <definition>.
example: text used to clarify abstract rules by applying them literally.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 3.2 VOID
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 3.3 Abbreviations
| For the purposes of the present document, the abbreviations given in TR 21.905 [1] and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905 [1].
A2P Application to Person
ADC Application Data Channel
API Application Programming Interface
DC Data Channel
DCAR Data Channel Application Repository
DCMF Data Channel Media Function
DCMTSI Data Channel Multimedia Telephony Service for IMS
DCSF Data Channel Signalling Function
IMS IP Multimedia Subsystem
MF Media Function
MRF Media Resource Function
NEF Network Exposure Function
NF Network Function
OMA Open Mobile Alliance
P2A Person to Application
P2A2P Person to Application to Person
P2P Person to Person
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 4 Architectural Assumptions and Requirements
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 4.1 Architectural Assumptions
| The following Architectural Assumptions are applicable to this Study:
- The IMS and IMS DC architecture defined in TS 23.228 [2] is used as the basis for any enhancements.
- An AS may be trusted or untrusted.
- The functionality of existing standardized APIs (e.g. OMA APIs) exposing MMTEL services may be used where applicable when the MMTEL service does not depend on IMS DC media.
- OMA APIs are not extended to support Data Channel and the principles outlined in TS 23.228 [2] Annex AD (IMS Subscribe/Notify framework for event monitoring).
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 5 Key Issues
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 5.1 Key Issue #1: ADC establishment in alerting phase for early media
| This key issue will investigate how to support IMS application data channel establishment in alerting phase to provide early media for a normal IMS DC session.
This Key Issue will study:
- Whether and how the capability of supporting ADC establishment in alerting phase is exchanged between UE and IMS network.
- How the use of ADC during alerting phase is controlled.
- Whether and how to download a specific list of DC applications that can be used during alerting phase.
- How to establish and terminate an ADC during alerting phase.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 5.2 Key Issue #2: Enhancements to capability exposure framework for eMMTel services
| This Key Issue will investigate how to enhance IMS capability exposure framework to expose IMS capability for eMMTel services (voice/video) and related events besides Data Channel, supporting IMS session establishment, modification and release to enable eMMTel services that may require enhancing audio/video services with DC capabilities.
Solutions should describe how to enhance Rel-19 IMS event exposure framework and event definition to support IMS DC events supported by DCSF (e.g. application downloading event) and non-DC events, including subscriber specific and non-subscriber specific supported by IMS AS.
NOTE 1: This Key Issue will analyse relevant use cases, the gap between OMA and 3GPP APIs and specify exposure functionality missing from OMA APIs.
NOTE 2: This Key Issue does not intend to transfer the responsibility of OMA API(s) to 3GPP.
This Key Issue will study:
- Identification of new events and/or capabilities to be exposed, in addition to those already listed in Table AD.2.5.3-1 of TS 23.228 [2].
- Assessment of whether existing OMA APIs support the exposure of the newly identified events and/or capabilities.
- Possible enhancement of exposure functionality where the assessment above identifies gaps in supporting exposure of the newly identified events and/or capabilities.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 5.3 Key Issue #3: Supporting dynamic configuration of MDC2 endpoints
| This Key Issue will investigate whether and how to define interfaces between DCSF and NEF/AF to complete existing P2A/P2A2P Data Channel procedures by specifying DC3/DC4 interfaces to enable dynamic configuration of MDC2 endpoints.
This Key Issue will study:
- the services and information elements that are exposed via DC3/DC4 for UE initiated P2A and P2A2P ADC session establishment.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6 Solutions
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.0 Mapping of Solutions to Key Issues
| Table 6.0-1: Mapping of Solutions to Key Issues
Key Issues
Solutions
KI#1
KI#2
KI#3
#1
X
#2
X
#3
X
#4
X
#5
X
#6
X
#7
X
#8
X
#9
X
10
X
11
X
12
X
13
X
14
X
15
X
16
X
17
X
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.1 Solution #1: ADC establishment in alerting phase initiated by the UE
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.1.0 High-level solution Principles
| This solution addresses Key Issue #1: DC establishment in alerting phase for early media.
The solution reuses Rel-19 TS 23.228 [2] with the addition that
- Based on the UE request (via a specific root URL) , the DSCF sends to the UE(s) an application list related with the alerting phase- The calling party receive BDC from the terminating network during alerting phase.
- The UE(s) decide whether and which application to download (as for Rel-19 specifications).
- The UE(s) decide when to release an application that was running in alerting phase; they may decide to continue this application after the alerting phase has finished. (as for Rel-19 specifications).
The solution may e.g. be used for the case where the originating UE1 calls a help centre and is immediately provided with an App that the user will be using when interacting with the call centre.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.1.1 Description
| This solution provides a procedure on how to establish a P2A ADC in alerting phase.
Editor's note: Current version of the solution supports P2A ADC establishment in alerting phase. it is FFS whether it can support other forms of ADC, i.e. P2A2P and P2P.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.1.2 Procedure
| The pre-condition of ADC establishment in alerting phase is the capability negotiation during the IMS registration phase on whether the UE supports the DC in alerting phase.
Based on the IMS DC capability negotiation defined in clause AC.7.0 of TS 23.228 [2], when the UE supporting DC in alerting phase and the UE is allowed to use DC in alerting phase, it includes the media feature tag of supporting DC in alerting phase in the Contact header field of the initial REGISTER request and any subsequent REGISTER request to allow the home IMS network to discover its capability of supporting DC in alerting phase.
If the IMS network supports DC in alerting phase, the S-CSCF includes a Feature-Caps header field indicating its capability of supporting DC in alerting phase in the 200 OK response to the initial and any subsequent REGISTER request, which is used by the UE to discover the IMS data channel capability of its home IMS network.
The UE may receive a Feature-Caps header field indicating IMS's capability of supporting DC in alerting phase in the 200 OK response to a subsequent REGISTER request when the network starts supporting DC in alerting phase after successful initial registration of the UE.
When the UE supporting DC in alerting phase initiates an IMS session and if the UE is allowed to use DC in alerting phase, it includes the media feature tag in the Contact header field of the initial INVITE message, regardless of data channel media being part of the SDP or not.
The UE shall not include the media feature tag in the Contact header field and data channel media description in the SDP offer of the initial INVITE request or any subsequent re-INVITE request message, if the S-CSCF has not included the capability indication of supporting DC in alerting phase in the Feature-Caps header field in the 200 OK response either to the initial REGISTER or a subsequent REGISTER request message.
If the UE is not configured whether to use DC in alerting phase, it is up to UE implementation whether or not to include the media feature tag in the Contact header field.
With a successful capability negotiation about the support of DC in alerting phase, the procedure is illustrated in Figure 6.1.2-1, which is based on clause AC.7.2.2 in TS 23.228 [2].
Figure 6.1.2-1: Procedure of ADC establishment in alerting phase
The steps in the procedure are as follows:
1. Same as step 1-14 in clause AC.7.1 of TS 23.228 [2], UE#1 initiates a SIP INVITE towards the IMS AS. After validating the user's subscription, the IMS AS notifies the DCSF about DC support. The DCSF provides the IMS AS with instructions and media information for establishing BDC. The IMS AS allocates resources with MF accordingly and updates the INVITE with the negotiated media. The updated SIP INVITE is forwarded to the terminating network/UE#2 for terminating network negotiation.
2-3. UE#2 and terminating network returns an 18x response with the SDP answer to BDC to originating network. According to the received SDP answer, MF may update data channel media resource information for UE#2.
4. The IMS AS notifies the successful session establishment event, Nimsas_SessionEventControl Notify (SessionEstablishmentSuccessEvent, Session ID, Media Info List) to DCSF.
5. The DCSF responds to the Nimsas notification request.
6. The SIP message18x is forwarded to UE#1 which indicates the BDC has been established and that the alerting phase has started.
7. The originating network P-CSCF executes QoS procedure for BDC as specified in TS 23.203 [4] and TS 23.503 [5].
8. PRACK and 200 OK for PRACK procedure are performed.
9-10. When the BDCs have been established between terminating MF and UE#1/UE#2, the DC application list for alerting phase and DC application for alerting phase is requested and downloaded to UE#1 and UE#2 from terminating DCSF.
The DCSF provides via MF the application list for alerting phase from the received specific URL , further to UE#1 and UE#2, based on their data channel capabilities and their choices.
The UE(s) use a specific URL related with the fact that they request a list of applications available in the alerting phase
Editor's note: It is FFs how the UE is alerted to initiate the download.
11. UE#1 initiates a P2A ADC for the selected DC application for alerting phase. UE#1 sends SIP UPDATE with an updated SDP to IMS AS via originating network P-CSCF and S-CSCF. The updated SDP contains the BDC information, as well as the requested ADC and the associated DC application binding information.
12. Same as step 3-14 of clause AC.7.2.2-1 in TS 23.228 [2], the IMS AS notifies the DCSF about DC support. The DCSF instructs the IMS AS to establish MF anchoring via MDC2. The IMS AS provisions the necessary media resources at the MF and returns the information to the DCSF. The DCSF then initiates ADC setup toward the DC Application Server via DC3/DC4. After receiving the DC AS's response with its own MDC2 media resource, the DCSF instructs the IMS AS to update the MF resource accordingly. The IMS AS completes the MF configuration and notifies the DCSF.
13-14. IMS AS sends the SIP UPDATE to remote network side and UE#2, via the originating S-CSCF, which does not include a request in the SDP for the ADC.
15-17. UE#2 and terminating network returns a SIP UPDATE 200 OK response with SDP answer for audio/video/bootstrap.
18. IMS AS notifies the DCSF about the successful result of media confirmation.
19. DCSF replies to the notification.
20. The IMS AS includes SDP answer for ADC to UE#1 in SIP UPDATE 200 OK response that it sends towards the UE 1 via the S-CSCF and P-CSCF.
21. The originating network P-CSCF executes QoS procedure for ADC media based on the SDP answer information from the 200 OK response.
22. SIP UPDATE 200 OK forwarded to UE#1 with ADC SDP answer.
23. The ADC between UE#1 and DC AS is established via MF. MF forwards data channel traffic between UE#1 and DC AS based on MDC2 media point information.
24-25. UE#2 answers the call and SIP INVITE 200 OK response is sent to the IMS AS via I/S-CSCF.
26. The IMS AS notifies the DCSF, via Nimsas_SessionEventControl_Notify (Session ID) of the call state change.
27. DCSF replies to the notification.
28-29. SIP INVITE 200 OK forwarded to UE#1 which indicates the call is answered and ACK procedure is performed.
30. Both UE#1 and UE#2 retrieve a new application list via BDC since call is answered. The new list supersedes the previously downloaded list. The UEs send request to MF to request application list by providing a root URL, via the established BDC with its data channel capabilities. The DCSF provides the application list from the root URL provided by UE#1 via MF, further to UE#1 and UE#2, based on their data channel capabilities and their choices through MF.
31. Subsequent procedures for DC application, as defined in clause AC.7.2 of TS 23.228 [2]. Depending on the application and user interaction, the UE(s) determine when to release the application initiated in alerting phase but this aspect is out of scope of specifications and does not require a new UE behaviour.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.1.3 Impacts on Services, Entities and Interfaces
| The solution has the following impacts:
UE:
- supporting retrieving DC application list and DC application for alerting phase; this includes providing a specific URL to get from DCSF the application list for alerting phase,
- supporting ADC establishment in alerting phase.
IMS AS:
- supporting negotiation of ADC in alerting phase.
Editor's note: Impacts on IMS AS and DCSF are FFS.
DCSF:
- providing DC application list based on the specific URL received from the UE and associated with the application list for alerting phase.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.2 Solution #2: Support for NW initiated data channel application in early media session with subscription
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.2.1 High level principles
| This solution has the following high level principles, in order to support IMS data channel application in early media session:
- Data Channel Application in Alerting Phase (DCAP) service can be configured by the user and stored in the HSS as part of the subscription data.
- IMS AS triggers the application data channel establishment based on UE subscription data when receives INVITE from remote UE.
- IMS AS performs as a B2BUA to update the SDP for data channel in early session and media flow for the main session.
- IMS AS activates the application data channel upon receiving 180 Ringing from the serving UE.
- The application data channel may coexistent with other early session services, e.g. CAT, CRS.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.2.2 Description
| This is a solution for Key issue #1 " DC establishment in alerting phase for early media".
The user of UE#2 is able to subscribe to the Data Channel Application in Alerting Phase (DCAP) service, including activate (or de-activate) the service and update the settings, e.g. to change by configuration the active data channel application. The DCAP subscriber is able to refine the data channel application selection behaviour with configured rules, e.g. time, calling party's location, called party's location, the identity of the calling and called party. The DCAP service is able to select the appropriate data channel application according to the rules.
DCAP is a terminating network service, but can also have an originating network functional component. That is, DCAP can be selected on behalf of the called subscriber for presentation to the calling party, but the calling (IMS) subscriber can also subscribe to and activate the DCAP service.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.2.3 Procedures
| The overall procedures of data channel establishment in early media phase are illustrated in figure 6.2.3-1.
Figure 6.2.3-1: ADC establishment in early media phase
0. The user of UE#2 subscribes Data Channel Application in Alerting Phase (DCAP) service to his/her serving IMS network. The data channel application to be initiated in alerting phase is selected by the user and configured in UE#2's subscription data.
1. UE#1 sends a SIP INVITE targeting UE#2. The initial SDP contains offers for audio/video flow, bootstrap data channel. UE#1 indicates support for preconditions, reliable provisional responses and indication of early-session supported.
2. Base on UE#2’s subscription data for DCAP service, the IMS AS decides to trigger the pre-configured data channel application to UE#1 in early media session. The IMS AS sends Nimsas_SessionEventControl_Notify to the selected DCSF to notify the session establishment, including the application data channel (inactive) and bootstrap data channel. The IMS AS may add the remote bootstrap data channel to UE#1 (BDC from UE#1 to the DCSF in the terminating network) if not presented.
3. The DCSF decides whether the requested data channels are allowed and determines the control policy and media information for the application data channel and bootstrap data channels.
4. The DCSF invokes the Nimsas_MediaControl_MediaInstruction instructing the IMS AS how to set up the application data channel and bootstrap data channels with MF both for originating and terminating side.
5. The IMS AS instruct the MF to allocate required bootstrap data channels. The IMS AS indicates to the DC AS that the media stream of ADC is inactive.
6. The IMS AS responds to the MediaInstruction request received in step 4.
7. The DCSF indicate the DC AS reserving the media resource for the application data channel and MDC2 end points to the MF. The DCSF indicates to the DC AS that the media stream of ADC is inactive.8. The DCSF responds to the Notify Request received in step 2.
9. The IMS AS sends the INVITE to UE#2.
10. UE#2 determines the subset of the media flows and responds with an Offer Response message back to the IMS AS.
11. The IMS AS forwards the Offer Response message to UE#1.
12. The IMS AS initiates an UPDATE message to UE#1, which includes the SDP for the and remote bootstrap data channel and inactive application data channel for early session. The IMS AS may add a remote bootstrap data channel with UE#1 if not request in the INVITE message.
13. Based on UE implementation and local policy, UE#1 may alert the user that an application data channel in early media is triggered. The user can decide to accept or reject the application.
14. If accepted by the user, UE#1 reserves the media resource for the application data channel and bootstrap data channel. UE#1 sends the200 OK, which acknowledges the UPDATE message to the IMS AS.
15. The BDC between UE#1 and the DCSF is established as part of the SDP Offer in the early session. If the requested data channel application is not locally available, UE#1 downloads the data channel application via the established BDC and update the early session with the IMS AS.
16. The IMS AS forwards the PRACK message to UE#2.
17-18. UE#2 reserves resource and sends a SIP 200 (OK) response for the SIP PRACK request to UE#1. The application data channel between the DC AS and UE#1 is established and UE#1.
19. UE alerts the user and sends Ringing indication to the IMS AS.
20. The IMS AS decides to activate the pre-configured data channel application to UE#1.
21. The IMS AS instructs the MF to activate the media resource for the application data channel. The IMS AS may notify the DC AS to activate the application towards UE#1.
22. The IMS AS updates the IMS session with UE#1 to activate the application data channel in early session.
23. UE#1 confirms the activation of the application data channel.
24. The interaction between UE#1 and the DC AS via application data data channel in early session is started.
25. UE#2 accepts the session and sends 200 OK to the INVITE.
26-27. The IMS AS updates UE#1 with SDP offer to initiate the media flow of the main session and terminate the application data channel in the early session.
28. The IMS AS instructs the MF to update the media resource and terminate the application data channel.
29-30. The IMS AS forwards 200 OK to UE#1. UE#1 sends ACK to UE#2.
31. The media flow of the main session between UE#1 and UE#2 starts. The requested BDCs for both UE#1 and UE#2 in the main session are established.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.2.4 Impacts to Services, Entities and Interfaces
| UE:
- Indicates supporting of early media session in SIP INVITE message, as specified in existing specification, e.g. TS 24.229 [6].
- Supports application data channel in the early session.
- Notifies user the requested data channel application in early session.
HSS:
- Supports management of user subscription for Data Channel Application in Alerting Phase (DCAP) service.
- Supports the user configuration of data channel application(s) in alerting phase.
IMS AS:
- Triggers the application data channel establishment when receiving INVITE from the originating UE.
- Activates the established application data channel when receiving 180 Ringing from the terminating UE.
- Terminates the application data channel when the call accepted by the terminating UE.
- Performs as B2BUA to update the media flow in the SDP exchanged with UE.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.3 Solution #3: IMS ADC establishment during alerting phase when initial INVITE does have BDC offer
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.3.0 High-level solution Principles
| The main principle of this solution for support of application data channel establishment during alerting phase of normal IMS DC session as follows:
- The calling party will receive bootstrap data channel offer from the terminating network during alerting phase.
- The calling party will establish the bootstrap data channel, download the application and based on the interest, trigger application data channel establishment by selecting one particular application from the terminating network.
- The called party after getting notification about the application running status in the calling party device, will establish bootstrap data channel, download the application and once accepts the session, the call will be established.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.3.1 Description
| This paper proposes solution for supporting application data channel establishment during alerting phase when calling party has offered bootstrap data channel in the initial INVITE.
In this proposal, the calling party and the called party will establish bootstrap data channel(s), download application or application list and establish application data channel while call has not yet been established.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.3.2 Procedures
| The procedure of establishing application data channel during alerting phase.
Figure 6.3.3-1: ADC establishment in early media phase
1. UE-A sends an initial SIP INVITE request to the originating IMS AS through P-CSCF and S-CSCF with an initial SDP offer for audio/video and for the bootstrap data channel establishment with bootstrap DC stream ID. The SDP contains bootstrap data channel offers for both the originating and the terminating side. P-CSCF may include the P-Early-Media header field to indicate the support for early media authorization in the initial SIP INVITE request. NOTE: The UE is assumed to support P-Early-Media.
2-13. Steps 2-13 of clause AC.7.1 in TS 23.228 [2] apply.
14. UE-B returns a SIP 18X response with an SDP answer for bootstrap data channel to the originating network. The terminating IMS AS includes P-Early-Media header field in the SIP 18X response indicating early media is allowed based on the SDP answer not including any application data channel.
15. The bootstrap data channel has been established between originating MF and UE-A. The UE shall request for a data channel application or data channel applications list from the MF via the established bootstrap data channel. The DCSF provides the data channel application or the data channel application list to UE-A through MF. UE-A may download the data channel application or the data channel application list through the established bootstrap data channel..
16. The bootstrap data channel has also been established between terminating MF and UE-A.
17. UE-A sends a SIP PRACK request towards the terminating side before or during step 15.
18. UE-B returns a SIP 200 OK response for the PRACK. Simultaneously, UE-B may alert the terminating user that the incoming session is a normal IMS DC session.
19. After the DC application is downloaded, UE-A initiates a SIP UPDATE request to update the IMS session for adding the application data channel and to inform UE-B about the associated DC application binding information. The IMS AS shall notify DCSF about the media change request event and request MF to allocate media resources for the application data channels based on instructions received from the DCSF if MF is anchoring application data channel. Once acknowledgement is received from the DCSF, the IMS AS shall send the update request to the terminating side and UE-B through S-CSCF. The SIP UPDATE request with the modified SDP offer includes the negotiated bootstrap data channel media description, requested application data channel media description and the associated data channel application binding information.
The IMS AS includes P-Early-Media header field in the SIP UPDATE request to indicate that early media is allowed based on the previously received SDP answer not indicating any application data channel.
20-22. The bootstrap data channel has been established between the originating MF and UE-B. The bootstrap data channel has also been established between the terminating MF and UE-B. UE-B may alert the terminating user that the data channel application is running in UE-A and to download the data channel application if not already available in UE-B. UE-B shall request for the data channel application and the DC application list from the MF for downloading via the established bootstrap data channels if needed.
23-29. When the DC application is available in UE-B, then the terminating network sends a 200 OK response to the originating side based on which the IMS AS notifies DCSF of the successful bootstrap DC and application DC establishment. Once the DCSF responds to the session establishment request, 200 OK response is forwarded to the UE-A. The originating network P-CSCF executes QoS procedures for bootstrap data channel. The IMS AS may include P-Early-Media header field in the 200 OK response to the SIP UPDATE request to indicate that early media is allowed. UE-B alerts the terminating user for running the DC application.
30. UE-B returns a SIP 180 response indicating that UE-B is ringing.
31. Once UE-B accepts to run the DC application, UE-B answers the IMS session. UE-B sends 200 OK to the initial SIP INVITE request and subsequent procedures follow.
Editor's note: The consideration on originating user consent of NW initiated ADC during alerting phase is FFS.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.3.3 Impacts on Services, Entities and Interfaces
| Editor's note: Impacts are FFS.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.4 Solution #4: IMS ADC establishment during alerting phase when initial INVITE does not have BDC offer
| |
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.4.0 High-level solution Principles
| The main principle of this solution for support of application data channel establishment during alerting phase of normal IMS DC session as follows:
- The calling party will receive bootstrap data channel offer from the terminating network during alerting phase.
- The calling party will establish the bootstrap data channel based on the interest, download the application and , trigger application data channel establishment by selecting one particular application from the terminating network.
- The called party after getting notification about the application running status in the calling party device, will establish bootstrap data channel, download the application and once accepts the session, the call will be established.
- It is assumed that the interactive ring back tone will not co-exist with legacy ring back tone.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.4.1 Description
| This clause proposes a solution for supporting application data channel establishment during alerting phase when calling party has not offered bootstrap data channel in the initial INVITE.
In this proposal, one scenario is explained how the called party (e.g. restaurant customer service) network is providing bootstrap data channel offer to the calling party during alerting phase so that the calling party can establish bootstrap data channel, download the application or application list and then session is established once called party user accepts it.
Scenario.
- The calling party triggers a normal call to restaurant customer service with intention of reserving seats for lunch.
- The called party has taken an interactive ringback tone service which means whenever any incoming session is received for that restaurant, the network will offer bootstrap data channel and list of applications provided by the restaurant.
- The calling party will establish the bootstrap data channel, download the list of applications and select "seat reserve" application.
- The called party based on the notification of running application on the calling party side will download the application if not available and then accepts the session.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.4.2 Procedures
| The procedure of establishing application data channel during alerting phase.
Figure 6.4.3-1: ADC establishment in early media phase
0. The terminating user has opted for an interactive ring back tone service from its network. That is, for every incoming SIP INVITE, the network will send an SDP offer for bootstrap data channel and a list of applications towards the originating UE-A.
1-2.UE-A sends an initial SIP INVITE request to the terminating side through P-CSCF and S-CSCF with an initial SDP offer for audio/video. P-CSCF may include the P-Early-Media header field to indicate the support for early media authorization in the initial SIP INVITE request.
NOTE: The UE is assumed to support P-Early-Media.
3. UE-B returns a SIP 18X response with an SDP answer for audio/video session to the originating network. The terminating IMS AS includes P-Early-Media header field in the SIP 18X response indicating early media is allowed.
4-5. UE-A sends a SIP PRACK request towards the terminating side and UE-B returns a SIP 200 OK response for the PRACK.
6. UE B sends 180 Ringing.
6A. Since the terminating user has subscribed to an interactive ringback tone service from its network, the terminating IMS AS triggers a SIP UPDATE towards the originating side. The SIP update contains the SDP offer for audio/video and bootstrap offer for the originating side. Steps 2-10 of clause AC.7.1 in TS 23.228 [2] apply in which the terminating IMS AS determines to notify the terminating DCSF based on DC subscription and UE-B DC capability and the DCSF instructs the IMS AS and MF to establish the resource required for the terminating side. In this scenario, the DCSF allows for application selection from the remote UE, even before the application has been selected by the peer UE.
7. IMS AS sends the modified SIP UPDATE request including the SDP offer for audio/video and bootstrap data channel adding media information of MF to the originating IMS AS.
8. Originating IMS AS determines to notify DCSF based on DC subscription and UE DC capability. If the network does not support DC service or UE-A does not have DC capability, then the originating network sends an SDP answer to setup only audio/video session and remove any bootstrap data channel media information.
9. Originating IMS AS forwards the SIP UPDATE with the SDP offer for audio/video and bootstrap data channel to the originating UE-A.
10. Based on UE implementation and local policy, UE-A may alert the user that bootstrap DC for early media has been offered and the user can accept or reject the bootstrap DC setup.
11. If the user accepts the offer, the bootstrap DC shall be established between the terminating MF and UE-A. The UE shall request for a data channel application or data channel applications list from the MF via the established bootstrap data channel. The DCSF provides the data channel application or the data channel application list to UE-A through MF. UE-A may download the data channel application or the data channel application list through the established bootstrap data channel.
12. Once the bootstrap data channel has been established, UE-A sends 200 OK response for the SIP UPDATE request to the terminating side.
13. UE-B alerts the terminating user to indicate an incoming IMS session.
14. After the DC application is downloaded, UE-A initiates a SIP UPDATE request to update the IMS session for adding the application data channel and to inform UE-B about the associated DC application binding information. The IMS AS shall notify DCSF about the media change request event. Once acknowledgement is received from the DCSF, the IMS AS shall send the update request to the terminating side and UE-B through S-CSCF. The SIP UPDATE request with the modified SDP offer includes the negotiated bootstrap data channel media description, requested application data channel media description and the associated data channel application binding information.
The IMS AS includes P-Early-Media header field in the SIP UPDATE request to indicate that early media is allowed based on the previously received SDP answer not indicating any application data channel.
15. The bootstrap data channel has been established between the terminating MF and UE-B. UE-B may alert the terminating user that the data channel application is running in UE-A and to download the data channel application if not already available in UE-B. UE-B shall request for the data channel application and the DC application list from the MF for downloading via the established bootstrap data channels if needed.
16. After the DC application is downloaded, UE-B alerts the terminating user for running the DC application.
17-22. The terminating network sends a 200 OK response to the originating side based on which the IMS AS notifies DCSF of the successful bootstrap DC and application DC establishment. Once the DCSF responds to the session establishment request, 200 OK response is forwarded to the UE-A. The originating network P-CSCF executes QoS procedures for bootstrap data channel and application data channel. The IMS AS may include P-Early-Media header field in the 200 OK response to the SIP UPDATE request to indicate that early media is allowed.
23. Application data channel has been established between UE-A and UE-B. The application data channel media in the alerting phase is exchanged between UE-A and UE-B.
24. UE-B answers the call and sends 200 OK to the SIP INVITE. The ADC established during alerting phase is terminated after 200 OK to INVITE is sent by UE B.
25. Subsequent procedures continue for the main session.
NOTE: The DCSF and MF shown in the figure belongs to originating IMS network.
|
7d12b515375832bdb38ea519e14adcb0 | 23.700-56 | 6.4.3 Impacts on Services, Entities and Interfaces
| This solution may have the following impacts to existing entities and interfaces:
IMS AS:
- Terminating IMS AS will provide a new offer for IMS bootstrap data channel based on the interactive ringback tone service subscription of called party user.- Terminating IMS AS will notify the session establishment to the selected DCSF including the bootstrap data channel although the incoming INVITE does not have the bootstrap DC offer.
UE:
- Supports bootstrap DC and application DC establishment during alerting phase.
- Notifies user the incoming bootstrap DC offer during the alerting phase.
|
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