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Content for  TR 22.837  Word version:  19.3.0

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0  Introductionp. 9

Wireless sensing technologies aim at acquiring information about a remote object or environment and its characteristics without physically contacting it. The perception data of the object and its surrounding can be utilized for analysis, so that meaningful information about the object or environment and its characteristics can be obtained.

1  Scopep. 10

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  Referencesp. 10

The following documents contain provisions which, through reference in this text, constitute provisions of the present document.
  • References are either specific (identified by date of publication, edition number, version number, etc.) or non-specific.
  • For a specific reference, subsequent revisions do not apply.
  • For a non-specific reference, the latest version applies. In the case of a reference to a 3GPP document (including a GSM document), a non-specific reference implicitly refers to the latest version of that document in the same Release as the present document.
[1]
TR 21.905: "Vocabulary for 3GPP Specifications".
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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.
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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.
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IEEE 802.11-18/0611r16: "Wireless LANs, WiFi Sensing Uses Cases"
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TEM STANDARDS TEM STANDARDS AND RECOMMENDED PRACTICE: https://unece.org/fileadmin/DAM/trans/main/tem/temdocs/TEM-Std-Ed3.pdf
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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.
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TR 22.856: "Localized Mobile Metaverse Services".
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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.
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"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.
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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.
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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.
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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.
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Eradication of elephant mortality and injury due to railway accidents through automatic tracking and alert system in IEEE Conference Publication, IEEE Xplore
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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.
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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.
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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.
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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.
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[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]
[30]
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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]
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]
[42]
[43]
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/.
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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.
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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.
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R. Bosch, "LRR3 3rd Generation Long-Range Radar Sensor," Robert Bosch GmbH, Germany, 2009.
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Continental, A.G., ARS 408-21 Premium Long RangeRadar Sensor 77 GHz.ARS, pp.408-21.
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F. Engels et. al, "Automotive Radar Signal Processing: Research Directions and Practical Challenges," in IEEE JSTSP, June2021.
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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.
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3  Definitions of terms, symbols and abbreviationsp. 13

3.1  Termsp. 13

For the purposes of the present document, the terms given in TR 21.905 and the following apply. A term defined in the present document takes precedence over the definition of the same term, if any, in TR 21.905.
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.
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.
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.
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.
  • 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.
  • 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  Symbolsp. 14

For the purposes of the present document, the following symbols apply:

3.3  Abbreviationsp. 14

For the purposes of the present document, the abbreviations given in TR 21.905 and the following apply. An abbreviation defined in the present document takes precedence over the definition of the same abbreviation, if any, in TR 21.905.

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