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5 Functional service requirements of Ambient IoT
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5.1 General
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The functional requirement for Ambient IoT service includes 6 aspects, i.e.
- Communication;
- Positioning/location;
- Management;
- Collected information and network capability exposure;
- Charging;
- Security and privacy
The Ambient IoT devices have some special characteristics such as Energy harvesting, Low complexity, Low data rates, Life span, and Reachability, etc.
Ambient IoT capable UEs are 3GPP UEs with the capability to communicate with an Ambient IoT device.
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5.2 Functional service requirements of Ambient IoT
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5.2.1 Communication aspects
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The 5G system shall be able to support 5G network or an Ambient IoT capable UE to communicate with a group of Ambient IoT devices simultaneously.
The 5G network shall support a mechanism to authorize an Ambient IoT capable UE to communicate with an Ambient IoT device.
The 5G system shall be able to support mechanisms to communicate:
- between an Ambient IoT device and the 5G network using Ambient IoT direct network communication or Ambient IoT indirect network communication, or
- between an Ambient IoT device and Ambient IoT capable UE using Ambient IoT device to UE communication.
NOTE: Examples of the communication between 5G network/Ambient IoT capable UE and Ambient IoT devices can include periodic sensor reporting or network-initiated inventory.
The 5G system shall provide suitable mechanisms to support communication between a trusted and authorized 3rd party and an Ambient IoT device or group of Ambient devices.
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5.2.2 Positioning
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The 5G system shall support location services for Ambient IoT devices (e.g., to locate Ambient IoT devices using absolute or relative positioning methods)
NOTE 1: The intention is not to use Ambient IoT devices to locate other Ambient IoT devices.
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5.2.3 Management
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The 5G network shall support suitable management mechanisms for an Ambient IoT device or a group of Ambient IoT devices.
The 5G system shall support a mechanism to:
- disable the capability to transmit RF signals for one or more Ambient IoT device that is / are currently able to transmit RF signals
- enable the capability to transmit RF signals for one or more Ambient IoT device that is / are currently disabled to transmit RF signals
Based on operator policy, the 5G system shall provide a suitable mechanism to permanently disable the capability of an Ambient IoT device or a group of Ambient IoT devices to transmit RF signals.
Subject to operator policy and regulatory requirements, the 5G system shall support suitable mechanisms for the Ambient IoT device to move between one or more networks and countries.
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5.2.4 Exposure
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Subject to user consent, operator policy and 3rd party request, the 5G system shall be able to obtain data from Ambient IoT devices (e.g. sensor data) and provide it to a trusted 3rd party via the 5G network.
Subject to user consent, operator’s policy and 3rd party request, the 5G system shall provide information about an Ambient IoT device or a group of Ambient IoT devices (e.g. position) to the trusted 3rd party via the 5G network.
The 5G system shall enable an authorized 3rd party to instruct the 5G network to trigger a group of Ambient IoT devices in an specific area and which action the Ambient IoT devices need to perform when triggered (e.g. send ID, receive further information, send measurement value).
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5.2.5 Charging
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The 5G system shall be able to collect charging information in a suitable way for using Ambient IoT services on per Ambient IoT device basis or a group of Ambient IoT devices (e.g., total number of communications per charging period).
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5.2.6 Security and privacy
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The 5G system shall enable security protection suitable for Ambient IoT, without compromising overall 5G security protection.
The 5G system shall be able to provide a mechanism to protect the privacy of information (e.g., location and identity) exchanged during communication between an Ambient IoT device and the 5G network or an Ambient IoT capable UE.
Based on subscription and operator policies, the 5G system shall authorize an Ambient IoT capable UE to communicate with a specific Ambient IoT device or with a group of Ambient IoT devices.
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6 Performance service requirements of Ambient IoT
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6.1 General
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Ambient IoT service can be categorized into 4 categories, namely inventory, sensor data collection, tracking and actuator control. The corresponding performance services requirements are listed in the following subclauses.
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6.2 Performance service requirements for Inventory
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Table 6.2-1 KPIs for inventory
Scenarios
Max. allowed end-to-end latency
Communication service availability
Reliability
User-experienced data rate
Message size
Device density
Communication range (Note 1)
Service area dimension
Device speed
Transfer interval
Positioning service latency
Positioning service availability
Positioning accuracy
Remark
Inventory or asset management
Typically, seconds level
99%
NA
<2 kbit/s
96/256 bits
<1.5 million devices/km²
indoor only
(Note 2)
30 m – 50 m indoor,
200 m - 400 m outdoor
1 km² – 10 km²
3 km/h – 10 km/h
NA
NA
NA
3 m indoor,
cell-level outdoor
NOTE 1: The communication range is the communication distance between the ambient IoT device and the 5G network or between the ambient IoT device and an ambient IoT capable UE.
NOTE 2: The device density is much lower in outdoors as only a subset of assets (e.g., stored indoors) will be in transit, and a much larger area for transit applies.
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6.3 Performance service requirements for sensor data collection
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Table 6.3-1 KPIs for sensor data collection
Deployment
Scenarios
Max. allowed end-to-end latency
Communication service availability
Reliability
User-experienced data rate
Message size
Device density
Communication range
(Note 1)
Service area dimension
Device speed
Transfer interval
Positioning service latency
Positioning service availability
Positioning accuracy
Remark
Indoor
Room environment monitoring (e.g. domicile, machinery)
20 s - 30 s
99 %
NA
<1 kbit/s
<100 bits
1.5 devices/m²
10 m - 30 m
NA
Stationary
NA
NA
NA
NA
Indoor agriculture and husbandry
>10 s
99.9%
NA
<1 kbit/s
Typically,
<1,000 bits
1 device /m²
30 m - 200 m
6,000 m² - 30,000 m²
Quasi-stationary
15 mins - 30 mins
NA
NA
NA
Outdoor
Smart grid
1 s
99%
NA
<1 kbit/s
Typically,
<800 bits
< 10,000 devices /km²
Typically, 50 m - 200 m
[several km² up to 100,000 km²]
Stationary
5 mins - 15 mins
NA
NA
several 10 m
Outdoor husbandry and logistics
Typically, > tens of seconds
99%
NA
<0.5 kbit/s
Typically,
[<800 bits]
<5,200 devices/ km²
[300 m - 500 m]
430,000 m²
≤ 3 km/h
15 mins
NA
NA
NA
Smart city
10 s - 30 s
99%
NA
<1 kbit/s
Typically,
<800 bits
<1,000 devices/ km²
300 m - 500 m
City wide including rural areas
Stationary
15 mins
NA
NA
NA
NOTE: The communication range is the communication distance between the ambient IoT device and the 5G network or between the ambient IoT device and an ambient IoT capable UE.
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6.4 Performance service requirements for tracking
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Table 6.4-1 KPIs for tracking
Deployment
Scenarios
Max. allowed end-to-end latency
Communication service availability
Reliability
User-experienced data rate
Message size
Device density
Communication range
(Note 1)
Service area dimension
Device speed
Transfer interval
Positioning service latency
Positioning service availability
Positioning accuracy
Remark
Indoor
Indoor tracking
1 s
99.9%
NA
<1 kbit/s
<1 kbits
25 devices /100 m²
-
250 devices /100 m²
10 m
200 m²
up to 3km/h
60 mins
1 s
90%
1 m - 3 m, 90% availability
Outdoor
Outdoor tracking
1 s
99.9%
NA
<1 kbit/s
<1 kbits
≤10 devices/ 100 m²
500 m
Up to the whole PLMN
up to 10 km/h
60 mins
1 s
95%
several 10 m
NOTE: The communication range is the communication distance between the ambient IoT device and the 5G network or between the ambient IoT device and an ambient IoT capable UE.
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6.5 Performance service requirements for actuator control
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Table 6.5-1 KPIs for actuator control Deployment Scenarios Max. allowed end-to-end latency Communication service availability Reliability User-experienced data rate Message size Device density Communication range (Note 1) Service area dimension Device speed Transfer interval Positioning service latency Positioning service availability Positioning accuracy Remark Indoor Indoor actuator control Several seconds 99% NA 2 kbit/s <100 Bytes <1.5 million/km² 50 m <250 m² for home, and 15,800 square meters for supermarket stationary 20 mins - 120 mins NA NA 3 m to 5 m indoor Outdoor Outdoor actuator control for large coverage Several seconds 99% N/A NA 128 bit (DL) NA [500] m outdoors 40,000 m2 - 4,000,000 m2 Static NA NA NA NA Outdoor actuator control for medium coverage Several seconds 99% NA <2 kbit/s <200 bits <20 devices/100 m² 200 m City wide including rural areas Static NA NA NA NA NOTE: The communication range is the communication distance between the ambient IoT device and the 5G network or between the ambient IoT device and an ambient IoT capable UE. Annex A (informative): Change history Change history Date Meeting TDoc CR Rev Cat Subject/Comment New version 2023-11 SA1#104 S1-233257 - Draft provided by editor. Note: corresponding WID not yet approved, so no official TS number 0.1.0 2023-12 SA#102 SP-231405 - MCC clean-up for presentation for one-step approval to SA. Corresponding WID to be submitted at the same time as this TS, then a TS number will be allocated. 1.0.0 2023-12 SA#102 SP-231750 - TR number known and added: TR 22.369 1.0.1 2023-12 SA#102 - Approved at SA#102 19.0.0 2024-03 SA#103 SP-240202 0001 3 F TS.22.369_adding the abbreviation 19.1.0 2024-06 SA#104 SP-240785 0006 1 D add the definition pointer of Ambient IoT device 19.2.0 2024-09 SA#105 SP-241145 0007 D Clarify that two Ambient IOT requirements are independent of each other 19.3.0
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1 Scope
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The present document describes use cases and potential requirements for enhancement of the 5G system to provide sensing services addressing different target verticals/applications, e.g. autonomous/assisted driving, V2X, UAVs, 3D map reconstruction, smart city, smart home, factories, healthcare, maritime sector.
Use cases focus on NR-based sensing, while some use cases might make use of information already available in EPC and E-UTRA (e.g. cell/UE measurements, location updates). This study will not lead to impacts on EPC and E-UTRA. Some use cases could also include non-3GPP type sensors (e.g. Radar, camera).
The aspects addressed in the present document include collecting and reporting of sensing information, sensing related KPIs. Security, privacy, regulation and charging are additional topics of concern.
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2 References
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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.
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[5] F. Liu et al., "Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond," in IEEE Journal on Selected Areas in Communications, doi: 10.1109/JSAC.2022.3156632.
[6] T. S. Rappaport, G. R. MacCartney, M. K. Samimi and S. Sun, "Wideband Millimeter-Wave Propagation Measurements and Channel Models for Future Wireless Communication System Design," in IEEE Transactions on Communications, vol. 63, no. 9, pp. 3029-3056, Sept. 2015, doi: 10.1109/TCOMM.2015.2434384.
[7] C. Han, Y. Bi, S. Duan and G. Lu, "Rain Rate Retrieval Test From 25-GHz, 28-GHz, and 38-GHz Millimeter-Wave Link Measurement in Beijing," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 8, pp. 2835-2847, Aug. 2019, doi: 10.1109/JSTARS.2019.2918507.
[8] IEEE 802.11-18/0611r16: “Wireless LANs, WiFi Sensing Uses Cases”
[9] TEM STANDARDS TEM STANDARDS AND RECOMMENDED PRACTICE: https://unece.org/fileadmin/DAM/trans/main/tem/temdocs/TEM-Std-Ed3.pdf
[10] S. Saponaraet. al, "Radar-on-Chip/in-Package in Autonomous Driving Vehicles and Intelligent Transport Systems: Opportunities and Challenges," inIEEE Sig. Proc. Mag., Sept. 2019.
[11] 3GPP TR 22.856, "Localized Mobile Metaverse Services".
[12] J. Hasch, E. Topak, R. Schnabel, T. Zwick, R. Weigel and C. Waldschmidt, "Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band," inIEEE Transactions on Microwave Theory and Techniques, vol. 60, no. 3, pp. 845-860, March 2012.
[13] “Velodyne™ LiDAR VPL-16 User Manual,” 63-9243 Rev. E, Velodyne™ LiDAR, https://velodynelidar.com/wp-content/uploads/2019/12/63-9243-Rev-E-VLP-16-User-Manual.pdf.
[14] Liu, A., Huang, Z., Li, M., Wan, Y., Li, W., Han, T.X., Liu, C., Du, R., Tan, D.K.P., Lu, J. and Shen, Y., 2022. A survey on fundamental limits of integrated sensing and communication. IEEE Communications Surveys & Tutorials, 24(2), pp.994-1034.
[15] https://medium.com/desn325-emergentdesign/s-l-a-m-and-optical-tracking-for-xr-cfabb7dd536f.
[16] Dwivedi, S., Shreevastav, R., Munier, F., Nygren, J., Siomina, I., Lyazidi, Y., Shrestha, D., Lindmark, G., Ernström, P., Stare, E. and Razavi, S.M., 2021. Positioning in 5G networks. IEEE Communications Magazine, 59(11), pp.38-44.
[17] T. Murakami et al, “Wildlife Detection System Using Wireless LAN Signal,” in NTT Technical Review vol.17, No.6, pp. 45-48, June 20019, https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201906fa13.pdf&mode=show_pdf.
[18] Eradication of elephant mortality and injury due to railway accidents through automatic tracking and alert system in IEEE Conference Publication, IEEE Xplore
[19] Impact of wild animals (deer and bears) on train operations 210616_KO_Animal2.pdf (jrhokkaido.co.jp) (in Japanese) [z] Rail Industry Safety Induction Handbook: https://railsafe.org.au/__data/assets/pdf_file/0009/32022/Rail-Industry-Safety-Induction-RISI-Handbook-V5.1.pdf.
[20] S. M. Patole, M. Torlak, D. Wang and M. Ali, "Automotive radars: A review of signal processing techniques," inIEEE Signal Processing Magazine, vol. 34, no. 2, pp. 22-35, March 2017.
[21] Society of Automotive Engineers (SAE), “Taxonomy and definition for terms related to Driving automation systems for on-Road Motor Vehicles”, https://www.sae.org/standards/content/j3016_202104/.
[22] Census of Fatal Occupational Injuries Summary, 2020, https://www.bls.gov/news.release/cfoi.nr0.htm.
[23] Javed MA, Muram FU, Hansson H, Punnekkat S, Thane H. Towards dynamic safety assurance for Industry 4.0. Journal of Systems Architecture. 2021 Mar 1; 114:101914.
[24] American National Standards Institute/Industrial Truck Safety Development Foundation, Safety standard for driverless, automatic guided industrial vehicles and automated functions of manned industrial vehicles, December 2019, 2019, [Online] http://www.itsdf.org.
[25] Moore, Erik George, "Radar Detection, Tracking and Identification for UAV Sense and Avoid Applications" (2019). Electronic Theses and Dissertations. 1544.
[26] https://www.bosch-mobility-solutions.com/en/solutions/assistance-systems/blind-spot-detection/.
[27] Soatti, Gloria, et al. "Enhanced vehicle positioning in cooperative ITS by joint sensing of passive features." 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2017.
[28] 5GAA_White_Paper_C-V2X Use Cases Volume II: Examples and Service Level Requirements.
[29] https://www.ieee802.org/11/Reports/tgbf_update.htm.
[30] https://mentor.ieee.org/802.11/dcn/20/11-20-1712-02-00bf-wifi-sensing-use-cases.xlsx.
[31] X. Liu, J. Cao, S. Tang and J. Wen, "Wi-Sleep: Contactless Sleep Monitoring via WiFi Signals," 2014 IEEE Real-Time Systems Symposium, 2014, pp. 346-355, doi: 10.1109/RTSS.2014.30.
[32] Chen V C. The micro-Doppler effect in radar. Artech house, 2019.
[33] 3GPP TS 22.261: “Service requirements for the 5G system”.
[34] A. Chebrolu, "FallWatch: A Novel Approach for Through-Wall Fall Detection in Real-Time for the Elderly Using Artificial Intelligence", 2021 Third International Conference on Transdisciplinary AI (TransAI), 2021, pp. 57-63, doi: 10.1109/TransAI51903.2021.00018, https://ieeexplore.ieee.org/document/9565618.
[35] B. A. Alsaify et al., “A CSI-Based Multi-Environment Human Activity Recognition Framework” Applied Sciences 12, no. 2: 930, 2022. https://doi.org/10.3390/app12020930.
[36] U. Saeed U et al., "Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living", Electronics 10(18):2237, 2021. https://doi.org/10.3390/electronics10182237.
[37] C. Dou, H. Huan, "Full Respiration Rate Monitoring Exploiting Doppler Information with Commodity Wi-Fi Devices". Sensors 21, 3505, 2021. https://doi.org/10.3390/s21103505.
[38] J. Pu, H. Zhang, "RF-Heartbeat: Robust and Contactless Heartbeat Monitoring Based on FMCW Radar", 2021. TechRxiv Preprint. https://doi.org/10.36227/techrxiv.15021645.v2.
[39] H. V. Habi and H. Messer, “Recurrent Neural Network for Rain Estimation Using Commercial Microwave Links,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 5, pp. 3672-3681, May 2021, doi: 10.1109/TGRS.2020.3010305.
[40] Roberto Opromolla, etc., “Perspectives and Sensing Concepts for Small UAS Sense and Avoid”, 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC).
[41] https://www.rwjbh.org/trinitas-regional-medical-center/treatment-care/sleep-disorders/sleep-apnea/.
[42] https://my.clevelandclinic.org/health/articles/10881-vital-signs.
[43] 3GPP TR 22.855, "Study on Ranging-based Services".
[44] Guoxuan Chi, et. al., "Wi-Drone: Wi-Fi-based 6-DoF Tracking for Indoor Drone Flight Control", MobiSys 22, Association for Computing Machinery, 2022.
[45] Report on Automated Valet Parking: technology assessment and use case implementation description – 5G Automotive Association (5gaa.org). https://5gaa.org/news/report-on-automated-valet-parking-technology-assessment-and-use-case-implementation-description/.
[46] Nie Y B , Zhang L . Main amendments to Working Safety Regulation of State Grid Company(Dynamical Part for Hydrodynamic Power Plant)[J]. East China Electric Power, 2008.
[47] Giuseppe Fragapane, René de Koster, Fabio Sgarbossa, Jan Ola Strandhagen, Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda, European Journal of Operational Research, Volume 294, Issue 2,2021, Pages 405-426.
[48] Li S, Li X, Lv Q, et al. WiFit: Ubiquitous bodyweight exercise monitoring with commodity wi-fi devices, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, IEEE, 2018: 530-537.
[49] https://www.synopsys.com/automotive/autonomous-driving-levels.html.
[50] R. Bosch, “LRR3 3rd Generation Long-Range Radar Sensor,” Robert Bosch GmbH, Germany, 2009.
[51] Continental, A.G., ARS 408-21 Premium Long RangeRadar Sensor 77 GHz.ARS, pp.408-21.
[52] F. Engels et. al, "Automotive Radar Signal Processing: Research Directions and Practical Challenges," in IEEE JSTSP, June2021.
[53] I. Greshamet al., "Ultra-wideband radar sensors for short-range vehicular applications," inIEEE Transactions on Microwave Theory and Techniques, vol. 52, no. 9, pp. 2105-2122, Sept. 2004.
[54] AinsteinAutomotive Safety Radar T-79 short-range radar: https://ainstein.ai/vehicle-radar/short-range-wideband-high-resolution-automotive-radar-sensor/.
[55] National Academies of Sciences, Engineering, and Medicine. 1995. Virtual Reality: Scientific and Technological Challenges. Washington, DC: The National Academies Press. https://doi.org/10.17226/4761.
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3 Definitions of terms, symbols and abbreviations
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3.1 Terms
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For the purposes of the present document, the terms given in 3GPP 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 3GPP TR 21.905 [1].
3GPP sensing data: data derived from 3GPP radio signals impacted (e.g. reflected, refracted, diffracted) by an object or environment of interest for sensing purposes, and optionally processed within the 5G system.
5G Wireless sensing: 5GS feature providing capabilities to get information about characteristics of the environment and/or objects within the environment (e.g. shape, size, orientation, speed, location, distances or relative motion between objects, etc) using NR RF signals and, in some cases, previously defined information available in EPC and/or E-UTRA.
Human motion rate accuracy describes the closeness of the measured value of the human body movement frequency caused by part(s) (e.g. chest) of the target object (i.e. human body) to the true value of the human body movement frequency.
non-3GPP sensing data: data provided by non-3GPP sensors (e.g. video, LiDAR, sonar) about an object or environment of interest for sensing purposes.
Sensing assistance information: information that is provided to 5G system and can be used to derive sensing result. This information does not contain 3GPP sensing data.
NOTE 1: Examples of sensing assistance information are map information, area information, a UE ID attached to or in the proximity of the sensing target, UE position information, UE velocity information and etc.
Sensing contextual information: information that is exposed with the sensing results by 5G system to a trusted third party which provides context to the conditions under which the sensing results were derived. This information does not contain 3GPP sensing data.
NOTE 2: Examples includes map information, area information, time of capture, UE location and ID. This contextual information can be required in scenarios where the sensing result is to be combined with data from other sources outside the 5GS.
Sensing group: a set of sensing transmitters and sensing receivers whose location is known and whose sensing data can be collected synchronously.
Sensing measurement process: process of collecting sensing data.
Sensing receiver: a sensing receiver is an entity that receives the sensing signal which the sensing service will use in its operation. A sensing receiver is an NR RAN node or a UE. A Sensing receiver can be located in the same or different entity as the Sensing transmitter.
Sensing result: processed 3GPP sensing data requested by a service consumer.
Sensing signals: Transmissions on the 3GPP radio interface that can be used for sensing purposes.
NOTE 3: The definition refers to NR RF signals and, in some cases, previously defined information available in EPC and/or E-UTRA can be used, without leading to impacts on EPC and E-UTRA.
Sensing transmitter: a sensing transmitter is the entity that sends out the sensing signal which the sensing service will use in its operation. A Sensing transmitter is an NR RAN node or a UE. A Sensing transmitter can be located in the same or different entity as the Sensing receiver.
Target sensing service area: a cartesian location area that needs to be sensed by deriving characteristics of the environment and/or objects within the environment with certain sensing service quality from the impacted (e.g. reflected, refracted, diffracted) wireless signals. This includes both indoor and outdoor environments.
Moving target sensing service area: the case where a target sensing service area is moving according to the mobility of a target from sensing transmitter’s perspective.
Transparent sensing: sensing measurements are communicated such that they can be discerned and interpreted by the 5G system, e.g. the data is communicated using a standard protocol to an interface defined by the 5G system.
The following KPIs apply to the definition of the use cases on sensing quantitative requirements:
- Accuracy of positioning estimate describes the closeness of the measured sensing result (i.e. position) of the target object to its true position value. It can be further derived into a horizontal sensing accuracy – referring to the sensing result error in a 2D reference or horizontal plane, and into a vertical sensing accuracy – referring to the sensing result error on the vertical axis or altitude.
- Accuracy of velocity estimate describes the closeness of the measured sensing result (i.e. velocity) of the target object’s velocity to its true velocity.
- Confidence level describes the percentage of all the possible measured sensing results that can be expected to include the true sensing result considering the accuracy.
- Sensing Resolution describes the minimum difference in the measured magnitude of target objects (e.g. range, velocity) to be allowed to detect objects in different magnitude.
- Missed detection probability is the conditional probability of not detecting the presence of target object/environment when the target object/environment is present. This probability is denoted by the ratio of the number of events falsely identified as negative, over the total number of events with a positive state. It applies only to binary sensing results.
NOTE 4: An event with a positive state refers to the presence of the characteristics of a target object or environment, including the event falsely identified as being negative and truly identified as being positive
- False alarm probability is the conditional probability of falsely detecting the the presence of target object/environment when the target object/environment is not present. This probability is denoted by the ratio of the number of events falsely identified as being positive, over the total number of events with a negative state. It applies only to binary sensing results.
NOTE 5: An event with a negative state refers to the non-presence of the characteristics of a target object or environment, including the event falsely identified as being positive and truly identified as being negative
- Max sensing service latency: time elapsed between the event triggering the determination of the sensing result and the availability of the sensing result at the sensing system interface.
- Refreshing rate: rate at which the sensing result is generated by the sensing system. It is the inverse of the time elapsed between two successive sensing results.
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3.2 Symbols
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For the purposes of the present document, the following symbols apply:
<symbol> <Explanation>
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3.3 Abbreviations
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For the purposes of the present document, the abbreviations given in 3GPP 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 3GPP TR 21.905 [1].
<ABBREVIATION> <Expansion>
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4 Overview
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5G Wireless sensing is a technology enabler to acquire information about characteristics of the environment and/or objects within the environment, that uses radio waves to determine the distance (range), angle, or instantaneous linear velocity of objects, etc. The 5G wireless sensing service relies on analyzing the transmissions, reflections, and scattering of wireless sensing signals.
This technical report investigates the potential of integrated sensing and communication technology for enabling new services and use cases for various industries. 5G wireless sensing service, as part of a cellular network provides new possibilities for enhanced usage of the telecommunication infrastructure in areas of object detection and tracking, environment monitoring and human motion monitoring. It provides input to various verticals - UAVs, smart home, V2X, factories.
The use cases examined in the report cover a wide range of applications, including:
• Object and intruder detection for smart home, on a highway, for railways, for factory, for predefined secure areas around critical infrastructure
• Collision avoidance and trajectory tracking of UAVs, vehicles, AGVs
• Automotive maneuvering and navigation
• Public safety search and rescue
• Rainfall monitoring and flooding
• Health and sports monitoring
Use cases focus is on 5G wireless sensing and some of the use cases could include non-3GPP type sensors (e.g. Radar, camera).
5G wireless sensing service also brings challenges related to confidentiality and privacy. There is a need to protect the sensing data from unauthorized access, interception and eavesdropping, but also to make sure there is compliance with regulation and user awareness.
In summary, it is considered beneficial for 3GPP specifications to address 5G system support of different use cases and service requirements for Integrated Sensing and Communication.
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5 Use cases
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5.1 Use case of intruder detection in smart home
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5.1.1 Description
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Sensing in smart home is a kind of the typical scenarios of indoor/local-area sensing [8]. Considering people spends most of lifetime indoor, how to improve the user experience for indoor scenario is important. Nowadays, various 5G UEs, e.g. wearable device, sensor, smart phone and customer premise equipment (CPE), are deployed at home. In order to enjoy more comfortable and convenient indoor life, various devices are connected via wireless signals to build a smart home platform.
In addition to communication purposes, wireless signals can also be used for sensing, e.g., monitoring the home environment continuously.
For intruder detection in smart home scenario, due to the activities of indoor object or human, the 3GPP signal measured by UE or network would be influenced. By analysing and collecting the sensing information such as Doppler frequency shift, amplitude change and phase change, the behaviour of indoor object or human could be detected as shown in following figure 5.1.1-1 which takes sensing entity that transceives (transmits and receives) the signal case as example.
.
Figure 5.1.1-1 An example of sensing operation of UE
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5.1.2 Pre-conditions
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Mary and her husband Tom live in a house with little daughter Alice.
On every working day, Mary and Tom have to leave home to work, and Alice needs to go to school. Since the community where the house is located is not stable, Mary and Tom have concern on the safety of their property.
In order to address their concerns, considering protecting the personal privacy and save family cost, Mary sets up some 5G CPEs (i.e. UE) in each room at home, which support sensing functionalities.
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5.1.3 Service Flows
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Mary and all her family members travel to Hawaii in a holiday. At this time, her house is empty. Since she worries about the safety of property, she enables the sensing service on intruder detection of the 5G CPEs (i.e. UE) at home.
Mary’s CPE (i.e. UE) in the living room is activated to perform the sensing operation. While the 5G CPE transmit 5G signals to provide communication services at home, the reflected signals are also received and measured at the CPE as sensing information. The CPE reports the sensing information to 5G network or further process locally. Via the analysing the differences between the 5G signals and the received reflected signals provided by sensing service performed by 5G system, any potential intruder will not be missed.
Also, Mary’s CPE in the living room can work with other 5G UEs in other rooms. The CPE discovers that the living room has another 5G device (i.e. UE) which could assist the sensing service as secondary device via direct device connection. The connectivity used in this case is direct device connection, and CPE and this 5G device play as the role of transmitter and receiver, respectively. The receiver measures the 5G signal (e.g., number of detected transmission paths), then provides sensing information to 5G network or further process locally. Via the analysing the differences between the 5G signals and the received reflected signals provided by sensing service performed by 5G system, any potential intruder will not be missed.
An intruder breaks into Mary’s house someday. The sensing service provided by 5G network system assists detecting that the presence of an intruder based on analysing the change of collected signals is aligned with the known feature of the activities of indoor human, and the alarm of intruder is sent to Mary’s smart phone. Mary calls the police for help, and the property is protected.
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5.1.4 Post-conditions
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Thanks to the sensing service provided by 5G UE and network, an intruder is found when Mary is out of home.
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5.1.5 Existing features partly or fully covering the use case functionality
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None.
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5.1.6 Potential New Requirements needed to support the use case
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[PR 5.1.6-1] The 5G network shall provide a mechanism for an operator to authorize a UE for sensing, e.g., based on location.
[PR 5.1.6-2] The 5G system shall support a UE to perform sensing measurement process based on the trusted third-party’s request.
[PR 5.1.6-3] The 5G system shall provide mechanisms for an operator to only collect or expose the sensing information requested by a trusted third-party according to agreement.
[PR 5.1.6-4] The 5G system shall support UE to perform sensing measurement process using signals received from other UE(s).
[PR 5.1.6-5] The 5G system shall support UE to perform sensing measurement process in licensed or unlicensed band.
[PR 5.1.6-6] The 5G system shall be able to provide the sensing service with following KPIs:
Table 5.1.6-1 Performance requirements of sensing results for intruder detection in smart home
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Intruder detection in smart home
Indoor
95
≤10
≤10
N/A
N/A
N/A
N/A
<1000
< 1
< 5
< 2
NOTE: The terms in Table 5.1.6-1 are found in Section 3.1.
NOTE: In this use case UE is acting as sensing transmitter and/or sensing receiver. This is an example and other options can also be valid.
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5.2 Use case on pedestrian/animal intrusion detection on a highway
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5.2.1 Description
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Transportation as a basic and essential industry plays one of the important roles in a human’s life. Making transportation smarter can make life more convenient and benefit economic development. Highways are an important part of smart transportation. Due to the strong road safety demand on smart transportation, it is necessary to monitor the road situation so as to make appropriate management of road traffic, give guidance or assistance information to vehicles and/or highway traffic safety administration [2].
For example, major accidents caused by pedestrians or animals crossing highways occur frequently [3] [4]. Currently, the highway supervision systems are mainly based on traditional sensors (e.g. radars, cameras) equipped in the roadside infrastructure, but there are still many problems in road supervision system, e.g. it only has partial coverage along the roadside, and the radar may be dedicated for a single usage which requires deploying different types of transportation radars in the same place to satisfy the respective sense use cases and requirements in the area of interest.
Base stations on the roadside are already used to provide 5G coverage for communication, and the radio signals can also be used to sense the environment for object detection.
The assumption of this use case is the following:
- There is at least 10km long and 33m wide dual three-lane carriageway, which has a central reservation to separate the carriageways, six 3.75m wide lanes and two 3.00m wide shoulders to permit emergency stops [9].
- The size and typical velocity of traffic participant is described in the Table 5.2.1-1.
Table 5.2.1-1
Size
(Length x Width x Height)
Typical velocity
Pedestrian
(Adult)
0.5m x 0.5m x 1.75m
5km/h [9]
Animal
(Sheep/deer)
1.5m x 0.5m x 1 m
5km/h [9]
Vehicle
4m x 1.75m x 1.5m
60km/h - 120km/h
As described in the figure 5.2.1-1, when the pedestrian/animal standing at the outermost side of the shoulder starts walking on the traffic lane, it means the highway intrusion happens. The distance that pedestrian/animal move perpendicular to the traffic lane (i.e. y direction in the figure 5.2.1-1) is more sensitive for road safety, compared to the distance parallel to the traffic lane (i.e. x direction in the figure 5.2.1-1).
Figure 5.2.1-1: Intrusion detection on a dual three-lane carriageway
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5.2.2 Pre-conditions
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Good partnership and cooperation are established between the road supervision department and Mobile Operator#A in City#B. Requested by the supervision departments for the sensing service, the suitable base stations around/along a highway are selected, which enable Mobile Operator#A to constantly sense the road situation including moving objects (e.g. vehicles and pedestrians). The sensing signal emitted from the base station arrives at vehicles/pedestrians/objects on the road and is bounced (reflected) back to the transmitting base station.
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5.2.3 Service Flows
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Figure 5.2.3-1: Pedestrian/animal intrusion detection
1. Fei is a tourist, who is taking a taxi to enjoy the view around the highway in City #B. The base stations around/along the highway constantly sense the road situation. While the taxi is driving on the highway, Fei rolls down the window to take some pictures. Suddenly his mobile phone falls out the window.
2. Fei tells the driver to stop and cautiously gets out of the taxi. He crosses the highway and wants to find his mobile phone. Meanwhile, some animals (e.g. sheep and deer) from a farm near the highway approach the road. More and more surrounding vehicles are passing at very high speed. The pedestrian and animals are detected and closely tracked with sufficient accuracy in the sensing area of a base station, and then the 3GPP sensing data is transferred to the core network from the RAN and further processed into the sensing results in the core network.
3. The sensing results are exposed by the Mobile Operator #A to the road supervision departments and map provider. The map provider adds the position of the vulnerable pedestrian and animals into the HD dynamic maps and transmits warning messages to the vehicles approaching them. Alternatively, RSUs are connected to the traffic control centre for management and control purposes. RSUs transmit warning messages to the vehicles approaching them. The staff working for supervision departments immediately responds to the emergency, launching temporary traffic management, and rushes to the emergency site to fetch the mobile phone for Fei and drive the animals away from the highway.
4. Finally, Fei and animals leaves the highway safely. Potential road accident(s) caused by the pedestrian/animal intrusion are avoided.
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5.2.4 Post-conditions
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Thanks to the area-coverage, long-distance sensing capability of the base station (which provides a bird’s-eye-view for monitoring the highway environment) the precision and efficiency of highway management and safety supervision is improved. The network-based sensing can provide timely, continuous, accurate, and comprehensive sensing results, which is a reliable basis for highway safety services.
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5.2.5 Existing features partly or fully covering the use case functionality
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None.
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5.2.6 Potential New Requirements needed to support the use case
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[PR 5.2.6-1] The 5G system shall be able to support a base station to perform sensing.
[PR 5.2.6-2] The 5G system shall be able to support means to select suitable base station(s) to perform sensing, e.g. based on the base station’s location, sensing capability, and the sensing service information requested by trusted third party application.
[PR 5.2.6-3] The 5G system shall be able to support means to configure the sensing operation of a base station(e.g. authorization, sensing activation and/or deactivation, sensing duration, sensing accuracy, target sensing location area).
[PR 5.2.6-4] The 5G system shall be able to support means to enable a base station to transfer 3GPP sensing data to the core network.
[PR 5.2.6-5] The 5G system shall be able to support means to enable the core network to process 3GPP sensing data for obtaining sensing results.
[PR 5.2.6-6] Based on operator’s policy, the 5G system shall expose a suitable API to a trusted third party to provide the information regarding sensing results.
[PR 5.2.6-7] The 5G system shall be able to support charging data collection for the sensing services (e.g. considering service type, sensing accuracy, target area, duration) requested by a trusted third-party application.
[PR 5.2.6-8] Subject to operator’s policy, the 5G network may provide secure means for the operator to expose information on sensing service availability (e.g., if sensing service is available and the supported KPIs) in a desired sensing service area location to a trusted third-party.
[PR 5.2.6-9] The 5G system shall be able to support the following KPIs:
Table 5.2.6-1 Performance requirements of sensing results for pedestrian/animal intrusion detection
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Pedestrian/animal intrusion detection on a highway
Outdoor (Highway)
95
≤1
N/A
N/A
N/A
N/A
N/A
≤5000
≤ 0.1
≤5
≤5
NOTE: The terms in Table 5.2.6-1 are found in Section 3.1.
NOTE: In this use case base station is acting as sensing transmitter and/or sensing receiver. This is an example and other options can also be valid.
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5.3 Use case on rainfall monitoring
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5.3.1 Description
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Rainfall monitoring is a topic of great importance for several application contexts: hydraulic structure design, agriculture, weather forecasting, climate modelling, etc. At present, the most widely used measurement method is rain gauge.
Traditional rainfall monitoring use rain gauges, which are located at a particular location. Wide-area rainfall monitoring using traditional rain gauges would be costly. The base stations are deployed by the operators with radio cell planning that could cover a wider area. With base stations monitoring the rainfall, for example rain rate (mm/h), it could obtain a horizontally wider-area measurement.
Radio signals, as they propagate through the atmosphere, are reduced in intensity by constituents of the atmosphere. Oxygen and water vapor are the two major components which are responsible for the signal absorption. If it is a rainy day, an additional attenuation caused by rain further increases the propagation path loss. [7] The rain attenuation depends on the size and distribution of the water droplets, hence, by quantifying and modelling the base station signal measurements, we are able to know the rain rate.
The mmWave bands, such as 28GHz and 38GHz have been used to assess coverage, large-scale path loss, and fading and multipath effects [6]. Since the 28 GHz and 38 GHz bands are also licensed for wireless backhaul communications, these frequencies can be used for rainfall monitoring [7].
The granularity of the rainfall monitoring could be smaller than the traditional measurements.
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5.3.2 Pre-conditions
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Peter is a farmer who takes care of a big farm that grows different crops. Peter needs to monitor the rainfall of his farm to manage reasonable irrigation, drainage and fertilizer. When there is less rainfall, Peter can select reasonable irrigation plans to improve the farmland water content condition. When there is high rainfall, Peter should improve the drainage system and fertilize the crops to avoid crop losses.
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5.3.3 Service Flows
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1) Peter has a subscription for the premium service of rainfall monitoring for a more granular location.
2) Peter is at daily working routine and wants to check the timely rainfall information from the weather application on his phone.
3) The RAN obtains the NR based 3GPP sensing data every hour and the 5G system processes the 3GPP sensing data to obtain sensing results and exposes the NR based sensing results to the weather application via the core network.
4) Based on the sensing results above, the application server obtains the rainfall information (i.e. rainfall and whether it is raining) associated with location information.
5) Peter obtains timely rainfall information from weather application on his phone.
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5.3.4 Post-conditions
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Peter could check the rainfall information at any time on his phone. Based on the timely rainfall information, Peter could plan the irrigation, drainage and fertilizer for the crops in his farm.
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5.3.5 Existing feature partly or fully covering use case functionality
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None.
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5.3.6 Potential New Requirements needed to support the use case
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[PR 5.3.6-1] The 5G network shall support collection of the NR based 3GPP sensing data from the base station.
[PR 5.3.6-2] Based on operator’s policy, the 5G system shall support mechanisms to process the 3GPP sensing data to derive the sensing results.
[PR 5.3.6-3] Based on operator’s policy, the 5G system shall provide mechanisms to expose NR based sensing results with sensing contextual information, e.g. location, to a trusted third-party application via the core network.
[PR 5.3.6-4] The 5G system shall support sensing services with KPIs as given in Table 5.3.6-1.
Table 5.3.6-1 Performance requirements of sensing results for rainfall monitoring
Scenario
Sensing service area
Confidence level [%]
Rainfall estimation accuracy
(for a target confidence level)
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Rainfall monitoring
outdoor
95
[1mm/h]
NOTE 2
N/A
N/A
N/A
N/A
N/A
N/A
1 min
10min, application configurable
5
5
NOTE 1: The terms in Table 5.3.6-1 are found in Section 3.1.
NOTE 2: For rainfall rain rate >1 mm/h[39]. Rainfall estimation accuracy describes the closeness of the measured rainfall estimation to its true rainfall value.
NOTE: In this use case base station is acting as sensing transmitter and/or sensing receiver. This is an example and other options can also be valid.
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5.4 Use Case on Transparent Sensing Use Case
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5.4.1 Description
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In general, a UE senses using either or combination of the non-3GPP sensors such as camera, Lidar, 3GPP-based sensing. In 3GPP 5G wireless sensing, the Sensing transmitters and Sensing receivers sense for stationary and moving Objects around them – using time-difference-of-arrival (TDoA), angle-of-arrival (AoA), angle-of-departure (AoD) measurements, RSSI etc. as shown in Figure 5.4.1-1 [14]. Transparent sensing is a use case in which 3GPP sensing data is captured by Sensing transmitter and/or Sensing receiver and communicated so that the 5GS is aware of the 3GPP sensing data, while the non-3GPP sensing data is the result of non-3GPP sensors and is transparent to 5GS. From this information, service enablers can be defined. One example of such information is location data, whose corresponding service enabler is Location Based Services.
Figure 5.4.1-1: BS and UE sensing Objects
In this use case, non-3GPP sensing data is made available to the 5GS, and the requirements for this exposure are considered. The data so obtained can be used for diverse purposes. One such purpose is Localization (identifying both a three- dimensional position and orientation.) Transparent Sensing data used for Localization is described in TR 22.856 [11].
Figure 5.4.1-2: Opaque and Transparent Sensing Data
The distinguishing characteristic of this use case is that the non-3GPP sensing data is provided to the 5GS itself.
The application server receiving 'transparent non-3GPP sensing data' as shown in figure 5.4.1-2 can be operated by the MNO. This enables the MNO to provide specific processing to produce 'combined sensing results as a service,' where the sensing data is supplied by non-3GPP sensors owned and operated by third parties, subscribers, etc.
In this use case it is the 5GS that receives 3GPP and non-3GPP sensing data, not a third party.
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5.4.2 Pre-conditions
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A UE has access to one or more sensors. In this use case. the UE has access to four sensors: NR-based sensing, 3D LiDAR, an RGB Camera and a Smart Phone Camera. The sensors' physical configuration is known (e.g. the cameras are 10 cm apart). The NR-based sensing capabilities of the UE and its connected BS are used to capture information about the nearby environment by the UE.
A mobile network MN supports the acquisition of non-3GPP sensing data. We term this support by the network a 'non-3GPP sensing data consuming service'.
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5.4.3 Service Flows
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The user U activates a mechanism to enable Non-3GPP sensing data acquisition that can be collected at U's UE.
The user U provides this non-3GPP sensing data via the 5GS. This process is analogous to activating or enabling a location tracking service.
MN acquires sensing data provided by U's UE, for a period of time.
MN can also acquire 3GPP sensing data. 3GPP-RF sensing data can be processed only in 5GS to derive sensing results. The sensing results and the Non-3GPP sensing data can be combined to produce a combined sensing result.
The user U deactivates the mechanism to provide non-3GPP sensing data to the 5GS.
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5.4.4 Post-conditions
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The non-3GPP sensing data acquired by the 5GS is processed in order to enable other services. The processed information can for example provide 'Spatial Localization' information that can be exposed to authorized third parties, as discussed in 22.856 [11]. "Spatial Localization Use Case".
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5.4.5 Existing feature partly or fully covering use case functionality
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Positioning in 5G Networks been proposed in 3GPP release-16, it specifies positioning signals and measurements for the 5G NR. In release-16, 5G Positioning architecture extends 4G positioning architecture by adding Location Management Function (LMF) and Transmission reception points (TRP). 5GS provides new positioning methods based on multi-cell round-trip time measurements, multiple antenna beam measurements, to enable downlink angle of departure (DL-AoD) and uplink angle of arrival (UL-AoA) [15][16]. The Rel-17 5G system supports positioning of the device-based but not device-free – objects that do not radiate EM signals [14][15][16].
The 5GS already supports transport of non-3GPP sensor data. The table below provides indicative performance requirements for media used for sensor information communication.
Sensor Type
Uplink KPI
Remarks
3D Lidar
30 Mbps
An example 3D LiDAR: 16 channel, 0.3M data points, dual return mode
2 bytes distance, 1byte [13]
Industrial RGB Camera
16 ~ 800 Mbps
2,592 x 2,048 x 10bits x 2.5 Hz x 6 EA, compression ratio 2%
Smart Phone Camera
4 ~ 200 Mbps
2,160 x 2,880 x 8bits x 1 Hz x 4 EA, compression ratio 2%
Table 5.4.5-1: Performance Requirements (already possible to fulfill with the 5GS)
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5.4.6 Potential New Requirements needed to support the use case
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[PR 5.4.6-1] Subject to user consent and national or regional regulatory requirements, based on operator policy, the 5GS shall support a mechanism to receive uplink non-3GPP sensing data from authorized non-3GPP sensors.
NOTE 1: This requirement assumes there is some functionality in the 5GS to discern and interpret the acquired 3GPP and non-3GPP sensing data.
[PR 5.4.6-2] Subject to user consent and national or regional regulatory requirements, based on operator policy, the 5GS shall support a mechanism to expose sensing results to trusted third parties.
[PR 5.4.6-3] Subject to user consent and national or regional regulatory requirements, based on operator policy, the 5GS shall support a mechanism to expose combined results to trusted third-parties.
[PR 5.4.6-4] Subject to user consent, network operator policy and national or regional regulatory requirements, the 5GS shall support a mechanism to enable Sensing transmitters and Sensing receivers to acquire 3GPP sensing data to capture information about the nearby environment and for this to be combined with Non-3GPP sensing data to produce a combined sensing result.
NOTE 2: This requirement does not imply or allow 3GPP sensing data to be exposed to third parites. This data is considered confidential.
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5.5 Use case on sensing for flooding in smart cities
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5.5.1 Description
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Due to the climate change in recent years, a larger amount of rain sometimes falls within a short duration of time inside a small area. This result, in particular in urban areas, in inundation and flooding even in areas where these did not happen in the past. When flooding is about to happen on roads, people might enter areas getting in danger without knowing it. Once flooding really happens there, this might result in loss of human life. At places where flooding is expected to occur, monitoring of flooding is performed using cameras and other sensors. However, due to the recent climate change, it can be difficult to recognize places where flooding is expected to occur. Using radio waves, it is possible to recognize places where flooding occurs in an efficient way.
NOTE: There has been a related trend, although a mobile communication is not directly involved so far and it's monitoring of the river, not of the road as this use case deals with. In Japan, MLIT (Ministry of Land, Infrastructure, Transport and Tourism) takes care of the river administration and supervises water level observation of rivers to prevent and predict flooding. In the past, water-level gauges were only sparsely deployed along the river. Water levels at places where those gauges were not placed were estimated based on water levels observed some distance away where such gauges were placed. Detailed degree of possibility of flooding at each place was not directly understood. To improve this situation, MLIT has encouraged to develop a low-cost water-level gauge and has started placing such gauges e.g., at places that are relatively prone to flooding or show a specific water behavior due to the form of the river, or that are close to hospitals or important facilities. Disaster Information for River is now available at https://www.river.go.jp/e/ for public.
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5.5.2 Pre-conditions
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Good partnership and cooperation are established between Mobile Operator #A and administrators of roads such as a local government in City #B. Mobile Operator #A constantly senses the surface of the road and informs results of sensing to the administrator of the road.
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5.5.3 Service Flows
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Figure 5.5.3-1: Sensing for flooding in smart cities
1. Base stations owned by Mobile Operator #A are deployed around the road. Mobile Operator #A carries out sensing of the surface of the road in City #B. This sensing is performed using radio wave. Results of sensing information, incl. whether flooding occurs on the road, are informed to the administrator of the road in City #B.
2. The administrator of the road usually monitors the state of flooding on the road using information from sensors including information from Mobile Operator #A. In addition, in the case of heavy rain, the administrator can request Mobile Operator #A to increase frequency of monitoring of situation of roads and Mobile Operator #A monitors the situation more frequently responding to this request.
3. If there is information received that flooding occurs, the administrator advises people in the areas concerned to evacuate the areas. The administrator advises via mobile networks.
4. People who received the advice evacuate the areas or do not enter such areas.
5. Now City #B trusts Mobile Operator #A and allows it to advise people about evacuation without City #B's intervention in case of flooding. Next time a similar flooding occurs, Mobile Operator #A sends advice for evacuation directly to people.
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5.5.4 Post-conditions
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Damage of the flooding has been kept at minimum.
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5.5.5 Existing features partly or fully covering the use case functionality
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None.
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5.5.6 Potential New Requirements needed to support the use case
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[PR 5.5.6-1] Subject to operator policy, the 5G system shall be able to provide sensing result indicating disasters or other emergencies (e.g., flooding) in a given geographic area to authorized third parties in a timely manner.
[PR 5.5.6-2] Subject to regional or national regulatory requirements and operator policy, the 5G system shall be able to provide its public warning system with a warning notification based on sensing result indicating disasters or other emergencies (e.g., flooding) in a given geographic area in a timely manner.
[PR 5.5.6-3] Subject to operator policy, it shall be possible for an authorized third party to configure the 5G system to initiate sensing for disasters or other emergencies (e.g., flooding) in a given geographic area.
[PR 5.5.6-4] The 5G system shall be able to support the following KPIs:
Table 5.5.6-1 Performance requirements of sensing for flooding in smart cities
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
sensing for flooding in smart cities
Outdoor
95
≤10
[≤0.2]
NOTE 2
N/A
N/A
N/A
N/A
≤ 1min
NOTE 3
< 1min
NOTE 3
< 0.1
< 3
NOTE 1: The terms in Table 5.5.6-1 are found in Section 3.1.
NOTE 2: This value is for the water level. Description related to NOTE in clause 5.5.1 suggests 0.01 m. [≤0.2] is derived from the water level where people feel difficulty in walking.
NOTE 3: Description related to NOTE in clause 5.5.1 suggests 2 minute-interval monitoring when the water level of the river rises quickly.
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5.6 Use case on intruder detection in surroundings of smart home
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5.6.1 Description
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Detection of an intruder including a person or a harmful animal into a private property is an important piece to ensure residents at home in the private property feel comfortable and secure. For the surroundings monitoring, various technologies, such as cameras, infrared cameras, and microwave radars are being used. However, these technologies require line-of-sight, and therefore locations which can be monitored may be limited.
Wireless signals make it possible to monitor locations without line-of-sight and to monitor wider areas [17]. Sensing by wireless signals can complement the afore-mentioned technologies and can improve accuracy of the detection. Sensing by wireless signals gives residents time to prepare against intruders or to drive them away.
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5.6.2 Pre-conditions
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UEs such as smart phones and consumer premise equipment are installed inside a house, in particular, near a wall or a window. Residents have a contract with a mobile operator for the UEs.
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5.6.3 Service Flows
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Figure 5.6.3-1: Intruder detection in surroundings of smart home
1. The UEs such as smart phones and CPE communicate with base stations in the outdoor or in the indoor and monitor 3GPP signals which are influenced by outdoor objects such as humans and animals. In addition, the UEs communicate with base stations of the mobile operator and monitor the radio wave state between the UEs and the base stations.
2. When an intruder enters the site, the radio signals are changed. The core network processes the data and yields sensing result indicating detection of the intruder.
NOTE: Cases that such an intruder or an animal is already indoor are addressed in the use case in clause 5.1.
3. The residents are informed of detection of the intruder.
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5.6.4 Post-conditions
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The residents report to the police or the security service and request them to take an appropriate action.
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5.6.5 Existing features partly or fully covering the use case functionality
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None.
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5.6.6 Potential New Requirements needed to support the use case
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[PR 5.6.6-1] Subject to operator policy, the 5G system shall be able to collect 3GPP sensing data and yield sensing result from the data for detection of outdoor objects.
[PR 5.6.6-2] The 5G system shall be able to support the following KPIs:
Table 5.6.6-1 Performance requirements of intruder detection in surroundings of smart home
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
intruder detection in surroundings of smart home
Outdoor
95
≤2
N/A
N/A
N/A
N/A
N/A
≤1000
< 1
< 0.1
< 5
NOTE: The terms in Table 5.6.6-1 are found in Section 3.1.
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5.7 Use case on sensing for railway intrusion detection
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5.7.1 Description
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Extensive railway deployment and the changing wildlife habitat area due to the changing global environment has led to increase of crash of wildlife to trains. Once a crash happens, its recovery costs, takes time, and impairs convenience [18], [19]. Such a crash should be avoided, but it appears difficult to proactively predict wildlife's intrusion onto railway track. It's different from e.g., weather forecast. Passively detecting wildlife's intrusion onto railway track appears an option to take. Monitoring with cameras serves the same purpose. However, this requires LOS (i.e., line of sight) and a dense deployment of cameras, which is not necessarily efficient. Another traditional mechanism using fibre optic sensing techniques is costly and requires manual intervention, making it very difficult to meet the increasing demand for railway monitoring. Thanks to the 5G NR based sensing, the base station as transmitter and receiver along the railway can constantly sense the railway situation such as railway intrusion.
The assumption of this use case is the following:
- There is at least 300km train line as depicted in the figure 5.7.1-1[19] owned by railway operator. The safe place is a place where a person and their equipment cannot be struck by rail traffic, which is used for minimizing damage caused by possible railway accident or crash and for ensuring safe operation of railway. The danger zone is anywhere within 3m horizontally from the nearest track.
- The typical size and velocity of intruder and train in this use case are described in the Table 5.7.1-1.
Table 5.7.1-1
Size
(Length x Width x Height)
Velocity
Intruder
Pedestrian(Adult):
0.5m x 0.5m x 1.75m
5km/h
Animal(Sheep/deer):
1.5m x 0.5m x 1 m
5km/h
Trains
24m x 3.5m x 3 m
100km/h - 350km/h
When the intruder standing at the outermost side of safe place starts walking on the danger zone, it means the intrusion happens. The distance that intruder move perpendicular to the railway track is more sensitive for road safety, compared to the distance parallel to the railway track.
Figure 5.7.1-1
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5.7.2 Pre-conditions
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Base stations are deployed near and along a railway track which enable the mobile operator to constantly sense the railway including intruder (e.g., pedestrians and animal). For sensing, signaling transmitted by a base station is influenced or bounced by objects around the railway and then monitored by the base station and other base stations. Sensing result is being notified to a railway operator by the mobile operator. The railway operator knows locations of trains.
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5.7.3 Service Flows
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Figure 5.7.3-1 Railway intrusion detection
1. Base stations are deployed near and along a railway track. In order to acquire the sensing information of railway, railway operator requests sensing service from mobile operator. The mobile operator configures the base stations along the train line to perform sensing. Suddenly, an intruder (e.g. pedestrian or animal) is walking on the danger zone.
2. The 3GPP sensing data is reported from base stations and further processed into the sensing results by the core network. The mobile operator exposes the sensing results to the railway operator. Based on the sensing results, the location of the intruder can be estimated.
3. Trains running on the railway track measure their own location and velocity. These trains inform that information to a controller of the railway operator.
4. The controller identifies a train that is affected by an intruder based on the sensing results from mobile operator and train's location and velocity.
5. The controller orders the train to slow down or stop. In addition, the staff working for railway operator immediately responds to the emergency. The intruder leaves the danger zone safely.
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5.7.4 Post-conditions
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The controller judges the intruder is gone and safety can be ensured. The controller permits the train to start again or speed up.
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5.7.5 Existing feature partly or fully covering use case functionality
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TBD.
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5.7.6 Potential New Requirements needed to support the use case
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[PR 5.7.6-1] Subject to operator policy, the 5G system shall enable the core network to collect and aggregate 3GPP sensing data data from RAN.
[PR 5.7.6-2] Subject to operator policy, the 5G system shall enable the core network to expose a suitable API to provide the information regarding sensing results to authorized third parties.
[PR 5.7.6-3] The 5G system shall be able to support the following KPIs:
Table 5.7.6-1 Performance requirements of sensing results for railway intrusion detection
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Intrusion detection on a railway
Outdoor (Along railway)
95
≤1.5
N/A
N/A
N/A
N/A
N/A
˂1500
≤ 0.1
2
2
NOTE: The terms in Table 5.7.6-1 are found in Section 3.1.
NOTE: In this use case base station and UE is acting as sensing transmitter and/or sensing receiver. This is an example and other options can also be valid.
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5.8 Use Case on Sensing Assisted Automotive Maneuvering and Navigation
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5.8.1 Description
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To support smart transportation and autonomous driving, more vehicle and devices are equipped with sensing technologies. For example, cameras, Radar, and Lidar systems are the most used sensors by the automotive industry to maintain the perception for autonomous vehicles at various levels of autonomy. Accurate sensing results are crucial to enable the safe and reliable control of the vehicles.
Due to the mounting position of the sensors (e.g., 3GPP based sensors) information collected from a single vehicle's sensors can not be sufficient or accurate enough to satisfy the advanced automotive use cases, e.g., autonomous driving, coordinated maneuver, etc. Therefore, the 5G system could coordinate sensing to get sensing data from various sources and generate sensing results which could be consumed at the vehicle and used for the vehicular control and driver assistance, e.g., feed into the Automated Driving System (ADS) in the car [21]. The 3GPP sensing data collected by the UE can be sent alongside relevant sensing information to other sensing entities (including other vehicles, roadside units, and network) for further processing (if required) before sharing with a third-party application as shown in Figure 5.8.1-1.
The network facilitated NR based sensing described above could significantly improve the sensing reliability and quality, enabling new and advanced automotive use cases.
Figure 5.8.1-1: 5G System Assisted Automotive maneuvering and navigation
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5.8.2 Pre-conditions
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In this use case, Joe and Bob’s vehicles are equipped with 3GPP-based sensing technology. Non-3GPP sensors like radar, camera and Lidar sensors could also be available in the vehicles. Additionally, the vehicles are capable of 5G communications, including direct communication with other vehicles, communication with 5G system via RAN entities.
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5.8.3 Service Flows
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5G system assisted coordination of sensing service
Step 1 (Network provides configurations and policies): When Bob’s car registers for 3GPP sensing service, the network provides policies and configurations to enable UEs take appropriate actions during sensing e.g., obtaining 3GPP sensing data from another UEs/RAN entities. For example, the policies provided by network could provide guidance for the discovery UEs/RAN entities with appropriate NR RF sensing capabilities, when to trigger requests, when to stop sending requests, messaging formats, the communication configurations (such as which 5G communication mode to use and under which conditions), the sensing configurations (such as which role i.e. transmitter/receiver, to use by a particular node for a particular sensing task, etc.. These polices and configurations could be updated frequently by the network based on e.g., network conditions, mobility pattern, etc.
Step 2 (Bob determines his sensors are blocked): Bob's sensor(s) is(are) blocked by Joe's vehicle, and cannot adequately detect its surroundings (e.g., detect if there is another vehicle in front). This could result in the vehicle miscalculating the needed distance to stop before a traffic light. In other cases, Joe's vehicle could also reduce the valid sensing region and result in misdetection of incoming vehicles size or shape, especially near intersections. The sensing results cannot fully satisfy the autonomous driving needs and requirement.
Step 3 (Bob recognizes need for sensing inputs): Due to unsatisfactory autonomous driving needs and requirements, the UE in Bob's vehicle is notified that its sensors are blocked and needs 5G System assistance for coordination of the sensing service.
Step 4 (Bob’s vehicle discovers Joe’s vehicle): With the policies and configurations provided by the 5G system, Bob’s vehicle can search for neighbouring UEs/RAN entities or ask the network to provide recommendations for UEs/RAN entities (e.g. considering the current network conditions in the target sensing area) and their 3GPP NR RF sensing capabilities (e.g., if UE/RAN entity supports sensing service). This information would be used to discover other vehicles and RAN entities with 3GPP NR RF sensors that can support sensing in the area. In this example, Bob's vehicle discovered Joe's vehicle could be useful in providing sensing inputs.
Step 5 (Bob’s vehicle connects to Joe’s vehicle): Bob's vehicle then establishes 5G communication connection with Joe's vehicle and/or RAN entities as shown in Figure 5.8.3-1. The most suitable 5G communication mode (e.g., broadcast, unicast, etc) is determined by the Bob’s vehicle based on 5G system configuration and policies.
Step 6 (Bob's vehicle requests sensing info from Joe’s vehicle). The request could indicate the information needed to perform sensing, e.g., the additional region to be covered, additional sensing target, synchronization info, etc.
Step 7 (Joe sends sensing results/3GPP sensing data to Bob’s vehicle) Based on the information provided by Bob’s vehicle; Joe sends Bob 3GPP sensing data identifying objects in its surroundings. It is important to note that when 3GPP sensing data is shared between Joe and Bob, it is expected to be performed in compliance with operator policy on the use of the operator resources (e.g., licensed/unlicensed spectrum).
Step 8a (Bob processes 3GPP sensing data locally) Based on the fact that Bob’s has non-3GPP sensors (e.g., camera, Lidar), Bob’s car can combine the 3GPP sensing data from Joe’s vehicle with other sensors.
Step 8b (The 5G System expose sensing results to third-party application) Additionally or alternatively Bob can share sensing results and non-3GPP sensing data from the camera and Lidar within the 5G System and then it is exposed by the 5G System to a third-party application server for combination by the third-party. It is important to note that contextual information is information forwarded alongside the sensing results which provide context to the conditions under which the sensing results were derived. This contextual information can be used in scenarios where the sensing result is to be combined with data from other sources. It should also be noted that in case contextual information is required, this information should be shared with the appropriate consent, permissions and subject to operator policy.
Figure 5.8.3-1: 5G system assisted automotive maneuvering and navigation
With the sensing information provided by Joe’s vehicle and the network, Bob’s vehicle obtains a full map of the region. The autonomous driving algorithm can make corresponding decisions reliably.
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5.8.4 Post-conditions
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Using 5G system assistance, Bob’s vehicle would be able to achieve highly reliable navigation capacity, by coordinating the operation with other vehicles to collaborate with other sensing devices to improve quality. With high-quality sensing results, advanced smart transportation use cases and autonomous driving could be achieved.
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5.8.5 Existing features partly or fully covering the use case functionality
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V2X communication supports the information exchange among the vehicles, between vehicle and infrastructure or network.
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5.8.6 Potential New Requirements needed to support the use case
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[PR 5.8.6-1] The 5G system shall be able to support mechanisms to control UEs and RAN entities for a sensing service.
NOTE 1: In the requirement above, control can include configuration such as sensing specific policies and settings (e.g., conditions for triggering sensing requests, location, etc.) coordinated amongst UE and RAN entities.
[PR 5.8.6-2] For a sensing service, the 5G system shall be able to support mechanisms for the UEs and RAN entities to provide 3GPP sensing data.
NOTE 2: This requirement can cover scenarios making use of information already available in the EPC and E-UTRA (assuming no new functionalities are required in the EPC and E-UTRA).
[PR 5.8.6-3] The 5G system shall be able to support an authorized UE in the discovery of UEs and selection of RAN entities with the required 3GPP NR RF sensing capabilities for the sensing service.
[PR 5.8.6-4] Subject to user consent and regulations, based on operator policy, the 5G system shall be able to provide means to authorize and configure a UE for sensing operation (e.g., based on location, time, etc) and for establishing the communication connection needed to assist the sensing service.
NOTE 3: The above requirement assumes that the communication connection used for assisting sensing service (e.g. for transferring 3GPP sensing data or sensing results) can include existing communication connection modes such as direct network communication, direct device connection under network coverage and indirect network connection [33].
[PR 5.8.6-5] Subject to user consent and regulations, based on operator policy, the 5G system shall be able to support exposure of sensing results and sensing contextual information (e.g. UE location), to a trusted third-party application.
[PR 5.8.6-6] The 5G system shall be able to provide means for the 5G network to activate and/or deactivate sensing service in the target sensing area based on network conditions (e.g., network-load).
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5.9 Use case on AGV detection and tracking in factories
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5.9.1 Description
|
Improving safety and work conditions in factories and industrial environments is a critical component for industry 4.0. Replacing communication cables with wireless connections has already positively changed the factory environment, by providing reliable ethernet-like communications, and enabling time-sensitive networking over the air. Nevertheless, despite automation and improvement, accidents in factories still occur, leaving room for improvement. Indeed, 5250 fatal work injuries were recorded in the US only in 2018, according to the Bureau of Labour Statistics [22], a 2% increase from 2017.
Automated Guided Vehicles (AGVs) are key components of the new smart factories, used for a variety of tasks such as heavy or hazardous materials transportation and distribution. Simultaneous presence of AGVs and human workers at the industrial side creates safety challenges and calls for stringent safety requirements [23]. For example, the driverless, automated guided industrial vehicles ANSI/ITSDF B56.5 [24] safety standard requires that “the AGV shall detect and avoid both static and dynamic obstacles appearing in the path of travel direction”. Reliable detection of AGV/human presence or proximity is therefore an important safety criterion.
5G system can be deployed in a factory which uses RAN entities and/or UEs to measure 3GPP sensing data, that are made available to sensing management entities in order to derive sensing results such as the detection of the presence or proximity of AGVs and humans. This use case assumes support of NR-based RF sensing.
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5.9.2 Pre-conditions
|
Company #A operates multiple AGV in its factory. Each AGV is programmed to perform certain tasks, such as transporting large containers from point #B to point #C following a programmed route. AGVs can be of various sizes and operate at different speeds and locations. In a factory, workers are dispersed throughout the area, performing different tasks. Workers-AGVs interactions are a source of potential injuries, and extra care needs to be taken to avoid any harm.
The factory deploys 5G based integrated communication and sensing system with RAN entities and UEs throughout the factory floor. The RAN entities deployment is done to optimize communication, positioning, and sensing.
The RAN entities and/or UEs perform sensing operations over certain target areas throughout the factory. The deployed RAN entities (or a subset of the RAN entities) transmit sensing reference signals, which are received by a subset of RAN entities and/or selected UEs. Some UEs are authorized and configured to monitor the sensing reference signals and report 3GPP sensing data to a sensing entity in the 5G system. The sensing entity can be deployed either locally in the factory or in the cloud/edge.
In this use case it is important to note that AGVs do not actively participate in the sensing signals transmission or reception, and hence it is more applicable to AGVs which are not equipped with UEs, e.g., legacy AGVs. For those AGVs with UEs, the UEs can be helpful in sensing and tracking humans on the factory floor.
5.9.3 Service Flows
Figure 5.9.3-1: AGV presence and proximity detection
1. Alex is working in his section of the factory (shown in the lower left area in Figure 5.9.3-1), performing regular maintenance work around a conveyor belt.
2. An AGV, AGV#1, is approaching the area where Alex is working, carrying a heavy load to be placed at a designated location next to the conveyor belt.
3. Using the 3GPP sensing data from the RAN entities and the UE carried by Alex, the sensing entity processes the data to obtain sensing results and detects the proximity of the AGV1 to Alex. The sensing results are shared with a safety monitoring application of the factory, and a notification is sent to Alex to warn him of the approaching AGV.
4. Another AGV, AGV#2, enters an area (lower right area in Figure 5.9.3-1) with increased risk for workers due to higher workers presence and higher equipment and machines density. Based on the 3GPP sensing data from RAN entities, the sensing entity processes the data to obtain sensing results and detects the presence of AGV#2 and exposes the detection event to the factory safety monitoring application. The safety monitoring application triggers a warning sound to warn the workers (e.g., John and Emma) in that area of the approaching AGV.
Note that, in this scenario, none of the UEs was involved in the sensing session. However, the sensing entity can use 3GPP sensing data from UEs in the area (e.g., UE carried by Emma) in its sensing processing if available.
5. In another scenario, John (lower right area in Figure 5.9.3-1), working on his section, not having a UE, is being tracked using 3GPP sensing data from RAN entities and/or UEs. When John comes in proximity with an AGV, which has or does not have a UE, a warning message is sounded to alert John.
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5.9.4 Post-conditions
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Thanks to the warning messages, workers are safe and potential accidents caused by workers-AGVs interactions are avoided. By leveraging the sensing capability of the 5G based integrated communication and sensing system, the factory safety supervision is upgraded, and workers safety is enhanced.
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5.9.5 Existing features partly or fully covering the use case functionality
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None.
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5.9.6 Potential New Requirements needed to support the use case
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NOTE 1: The following requirements apply to networks managed by PLMN or NPN.
[PR 5.9.6-1] The 5G system shall be able to provide means to support NR-based sensing in a certain area or location.
[PR 5.9.6-2] Based on operator policy and location area, the 5G system shall be able to provide means to support per-UE authorization for NR-based sensing.
[PR 5.9.6-3] The 5G system shall be able to support means to enable RAN entities and UEs to transfer 3GPP sensing data to sensing processing entities in the 5G system responsible for processing and aggregation of the 3GPP sensing data.
NOTE 2: The “Sensing processing entities” in the above requirement refer to one or more entities in the 5G system responsible for aggregating and processing of 3GPP sensing data (e.g., core network).
[PR 5.9.6-4] Based on operator’s policy, the 5G system shall be able to support means to expose sensing results to a trusted third-party application.
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5.10 Use case on UAV flight trajectory tracing
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5.10.1 Description
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With the development of UAV technologies and the increase of demands on rapid logistics, aerial photographing, environmental monitoring and public security, a variety of commercial UAV services gradually become reality.
Normally the commercial UAVs fly based on predetermined flight routes, following regulated positions, heights, speeds, and directions. E.g., a package-delivery UAV flies from the package sender to the package recipient; a task-execution (such as environmental monitoring) UAV flies from the UAV airport to the target area.
On-route flying is important for these commercial UAVs. Their flight routes are optimized and permitted by UAV service operators, UAV management department, or USS (Uncrewed Aerial System Service Supplier)/UTM (Uncrewed Aerial System Traffic Management). Usually, they have the shortest flight distance, avoid no-fly zone, and keep safe distance from obstacles (e.g., building, trees, hills) and other commercial UAVs.
Although a UAV is equipped with sensors to keep itself along the flight route, the external UAV flight trajectory tracing function is still necessary because these sensors sometimes are restricted. E.g., the camera is impacted by light situation; the UAV-borne radar is impacted by rainfall or snowfall, etc. If these events occur, UAV cannot correctly decide its own position, height or speed, and thus cannot follow the traced route.
Although there exist dedicated UAV surveillance equipment and radar, their large-scale deployment has great challenges due to lack of available sites and high installation and maintenance cost.
In comparison, using the 5G system can provide a cost-effective way to trace these UAVs, e.g., 5G network infrastructures with ubiquitous coverage can better trace the flight trajectory of each UAV.
Specifically, 5G RAN entities can rely on radio sensing to obtain the information on UAV position and motion (e.g., distance, angle) and send 3GPP sensing data to a sensing processing entity located in the 5G system.
As shown in Figure 5.10.1-1, the UEs that are connected to the 5G RAN entities can be configured to assist in the sensing operations, which can increase the sensing coverage, provide more positioning reference points, and improve sensing result accuracy and robustness. This improvement is a result of higher density of UEs compared to the base stations, which increases the probability that some UEs are located in positions that have shorter distance away from UAV than 5G RAN entities (e.g., UAV located in the middle of two 5G RAN entities while UE locates under UAV), or some UEs are located in the reflection directions that have larger radar cross section (RCS) than 5G RAN entities considering the UAV RCS variation in different reflection directions.
The 5G sensing processing entity can collect the sensing data from one or multiple network infrastructures.
The 5G network operator can provide the UAV flight trajectory tracing service to a trusted third-party application (e.g., UAV service operator, UAV management department, USS/UTM) as requested.
Figure 5.10.1-1: UAV flight trajectory tracing by 5G system
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5.10.2 Pre-conditions
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A UAV operator/UTM provides package delivery service in an area which is covered by 5G network. The UAV operator/UTM subscribes to the UAV flight trajectory tracing service from the 5G network operator.
The UAV operator/UTM provides the 5G network operator the characteristics of the UAV to be sensed, time and space (covering the regulated UAV flight routes and possible off-route locations) of the UAV flight trajectory tracing service.
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5.10.3 Service Flows
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When the appointed time starts, 5G network operator activates the UAV flight trajectory tracing function at the appointed space until the appointed time ends.
The UAV operator controls UAV#1 to take off from package delivery source and fly toward package delivery destination along a regulated flight route.
By radio sensing, a set of 5G base stations and UEs detect UAV#1, and then estimate the position and motion related metrics (e.g., distance, angle) as well as the target object is in coverage, resulting in 3GPP sensing data. The 5G RAN and UEs then send the 3GPP sensing data to the 5G sensing processing entity.
In certain cases, during the flying course, based on sensing and location information, if it is detected that UAV#1 has left the coverage of an old base station and entered the coverage of a new base station, the old base station could stop radio sensing and operate in a power saving mode. The new base station starts and keeps on sensing UAV#1 until it is out of coverage. Note that the determination that the UAV#1 has left the coverage of a base station or not could be determined based on the UAV positions and velocities estimated at the 5G sensing processing entity. Therefore, the network could then decide to activate and deactivate sensing in certain base stations based on this information. In other cases, the network could configure a start and stop of sensing operations for a base station based on a specified time period. In some other cases, during the flying course of the UAVs, based on location information, flying trajectory, sensing requirements, network conditions (e.g. network load) etc., if it is detected that the sensing coverage of the current base station monitoring UAV#1 has weakened and/or a new base station is available that can provide better sensing coverage to monitor UAV#1, a proactive sensing handover can be triggered. This would be useful for the sensing service continuity.
The 5G sensing processing entity collects the UAV 3GPP sensing data from one or multiple RANs and UEs, and estimates the positions and velocities, and sends in real time the sensing results (e.g., UAV positions, velocities) to the UAV operator and/or UTM.
Based on the received sensing results, the UAV operator and/or UTM traces the flight trajectory of UAV#1. Once the UAV operator and/or UTM detects an off-route event, it further steers UAV#1.
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5.10.4 Post-conditions
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UAV#1 delivers package to the destination along the traced flight route or its off-route behavior is sensed.
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22.837
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5.10.5 Existing features partly or fully covering the use case functionality
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None.
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22.837
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5.10.6 Potential New Requirements needed to support the use case
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[PR 5.10.6-1] Based on operator policy, request from UTM and sensing configuration (e.g. sensing area), the 5G system shall be able to support RAN entities and UEs in sensing the characteristics of an airborne object of interest (e.g., UAV), including generating 3GPP sensing data related to the object’s location and motion metrics (see examples in Table 5.10.6-1).
[PR 5.10.6-2] The 5G system shall be able to support means to authorize RAN entities and UEs in certain location area generating and reporting 3GPP sensing data (e.g., related to a UAV position, velocity) to a 5G sensing processing entity.
NOTE 1: The requirement above assumes that the 3GPP sensing data is post-processed in 5G sensing processing entity which is located within the 5G system.
[PR 5.10.6-3] The 5G system shall be able to support means to process the 3GPP sensing data and expose in real time the sensing results (e.g., related to a UAV position, velocity) from a 5G sensing processing entity to a trusted third-party application.
[PR 5.10.6-4] The 5G system shall support energy efficient sensing operations.
NOTE 2: Examples of energy efficient sensing operations can include temporarily disabling sensing transmitters and receivers that are not involved in sensing and communication operations or adjusting the sensing operation parameters (e.g. sensing frequency).
[PR 5.10.6-5] Subject to operator’s policy, the 5G network may provide secure means for the operator to expose information on sensing service availability (e.g., if sensing service is available and the supported KPIs) in a desired sensing service area location to a trusted third-party.
[PR 5.10.6-6] The 5G system shall be able to provide the means for supporting sensing service continuity.
[PR 5.10.6-7] The 5G system shall support sensing services with KPIs as given in Table 5.10.6-1.
Table 5.10.6-1 Performance requirements of sensing results for UAV flight trajectory tracing
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution (horizontal/vertical)
[mxm]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
UAV flight trajectory tracing
Outdoor
N/A
1-2
1-2
1-2
1-2
1m x 1m ~10m x 10m NOTE 2
1m/s x 1m/s ~ 10m/s x 10m/s NOTE 3
100~1000 NOTE 4
1Hz
NOTE 5
5
5
NOTE 1: The terms in Table 5.10.6-1 are found in Section 3.1.
NOTE 2: To detect the UAV existence (e.g., for intrusion detection), the sensing resolution of distance is 10m [25]. To track the UAV flying (e.g., for collision detection and warning), the sensing resolution of distance is 1m [25].
NOTE 3: To detect the UAV existence, the sensing resolution of velocity is 10m/s [25]. To track the UAV flying, the sensing resolution of velocity is 1m/s [25].
NOTE 4: To realize 1m granularity tracking, when the velocity resolution is 1~10m/s, the maximum corresponding sensing service latency is 0.1~1s.
NOTE 5: Echodyne MESA-DAATM has approximate 1Hz scan rate [40].
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5.11 Use case on sensing at crossroads with/without obstacle
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5.11.1 Description
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The various ways of transportation (e.g. vehicles, walking people, motor vehicle, non-motor vehicle) and the dense buildings make the traffic condition complicated. Typically, traffic accidents often happen at the crossroads for example the pedestrians suddenly rush to the road from the invisible place (e.g., behind the high buildings, behind the tall trees), which cause an urgent need to monitor the real-time road status for all days, thus with the collaboration of trusted third-party e.g. map service provider or ITS management platform, driving warning or assistant driving information can be provide timely to vehicles.
The road status includes vehicle moving information, VRU (Vulnerable Road User) information (e.g. VRU location, VRU moving direction, VRU moving speed, etc.), abnormal vehicle behaviour, road obstacles and road condition.
The road status information can be sensed by the cameras and radars on RSU (Road Side Unit). But considering the crossroad condition is very complicated, there are always some blind points. 5G based sensing can provide sensing information to fill these gaps.
For example, it is expected that the base station can sense the surrounding environment e.g. the road, and send the 3GPP sensing data to the core network. The core network can carry out systematic calculation and analysis of the 3GPP sensing data for outputting the sensing result. Such sensing result can be sent to a trusted third-party e.g. map service provider for combination with navigation map data, so as to make the driver aware of the congestion and traffic accidents in advance, and effectively increase the comfort and safety of driving. The base station sensing operations could improve the real-time map service with high reliability and quality.
But in some cases of above, the obstacles (e.g., high buildings or trees) block the transmission of radio signals. The availability and accuracy of the sensing service for the target objects which are located in the area will be greatly impacted.
To guarantee sensing service in this area, multiple 5G system sensing entities can work together.
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5.11.2 Pre-conditions
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Network operator “VV” has released a sensing service for road status sensing and has deployed base stations especially at multiple crossroads to continuously sense the road status.
Due to the high buildings (e.g. Building A) near the crossroads, there are some areas with obstacles for 5G base stations. Some 5G system sensing entities are further deployed by the network operator ‘VV’ to help radio signal transmission and collect 3GPP sensing data.
Network operator “VV” has a collaboration with the ITS management department that the user who has registered the Network operator “VV”’s “road status sensing service” can receive real-time road status information, driving warning or assistant driving information from ITS management platform.
Bob has registered the road status sensing service from Network operator “VV”.
Network operator “VV” can also deliver the real-time road information and the real time location/ trajectory of vehicles to a map service provider. The map service provider can provide “assisted driving service” based on this information.
Bob has a vehicle with the “assisted driving service” provided by the map service provider.
Bob drives the vehicle from home to the company in the morning of a working day.
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5.11.3 Service Flows
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Figure 5.11.3-1. Sensing at crossroads with/without obstacle
1. The 5G base station continuously collects 3GPP sensing data of the road status and the sensing result is continuously reported to the trusted third-party (e.g. the map service provider or ITS management platform) by 5G network according to the preconfigured refresh rate (e.g. in the midnight, it uses slow refresh rate with 0.2Hz, and in the working day morning, it uses fast refresh rate with 10Hz). The refresh rate can be adjusted according to the trusted third-party demand and network operator’s policy.
2. In the working day morning, Bob has started his road status sensing service when he begins driving his vehicle to his office.
3. Bob drives his vehicle from home to his office and started assisted driving service. The map service provider sends the road sensing request to the 3GPP core network.
4. In the crossroad, there are some higher buildings. It is difficult for Bob to timely detect other vehicles and VRUs in the area. As example in figure 5.11.3-1, Bob is driving his vehicle and crossing the crossroad toward the southeast of the crossroad. Linda is driving her motorcycle on a side road toward the main road which is also the southeast of the crossroad. The line of sight between Bob and Linda is blocked by the high building A which is at a corner of the intersection.
5. Linda’s motorcycle activity is continuously sensed by the base station under the help of other 5G system sensing entities.
6. The 5G system collects and associates the multiple 3GPP sensing data from multiple base stations with the crossroad location. Considering the obstacles (e.g., high buildings or trees) in this area, it impacts the sensing quality and availability of the 3GPP sensing data from the blocked base stations. So, the 5G system needs to select suitable 3GPP sensing data to derive the sensing result to guarantee the availability of the sensing service.
7. The motorcycle sensing result which includes the motorcycles moving speed, moving direction, position etc. is periodically reported to the the map service provider and ITS management platform.
8. The other vehicles in the crossroad have been sensed and related sensing result are also reported to the map service provider and ITS management platform.
9. The map service provider fuses the sensing result with the map and then sends to the Tom’s vehicle.
10. According to the continuously received motorcycle sensing results, the ITS management platform can analyze and identify that there will be a potential collision risk between Bob and Linda. The collision warning then is sent to Bob.
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5.11.4 Post Conditions
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Bob’s vehicle receives the real-time map information which warns Bob that there is another cross-direction motorcycle driving towards his vehicle. Bob stops his vehicle before the crossroad to avoid a potential collision. With the assistance of RAN sensing, Bob arrives in the company safely and easily. Bob starts the daily work in the office.
Bob receives the warning and drives safely through the crossroads.
Linda can also ride safely to the crossroad. The potential risk of collision is avoided.
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e8cee4e428329a7668584ba76bf8de13
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22.837
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5.11.5 Existing features partly or fully covering the use case functionality
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None.
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e8cee4e428329a7668584ba76bf8de13
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22.837
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5.11.6 Potential New Requirements needed to support the use case
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[PR 5.11.6-1] The 5G system shall be able to support a mechanism to provide available sensing service in a target sensing service area.
[PR 5.11.6-2] The 5G RAN shall be able to collect 3GPP sensing data from requested target sensing service area according to the operator’s policy.
NOTE 1: The operator policy means to configure the target sensing service area, real time 3GPP sensing data collection or periodic collection etc.
[PR 5.11.6-3] The 5G system shall be able to report the sensing result to the trusted third-party with refresh rate which is requested by the trusted third-party e.g. a map service provider, and controllable by the operator, according to a business agreement.
NOTE 2: The sensing result can be the target object’s size, shape, position, moving direction, moving speed, etc.
[PR 5.11.6-4] The 5G system shall support means for a trusted third-party application, e.g. a map service provider to configure sensing per location.
[PR. 5.11.6-5] The 5G system shall be able to support the sensing service with given KPIs in Table 5.11.6-1.
Table 5.11.6-1 Performance requirements of sensing results for sensing at crossroads with/without obstacle
Scenario
Sensing service area
Confidence level [%]
Accuracy of positioning estimate by sensing (for a target confidence level)
Accuracy of velocity estimate by sensing (for a target confidence level)
Sensing resolution
Max sensing service latency[ms]
Refreshing rate [s]
Missed detection [%]
False alarm [%]
Horizontal
[m]
Vertical
[m]
Horizontal
[m/s]
Vertical
[m/s]
Range resolution
[m]
Velocity resolution (horizontal/ vertical)
[m/s x m/s]
Sensing at crossroads with/without obstacle
Outdoor
95
≤1
N/A
N/A
N/A
N/A
N/A
≤100
NOTE 2
≤ 0.1
≤5
≤5
NOTE 1: The terms in Table 5.11.6-1 are found in Section 3.1.
NOTE 2: The value is sourced from [28].
NOTE: In this use case base station is acting as sensing transmitter and/or sensing receiver. This is an example and other options can also be valid.
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5.12 Use case on Network assisted sensing to avoid UAV collision
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