ETSI GR ISC 002 V1.1.1 (2025-08)

Integrated Sensing And Communications (ISAC); Channel Modelling, Measurements and Evaluation Methodology

DGR/ISC-002

General Information

• Status: Not Published
• Technical Committee: ISAC - Integrated Sensing And Communications
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 13-Aug-2025
• Completion Date: 05-Aug-2025

Standards Content (Sample)

GROUP REPORT
Integrated Sensing And Communications (ISAC);
Channel Modelling, Measurements and
Evaluation Methodology
Disclaimer
The present document has been produced and approved by the Integrated Sensing And Communications (ISAC) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR ISC 002 V1.1.1 (2025-08)

Reference
DGR/ISC-002
Keywords
channel modelling, evaluation, ISAC,
measurement
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3 ETSI GR ISC 002 V1.1.1 (2025-08)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
Executive summary . 5
Introduction . 6
1 Scope . 7
2 References . 7
2.1 Normative references . 7
2.2 Informative references . 7
3 Definition of terms, symbols and abbreviations . 10
3.1 Terms . 10
3.2 Symbols . 10
3.3 Abbreviations . 10
4 State-of-the-art ISAC channel modelling approaches . 12
4.0 General . 12
4.1 Related academic research . 12
4.2 IEEE 802.11bf . 13
4.3 3GPP . 14
4.3.1 Existing Channel Models (ETSI TR 138 901) . 14
4.3.2 SA1/SA2 . 16
4.3.3 RAN1 . 16
4.4 Other forums review . 18
4.4.1 ATIS Next G Alliance . 18
4.4.2 ETSI ISG RIS . 18
4.4.3 ETSI ISG THz . 18
4.4.4 INTERACT Cost Action . 18
4.4.5 ITU-R . 19
5 Proposed ISAC channel modelling approaches . 20
5.0 General . 20
5.1 Use cases, scenarios and frequency bands . 20
5.2 RCS Modelling . 21
5.2.1 Definition . 21
5.2.2 RCS Model Dependencies . 21
5.2.3 RCS Model Boundaries . 22
5.2.4 RCS Modelling Approaches . 23
5.2.5 Multipoint target modelling . 23
5.2.6 Segmented Object Average RCS . 24
5.2.7 Number of Object Segments . 24
5.2.8 RCS fading model . 25
5.2.9 Views on RCS incorporation in ETSI TR 138 901 . 26
5.2.10 Electromagnetic based RCS Modelling . 27
5.3 Micro-Doppler Modelling . 31
5.3.1 Micro-Doppler definition . 31
5.3.2 Micro-Doppler Dependencies . 31
5.3.3 Proposed Modelling . 32
5.3.4 Evaluation methodology and feasibility analysis . 34
5.3.4.1 Simulation on link-level system . 34
5.3.4.2 Sensing parameters estimation . 35
5.3.4.3 Classification of micro-Doppler mode . 36
5.4 Micro-deformation channel modelling . 38
5.4.1 Definition . 38
5.4.2 Proposed methodology for modelling . 38
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4 ETSI GR ISC 002 V1.1.1 (2025-08)
5.4.3 Evaluation methodology and feasibility analysis . 42
5.4.3.1 Simulation assumption . 42
5.4.3.2 Feasibility analysis based on simulation results . 43
5.5 Rain Attenuation Modelling . 47
5.5.1 Definition . 47
5.5.2 Rain Attenuation Model Dependencies . 47
5.5.3 Rain Attenuation Model Application . 47
5.5.4 Rain Attenuation Modelling Approaches . 48
5.5.5 Results Anal ys is . 49
5.5.6 Model Verification . 50
5.5.7 Proposed Model . 52
6 Conclusions and recommendations . 52
Annex A: Measurement campaigns and emulations for micro-Doppler . 54
A.1 Measurement campaign . 54
A.2 Ray-tracing simulation . 56
Annex B: Measurement campaigns for Rain Attenuation . 59
B.1 Measurements for Rain Attenuation Modelling . 59
History . 62

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5 ETSI GR ISC 002 V1.1.1 (2025-08)
Intellectual Property Rights
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Integrated Sensing And
Communications (ISAC).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
Executive summary
The present document provides an overview of the current state-of-the-art in ISAC channel modelling approaches and
summarizes their key limitations.
In addition, the present document provides ISAC channel modelling enhancements, including RCS modelling for
complex objects, micro-Doppler modelling for small-scale motion recognition, micro-deformation modelling for
structural health monitoring, and rainfall attenuation modelling for power loss caused by precipitation.
Finally, the present document presents conclusions and recommendations for future work addressing required
developments for ISAC systems and radio access network architectures, including security, privacy, trustworthiness,
and sustainability.
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6 ETSI GR ISC 002 V1.1.1 (2025-08)
Introduction
There is growing interest in ISAC across the broader research ecosystem, including global standardization bodies,
industrial stakeholders, academia, and regional collaborative projects. The present document provides a study on ISAC
channel modelling, which can serve as a basis for evaluating the performance of future 6G ISAC solutions.

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7 ETSI GR ISC 002 V1.1.1 (2025-08)
1 Scope
The present document outlines the scope as follows:
• Develop advanced ISAC channel models and validation through measurement campaigns and emulations, that
can fill the gaps of existing channel models.
• Validate the models through feasibility analysis and measurement campaign.
• Identify and describe the corresponding use cases and the potentially suitable frequency bands.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's
understanding, but are not required for conformance to the present document.
[i.1] IEEE 802.11-21bf™: "Channel Models for WLAN Sensing Systems".
[i.2] ETSI TR 138 901 (V18.0.0): "5G; Study on channel model for frequencies from 0.5 to 100 GHz
(3GPP TR 38.901 version 18.0.0 Release 18)".
[i.3] Chen Victor C. et al.: "Micro-Doppler effect in radar: phenomenon, model, and simulation study",
IEEE™ Transactions on Aerospace and electronic systems, vol. 42.1, pp. 2-21, 2006.
[i.4] Liu Ting et al.: "Channel Modelling on Micro-Doppler Feature of a Pedestrian at 77 GHz",
IEEE™ Conference on Antenna Measurements and Applications (CAMA). IEEE™, 2023.
[i.5] Tahmoush Dave: "Review of micro‐Doppler signatures", IEEE™ Conference on Antenna
Measurements and Applications (CAMA), 2023.
[i.6] Chen Victor C.: "The micro-Doppler effect in radar", Artech house, 2019.
[i.7] ETSI TS 103 789 (V1.1.1): "Short Range Devices (SRD) and Ultra Wide Band (UWB); Radar
related parameters and physical test setup for object detection, identification and RCS
measurement".
[i.8] Mahafza Bassem R.: "Radar systems analysis and design using MATLAB", Chapman and
Hall/CRC, 2005.
[i.9] Gong J., Yan J., Li D., Hu H., Kong D.: "Using a Pair of Different-Sized Spheres to Calibrate
Radar Data in the Microwave Anechoic Chamber", Applied Sciences. 5 September 2022,
12(17):8901.
[i.10] C. Uluisik, G. Cakir, M. Cakir and L. Sevgi: "Radar Cross Section (RCS) modelling and
simulation, part 1: a tutorial review of definitions, strategies, and canonical examples", in IEEE™
Antennas and Propagation Magazine, vol. 50, no. 1, pp. 115-126, February 2008.
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8 ETSI GR ISC 002 V1.1.1 (2025-08)
[i.11] K. Guan et al.: "On the Influence of Scattering From Traffic Signs in Vehicle-to-X
Communications", in IEEE™ Transactions on Vehicular Technology, vol. 65, no. 8,
pp. 5835-5849, August 2016, doi: 10.1109/TVT.2015.2476335.
[i.12] L. R. Danoon and A. K. Brown: "Modelling Methodology for Computing the Radar Cross Section
and Doppler Signature of Wind Farms", in IEEE™ Transactions on Antennas and Propagation,
vol. 61, no. 10, pp. 5166-5174, October 2013, doi: 10.1109/TAP.2013.2272454.
[i.13] R. E. Kell: "On the derivation of bistatic RCS from monostatic measurements", in Proceedings of
the IEEE™, vol. 53, no. 8, pp. 983-988, August 1965, doi: 10.1109/PROC.1965.4077.
[i.14] M. Borgese, S. Genovesi, G. Manara and F. Costa: "Radar Cross Section of Chipless RFID Tags
and BER Performance", in IEEE™ Transactions on Antennas and Propagation, vol. 69, no. 5,
pp. 2877-2886, May 2021, doi: 10.1109/TAP.2020.3037800.
[i.15] ETSI GR ISC 001: "Integrated Sensing And Communications (ISAC); Use Cases and Deployment
Scenarios".
[i.16] ETSI GR ISC 003: "Integrated Sensing And Communications (ISAC); System and RAN
Architectures".
[i.17] ETSI GR ISC 004: "Integrated Sensing And Communications (ISAC); Security, Privacy,
Trustworthiness and Sustainability".
[i.18] "Channel Measurements and Modelling for Joint/Integrated Communication and Sensing, as well
as 7-24 GHz Communication", Next G Alliance, July 2024.
[i.19] ETSI GR RIS 003: "Reconfigurable Intelligent Surfaces (RIS); Communication Models, Channel
Models, Channel Estimation and Evaluation Methodology".
[i.20] ETSI GR RIS 006: "Reconfigurable Intelligent Surfaces (RIS); Multi-functional Reconfigurable
Intelligent Surfaces (RIS): Modelling, Optimization, and Operation".
[i.21] ETSI GR RIS 007: "Reconfigurable Intelligent Surfaces (RIS); Near-field Channel Modeling and
Mechanics".
[i.22] Y. Miao et al.: "Dual-band mmWave measurements of human body scattering and blockage
effects using distributed beamforming for ISAC applications", European Conference on Antennas
and Propagation (EuCAP), 2024.
[i.23] H. C. A. et al.: "Modelling Micro-Doppler Signature of Drone Propellers in Distributed ISAC",
IEEE™ Radar Conference (RadarConf24), Denver, CO, USA.
[i.24] J. Chuang et al.: "Quasi-deterministic channel propagation model for human sensing: Gesture
recognition use case", IEEE™ Open Journal of Antennas and Propagation, vol. 5(3), pp. 557-572,
2024.
[i.25] A. Ziganshin et al.: "A Scalable Hybrid Channel Model for ISAC Evaluation", International
Conference on Microwaves for Intelligent Mobility, 2024.
[i.26] V. C. Chen, F. Li, S. S. Ho and H. Wechsler: "Micro-Doppler effect in radar: phenomenon, model,
and simulation study", in IEEE™ Transactions on Aerospace and Electronic Systems, vol. 42,
no. 1, pp. 2-21, January 2006.
[i.27] Passafiume M., Rojhani N., Collodi G., Cidronali A.: "Modelling Small UAV Micro-Doppler
Signature Using Millimeter-Wave FMCW Radar", Electronics 2021.
[i.28] Huang et al.: "MAJoRCom: A Dual-Function Radar Communication System Using Index
Modulation", IEEE™ Trans. Signal Proc., vol. 68, no. 5, pp. 3423-3438, May 2020.
[i.29] P. Kumari et al.: "IEEE 802.11ad-Based Radar: An Approach to Joint Vehicular
Communication-Radar System", IEEE™ Transactions on Vehicular Technology, vol. 67, no. 4,
pp. 3012-3027, April 2018.
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[i.30] Z. Cheng et al.: "Transmit Sequence Design for Dual-Function Radar-Communication System
With One-Bit DACs", IEEE™ Transactions on Wireless Communications, vol. 20, no. 9,
pp. 5846-5860, September 2021.
[i.31] F. Liu et al.: "Radar-Assisted Predictive Beamforming for Vehicular Links: Communication
Served by Sensing", IEEE™ Transactions on Wireless Communications, vol. 19, no. 11,
pp. 7704-7719, November 2020.
[i.32] Rahman et al.: "Joint Communication and Radar Sensing in 5G Mobile Network by Compressive
Sensing", IET Communications, vol. 14, pp. 3977-3988, December 2020.
[i.33] Kumari et al.: "JCR70: A Low-Complexity Millimeter-Wave Proof-of-Concept Platform for a
Fully-Digital SIMO Joint Communication-Radar", IEEE™ Open J. Vehic. Tech., vol. 2,
pp. 218-234, March 2021.
[i.34] C. Baquero Barneto et al.: "Millimeter-Wave Mobile Sensing and Environment Mapping: Models,
Algorithms and Validation", IEEE™ Trans. Vehic. Tech., vol. 71, no. 4, pp. 3900-3916,
April 2022.
[i.35] A. Ali et al.: "Leveraging Sensing at the Infrastructure for mmWave Communication", IEEE™
Communications Magazine, vol. 58, no. 7, pp. 84-89, July 2020.
[i.36] D. He, B. Ai, K. Guan et al.: "The Design and Applications of High-Performance Ray-Tracing
Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial", IEEE™
Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, 2019.
[i.37] D. He, K. Guan, D. Yan et al.: "Physics and AI-based digital twin of multi-spectrum propagation
characteristics for communication and sensing in 6G and beyond", IEEE™ Journal on Selected in
Communications, vol. 41, no. 11, pp. 3461-3473, 2023.
[i.38] A. S. Glassner, Ed.: "An Introduction to Ray Tracing", London, U.K., Academic Press, 1989.
[i.39] V. Degli-Esposti, F. Fuschini, E. M. Vitucci and G. Falciasecca: "Measurement and modelling of
scattering from buildings", IEEE™ Trans. Antennas Propag., vol. 55, no. 1, pp. 143-153,
January 2007.
[i.40] 3GPP TR 22.837: "Study on Integrated Sensing and Communication".
[i.41] 3GPP TS 22.137: "Integrated Sensing and Communication".
[i.42] Recommendation ITU-R P. 838-3 (2005): "Specific attenuation model for rain for use in
prediction methods".
[i.43] Recommendation ITU-R P. 530-18 (2022): "Propagation data and prediction methods required for
the design of terrestrial line-of-sight systems".
[i.44] A. A. H. Budalal et al.: "Modification of distance factor in rain attenuation prediction for
short-range millimeter-wave links", IEEE™ Antennas and Wireless Propagation Letters, vol. 19,
no. 6, pp. 1027-1031, 2020.
[i.45] H. Park, J. Choi, H. Bae et al.: "Measurement and statistics of rain attenuation on terrestrial link at
rd
European Microwave Conference (EuMC), pp. 706-709,
240, 270 and 300 GHz", 2023 53
2023. .
[i.46] P. Valtr and P. Pecha.: "On distance factor in rain attenuation predictions", Proceedings
th
13 European Conference on Antennas and Propagation (EuCAP), pp. 1-3, 2019.
[i.47] L. Luini, G. Roveda, M. Zaffaroni et al.: "The impact of rain on short E -Band radio links for 5G
mobile systems: Experimental results and prediction models", IEEE™ Transactions on Antennas
and Propagation, vol. 68, no. 4, pp. 3124-3134, 2020.
[i.48] 3GPP RP-242348: "Study on channel modelling for Integrated Sensing And Communication
(ISAC) for NR".
[i.49] 3GPP SP-231754: "New SID on Study on Architecture Enhancement to support Integrated
Sensing and Communication".
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10 ETSI GR ISC 002 V1.1.1 (2025-08)
[i.50] ETSI TS 122 261: "5G; Service requirements for the 5G system (3GPP TS 22.261)".
[i.51] 3GPP SP-241391: "New Study on 6G Use Cases and Service Requirements".
[i.52] 3GPP TR 22.870: "Study on 6G Use Cases and Service Requirements; Stage 1".
[i.53] 3GPP SP-250833: "Revised SID on Study on Stage 2 for Integrated Sensing and Communication".
[i.54] 3GPP RP-251861: "New SID: Study on Integrated Sensing And Communication (ISAC) for NR".
[i.55] 3GPP RP-251567: "Introduction of Rel-19 channel model for ISAC".
[i.56] "Time-Harmonic Electromagnetic Fields", vol. 9, New York: McGraw Hill, 1961.
[i.57] ETSI TR 138 901 (V19.0.0): "5G; Study on channel model for frequencies from 0.5 to 100 GHz
(3GPP TR 38.901 version 19.0.0 Release 19)".
[i.58] Next G Alliance Report Phase II: "Channel Measurements and Modeling for Joint/Integrated
Communication and Sensing, as well as 7-24 GHz Communication" 2025.
[i.59] Recommendation ITU-R P. 837-7 (2017): "Characteristics of precipitation for propagation
modelling".
3 Definition of terms, symbols and abbreviations
3.1 Terms
Void.
3.2 Symbols
For the purposes of the present document, the following symbols apply
MHz MegaHertz
mmWave Millimetre-Wave
THz Terahertz
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
rd
3GPP 3 Generation Partnership Project
th
5G 5 Generation
th
5GA 5G-Advanced6G 6 Generation
ABG Alpha-Beta-Gamma
AGV Automated Guided Vehicle
AoA Azimuth angle of Arrival
AoD Azimuth angle of Departure
AS Angular Spread
ASA Azimuth angle Spread of Arrival
ASD Azimuth angle Spread of Departure
ATIS Alliance for Telecommunications Industry Solutions
BIRA BIstatic RAdar measurement facility
BS Base Station
CDL Clustered Delay Line
CN Core Network
CSI Channel State Information
CST Computer Simulation Technology
CTF Channel Transfer Function
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11 ETSI GR ISC 002 V1.1.1 (2025-08)
CW Continuous Wave
DAC Data Acquisition Card
DDHC Data-Driven Hybrid Channel
DGR Draft Group Report
DS Delay Spread
EM ElectroMagnetic
EO Environment Object
FFT Fast Fourier Transform
FR1 FRequency 1
FR2 FRequency 2
FR3 FRequency 3
GCS Global Coordinate System
gNB 5G Node B
GPS Global Positioning System
IEEE Institute of Electrical and Electronics Engineers
ISAC Integrated Sensing And Communications
ITU-R International Telecommunication Union Radiocommunication
LoS Line of Sight
LSP Large-Scale Parameters
MBET Monostatic-to-Bistatic-Equivalent Theorem
ME Mean Error
METIS Mobile and wireless communications enablers for the Twenty-twenty Information Society
MPC MultiPath Components
NGA Next G Alliance
NLoS Non-Line of Sight
NR New Radio
OFDM Orthogonal Frequency Division Multiplexing
PCA Principal Component Analysis
PDP Power Delay Profile
PEC Perfectly Electrically Conducting
PL Path Loss
PRI Pulse Repetition Interval
RAN Radio Access Network
RAN1 Radio Access Network working group 1
RCS Radar Cross Section
RMa Rural Macrocell
RF Radio Frequency
RFPA Radio Frequency Power Amplifier
RIS Reconfigurable Intelligent Surface
RMSE Root Mean Square Error
RT Ray Tracing
RX Receive
SA Service and system Aspects
SA1 SA working group 1
SA2 SA working group 2
SF Sensing Function
SNR Signal to Noise Ratio
SSP Small-Scale Parameters
ST Sensing Target
SVM Support Vector Machine
TR Technical Report
TRP Transmission and Reception Point
TS Technical Specification
TSG Technical Specification Group
TU Technische Universität
TX Transmit
UAV Unmanned Aerial Vehicle
UC Use case
UE User Equipment
UMi Urban Microcell
UMa Urban macrocell
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USRP Universal Software Radio Peripheral
V2I Vehicle to Infrastructure
V2V Vehicle to Vehicle
V2X Vehicle to Everything
WG Working Group
WP5D Working Party 5D
XPR Cross Polarization Ratio
ZoA Zenith angle of Arrival
ZoD Zenith angle of Departure
ZSA Zenith angle Spread of Arrival
ZSD Zenith angle Spread of Departure
4 State-of-the-art ISAC channel modelling approaches
4.0 General
In this clause, ISAC channel models from the current state-of-the-art are reviewed and summarized, including those
from the academic literature, IEEE 802.11bf [i.1], and ETSI TR 138 901 [i.2].
4.1 Related academic research
Several academic papers have been reviewed and their key assumptions for ISAC channel modelling are summarized as
follows:
• "MAJoRCom: A Dual-Function Radar Communication System Using Index Modulation" [i.28]:
- A simple multi-path model is used.
- 1,9 GHz carrier is assumed.
• "IEEE 802.11ad-Based Radar: An Approach to Joint Vehicular Communication-Radar System" [i.29]:
- A simple Ricean channel model is used. LoS path is generated based on UE's location, but NLoS path is
generated based on random gaussian distribution.
- Channel gain is generated based on Radar Cross Section (RCS). Radar range will decrease substantially
for targets with low RCS (e.g. a pedestrian).
- Free space pathloss exponent 2 is used.
- 60 GHz unlicensed band is used.
• "Transmit Sequence Design for Dual-Function Radar-Communication System With One-Bit DACs" [i.30]:
- A simple LoS path based model. "Swerling-II" model was used, i.e. it is assumed to be constant during
each pulse duration but varying from pulse to pulse.
• "Radar-Assisted Predictive Beamforming for Vehicular Links: Communication Served by Sensing" [i.31]:
- The reflection coefficient (related to the generation of channel gain) is generated based on RCS.
- 30 GHz carrier frequency is used.
• "Joint Communication and Radar Sensing in 5G Mobile Network by Compressive Sensing" [i.32]:
- Manual cluster-based model: 3 clusters, AoA centre, moving speeds are randomly generated. In each
cluster, random rays are generated.
- 100 MHz bandwidth is used.
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13 ETSI GR ISC 002 V1.1.1 (2025-08)
• "JCR70: A Low-Complexity Millimeter-Wave Proof-of-Concept Platform for a Fully-Digital SIMO Joint
Communication-Radar" [i.33]:
- Cluster Delay Line (CDL) model.
- RCS is used to generate reflection coefficients. 5 dBsm and 8 dBsm (dB relative to square metres) are
used.
- 70 GHz carrier frequency is used.
• "Millimeter-Wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation" [i.34]:
- Ray tracing-based model.
- Subcarrier specific lambda, Far-field assumption.
- mmWave.
• "Leveraging Sensing at the Infrastructure for mmWave Communication" [i.35]:
- Ray tracing-based model.
- mmwave.
There is a rich literature on channel models for ISAC, including the above papers, covering:
• Lack of subTHz sensing channel modelling: the above papers discuss mmWave, radar frequency
(e.g. 70 GHz), or lower frequencies (FR1). There is a lack of publications for THz sensing channel
measurement.
• RCS is used for sensing cluster channel gain generation:
- The reflection coefficient is related to the RCS.
• Ray tracing is used to generate target-related rays/clusters generation.
• There is no consideration of the relationship with a communication channel.
4.2 IEEE 802.11bf
In IEEE 802.11bf [i.1], the Data-Driven Hybrid Channel (DDHC) Model is used to support the modelling of sensing
uncertainty. The DDHC Model is built based on two parts, a ray tracing method to model target-related rays, and an
autoregressive statistical method to model target-unrelated rays. The target-unrelated rays are defined as the rays
reflected from fixed objects and perturbed scatters; the target-related rays are defined as the rays reflected from the
moving targets. By using a real dataset collected from experiments or ray tracing, the final DDHC Model can be
obtained to better approximate the real channel model in a specific scenario. Here is the summary of
IEEE 802.11bf [i.1] sensing channel modelling:
• Data driven hybrid channel model is used:
- Target related rays are generated based on ray tracing:
 Ray tracing-based model is used:
- Define a living room scenario
- Target unrelated rays are generated by an autoregression model and the 11ax/11ay channel model:
 The target-unrelated rays are generated, by combining the autoregressive model and the channel
model in 11ax (for sub 7 GHz) or 11ay (for 60 GHz). The auto-regressive model is given in
equation (4.2-1):
����                    0 ≤ � ≤ �
�
� ��� = �       (4.2-1)
�
�� �� − � � + �1− ������  � > �
� � �
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 For 11ax:
���
� � �∅
�,�
���� = ∑ �� × �∑ � � ��� − � ��     (4.2-2)
�
��� � ��� �,� �.�
 for 11ay:
�
� ��
����
��� �� ��
�(�)= ∑ �� � �� � ��� − � ��        (4.2-3)
� � �
���
• Where � is the target-unrelated rays, � is the 11ax/ay channel model, and � and � are the coherent time
� �
hyper-parameters, respectively. In equation (4.2-2), � is the number of clusters, � is the number of rays in
th th th
each cluster, �� is the pathloss of the n cluster, � is the amplitude of the m ray in the n cluster, � is
� �,� �,�
th th th th
the time delay of the m ray in the n cluster, ∅ is the phase of the m ray in the n cluster. In
�,�
�� ��
th
equation (4.2-3), � is the total number of rays, � is the amplitude of the i ray, � and � are the
���� �
� �
th th
channel phasor vectors of the i ray, � is the time delay of the i ray.
�
4.3 3GPP
4.3.1 Existing Channel Models (ETSI TR 138 901)
The 3GPP channel model before Rel-19 [i.2] supports both a stochastic model and a model that is a hybrid of a
deterministic modelling (ray-tracing) and a stochastic modelling. It is designed for base station to UE communication
link (and vice versa) and supports both link level and system level simulations but lacks the sensing channel model
aspects. The model is parameterized for a set of communication scenarios. Various sensing scenarios such as
monostatic sensing or bistatic sensing channels are not modelled which makes it unsuitable for future sensing use cases.
The target modelling requirements are generally dependent on the particular Use Cases (UCs) and underlying
performance requirements. In particular, the following aspects can be identified as needing an update to support
sensing:
1) Support for mono-static and bi-static sensing: Depending on the device that transmits and/or receives sensing
signals, mono-static sensing and bi-static sensing scenarios are considered. For mono-static sensing,
mono-static gNB sensing and mono-static UE sensing need to be supported. For bi-static sensing, bi-static
gNB-to-UE (or UE-to-gNB) sensing, bi-static UE-to-UE sensing and bi-static gNB-to-gNB sensing need to be
supported. For each of these sensing scenarios, Large-Scale Parameters (LSPs) and Small-Scale Parameters
(SSPs) additionally need to be defined for sensing channel generation. Depending on the type of target to be
sensed and the use case, the altitude, RCS, LSP/SSP generation method, etc., of the target or device may be
different.
2) LoS/NLoS state determination for sensing channel: In most radar-based sensing channel modelling, it is
assumed that a target exists (i.e. the channel between the target and the sensing device is LoS state). This may
be sufficient for evaluating only sensing performance, but to evaluate performance from a system perspective
and evaluate integrated sensing and communication performance, an actual channel model including both LoS
and NLoS between the sensing device and target is needed. The existing distance-dependent LoS/NLoS state
decision probability model may be reused (in this case, the distance between the Tx device and the Rx device
should be replaced with the distance between the Tx sensing device and the target and the distance between the
target and the Rx sensing device), or based on measurement or channel sounding, and new models for sensing
LoS/NLoS state determination may be discussed. Additionally, parameters such as Delay Spread (DS),
Angular Spread (AS), shadowing factor, Path Loss (PL), etc. may be determined differently depending on the
LoS/NLoS state.
3) Large Scale Parameters (LSPs) for sensing channel: DS, AS, SF, PL, and Ricean K factor (K) can be different
from those of the communication channel. In a communication channel, since the base station and the UE have
different antenna heights, it is desirable to use different angular distributions for AoA and AoD. However, in
the case of mono-static sensing, since the Tx and Rx are co-located and their antenna height are the same, the
AoA and AoD could have the same distribution.
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4) Small Scale Parameters (SSPs) for sensing channel: This can be classified into the cluster/Ray modelling for
the sensing channel and the effect of mobility:
a) Cluster/Ray modelling for sensing channel: Deterministic or semi-deterministic Ray/Cluster modelling
can be considered. With deterministic modelling the location of the cluster is determined based on the
physical location and characteristics of the object (target or clutter) while semi-deterministic may model
the characteristics of the object stochastically. Delay, gain, and echo angle for a direct path toward a
sensing target are determined based on the physical location of a sensing target. Delays, gains, and angles
for indirect paths toward the sensing target are generated in a statistical manner based on measurement or
sounding results. If the target is located very close to the sensing device, the near-field effect can also be
considered. This may be seen as generating channels that include a single bounce toward/from the target
and channels that have multi-bounces toward/from the target. If the sensing target creates one or more
clusters and multiple rays exist within the one or more clusters, one of the rays corresponds to a direct
path and the rest of the rays correspond to indirect paths. Based on these rays, a multi-path channel can
be generated for a sensing target. There is also a need to discuss how the delay and angle of the
background channel are generated. The channels generated by the surrounding environment or clutter
can be distinguished from channels generated by targets. There is a need to further discuss how to
generate clusters/rays generation for mono-static sensing and bi-static sensing. When implementing a
cluster for a sensing object, it is needed to discuss whether to generate a cluster for the sensing object in
addition to the existing communication clusters or assign one or more clusters from the generated
clusters for communication with the sensing object.
b) Effect of mobility: The device mobility is already modelled in communication channel modelling, but
target mobility also needs to be considered.
5) Target Modelling:
a) RCS consideration: RCS needs to be characterized for the relevant sensing targets/scenarios:
i) Different sensing targets such as humans, vehicles, or AGV/AMR (smart factory) may have
different RCS values and human health signals (respiration/heartbeat channel signatures) may have
different modelling approaches e.g. using micro-Doppler modelling or variational RCS values.
ii) The channel modelling supports different related deployment scenarios (e.g. RCS characterization
for various Tx/Rx position with respect to the sensing target).
iii) For use cases involving target identification, posture recognition, orientation detection, etc., the
geometry of sensing targets should be modelled. Instead of a single RCS value, the scattering field
of a target can be model by multiple scattering centres with given relative locations and different
RCS models. The expanded multi-scattering centre uses multiple scattering points to abstractly
describe the electromagnetic scattering characteristics of the target, which can more accurately
restore the amplitude and the shape information of the target.
iv) It should be discussed if the RCS is modelled as part of the large-scale parameters in the path loss
or is modelled as part of the small-scale parameters on a target's ray/cluster.
6) XPR for sensing channel: In mono-static
...