3GPP TSG RAN WG1 #120bis                                                            R1-2501817
WuHan, China, April 7th – 11st, 2025

Source:	vivo
Title:	Views on Rel-19 ISAC deployment scenarios
Agenda Item:	9.7.1
Document for:	Discussion and Decision

Conclusions
In this contribution, we have discussed the views on ISAC channel calibration. The observations and proposals are summarized as follows.
Observation 1: Some sensing channel conditions, such as NLOS+NLOS, LOS+NLOS, NLOS+LOS, may not be calibrated if only best N Tx-ST-Rx links are calibrated.

For TRP-UE/UE-TRP bistatic, each pair of serving TRP-UE can be the sensing Tx-Rx pair, and the serving TRP selection can be based on the RSRP between TRP and UE.
Select all the Tx-ST-Rx links to draw the CDF figures for calibration.
RAN1 does not limit the deployment scenarios for calibration, and it is up to company inputs. 
RAN1 does not limit the sensing mode, and it is up to company inputs.  
Full convolution is used as a baseline for ISAC channel calibration.
Whether or how to perform power normalization should be determined for ISAC channel calibration if ray level 1-by-1 random coupling convolution between Tx-target link and target-Rx link is used.
RAN1 individually calibrates the target channel and background channel if the background channel is newly designed. Otherwise, RAN1 only calibrates the target channel.
For the purposes of large scale calibration for human indoor sensing scenarios, the parameters in Table 1 are proposed.
For the purposes of large scale calibration for human outdoor sensing scenarios, the parameters in Table 2 are proposed.
For the purposes of full calibration for human indoor sensing scenarios, the parameters in Table 3 are proposed.
For the purposes of full calibration for human outdoor sensing scenarios, the parameters in Table 4 are proposed.
For spatial consistency calibration of ISAC channel, select Case 5 or Case 6 for calibration.
Case 5: links between same UT and two nodes X/Y, subjected to correlation distance, i.e., link UT1-X and link UT1-Y, where nodes X/Y can be target or UT.
Case 6: links between same target and two nodes X/Y, subjected to correlation distance, i.e., link target1-X and link target1-Y, where nodes X, Y are different UTs.
RAN1 uses the target-specific method described in [3] to model the spatial consistency of ISAC channel.
For the purposes of spatial consistency calibration for human indoor sensing scenarios, the parameters in Table 5 are proposed.
For the purposes of spatial consistency calibration for vehicles sensing scenarios, the parameters in Table 6 are proposed.

3GPP TSG RAN WG1 Meeting #120b 	          	     	           R1-2501839
Wuhan, China, April 7th – 11th, 2025
Agenda Item:	9.7.1
Source: 	EURECOM
Title: 	Discussion on ISAC deployment scenarios and requirements
Document for:	Discussion and decision

Conclusions
In this contribution, the following proposals are put forward:
Proposal 1: The parameters of CM calibration for full calibration for UAV use cases are shown in Table 1.
Proposal 2: The parameters of CM calibration for large scale for UAV use cases are shown in Table 2.
Proposal 3: The parameters of CM calibration for full calibration for human indoor/outdoor use cases are shown in Table 3.
Proposal 4: The parameters of CM calibration for large scale for human indoor/outdoor use cases are shown in Table 4.
Proposal 5: The parameters of CM calibration for full calibration for Automotive vehicles use cases are shown in Table 5.
Proposal 6: The parameters of CM calibration for large scale for Automotive vehicles use cases are shown in Table 6.
Proposal 7: The parameters of CM calibration for full calibration for AGV use cases are shown in Table 7.
Proposal 8: The parameters of CM calibration for large scale for AGV use cases are shown in Table 8.
Proposal 9: The parameters of CM calibration for full calibration for objects creating hazards on roads/railways use cases are shown in Table 9.
Proposal 10: The parameters of CM calibration for large scale for objects creating hazards on roads/railways use cases are shown in Table 10.

3GPP TSG RAN WG1 #120bis			R1-2501926
Wuhan, China, April 7th – 11th, 2025

Agenda Item:	9.7.1
Source:	InterDigital, Inc.
Title:	Discussion on ISAC deployment scenarios
Document for:	Discussion and Decision

Conclusion 
In this contribution, we discussed the calibration parameters for scenarios associated with different sensing targets. 
Based on the discussion, we made the following observations and proposals:
Observation 1: Given the limited RAN1 meetings for Rel. 19, the calibration efforts should be streamlined across companies to ensure progress.
Observation 2: The modelling of EO remains under discussion in the agenda item 9.7.1.
Observation 3: Time-varying mobility model for sensing mobile transmitters/receivers and sensing targets is essential to support tracking of sensing targets over time.
Observation 4: AR model offers a simple generalized framework for defining values for time varying models, where values at a given time depend linearly on the values from previous instances.
Observation 5: The discrete velocities defined for a sensing target can be preserved in the time-varying model by applying discrete mapping functions such as nearest neighbour function to the determined values.
Observation 6: The time varying velocity modelling for a sensing transmitter/receiver or sensing target should be limited in terms of corresponding maximum and minimum velocity.
Observation 7: Micro-Doppler signatures can be used to distinguish between sensing targets and the unintended targets

Proposal 1: RAN1 prioritizes large-scale calibration first before progressing to full-scale calibration.
Proposal 2: Support spatial consistency calibration for the ISAC channel model.
Proposal 3: Support the following metrics for spatial consistency calibration: coupling loss, SINR, cross-correlation coefficient of delay, AOA and LOS/NLOS status between a pair of nodes, average varying rate of power, varying rate of delay, and average varying rate of AOA. 
Proposal 4: Exclude EO from the ISAC channel modelling calibration until its modelling is finalized.
Proposal 5: Prioritize the calibration of target channel before background channel.
Proposal 6: Use Table 1 for the large-scale and full-scale calibration for UMa-AV deployment scenario for UAV sensing target.
Table 1  : Large-scale and full-scale calibration parameters for UMa-AV scenario for UAV sensing

Proposal 7: Use Table 2 for the large-scale and full-scale calibration for indoor office deployment scenario for human sensing target.
Table 2 : Large-scale and full-scale calibration parameters for Indoor office scenario for human sensing

Proposal 8: Use Table 3 for the large-scale and full-scale calibration for InF-SH deployment scenario for AGV sensing target.
Table 3 : Large-scale and full-scale calibration parameters for InF-SH scenario for AGV sensing

Proposal 9: Time-varying velocity modelling for mobile sensing transmitter/receiver and sensing targets is supported for different sensing targets for future evaluations.
Proposal 10: For mobile sensing transmitters/receivers and sensing targets, define the time-varying velocity model based on an autoregressive (AR) model where the velocity depends on its value in previous time instances and is influenced by random factors.
Proposal 11: Incorporate the micro-Doppler signatures of the sensing targets, unintended targets for different deployment scenarios.

3GPP TSG-RAN WG1 Meeting #120-bis	R1- 2501935
Wuhan, China, April 7th – April 11th, 2025

Agenda Item:	9.7.1
Source:	NVIDIA
Title:	Deployment scenarios for integrated sensing and communication with NR
Document for:	Discussion
1	

Conclusion
In the previous sections, we discuss general aspects of deployment scenarios for ISAC in NR and make the following observations:
Observation 1: Wireless channel modelling needs to provide consistency and, above all, a correct representation of the frequency, spatial, and temporal correlation across base stations, devices, and objects in the environment.
Observation 2: ISAC evaluation without a propagation model grounded on the underlying physics of the scattering phenomena is simply unnatural, prone to modelling error and possibly a huge waste of effort for the industry.
Observation 3: Deterministic, physics-based modelling for wireless propagation, especially ray tracing, is essential for ISAC evaluation.
Observation 4: WLAN sensing Task Group IEEE 802.11bf has embraced ray tracing-based channel model for Wi-Fi sensing.
Observation 5: Though ray tracing has been considered in the map-based hybrid channel model in TR 38.901, the map-based hybrid model defined is not calibrated and has not been used in 3GPP simulation campaigns.
Observation 6: A common reference scenario defined for ray tracing not only can be used for ISAC evaluation but also other key technologies toward 6G, including reconfigurable intelligent surface (RIS), larger antenna arrays in new spectrum such as 7-24 GHz and sub-THz bands, and AI/ML, to name a few.
Based on the discussion in the previous sections we propose the following:
Proposal 1: Define a common reference scenario for ray tracing to be used in ISAC evaluation. 
Proposal 2: Select one the following options to define a common reference scenario for ray tracing to be used in ISAC evaluation:
Option 1: Real-scenario map that is a virtual representation of a real area on earth. 
Option 2: Synthetic-scenario map that is artificially constructed to mimic a certain physical environment such as urban macro, rural macro, indoor office, or indoor factory.
Proposal 3: Describe the scene geometry and the characteristics of the materials involved in the ISAC deployment scenarios to be used for ray tracing in ISAC evaluation.
Proposal 4: For the urban grid defined for V2X in TR 37.885, the scene geometry can be described by including assumption on building height, and assumptions on the materials for the buildings and roads can be included.
Proposal 5: Agree on the evaluation parameters for environment objects in automotive sensing scenarios assuming urban grid (ISAC – Automotive) as in Table 1.
Table 1: Evaluation parameters for environment objects in ISAC-Automotive 

3GPP TSG RAN WG1 #120bis		R1-2502002
Wuhan, China, April 7th – 11th, 2025

Source:	CATT, CICTCI
Title:	Discussion on ISAC deployment scenarios
Agenda Item:	9.7.1
Document for:	Discussion and Decision

Conclusion
In this contribution, we discuss the detailed scenarios of each use case for ISAC. The observation and proposals are summarized as follows:
Observation 1: Different cell sectorization methods have no impact on the result of large scale calibration.
Proposal 1: For UAV sensing scenarios, support large scale calibration parameters in Table 2.
Proposal 2: For Automotive sensing scenarios, for the EO type-2 in Urban Grid, when modelled, 
The number of buildings follows the road configuration and warp around rule in TR 37.885/36.885; for calibration, the number of buildings can be reduced for simplicity, e.g. to 9 (for a 7-cell layout with ISD=500m);
Consider shifting the road grids by ()  m and thus BSs are placed at the corner of the top of buildings;
4 walls per building are modelled. The impact of a wall is ignored if specular reflection condition is not met at the surface of the wall;
No need to introduce additional building size.
Proposal 3: For Automotive sensing scenarios, support large scale calibration parameters in Table 4.
Proposal 4: For UAV sensing scenarios, support full scale calibration parameters in Table 6.
Proposal 5: For Automotive sensing scenarios, support full scale calibration parameters in Table 8.

3GPP TSG RAN WG1 #120bis			R1-2502029
Wuhan, China, April 7th – 11th, 2025

Agenda item:		9.7.1
Source:	China Telecom
Title:	Discussion on ISAC deployment scenarios
Document for:		Discussion

Conclusions
In this contribution, we discuss ISAC deployment scenarios related issues and have following proposals:
Proposal 1: It should be reported which concatenation method of Tx-target and target-Rx link is used in channel model calibration.
Proposal 2: For full-scale calibration, support SIR as a metric to be calibrated.
Proposal 3: The SIR can be defined as follows: 

where S is defined as received power summation at sensing Rx of the signal across the sensing target from serving sensing Tx, I1 is the received signal power from target channel of other targets with the same pair of sensing Tx and Rx, and I2 is the received signal from other sensing Tx to the same sensing Rx.
Proposal 4: For calibrations for human sensing targets,
If monostatic sensing mode is considered, component A of the human for each scattering point should be set as -1.37dBsm.
If bistatic mode is performed in simulation, the same value as monostatic is supported for the component B1/B2 of bistatic human.
Proposal 5: Support simulation assumptions for large scale calibration for human sensing targets as listed in Table 1.
Proposal 6: Support simulation assumptions for full scale calibration for human sensing targets as listed in Table 2.

3GPP TSG RAN WG1 #120bis	R1- 2502051
Wuhan, China. 7th – 11th April 2025

Agenda item:		9.7.1
Source:	Nokia, Nokia Shanghai Bell
Title:	Discussion on ISAC Deployment Scenarios
WI code:	FS_Sensing_NR
Release:	Rel-19
Document for:		Discussion and Decision

Conclusion
In this contribution, we are making the following proposals.
Proposal 1: Consider our results in Figures 1 and 2 for the TRP-based monostatic UAV sensing scenario in the UMA-AV channel to the ISAC channel model calibration effort and capture them in the TR
Proposal 2: Consider our results in Figures 3 and 4 for the TRP-based bistatic UAV sensing scenario in the UMA-AV channel to the ISAC channel model calibration effort and capture them in the TR
Proposal 3: Discuss the proposed set of assumptions in Table 2 and our results in Figures 3 and 4 for the TRP-based monostatic Human Sensing Scenario in Indoor Factory deployment, and consider them in the ISAC channel model calibration effort
Proposal 4: Discuss the proposed set of assumptions in Table 2 and our results in Figures 5 and 6 for the TRP-based bistatic Human Sensing Scenario in Indoor Factory deployment, and consider them in the ISAC channel model calibration effort

References

3GPP TSG RAN WG1 #120-bis		R1- 2502054
Wuhan, China, April 7th – 11th, 2025

Source:	Tiami Networks
Title:	Discussion on ISAC deployment scenarios
Agenda Item:	9.7.1
Document for:	Discussion and Decision

Introduction:
The following agreements were achieved during RAN#120 meeting . 



Based on the agreed calibration parameters, we have provided the large-sale calibration results for target channel of the BS-monostatic UMa-AV UAV target, BS-UE bistatic UMa-AV UAV, and BS-monostatic UMa Human detection scenarios in Appendix A.
It is essential for ISAC calibration to be able to handle the difference in signal strength between the target reflections and background clutter. This is because traditional calibration methods might miss target channel errors because the background is so much stronger. That's why we need to calibrate the target and background channels separately - to properly check the RCS, positioning accuracy, and signal strength. Furthermore, we should consider controlled tests setups like a single transmitter-receiver pair instead of complicated full-system simulations. This helps spot errors in how we model radar reflections (RCS) and signal polarization. By checking basic measurements like signal strength and angle/delay variations, we can ensure the model is being implemented consistently across all companies’ efforts. 
Proposal 1: ISAC target-channel calibration should be considered assuming simple simulation setups to verify RCS and polarization modeling. Key metrics like received power, angle/delay spreads, and Doppler shifts must be standardized for consistent implementation.
Back to the evaluation parameters for the targets, we believe the minimum distance between the targets needs to be considered more carefully. At least for a UAV target, assuming a fixed value of 10m is too optimistic and might result in unrealistic target detection results. Hence, the minimum distance between sensing targets should be defined as no less than the physical dimensions of the largest target while avoiding arbitrary restrictions that could limit sensing resolution.

Proposal 2: The minimum distance between targets should be at least equal to the target's size.

Conclusion
Proposal 1: ISAC target-channel calibration should be considered assuming simple simulation setups to verify RCS and polarization modeling. Key metrics like received power, angle/delay spreads, and Doppler shifts must be standardized for consistent implementation.
Proposal 2: The minimum distance between targets should be at least equal to the target's size.


Appendix A
Coupling loss results for large-scale calibration. 

Figure 1. CDF of Coupling loss for the BS monostatic sensing for a human target in UMa.

Figure 2. CDF of Coupling loss for the BS monostatic sensing for a UAV target in UMa-AV.

Figure 3. CDF of Coupling loss for a BS-UE bistatic sensing for the UAV target in UMa-AV.

TDoc file conclusion not found

3GPP TSG RAN WG1 #120bis                                       R1-2502062
Wuhan, China, Apr 7th – 11th, 2025	

Source:	ZTE Corporation, Sanechips
Title:	Discussion on ISAC deployment scenarios
Agenda item:	9.7.1
Document for:	Discussion and Decision

Conclusion
In this contribution, we provide our views on ISAC deployment scenarios, and we have the following proposals:
Proposal 1: For ISAC channel modelling, do not calibrate combined channel.
Proposal 2: For target channel calibration, select Tx-Rx pair based on the 4 smallest power scaling factors of the target channel to simplify calibration workload.
Proposal 3: Coupling loss for target channel is defined as:
Large scale calibration: power scaling factor (pathloss, shadow fading, and RCS component A included)
Full calibration: power scaling factor, RCS B1/B2 and power of rays in Tx-target/target-Rx links (), Tx/Rx antenna pattern, 3 polarization matrixes
Proposal 4: Definitions of delay spread and angle spread refer to Annex A of TR 25.996, with the following consideration:
is replaced by  of 38.901 draft CR
 is replaced by , , , 
For delay spread, consider absolute delay  since it is agreed as mandatory feature.
Proposal 5: The following additional parameters should be considered for full calibration:
XPR of sensing target
Options for the concatenation of Tx-target and target-Rx link in the target channel
Proposal 6: Human is modelled as single scattering point. The location of the single scattering point can be modelled in a higher height than the geometric center.
For adult pedestrian, the height of single scattering point is modelled in 1.5m.

3GPP TSG-RAN WG1 #120bis	R1-2502067
Wuhan, China, April 7th – 11th, 2025
	
Agenda Item:	9.7.1
Source:	Panasonic
Title:	Discussion on ISAC Deployment Scenarios
Document for:	Discussion

Conclusion
In this document, we have discussed some details on ISAC deployment scenarios. We have the following proposals:
Proposal 1: For the purposes of large-scale calibration for UAV sensing targets, support to update (in red colour) the following calibration parameters in Table x. 
Table x. Simulation assumptions for large scale calibration for UAV sensing targets

Proposal 2: For the purposes of full calibration for UAV sensing targets, support to update (in red colour) the following parameters below in Table y. 
Table y. Simulation assumptions for full calibration for UAV sensing targets

3GPP TSG RAN WG1 #120bis			        R1-2502170
Wuhan, China, April 17th – 21st, 2025

Agenda item:	9.7.1
Source: 	CMCC, China Southern Power Grid
Title: 	Discussion on ISAC channel model calibration
Document for:	Discussion and Decision

Conclusions
In this contribution, we provide our views on channel model calibration and initial results on large scale calibration for UAV sensing target, and the following proposals are made:
Proposal 1: Propose to use assumptions in Table 2 to update and clarify the simulation assumptions for large scale calibration for UAV sensing targets.
Proposal 2: We provide our calibration result in Figure 1 and Figure 2 with assumptions in Table 2 , while detailed CDF data can be found in the attached file.
Proposal 3: Support the simulation assumptions for large scale calibration for Automotive Urban and Hazards in Table 3 and channel model calibration parameters for full calibration in Table 4.

3GPP TSG-RAN WG1 Meeting #120bis	   R1-2502207
Wuhan, China, April 07-11, 2025

Agenda Item:	9.7.1
Source:	Huawei, HiSilicon
Title:	Deployment scenarios for ISAC channel model 
Document for:	Discussion and Decision

Conclusions
This contribution illustrates how the SIR is calculated for TRP-TRP mono-static and bi-static sensing modes and for TRP-UE bi-static sensing mode. In addition, the simulation assumptions for the large-scale and full calibration of sensing vehicle in urban grid and on highway are summarized, respectively. The preliminary simulation results for sensing UAV is also provided. 
The proposals are summarized as follows:
Proposal 1: Adopt the SIR as one of the metrics for the ISAC channel model calibration. 
For TRP mono-static and bi-static, the SIR is calculated as in Table 1 for UAV.
For TRP-UE bi-static sensing mode, the SIR is calculated as in Table 2 for vehicle in urban grid.
For other sensing scenarios, the SIR calculation for the sensing modes defined in Table 1 or Table 2 applies similarly.
Proposal 2: Adopt the simulation assumptions from Table 3 with highlighted in cyan for the large scale and from Table 4 for the full calibration for sensing UAV, respectively. 
Proposal 3: Adopt the simulation assumptions in Table 5 for the large scale and full calibration for sensing vehicle in urban grid scenario.
Proposal 4: Adopt the simulation assumptions in Table 6 for the large scale and full calibration for sensing vehicle on highway.

3GPP TSG RAN WG1 #120bis                                R1-2502285
Wuhan, China, April 7th – 11th, 2025

Source:	OPPO
Title:          Discussion on ISAC channel model calibration
Agenda Item:   9.7.1
Document for:  Discussion and Decision

Description
In RAN1 #120 and previous meetings, the baseline of evaluation parameters for all five sensing scenarios were agreed. In this contribution, we discuss the assumptions and metrics for channel model calibration.
Calibration assumption
Simulation assumption for human indoor/outdoor
According to the discussion in previous meetings, communication scenarios are applied for related sensing scenarios. The Table 7.8-1 and Table 7.8-2 in TR38.901 for calibration could be starting point. Table 1 could be used as simulation assumption for human indoor/outdoor scenarios. Both TRP and UE are considered as sensing node for human case. The calibration shall consider TRP mono-static mode, TRP bi-static mode, TRP-UE bi-static mode, and UE bi-static mode.
Table-1 Large-scale calibration assumptions for human indoor/outdoor scenario


Table-2 Full calibration assumptions for human indoor/outdoor scenario

Proposal 1: Use simulation assumptions in Table 1 and Table 2 for calibration of human indoor/outdoor scenarios.
Simulation assumption for UAV
RAN1 has agreed most of assumptions for large-scale calibration for UAV scenario. Table 4 lists our preferred full-scale calibration assumptions for UAV scenarios.
Table-3 Full calibration assumptions for UAV scenario


Proposal 2: Use simulation assumptions in Table 3 for full-scale calibration of UAV scenario.

Calibration metrics
The following metrics defined for small scale are mainly collected from the target channel. 
CDF’s of ray-level power, delay and angles spread
For CDF of delay, each delay sample is the relative Tx-ST-Rx delays comparing to LOS+LOS delay.
For CDF of angle spread (including AOA/AOD/ZOA/ZOD), the angle spread is calculated based on Annex A in TR 25.996.
For each metric CDF, there can be two options in statistics collection.
Opt-1: All rays are collected for CDF statistics, including LOS*LOS ray, LOS*NLOS rays, NLOS*LOS rays, and NLOS*NLOS rays.
Opt-2: Only NLOS*NLOS rays are collected for CDF statistics.
Delay-to-power profile and angle-to-power profile
The profiles with pre-defined delay bins or angle bins (the angle could be one of AOA, AOD, ZOA, and ZOD). The power within a delay/angle bin is defined as total power of rays whose corresponding delay/angle falls into the bin.
The delay can be the absolute delay in delay-to-power profile.  
SNR/SINR that may matter to sensing instead of communication
The “S” part used to derive SNR/SINR may refer to useful rays for sensing instead of all rays carrying the sensing signal energy. The “noise” or “interference part” may also include certain energy carried in sensing signal. The details need RAN1 further discussions. 
Proposal 3: RAN1 considers the calibration metrics that are not used in communication channel model calibration, include:
CDF’s of ray-level power, delay and angle spread
Delay-to-power profile and angle-to-power profile
SNR/SINR that may matter to sensing instead of communication

Conclusion 
This contribution is concluded with the following observations and proposals. 
Proposal 1: Use simulation assumptions in Table 1 and Table 2 for calibration of human indoor/outdoor scenarios.
Proposal 2: Use simulation assumptions in Table 3 for full-scale calibration of UAV scenario.
Proposal 3: RAN1 considers the calibration metrics that are not used in communication channel model calibration, include:
CDF’s of ray-level power, delay and angle spread
Delay-to-power profile and angle-to-power profile
SNR/SINR that may matter to sensing instead of communication
Reference 
RP-234069 New SID: Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR
TR 38.901 Study on channel model for frequencies from 0.5 to 100 GHz
TR 36.777 Study on Enhanced LTE Support for Aerial Vehicles
TR 37.885 Study on evaluation methodology of new Vehicle-to-Everything (V2X) use cases for LTE and NR
TR 38.802 Study on New Radio Access Technology Physical Layer Aspects
R1-2405964 LS on Physical Properties of Sensing Targets in Automotive Scenarios for ISAC, 5GAA WG4
TR 25.996 Spatial channel model for Multiple Input Multiple Output (MIMO) simulations

TDoc file conclusion not found

3GPP TSG RAN WG1 Meeting #120bis		R1-2502325
Wuhan, China, April 7th – 11st 2025

Agenda item:	9.7.1
Source:	Sony
Title:	Discussion on ISAC Deployment Scenarios
Document for:	Discussion and Decision

Conclusion
In this contribution, we share our views on the details of ISAC deployment scenarios, particularly on channel model calibration aspects. We made the following observation and proposals: 
Observation 1: At higher frequency (FR2), the coupling loss results are worse than the lower frequency (FR1). Generally, the TRP-TRP monostatic provides better coupling loss results than the TRP-TRP bistatic in the lower part of CDF.
Proposal 1: Update the parameter name to the minimum 2D distances between pairs of Tx/Rx and sensing target.
Proposal 2: Further study on the CL of combined channel and the definition of SIR metric.
Proposal 3: Support the above full scale calibration assumptions table as the baseline. Further refinements are still needed, including removing CDF of per-cluster coupling loss for the background channel.

3GPP TSG-RAN WG1 Meeting #120bis		R1-2502378
Wuhan, China, April 7th – 11th, 2025

Agenda item:	9.7.1 
Title:	Discussion on ISAC Deployment Scenarios 
Source:	Samsung 
Document for:	Discussion and Decision

Conclusion
For EO type-2, major objects could be buildings. The shape of buildings defined by 3GPP represents the cell layout of an Urban Grid scenario, considering the inter-site distance (ISD) of an UMa, resulting in a 2D size of 433 m x 250 m, However, EO type-2 should not be limited to simply being a cell layout element but rather play the role of deterministic clutters within channels.
EO type-2s may take the form of buildings. Nonetheless, these forms of EO type-2 should no vary across different scenarios but rather have standardized shapes applicable to all scenarios.
RAN1 consider the sensing target as EO type-1 for each sensing target scenario in unintended/environment object category
RAN1 discuss the detailed shape and size for EO type-2 in unintended/environment object category 
In monostatic sensing modes, transmit and receive antennas could exist at the same location. Under such circumstances, sensing would typically employ full-duplex methods unless distance become excessively large, necessitating consideration of antenna panel separations designed to mitigate self-interference
Differences in antenna configurations affecting antenna gains in channel impulse response
RAN1 clarify the antenna configuration for monostatic sensing mode taking into account possible options:
Option #1: The total number of antenna elements in monostatic equal to that of bistatic. That means, if the total number of antennas in bistatic is N, then the number of transmitting antennas in monostatic is N/2, and the number of receiving antennas is also N/2
Option #2: The total number of antenna elements in monostatic double that of bistatic. That means, if the total number of antennas in bistatic is N, then the number of transmitting antennas in monostatic is N, and the number of receiving antennas is also N
RAN1 selects unified options and performs calibration
It’s not clear for Propagation Case 1, 2 and 3 involving LOS conditions. This is because while the power of the LOS path under LOS condition is , it’s ambiguous whether the power of the NLOS paths under LOS condition is  or .  considers only the power of the generated cluster, while  includes components representing relatively lower powers considering the specular reflection of LOS
RAN1 clarify the power term for channel coefficient generation between the below options:
The coupling loss get results the complex values even though the calibration assumed the single dual-pol isotropic antenna 
RAN1 consider the absolute value of coupling loss to derive CDF
Annex B.1.2 has parameter settings considering non-terrestrial UE environments, but its usability at high frequency band (more than 2 GHz frequency band) is unclear
Annex B.1.3 demonstrated usability at higher frequency bands because it already defined in TR 38.901, but considers terrestrial UE deploying environment
RAN1 discuss whether to prioritize usability at higher frequency band or the environment considered for fast fading model
RAN1 discuss the detailed simulation parameters of UAV scenario for calibration with Table 1
Table x. Simulation assumptions for full calibration for UAV sensing targets
When a human outdoor is assumed as a sensing target, their typical surroundings may include vehicles, people, trees, etc., and further, surrounding buildings and ground. These may interfere with sensing performance due to reflections in the sensing environment. In order to reflect more realistic situations, such modelling of EOs is required.
For human indoor and outdoor as sensing target, RAN1 discuss the detailed environment objects 
RAN1 discuss the detailed simulation parameters of human outdoor for calibration with Table 2
Table 2. Simulation assumptions for full calibration for Human outdoor sensing targets (full calibration)

RAN1 discuss the detailed simulation parameters of human indoor for calibration with Table 3
Table 3. Simulation assumptions for full calibration for Human indoor sensing targets (full calibration)
The main purpose for the introduction of the Urban Grid as a deployment scenario in ISAC channel to compensate for the absence of road geometry in basic outdoor scenarios. However, the cell layout of the Urban Grid defined in TR37.885 is only based on the UMa scenario. The road condition would also be significant elements in UMi-street canyon and Rural areas
Assuming a road width of 20 m, the cells of the UMa-based Urban Grid are defined as 433 m and 250 m. By inferring from this,  and  can be considered
RAN1 consider the road condition on UMi and RMa for Urban Grid scenarios
RAN1 discuss the detailed cell-layout for Highway scenarios between option 1 and 2 in TR36.885
RAN1 discuss the detailed simulation parameters of automotive for calibration with Table 5
Table 5. Simulation assumptions for full calibration for Automotive sensing targets (full calibration)

RAN1 discuss the detailed Indoor Factory scenario considering cell layout
RAN1 discuss the detailed simulation parameters of AGV for calibration with Table 6
Table 6. Simulation assumptions for full calibration for Human indoor sensing targets (full calibration)
Discuss whether link between sensing target and background environment is considered and necessity of definition of minimum distance between sensing target and environment object
RAN1 introduce all studied scenarios for ISAC in TR 38.901.
Discuss how to make working scope of channel modeling for 6 sensing modes and whether RAN1 need to focus on specific some of sensing mode considering with two types as below
Type #1: a common channel modelling framework can be applied for all 6 sensing mode with some adaptation for each mode (e.g., sensing TX, RX change between BS and UE)
Type #2: individual channel modelling should be studied for each sensing mode
RAN1 supports the channel parameters for ISAC with possible frequency dependency
RAN1 study and model the ISAC channel considering validation of efficacy for frequency bands

3GPP TSG RAN WG1 #120bis                                       R1-2502416
Wuhan, China, Apr 7th – 11th, 2025	

Source:	CALTTA
Title:	Discussion on ISAC deployment scenarios
Agenda item:	9.7.1
Document for:	Discussion and Decision

Conclusion
In this contribution, we provide our views on details of the calibration issues and parameters for UAV and automotive scenarios, and we have the following proposals:
Proposal 1: Support the calibration parameters in Table 2.1-1 for UAV scenario. 
Proposal 2: Support the calibration parameters in Table 2.2-1 for automotive vehicle scenario. 

3GPP TSG RAN WG1 #120-bis		             			          R1-2502418 
Wuhan, China, April 7th – 11th, 2025

Agenda Item: 		   9.7.1
Source: 			   BUPT, CMCC, X-Net
Title:                Discussion on ISAC channel calibration
Document for: 	      Discussion and Decision

Conclusions
In this contribution, we analyze and summarize the assumptions, metrics, RCS, and convolution methods for ISAC channel calibration.. The proposals are as follows:

Proposal 1: The target channel and Mono-static background channel need to be calibrated. The combined channel can be used solely for SIR/SINR calibration.
Proposal 2: The calibration of the ISAC channel model can follow three phases: large scale calibration, full calibration, and additional calibration (SC and EO).
Proposal 3: For large-scale calibration, it is recommended to use 19 cell sites and select the four TRPs with the strongest power scaling factors as the serving stations.
Proposal 4: For full calibration, it is recommended to use power scaling factors for the rapid identification of target links.
Proposal 5: For evaluating the overall performance of ISAC, interference primarily considers co-channel interference. For evaluating sensing performance only, interference can account for the combined impact of both the background channel of the link itself and co-channel links.
Proposal 6: Selecting fixed RCS values for use in channel model calibration.
Proposal 7：When Option 3 is used for calibration, NLOS-NLOS power normalization should be applied to align the power with that of Option 0, ensuring compliance with the objective physical law of energy conservation.
Proposal 8: When simulating the ST-Rx link, after generating channel parameters using existing TRs (or modifications based on existing TRs), the horizontal angle parameters (AOA, ZOA) and vertical angle parameters (AOD, ZOD) should be swapped to ensure the correctness of the link direction.

3GPP TSG RAN WG1 Meeting #120bis	 R1-2502451
Wuhan, China, April 7th – 11th, 2025

Source: 	Xiaomi
Title:	Deployment scenarios and evaluation assumptions for ISAC channel model
Agenda Item:	9.7.1
Document for:	Discussion and Decision

Conclusion
In this contribution, we discuss the remaining issues of evaluation parameters for the ISAC deployment scenarios. In addition, we propose detailed simulation assumptions for UAV, human indoor, and automotive vehicles sensing scenarios.
Proposal 1：EO is not included in the table for evaluation parameters of UAV sensing scenarios. It can be discussed whether to model EO type-1 in the performance evaluation.
Proposal 2: The fix value of min 3D distance between sensing targets at human indoor scenario is set to 1m.
Proposal 3: EO is not included in the table for evaluation parameters of human indoor scenario. It can be discussed whether to model EO type-1 in the performance evaluation.
Proposal 4: It is up to each company to select and report interested scenarios for calibrations. RAN1 will collect results only for a scenario if sufficient number of companies run the calibration.
Proposal 5: For background channel, only the mono-static background channel which introduced a new model is necessary to calibrate. 
Proposal 6: Only if SIR/SINR would be selected as one calibration metric, full calibration can be considered for the combined channel, both for mono- and bi-static sensing.
Proposal 7: For a specific sensing target, the 4 (sensing Tx, sensing Rx) pairs with the highest large-scale coupling loss should be considered for calibration of the target channel.
Proposal 8: Performance metrics for calibration should be determined for sensing, and the metrics in TR 38.901 can be considered as the starting point.
Large-scale calibration: Coupling loss
Full calibration: Coupling loss, CDF of Delay Spread and Angle Spread.
Proposal 9: The definitions of performance metrics for calibration should be clarified for sensing.
Coupling loss for large scale calibration: power scaling factor
Coupling loss for full calibration: power scaling factor + power of path (RCS B1/B2, power of rays in STX-SPST/SPST-SRX links included) + antenna gain + polarization effect
The definitions of the metrics in TR38.901 can be reused for CDF of Delay Spread and Angle Spread, wherein the communication channel between TRP and UE is replaced by the target channel between sensing Tx and sensing Rx.
Proposal 10: For background channel calibration for mono-static, calibration metrics include the coupling loss (large scale and full calibration) and CDF of delay spread and angle spread (full calibration).
The definitions of these metrics in TR38.901 can be reused, wherein communication channel between TRP and UE is replaced by the background channel between sensing Tx and one reference point.
Proposal 11: For calibration of UMa-AV, the simulation assumption in tableⅠ can be used as a starting point.
Proposal 12: For calibration of human indoor, the simulation assumption in tableⅡ can be used as a starting point.
Proposal 13: For calibration of highway, the simulation assumption in tableⅢ can be used as a starting point.

3GPP TSG RAN WG1 #120bis	R1-2502465
Wuhan, China, April 7th – 11th, 2025

Agenda Item:	9.7.1
Source:	Toyota ITC
Title:	Discussion on ISAC deployment scenarios
Document for:	Discussion and Decision

Conclusions
In this contribution we discussed our views on ISAC deployment scenarios and calibration. Our proposal is summarized as follows: 
Proposal 1: For the purposes of large-scale calibration and full calibration for automotive sensing targets, RAN1 to consider at least TRP monostatic, TRP-TRP bistatic, UE mono-static, and TRP-UE bi-static sensing modes.

3GPP TSG RAN WG1 #120bis                                                                          R1-2502588
Wuhan, China, April 7th – 11th, 2025

Agenda item:		9.7.1
Source:	 	Lenovo
Title:		Discussion on ISAC deployment scenarios 
Document for:		Discussion and decision

Conclusion
In this contribution, the views on the initial CM calibration have been shared with following observations and proposals:
Observation 1: For the purpose of the ISAC channel model calibration, it would not be sufficient to monitor the metrics only associated with the target channel, since the impact of the target channel may be lost/buried in the impact of modeling mismatches among companies for the background channel.
Proposal 1:  Consider separate metrics for calibration of the target channel, background channel and the combined ISAC channel, including at least the coupling loss, delay and angle spread each associated with the target channel, the background channel and the combined ISAC channel.
Observation 2: The channel modeling framework as discussed in AI 9.7.2 consists of various novel steps, e.g., modeling of the sensing target, modeling of the target channel, modifications to the background channel, combination step for the background and target channel.  
Proposal 2: In order to better calibrate the channel modeling implementation among companies, it is preferred to take a stepwise approach with a limited number of novel features present in each calibration step. 
Proposal 3: If the stepwise calibration process is agreed, the calibration assumptions and metrics can be discussed and defined at different steps, where link-level assumptions can be used for calibration of the target object modeling.
Proposal 4: The calibration metrics can be expanded to describe the supported modeling dynamic range for the background channel.
Proposal 5: All the 5 sensing target types and sensing modes are intended to be captured by the channel model calibration process based on the company contributions and scenario interest.
Proposal 6: For both monostatic and bistatic scenarios, the selection of the TRPs can be done based on the resulting coupling loss, and the 4 best links can be selected and separately reported/used to generate the desired metrics.
Proposal 7: For both monostatic and bistatic scenarios, the selection of the TRPs can be done based on the resulting coupling loss, and the 4 best links can be selected and separately reported/used to generate the desired metrics.
Proposal 8: Consider the calibration metrics for full calibration of the background channel and combined ISAC channel including the CDF curve for cluster coupling loss, i.e., the CDF of the coupling loss associated with different clusters.
Proposal 9: The UE selection can assume uniform UE distribution as TR 38.901 within the center cell, wherein selection of the TRP-UE links can be done like the bi-static TRP-TRP links, by taking the best four of the TRP-UE links to generate the calibration metrics.  
Proposal 10: For the UAV sensing scenarios with TRP-based sensing modes where the UAV is within a sufficient 3D distance from any of the sensing Tx/Rx nodes, the defined minimum distance requirement does not apply.  
Proposal 11: For calculation of the calibration metrics associated with the background channel of a monostatic sensing mode, the direct Tx-Rx leakage path can be removed from the calculation of the coupling loss in the background and combined ISAC channel.  
Proposal 12: The cell sector can be defined as a single isotropic sector covering the full azimuth angular range [0 360].

3GPP TSG RAN WG1 #120bis	R1-2502623
Wuhan, China, April 7th – 11th, 2025

Agenda Item:	9.7.1
Source:	Apple Inc.
Title:	Discussion on ISAC deployment scenarios
Document for:	Discussion/Decision

Conclusion
The following observations and proposals have been made in this contribution: 

Proposal 1: For channel model calibration, RAN1 should adopt the stochastic communication channel model calibration approach in TR 38.901 for ISAC channel modelling for 
large scale parameters, where fast fading is not included 
full calibration, which includes fast fading 
Spatial consistency

Proposal 2: On the issue of the spatial consistency model/parameters and for some parameters such as the mean and variance of the RCS, progress in Agenda Item 9.7.2 is needed. 

Proposal 3: On the Minimum 3D distances between pairs of Tx/Rx and sensing target for UMa and UMi: 
BS-to-target minimum distance = 10 meters 
UE-to-target minimum distance = 1 meter


Proposal 4: On the UE-to-UE channel model there are two options capture in 38.858: 
Option 1: 36.843 (D2D model) 
Option 2: Using UMi in 38.901 but changing BS height to UE’s height. 
Calibration should use Option 2.

Proposal 5: Finalize background channel modeling:
Monostatic Channel: model parameters 
Bi-static Channel: Agreement needed to use 38.901 communications channel

Proposal 6: For the purposes of large-scale calibration for human outdoor sensing targets, the following calibration parameters are proposed below in Table 1. 

Proposal 7: For the purposes of full calibration for human outdoor sensing targets, the following parameters are proposed below in Table 2. 

Proposal 8: For the purposes of large-scale calibration for human indoor sensing targets, the following calibration parameters are proposed below in Table 3. 

Proposal 9: For the purposes of full calibration for human indoor sensing targets, the following parameters are proposed below in Table 4. 

3GPP TSG RAN WG1 #120bis		 R1-2502714
Wuhan, China, April 7th – 11st, 2025
Source: 	MediaTek Inc.
Title:	Discussion on ISAC deployment scenario
Agenda item:	9.7.1
Document for:	Discussion

Conclusion 
In this contribution, it discusses the ISAC channel modelling calibration related issues with following proposals: 
Proposal 1: Suggesting only consider the target channel  calibration if background channel is modelled with the same communication channel procedure, and only considering the background channel calibration if additional new procedures are introduced.
Proposal 2: The detailed calibration parameters and corresponding calibration results refer to the Table 1 and figures from Figure 1 to Figure 6.
Table 1. Simulation assumptions for large scale calibration for UAV sensing targets

Proposal 3: Considering the limited meeting time for Rel-19, not to consider SIR/SINR as a calibration metric, and details discussion on sensing SIR/SINR can be in the future Release.
Proposal 4: Suggesting the following channel modelling calibration parameters Human indoor use cases.

3GPP TSG-RAN WG1 Meeting #120bis	Tdoc R1-2502725
Wuhan, China, April 7th – 11th, 2025

Agenda Item:	9.7.1
Source:	Ericsson
Title:	Discussion on ISAC Deployment Scenarios
Document for:	Discussion, Decision
Discussion
The objective of the study on channel modelling for Integrated Sensing And Communication (ISAC) for NR [1] includes the following: 

Most discussions of agenda item 9.7.1 in recent meetings focused on channel model calibration, including calibration mechanisms and parameters, while one important task of the agenda item is to collect ISAC channel parameters like those in section 7.2 of 38.901 for communication channel. We discuss the two parts of contents in this contribution. 

Calibration parameters for section 7.9.4 of 38.901
Large-scale calibration
In RAN1#120 meeting, both large-scale and full-scale calibration were agreed, but there was no consensus among companies on the relationship or mapping between large-scale/full calibration and target/background channel. There are also some ambiguities in the agreed large-scale calibration table for UAV scenario.
The first agreement above says ‘large scale parameters, where fast fading is not included’, which means that large-scale calibration doesn’t include fast fading parameters or steps. More specifically, it doesn’t involve the steps of cluster and ray-related small-scale parameters or the generation of target channel or background channel. It only relates to RCS component A and the large-scale parameters generated in steps 2, 3, and 4 according to TR 38.901, namely LOS/NLOS state, indoor/outdoor state, path loss between a BS and a UT, and delay spread (DS), angular spreads (ASA, ASD, ZSA, ZSD), Ricean K-factor, and shadow fading (SF). In short, large-scale calibration is only about large-scale parameters of target, sensing Tx and Rx, independent from target channel or background channel, which require small-scale parameters to generate.
The agreement in RAN1#120 ‘large-scale calibration doesn’t include fast fading’ means large-scale calibration doesn’t involve the steps of cluster and ray-related small-scale parameters or the generation of target channel or background channel.
Large-scale calibration is only about large-scale parameters of target, sensing Tx and Rx, independent from target channel or background channel, which require small-scale parameters to generate.
According to the agreement below, RCS component A is used in the equation to generate power scaling factor. A is a single value per target type. It is FFS whether different A values would exist for different sensing modes. 
There is no doubt that bi-static sensing mode requires bi-static RCS. However, there is some ambiguities between mono-static sensing mode and mono-static RCS. As discussed in [2], for mono-static sensing mode, a mono-static RCS value, where the incident angle and scattered angle are the same, is needed to generate the direct path of a target channel, and much more bi-static RCS values are needed for indirect paths. With 1:1 random ray pairing, one mono-static RCS value and nearly 1200 bi-static RCS values are needed to generate the direct path and indirect paths respectively. Both mono-static and bi-static sensing modes need at least thousands of bistatic RCS values for a target channel, while mono-static sensing mode may additionally need one mono-static RCS value for the direct path of target channel, if any. Therefore, regarding ‘FFS: this allows different values for monostatic and bistatic sensing, if needed’, we propose that the same RCS component A is used for monostatic and bistatic sensing.
Both mono-static and bi-static sensing modes need at least thousands of bistatic RCS values for a target channel, while mono-static sensing mode may additionally need one mono-static RCS value for the direct path of target channel, if any.
Regarding ‘FFS: this allows different values for monostatic and bistatic sensing, if needed’, the same RCS component A is used for monostatic and bistatic sensing modes.
Value A for mono-static RCS for small UAV and human model 1 were agreed in RAN1#120. Bi-static RCS model is still under discussion. It is unclear whether mono-static RCS and bi-static RCS will have corresponding different A values, leading to two power scaling factors for a target type, one for mono-static RCS and another one for bi-static RCS. Otherwise, the same A can be used for both mono-static RCS and bi-static RCS, with only one power scaling factor for a target type. In the latter case, how to determine the common A should be sorted out. There are three options in our companion contribution [2]. Value of RCS component A used for large-scale calibration depends on the progress of AI 9.7.2. Without further progress in AI 9.7.2, regardless of BS mono-static or bi-static sensing mode, the value A used for large-scale calibration should not be the RCS component A of mono-static RCS. 
It is unclear whether mono-static RCS and bi-static RCS will have corresponding different A values, leading to two power scaling factors for mono-static RCS and bi-static RCS for a target type. 
Otherwise, the same A will be used for both mono-static RCS and bi-static RCS, leading to one power scaling factor for a target type. There are three options in our companion contribution.
Without further progress in AI 9.7.2, regardless of BS mono-static or bi-static sensing mode, the value A used for large-scale calibration should not be the RCS component A of mono-static RCS. 
In the UAV large-scale calibration table agreed in RAN1#120, there is a discrepancy between the BS antenna configuration (which is isotropic) and the sectorization which assumes three sectors. Let’s consider the three sectors of the center BS. Based on the assumptions of 3 sectors per cell site, isotropic BS antenna, and 1 target uniformly distributed (across multiple drops) within the 3 sectors of the center cell, the three sectors of the center cell will generate the same TRP-target link, and TRP monostatic background channel, and TRP-TRP background channel will all be identical. This seems wasteful of simulation resources. We suggest using a single 360-degree “sector”, i.e. the BS is at the center of the cell. 
For the purpose calibration, a single dual-polarized isotropic antenna and 360-degree sectors can be assumed. 
For UAV large-scale calibration, the agreed metric is coupling loss (based on LOS pathloss). The usual definition of coupling loss includes the antenna gains. Given isotropic antenna pattern is used, coupling loss is equivalent to the power scaling factor. For clarity, we propose for any target type, the large-scale calibration metric is “Power scaling factor for the target channel”. Given isotropic antenna pattern is used, for large-scale calibration of any target type, the large-scale calibration metric is “Power scaling factor for the target channel”.
Given isotropic antenna pattern is used, for large-scale calibration of any target type, the large-scale calibration metric is “Power scaling factor for the target channel”.
According to LOS probability, UAVs at 200m height see 100% LOS condition for BS-UAV link. But this doesn’t hold for UE-involved sensing modes for UAV sensing scenario. Therefore, based on LOS pathloss can be removed from the metric for UAV large-scale calibration. For ground sensing targets, there is no need to limit the large-scale calibration to LOS condition.
LOS condition doesn’t hold for UE-involved sensing modes for UAV sensing scenario.
For UAV sensing scenario and UE-involved sensing modes, there is no need to limit the large-scale calibration to LOS condition. It is the same for ground sensing targets.
Methodology for full calibration
According to the agreement in RAN1#120, target channel, background channel and combined channel, if calibrated, are separately calibrated. in our view is the separate calibration refers to full calibration.
The conventional full calibration of target channel includes system simulations with multiple gNBs as sensing Tx and Rx. Even if BS are at fixed locations, the uniform distribution of a single target within the area of the center BS incurs uncertainty of selecting a number N of sensing Tx and Rx pairs for both BS mono-static and bi-static sensing modes. The random target distribution and the corresponding selection of some Tx/Rx pairs makes the geometry uncertain and becomes a challenge to compare companies’ implementations of angle-dependent RCS. It will be worse with the uniform distribution of UEs for UE-involved sensing modes.
The random target distribution and the corresponding selection of some Tx/Rx pairs makes the geometry uncertain and becomes a challenge to compare companies’ implementations of angle-dependent RCS. It will be worse with the uniform distribution of UEs for UE-involved sensing modes.
However, due to the deterministic nature of the target channel modelling, a simplified full calibration of target channel for a sensing mode can be performed using deterministically positioned target, Tx, and Rx, rather than a uniform distribution. The geometry of the Tx, Rx, and target, including movement of one or more of these, can be deterministically specified in the calibration assumptions. This would help reduce statistical uncertainties that could otherwise skew the statistics unless large number of seeds and drops are used.
The geometry of the Tx, Rx, and target, including movement of one or more of these, can be deterministically specified in the calibration assumptions.
The ISAC channel model involves the generation of many large-scale and small-scale random parameters. In order to isolate the target modeling and mitigate the random variations caused by stochastic factors, this is preferably not done using stochastic Tx-target and target-Rx links. Instead, since target modeling is inherently deterministic, the simplified full calibration of target channel can include the concatenation of two deterministic Tx-target and target-Rx links where the multipath is modeled deterministically using CDL models. It only requires a single Tx, Rx, and a target. 
In order to isolate the target modeling and mitigate the random variations caused by stochastic factors, the simplified full calibration of target channel can include the concatenation of two deterministic Tx-target and target-Rx links where the multipath is modeled deterministically using CDL models. It only requires a single Tx, Rx, and a target.
Regarding background channel, due to its stochastic nature, this calibration would need to involve many monostatic or bistatic Tx/Rx locations to generate stable statistics. ISAC background channel modelling reuses many different channel models and TRs for different sensing modes. Some of these models have previously been calibrated and do not need recalibration, while other are new. One new addition is monostatic background channel, which needs calibration. Depending on the progress in 9.7.2, some new or revised bistatic background channel features, including such as moving or low-power clusters, may be introduced. 
Since ISAC background channel model reuses many existing channel models and TRs for different sensing modes, there is no need to recalibrate some of these models, which have previously been calibrated.
Monostatic background channel is a new channel model and in need of calibration.
The necessity of calibration of combined channel depends on the progress in 9.7.2. A trivial addition of the two channels may not require any calibration, but there could be agreements on aspects such as cluster/path reduction, blocking by targets or Type 2 EOs influencing the background channel, normalization, etc, that may motivate a separate calibration step. Again, to reduce calibration complexity of combined channel, ISAC channel as the sum of background channels modelled with CDL model and target channel modelled with the concatenation of CDL models could be utilized. 
Calibration of combined channel is needed in case of agreements on aspects such as cluster/path reduction, blocking by targets or Type 2 EOs influencing the background channel, normalization, etc, 
To reduce calibration complexity of combined channel, ISAC channel as the sum of background channels modelled with CDL models and target channel modelled with the concatenation of CDL models could be utilized.
Parameters and metrics of full calibration
Full calibration relies on a complete channel model. There is no agreement on some topics in 9.7.2, such as “Fast fading model” for UAV scenario, “(u, std) for XPR of target”, “Component B1/B2 of bistatic RCS” and “The power threshold for path dropping after concatenation for target channel”, “The power threshold for removing clusters in step 6 in section 7.5, TR 38.901 for background channel”, and “Absolute delay”. These are all treated in 9.7.2 and will be captured in the relevant parts of the TR and should not be doubly specified also in the calibration assumptions table. To avoid parallel discussions on the same topics in the two agenda items with potentially conflicting outcomes, we suggest no duplicate discussions on the remaining channel modelling issues for calibration only.
There is no agreement on some topics in 9.7.2, such as “Fast fading model” for UAV scenario, “(u, std) for XPR of target”, “Component B1/B2 of bistatic RCS” and “The power threshold for path dropping after concatenation for target channel”, “The power threshold for removing clusters in step 6 in section 7.5, TR 38.901 for background channel”, and “Absolute delay”.
These remaining open issues of channel modelling are all treated in 9.7.2 and will be captured in the relevant parts of the TR and should not be doubly specified also in the calibration assumptions table. 
To avoid parallel discussions on the same topics in the two agenda items with potentially conflicting outcomes, we suggest no duplicate discussions on the remaining channel modelling issues for calibration only.
Full calibration metrics such as received power, mean delay/angle and delay/angular spread, and Doppler frequency shift determined on the concatenated links would be useful to validate that different companies’ implementations of the RCS modeling and the path concatenation give comparable results. We can use “Coupling loss for the target channel” and “Coupling loss for the background channel” as two important metrics. The commonly used definition is that the coupling loss is the ratio between the transmitted and the received power. Since the transmitted power is given, we need to determine the average received power, where the averaging is to be done over fast fading. Calculating an average received power over time, frequency of the channel coefficients should be a stable method. 
If XPR of target, such as (u, std) for XPR of target, is a calibration parameter, the purpose is to calibrate the generation of CPM of target. Otherwise, companies can assume [1 0; 0 -1] for calibration, i.e., no cross-polarization power introduced by the target. It is straight forward that coupling loss for target channel can be separately reported for co-polar and cross-polar coupling loss or the ratio between them, which can be determined by averaging the received power across time and frequency. 
Furthermore, the time evolution of the channel on concatenated links can be directly compared to validate the modeling of Doppler and spatial consistency. A fixed radial velocity can be used as a parameter to calibrate Doppler shift.
“Coupling loss for the target channel” and “Coupling loss for the background channel” are two important metrics for full calibration.
The coupling loss for target channel can be separately reported for co-polar and cross-polar coupling loss or the ratio between them.
Calculating an average received power over time, frequency of the channel coefficients is used to calculate coupling loss.
A fixed radial velocity can be used as a parameter to calibrate Doppler shift.
Evaluation parameters for section 7.9.1 of 38.901
A plan of new ISAC sections in 38.901 was agreed in RAN1#116bis, including section 7.9.1 for scenarios and section 7.9.4 for calibration. Both sections will capture ISAC parameters and correspond to section 7.2 and section 7.8 of communication channel in 38.901 respectively. In this contribution, we separately discuss parameters to be captured in the new sections 7.9.1 and 7.9.4 of TR 38.901.

Tables in section 7.2 of 38.901 define the values/value ranges of parameters the channel model is valid for, i.e., validity ranges. Validity ranges of the parameters depict the capabilities and limitations of the channel model and helps readers of the TR to better understand the capabilities of the model and prevent some future work in vain. For example, the channel model of indoor factory scenario is valid for rectangular room size of 20-160000 m2 and ceiling height of 5-15m or 5-25m. Note that the intention of parameters in section 7.2 is not for simulation or evaluation in future study items.
New section 7.9.1 will capture the ISAC counterparts of the parameters in section 7.2 of 38.901. For example, heights, velocities, and accelerations outside the validity ranges are not validated and cannot be accurately supported by the channel model. The parameter physical characteristics lists sizes of the objects, which RCS and XPR are specified in 38.901 and which may be modelled in the future study item. Note that channel model in TR 36.777 does not support vertical mobility, because it does not support spatial consistency in the vertical domain. If this limitation remains, this should be clearly stated to avoid work in vain later.
Tables in section 7.2 of 38.901 define the values/value ranges of parameters the channel model is valid for, i.e., validity ranges. Validity ranges of parameters depict the capabilities and limitations of the channel model and help reader of the TR to better understand the capabilities of the model and prevent some future work in vain.
There were some discussions and agreements on ISAC channel calibration. One rule of thumb is that calibration is not supposed to be complete and complicated to reduce companies’ efforts. However, the discussion on calibration parameters should not put a restriction on the validity of channel model. 
Discussion on calibration parameters should not put a restriction on the validity of channel model.
For applicable communication scenario, only one scenario was agreed for the calibration of one type of sensing target, but this parameter in section 7.9.1 should capture the complete list of communication scenarios, where the target type may appear and be modelled. 
3D mobility includes the range of velocity and range of acceleration for each type of intended/unintended targets. Note 3 in UAV agreement says time-varying velocity may be considered for future evaluations. It indicates only time-constant speeds are used for calibration, and however, ISAC channel model should be able to support time-varying velocity for the future evaluation of object tracking. We provide value ranges of acceleration for the parameter 3D mobility in Table 1.
We now discuss another two parameters. Values of vehicle sensing scenarios are copied below.
The parameter minimum 3D distance between sensing targets is needed for future evaluation to determine the sensing resolution. Option 1 is sufficient to avoid spatially overlapping targets, which would not happen in real world. But Option 2 sets a restriction of channel model, and the future sensing algorithms would not be able to support a smaller resolution.
For Type-2 EO, 413mx230mx20m is the size of a building in urban grid scenario and unreasonably large for a building in rural areas. A smaller building size can be considered for RMa scenario, e.g. 12mx10mx10m. If Type-2 EO is only modelled for specular reflection, the pre-requisites of specular reflection should be conformed with, 1) the Type-2 EO is of the same size as or larger than the first Fresnel zone, and 2) the incident angle equals the reflection angle. The radius of the first Fresnel zone is approximately given by:

for distances tx—target d₁ and target—rx d₂, and wavelength λ.  
Moreover, in the agreements, each table is for a sensing scenario and models just one type of sensing targets. Such simplification can reduce calibration effort, while multiple types of sensing targets may co-exist in a real scenario. One important use case of sensing is to avoid collision between different types of sensing targets, such as UAVs and birds, pedestrians/animals and vehicles/trains, AGVs and workers. In the future evaluations, companies can select multiple co-existing types of targets in a simulation. In this sense, one table of parameters in section 9.7.1 is sufficient, and there is no need to generate tables for separate sensing scenarios in section 9.7.1. 
Applicable communication scenario in section 7.9.1 should capture the complete list of communication scenarios, where the target type may appear and be modelled.
ISAC channel model should be able to support time-varying velocity for the future evaluation of object tracking.
Option 2: Fixed value for the parameter minimum 3D distance between sensing targets sets a restriction of channel model. the future sensing algorithms would not be able to support a smaller resolution.
Support Option 1: At least larger than the physical size of a sensing target for the parameter minimum 3D distance between sensing targets.
A smaller building size than 413mx230mx20m in urban grid can be considered at least for RMa scenario, e.g. 12mx10mx10m.
Two prerequisites should be met to model specular reflection of Type-2 EO: 1) the Type-2 EO is of the same size as or larger than the first Fresnel zone, and 2) the incident angle equals the reflection angle.
To reduce calibration effort, only one type of targets is modelled in a sensing scenario. However, multiple types of sensing targets may co-exist in a real scenario.
One table of parameters in section 9.7.1 is sufficient. There is no need to generate tables for separate sensing scenarios in section 9.7.1. 
With all the discussions, Table 1 summarizes ISAC parameters for section 7.9.1 of 38.901.
Support the parameters in Table 1 for section 7.9.1 of 38.901.
Table 1	ISAC parameters for section 7.9.1 of 38.901
Conclusion
In the previous sections we made the following observations: 
Observation 1	The agreement in RAN1#120 ‘large-scale calibration doesn’t include fast fading’ means large-scale calibration doesn’t involve the steps of cluster and ray-related small-scale parameters or the generation of target channel or background channel.
Observation 2	Both mono-static and bi-static sensing modes need at least thousands of bistatic RCS values for a target channel, while mono-static sensing mode may additionally need one mono-static RCS value for the direct path of target channel, if any.
Observation 3	It is unclear whether mono-static RCS and bi-static RCS will have corresponding different A values, leading to two power scaling factors for mono-static RCS and bi-static RCS for a target type.
Observation 4	Otherwise, the same A will be used for both mono-static RCS and bi-static RCS, leading to one power scaling factor for a target type. There are three options in our companion contribution.
Observation 5	LOS condition doesn’t hold for UE-involved sensing modes for UAV sensing scenario.
Observation 6	The random target distribution and the corresponding selection of some Tx/Rx pairs makes the geometry uncertain and becomes a challenge to compare companies’ implementations of angle-dependent RCS. It will be worse with the uniform distribution of UEs for UE-involved sensing modes.
Observation 7	There is no agreement on some topics in 9.7.2, such as “Fast fading model” for UAV scenario, “(u, std) for XPR of target”, “Component B1/B2 of bistatic RCS” and “The power threshold for path dropping after concatenation for target channel”, “The power threshold for removing clusters in step 6 in section 7.5, TR 38.901 for background channel”, and “Absolute delay”.
Observation 8	Tables in section 7.2 of 38.901 define the values/value ranges of parameters the channel model is valid for, i.e., validity ranges. Validity ranges of parameters depict the capabilities and limitations of the channel model and help reader of the TR to better understand the capabilities of the model and prevent some future work in vain.
Observation 9	Option 2: Fixed value for the parameter minimum 3D distance between sensing targets sets a restriction of channel model. the future sensing algorithms would not be able to support a smaller resolution.
Observation 10	To reduce calibration effort, only one type of targets is modelled in a sensing scenario. However, multiple types of sensing targets may co-exist in a real scenario.

Based on the discussion in the previous sections we propose the following:
Proposal 1	Large-scale calibration is only about large-scale parameters of target, sensing Tx and Rx, independent from target channel or background channel, which require small-scale parameters to generate.
Proposal 2	Regarding ‘FFS: this allows different values for monostatic and bistatic sensing, if needed’, the same RCS component A is used for monostatic and bistatic sensing modes.
Proposal 3	Without further progress in AI 9.7.2, regardless of BS mono-static or bi-static sensing mode, the value A used for large-scale calibration should not be the RCS component A of mono-static RCS.
Proposal 4	For the purpose calibration, a single dual-polarized isotropic antenna and 360-degree sectors can be assumed.
Proposal 5	Given isotropic antenna pattern is used, for large-scale calibration of any target type, the large-scale calibration metric is “Power scaling factor for the target channel”.
Proposal 6	For UAV sensing scenario and UE-involved sensing modes, there is no need to limit the large-scale calibration to LOS condition. It is the same for ground sensing targets.
Proposal 7	The geometry of the Tx, Rx, and target, including movement of one or more of these, can be deterministically specified in the calibration assumptions.
Proposal 8	In order to isolate the target modeling and mitigate the random variations caused by stochastic factors, the simplified full calibration of target channel can include the concatenation of two deterministic Tx-target and target-Rx links where the multipath is modeled deterministically using CDL models. It only requires a single Tx, Rx, and a target.
Proposal 9	Since ISAC background channel model reuses many existing channel models and TRs for different sensing modes, there is no need to recalibrate some of these models, which have previously been calibrated.
Proposal 10	Monostatic background channel is a new channel model and in need of calibration.
Proposal 11	Calibration of combined channel is needed in case of agreements on aspects such as cluster/path reduction, blocking by targets or Type 2 EOs influencing the background channel, normalization, etc,
Proposal 12	To reduce calibration complexity of combined channel, ISAC channel as the sum of background channels modelled with CDL models and target channel modelled with the concatenation of CDL models could be utilized.
Proposal 13	These remaining open issues of channel modelling are all treated in 9.7.2 and will be captured in the relevant parts of the TR and should not be doubly specified also in the calibration assumptions table.
Proposal 14	To avoid parallel discussions on the same topics in the two agenda items with potentially conflicting outcomes, we suggest no duplicate discussions on the remaining channel modelling issues for calibration only.
Proposal 15	“Coupling loss for the target channel” and “Coupling loss for the background channel” are two important metrics for full calibration.
Proposal 16	The coupling loss for target channel can be separately reported for co-polar and cross-polar coupling loss or the ratio between them.
Proposal 17	Calculating an average received power over time, frequency of the channel coefficients is used to calculate coupling loss.
Proposal 18	A fixed radial velocity can be used as a parameter to calibrate Doppler shift.
Proposal 19	Discussion on calibration parameters should not put a restriction on the validity of channel model.
Proposal 20	Applicable communication scenario in section 7.9.1 should capture the complete list of communication scenarios, where the target type may appear and be modelled.
Proposal 21	ISAC channel model should be able to support time-varying velocity for the future evaluation of object tracking.
Proposal 22	Support Option 1: At least larger than the physical size of a sensing target for the parameter minimum 3D distance between sensing targets.
Proposal 23	A smaller building size than 413mx230mx20m in urban grid can be considered at least for RMa scenario, e.g. 12mx10mx10m.
Proposal 24	Two prerequisites should be met to model specular reflection of Type-2 EO: 1) the Type-2 EO is of the same size as or larger than the first Fresnel zone, and 2) the incident angle equals the reflection angle.
Proposal 25	One table of parameters in section 9.7.1 is sufficient. There is no need to generate tables for separate sensing scenarios in section 9.7.1.
Proposal 26	Support the parameters in Table 1 for section 7.9.1 of 38.901.

References
RP-242348, Revised SID: Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR, Xiaomi, AT&T, 3GPP TSG RAN Meeting #105, September 2024
R1-2502726, Discussion on ISAC Channel Modelling, Ericsson, RAN1#120bis, April 2025
R1-2405964, LS on Physical Properties of Sensing Targets in Automotive Scenarios for ISAC, 5GAA, RAN1#118, August 2024
TR 22.837, Feasibility Study on Integrated Sensing and Communication, v19.1.0, September 2023
P.S.Bokare and A.K.Maurya, “Acceleration-Deceleration Behaviour of Various Vehicle Types,” World Conference on Transport Research, WCTR 2016 Shanghai. 10-15 July 2016
TR 36.878, Study on performance enhancements for high speed scenario in LTE, v13.0.0, January 2016

TDoc file conclusion not found

3GPP TSG RAN WG1 #120-bis	             			           	R1-2502731
Wuhan, China, April 7th – 11th, 2025

Agenda Item: 		9.7.1
Source: 		Moderator (AT&T)
Title: 	 FL Summary #1 on ISAC Scenarios and Calibrations
Document for: 	  Discussion and Decision

Conclusion
RAN1 will consider the recommendations for the physical characteristics (e.g., sizes, shapes, materials, velocities, etc.) of sensing targets and objects provided in 5GAA LS (R1-2405964), along with the relevant characteristics defined in 3GPP TRs, within the scope of the Rel-19 study item.
No LS response from RAN1 to 5GAA is necessary.
R1-2405964 is proposed to be NOTED.

Agreement
General principles for all sensing target deployment scenarios should consider the following:
“Sensing mode” is removed in the scenario tables, but may be included in the evaluation/calibration phase. Per the SI, all sensing modes are possible for the deployment scenarios.
“Sensing area” may be addressed as part of the sensing target distribution and/or Tx/Rx characteristics and/or cell layout.



Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#117 as a baseline.
Note: Additional parameters, value/value ranges are not precluded.

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
Note: further down-selection between the options in the table is not precluded.

RAN1#118-bis Agreements

Agreement
For Automotive sensing target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human sensing target scenarios, (indoor and outdoor), the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automated Guided Vehicles (AGV) target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For objects creating hazards use cases, RAN1 to consider the following table as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.

RAN1#119 Agreements

Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118 as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
NOTE: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automotive sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Automotive

Details on ISAC-Automotive scenarios are listed in Table x.


Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human (indoor and outdoor) sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Human

Details on ISAC-Human scenarios are listed in Table x.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.



Agreement
For AGV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-AGV

Details on ISAC-AGV are listed in Table x.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For Objects creating hazards sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Hazards

Details on ISAC-Hazards are listed in Table x.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


RAN1#120 Agreements for CM Calibration


Agreement
For ISAC channel modelling calibration, RAN1 considers both large-scale and full-scale calibration to include parameters and values for at least the following: 
large scale parameters, where fast fading is not included
full-scale calibration parameters, which includes fast fading.
NOTE0: one part of calibration work does not include additional components and does not include spatial consistency
FFS: whether spatial consistency is specified as an additional component for ISAC CM
NOTE1: additional calibrations including spatial consistency can also be considered case by case for different scenarios.
NOTE2: Inclusion of EO in ISAC CM calibrations can also be considered case by case for different scenarios.

Agreement
Calibration of ISAC CM includes separate calibration of the target channel and of the background channel
FFS: additional calibration for the combined channel (combination of target and background channel).


Agreement
For the purposes of large scale calibration for UAV sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for large scale calibration for UAV sensing targets

3GPP TSG RAN WG1 #120-bis	             			           	R1-2502732
Wuhan, China, April 7th – 11th, 2025

Agenda Item: 		9.7.1
Source: 		Moderator (AT&T)
Title: 	 FL Summary #2 on ISAC Scenarios and Calibrations
Document for: 	  Discussion and Decision

Conclusion
RAN1 will consider the recommendations for the physical characteristics (e.g., sizes, shapes, materials, velocities, etc.) of sensing targets and objects provided in 5GAA LS (R1-2405964), along with the relevant characteristics defined in 3GPP TRs, within the scope of the Rel-19 study item.
No LS response from RAN1 to 5GAA is necessary.
R1-2405964 is proposed to be NOTED.

Agreement
General principles for all sensing target deployment scenarios should consider the following:
“Sensing mode” is removed in the scenario tables, but may be included in the evaluation/calibration phase. Per the SI, all sensing modes are possible for the deployment scenarios.
“Sensing area” may be addressed as part of the sensing target distribution and/or Tx/Rx characteristics and/or cell layout.



Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#117 as a baseline.
Note: Additional parameters, value/value ranges are not precluded.

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
Note: further down-selection between the options in the table is not precluded.

RAN1#118-bis Agreements

Agreement
For Automotive sensing target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human sensing target scenarios, (indoor and outdoor), the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automated Guided Vehicles (AGV) target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For objects creating hazards use cases, RAN1 to consider the following table as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.

RAN1#119 Agreements

Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118 as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
NOTE: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automotive sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Automotive

Details on ISAC-Automotive scenarios are listed in Table x.


Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human (indoor and outdoor) sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Human

Details on ISAC-Human scenarios are listed in Table x.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.



Agreement
For AGV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-AGV

Details on ISAC-AGV are listed in Table x.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For Objects creating hazards sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Hazards

Details on ISAC-Hazards are listed in Table x.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


RAN1#120 Agreements for CM Calibration


Agreement
For ISAC channel modelling calibration, RAN1 considers both large-scale and full-scale calibration to include parameters and values for at least the following: 
large scale parameters, where fast fading is not included
full-scale calibration parameters, which includes fast fading.
NOTE0: one part of calibration work does not include additional components and does not include spatial consistency
FFS: whether spatial consistency is specified as an additional component for ISAC CM
NOTE1: additional calibrations including spatial consistency can also be considered case by case for different scenarios.
NOTE2: Inclusion of EO in ISAC CM calibrations can also be considered case by case for different scenarios.

Agreement
Calibration of ISAC CM includes separate calibration of the target channel and of the background channel
FFS: additional calibration for the combined channel (combination of target and background channel).


Agreement
For the purposes of large scale calibration for UAV sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for large scale calibration for UAV sensing targets

3GPP TSG RAN WG1 #120-bis	             			           	R1-2502733
Wuhan, China, April 7th – 11th, 2025

Agenda Item: 		9.7.1
Source: 		Moderator (AT&T)
Title: 	 FL Summary #3 on ISAC Scenarios and Calibrations
Document for: 	  Discussion and Decision

Conclusion
RAN1 will consider the recommendations for the physical characteristics (e.g., sizes, shapes, materials, velocities, etc.) of sensing targets and objects provided in 5GAA LS (R1-2405964), along with the relevant characteristics defined in 3GPP TRs, within the scope of the Rel-19 study item.
No LS response from RAN1 to 5GAA is necessary.
R1-2405964 is proposed to be NOTED.

Agreement
General principles for all sensing target deployment scenarios should consider the following:
“Sensing mode” is removed in the scenario tables, but may be included in the evaluation/calibration phase. Per the SI, all sensing modes are possible for the deployment scenarios.
“Sensing area” may be addressed as part of the sensing target distribution and/or Tx/Rx characteristics and/or cell layout.



Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#117 as a baseline.
Note: Additional parameters, value/value ranges are not precluded.

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
Note: further down-selection between the options in the table is not precluded.

RAN1#118-bis Agreements

Agreement
For Automotive sensing target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human sensing target scenarios, (indoor and outdoor), the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automated Guided Vehicles (AGV) target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For objects creating hazards use cases, RAN1 to consider the following table as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.

RAN1#119 Agreements

Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118 as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
NOTE: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automotive sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Automotive

Details on ISAC-Automotive scenarios are listed in Table x.


Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human (indoor and outdoor) sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Human

Details on ISAC-Human scenarios are listed in Table x.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.



Agreement
For AGV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-AGV

Details on ISAC-AGV are listed in Table x.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For Objects creating hazards sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Hazards

Details on ISAC-Hazards are listed in Table x.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


RAN1#120 Agreements for CM Calibration


Agreement
For ISAC channel modelling calibration, RAN1 considers both large-scale and full-scale calibration to include parameters and values for at least the following: 
large scale parameters, where fast fading is not included
full-scale calibration parameters, which includes fast fading.
NOTE0: one part of calibration work does not include additional components and does not include spatial consistency
FFS: whether spatial consistency is specified as an additional component for ISAC CM
NOTE1: additional calibrations including spatial consistency can also be considered case by case for different scenarios.
NOTE2: Inclusion of EO in ISAC CM calibrations can also be considered case by case for different scenarios.

Agreement
Calibration of ISAC CM includes separate calibration of the target channel and of the background channel
FFS: additional calibration for the combined channel (combination of target and background channel).


Agreement
For the purposes of large scale calibration for UAV sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for large scale calibration for UAV sensing targets

3GPP TSG RAN WG1 #120-bis	             			           	R1-2502734
Wuhan, China, April 7th – 11th, 2025

Agenda Item: 		9.7.1
Source: 		Moderator (AT&T)
Title: 	 FL Summary #4 on ISAC Scenarios and Calibrations
Document for: 	  Discussion and Decision

Conclusion
RAN1 will consider the recommendations for the physical characteristics (e.g., sizes, shapes, materials, velocities, etc.) of sensing targets and objects provided in 5GAA LS (R1-2405964), along with the relevant characteristics defined in 3GPP TRs, within the scope of the Rel-19 study item.
No LS response from RAN1 to 5GAA is necessary.
R1-2405964 is proposed to be NOTED.

Agreement
General principles for all sensing target deployment scenarios should consider the following:
“Sensing mode” is removed in the scenario tables, but may be included in the evaluation/calibration phase. Per the SI, all sensing modes are possible for the deployment scenarios.
“Sensing area” may be addressed as part of the sensing target distribution and/or Tx/Rx characteristics and/or cell layout.



Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#117 as a baseline.
Note: Additional parameters, value/value ranges are not precluded.

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
Note: further down-selection between the options in the table is not precluded.

RAN1#118-bis Agreements

Agreement
For Automotive sensing target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human sensing target scenarios, (indoor and outdoor), the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automated Guided Vehicles (AGV) target scenarios, the following table is used as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For objects creating hazards use cases, RAN1 to consider the following table as a starting point for deployment scenario parameters/values.
The detailed scenario description in this clause can be used for channel model calibration.
Note: Additional parameters, value/value ranges are not precluded.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.

RAN1#119 Agreements

Agreement
For UAV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118 as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-UAV

Details on ISAC-UAV scenarios are listed in Table x.

Table x. Evaluation parameters for UAV sensing scenarios
NOTE: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Automotive sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Automotive

Details on ISAC-Automotive scenarios are listed in Table x.


Table x. Evaluation parameters for Automotive sensing scenarios
NOTE1: calibration for UMi, Uma, RMa is not performed for the automotive scenario, but UMi, Uma, RMa can be considered for future evaluations of the automotive sensing target scenarios. Calibration for UMi, Uma, RMa is expected to be performed for another sensing scenario.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


Agreement
For Human (indoor and outdoor) sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Human

Details on ISAC-Human scenarios are listed in Table x.

Table x. Evaluation parameters for Human (indoor and outdoor) sensing scenarios

NOTE1: For the human (indoor and outdoor) sensing targets, additional communication scenarios can be considered for future evaluations. Channel model calibration for Urban Grid with outdoor humans is expected to be performed from Objects creating hazards on the road/railway sensing scenarios.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.



Agreement
For AGV sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-AGV

Details on ISAC-AGV are listed in Table x.

Table x. Evaluation parameters for Automated Guided Vehicles
NOTE1: For the AGV sensing targets, additional communication scenarios can be considered for future evaluations.
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.
NOTE3: RAN1 can further discuss narrowing down the number of sub-scenarios of InF


Agreement
For Objects creating hazards sensing target scenarios, the following table is agreed for deployment scenario parameters/values using the agreements from RAN1#118-bis as a baseline:

The detailed scenario description in this clause can be used for channel model calibration.

ISAC-Hazards

Details on ISAC-Hazards are listed in Table x.

Table x. Evaluation parameters for objects creating hazards 

NOTE1: For the objects creating hazards sensing targets, additional communication scenarios can be considered for future evaluations. 
NOTE2: A percentage of TRPs/UEs that have sensing capabilities may be considered for future evaluations.


RAN1#120 Agreements for CM Calibration


Agreement
For ISAC channel modelling calibration, RAN1 considers both large-scale and full-scale calibration to include parameters and values for at least the following: 
large scale parameters, where fast fading is not included
full-scale calibration parameters, which includes fast fading.
NOTE0: one part of calibration work does not include additional components and does not include spatial consistency
FFS: whether spatial consistency is specified as an additional component for ISAC CM
NOTE1: additional calibrations including spatial consistency can also be considered case by case for different scenarios.
NOTE2: Inclusion of EO in ISAC CM calibrations can also be considered case by case for different scenarios.

Agreement
Calibration of ISAC CM includes separate calibration of the target channel and of the background channel
FFS: additional calibration for the combined channel (combination of target and background channel).


Agreement
For the purposes of large scale calibration for UAV sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for large scale calibration for UAV sensing targets

3GPP TSG RAN WG1 Meeting #120-bis  	          	     		R1-2502820
Wuhan, China, April 7-11, 2025
_____________________________________________________________________Agenda item: 9.7.1
Source: LG Electronics
Title: 	Discussion on ISAC deployment scenarios
Document for: Discussion and decision

Conclusions
In this contribution, the ISAC deployment scenarios were discussed. The following observations and proposals were made as conclusions.
Proposal 1: Only a single type of sensing target that needs to be detected in the associated sensing scenario is dropped for evaluation.
Proposal 2: If the multiple types of sensing target objects are allowed for dropping in a sensing scenario, except the sensing target that needs to be detected in the sensing scenario, other types of sensing target objects are modeled as type-1 EO.
Proposal 3: In UAV sensing, in option C for the horizontal plane of 3D distribution of a sensing target, ‘the area not necessarily determined by cell boundaries’ is defined by a sphere space, which is represented by the 3D center position and the radius.
Proposal 4: In the automotive sensing scenario, only the wall having the single-bounce specular reflection point is modelled for evaluation.
Proposal 5: In human sensing, for the horizontal plane of 3D distribution of a sensing target, both option A and C are supported.
Option A: N targets uniformly distributed within one cell. 
Option C: N targets uniformly distributed within an area not necessarily determined by cell
Proposal 6: In human sensing, for option C for the horizontal plane of 3D distribution of a sensing target, ‘the area not necessarily determined by cell boundaries’ is defined by a 2D circle space, which is represented by the center position and the radius.
Proposal 7: For the human indoor sensing scenario, the walls, ceil and floor inside the indoor space are modeled as type-2 EO.
Proposal 8: In AGV sensing, for the horizontal plane of 3D distribution of a sensing target, option B is supported.
Option B: Uniformly distributed in horizontal plane
Proposal 9: For the automated guided vehicle sensing scenario, the walls, ceil and floor inside the factory are modeled as type-2 EO.
Proposal 10: In AGV sensing, for the horizontal plane of 3D distribution of a sensing target, option B is supported.
For the evaluation parameters, we have FFS point regarding the number of buildings as follows.
Proposal 11: In the hazardous object sensing scenario, only the wall having the single-bounce specular reflection point is modelled for evaluation.

3GPP TSG RAN WG1 #120-bis			R1-2502849
Wuhan, China, April 7th – 11th, 2025
Agenda item:	9.7.1
Source: 	Qualcomm Incorporated
Title: 	ISAC Deployment Scenarios
Document for:		Discussion

Conclusion
We have discussed ISAC deployment scenarios and made the following proposals:  
 Proposal 1: Support the following updates in the Evaluation parameter table for UAV sensing scenarios:
3D distribution in the horizontal plane, support:
Option A: N targets uniformly distributed within one cell
3D distribution in the vertical plane, support:
Option A: Uniform between 1.5m and 300m
sizes of UAV:
make Option 2 (0.3m x 0.4m x 0.2m) as baseline
minimum 3D distances between sensing target:
Support Option 1 (At least larger than the physical size of a target)

Proposal 2: For the UAV Evaluation parameter table, with regards to the Unintended objects we propose the following: 
For the purpose of sensing the i-th target, the N-1 remaining uniformly distributed targets in the cell are considered an unintended objects
Do not introduce Environment Objects Type 1 or Type 2

Proposal 3: Support the following updates in the Evaluation parameter table for humans sensing scenarios:
For the Rx/Tx mobility, 
support Option 3
For the 3D mobility, 
support Option 3
For the 3D distribution in indoor, 
support N=[1, 5] targets
For the 3D distribution in outdoor,
Option A: N targets uniformly distributed within one cell
For the EO in outdoor
Do not include any Environment object
For the EO in indoor
Include as an optional scenario, one or more of the:
4 outside walls, 
the ceiling and 
the ground floor 
Proposal 4: Support the following updates in the evaluation parameter table for automotive vehicle sensing scenarios:
With regards to the minimum 3D distances between targets:
Support Option 1
With regards to the environment objects:
EO Type 2 should be an optional modelling component in urban grid (blockage & pathloss is still modelled)
 4 walls (as EO type 2) per building of size 413m X 230m X Hm, where H > BS Height 

Proposal 5: Support the following updates in the evaluation parameter table for automotive vehicle sensing scenarios:
Rx/Tx Mobility for UEs
Option 3: Uniform distribution between 0km/h and 3km/h
3D mobility
Option 1: Uniform distribution in the range of up to 30 km/h
3D distribution
Option A: Uniformly distributed in the convex hull of the horizontal BS deployment
Minimum 3D distance between sensing targets
Option A: At least larger than the physical size of a target
Environment Objects
Include as an optional scenario, one or more of the:
4 outside walls, 
the ceiling and 
the ground floor 
Proposal 6: Support the following updates in the evaluation parameter table for automotive vehicle sensing scenarios:
3D mobility
Horizontal velocity: up to 10 km/h for humans and animals
Minimum 3D distance between sensing targets
Option 1: At least larger than the physical size of a target
Environment Objects
Add EO Type-2 according to the urban grid layout of 37.885 with the following details:
4 walls (as EO type 2) per building of size 413m X 230m X Hm (FR1) and 186m X 72m X Hm (FR2), where H > BS Height 

Proposal 7: For the purposes of large scale calibration for UAV sensing targets, the following revised calibration parameters are proposed below:

Proposal 8: For the purposes of large scale calibration for Automotive sensing targets, the following calibration parameters are proposed below:

Proposal 9: For the purposes of calibration for Automotive sensing targets, the UE monostatic sensing mode and the TRP-UE sensing mode should be prioritized. 
Proposal 10: For the purposes of calibration for Automotive sensing targets, support ISD=500 for FR1 and ISD=250 for FR2.
Proposal 11: Clarify the definition of the uniform distribution for determining effective environment height in UMa pathloss calculations. Based on the above options, below are provided two alternative updated definitions:
Include final value: The distribution is defined as , where  is defined as  and  is the largest integer such that .
Exclude final value: The distribution is defined as , where  is defined as  and  is the largest integer such that .

Proposal 12: Modify the UMa breakpoint distance calculation to account for UTs in the height range m. Below are provided two alternative solutions which provide different potential outcomes for effective environment heights in this range:
Update   to 
,
With this option, for any m, the effective environment height will be 1 meter with probability 1.
Add an additional case for UTs in the height range m: in this case, set  = 1 m with probability  and 12 m otherwise.

3GPP TSG RAN WG1 Meeting #120b		             			           	                   R1-2502922
Wuhan, China, April 7-11, 2025
Source 	: CAICT
Title 	: Considerations on ISAC deployment scenarios
Agenda Item	: 9.7.1.
Document for	: Discussion and decision 
Introductions 
In the last meeting for the study on ISAC deployment scenarios in RAN1#120, the following agreements about assumptions for ISAC channel model calibration have been reached:
In this paper, we will present our views on some remain issues about the assumptions of ISAC deployment scenarios for channel model calibration. 
Assumptions for Channel Model Calibration
General 
Issue1: whether to include additional calibration for the combined channel (combination of target and background channel)
From our view, since we have included calibrations for target channel and background channel separately, there is little necessity for calibration for the combined channel. However, we can set some principles for how to define the combination of target and background channel. The principles can include the following aspects:
the method of power normalization of combined channel, which will be determined in section 9.7.2
the number of background channels in the sensing scenarios, especially for the scenarios like InH and InF
the method to define interference from background channel to the target channel, which will impact on SINR and also the performance evaluation of ISAC solutions. However, if SINR is included in the metrics for channel calibration, the calibration of the combination of target and background channel is carried out accordingly.

Proposal1: Principles for how to define the combination of target and background channel is needed, which can include the following aspects: 
the method of power normalization of combined channel
the number of background channels
the method to define interference from background channels to the target channel

Issue2: what kind of calibration metrics are included in the channel calibribation
It is suggested to include coupling loss in serving sell and geometry based on LOS pathloss as metrics of channel model calibration for large scale. For full calibration, it will model fast fading channel with small scale of RCS and the calibration metrics further include CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA) from the serving cell (according to circular angle spread definition of TR 25.996).  As for SINR, if it is not included in the metrics, the definition of SINR should at least be given considering that it will has significant impact on the ISAC solution performance. 

Proposal 2:  Metrics for channel model calibration are suggested to use:
Coupling loss – serving cell (based on LOS pathloss)
Geometry (based on LOS pathloss) with and without white noise
CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA) from the serving cell (according to circular angle spread definition of TR 25.996) for full calibration
where 1) and 2) for large scale calibration and 1),2) and 3) for full calibration 

Issue3: how to define EO table in the channel calibration related to EO 
In last meeting, the EO table is put forward and not get much discussion. We can set a common EO table for channel calibration in this meeting. In the scenario like urban grid, the impact of EO-type2 on the solution evaluation might not be ignored and it is better to have some channel calibration related to EO-type2. For EO-type1, it is modelled in the same way of sensing target and there is no need to define EO-type1. 
 Proposal 3:  It is suggested to define a EO table related to EO-type2, and use the table as follows:


Assumptions for Different ISAC Scenarios
Assumptions for channel model calibration for UAV sensing is provided in Table1. 
Table 1  calibration parameters for UAV

Assumptions for channel model calibration for automotive vehicles sensing is provided in Table2. 
Table 2 calibration parameters for automotive vehicles

Assumptions for channel model calibration for indoor and outdoor humans sensing is provided in Table3. 
Table 3 calibration parameters for humans indoor and outdoor

Proposal 4:  It is suggested to use assumptions in Table1/2/3 for channel model calibration for sensing scenarios of UAV, automotive vehicles, and humans, respectively.
Conclusions
In this contribution, we have presented our views on the assumptions for channel model calibration and put forward the following proposals.

Proposal1: Principles for how to define the combination of target and background channel is needed, which can include the following aspects: 
the method of power normalization of combined channel
the number of background channels
the method to define interference from background channels to the target channel
Proposal 2:  Metrics for channel model calibration are suggested to use:
Coupling loss – serving cell (based on LOS pathloss)
Geometry (based on LOS pathloss) with and without white noise
CDF of Delay Spread and Angle Spread (ASD, ZSD, ASA, ZSA) from the serving cell (according to circular angle spread definition of TR 25.996) for full calibration
where 1) and 2) for large scale calibration and 1),2) and 3) for full calibration 

Proposal 3:  It is suggested to define a EO table related to EO-type2, and use the table as follows:

Proposal 4:  It is suggested to use assumptions in Table1/2/3 for channel model calibration for sensing scenarios of UAV, automotive vehicles, and humans, respectively.

References
RP-234069, New SID: Study on channel modelling for Integrated Sensing And Communication (ISAC) for NR, Nokia, Nokia Shanghai Bell, Dec. 2023.
3GPP RAN1#119, RAN1 Chair’s Notes, Nov. 2024. 
3GPP RAN1#120, RAN1 Chair’s Notes, Feb. 2025. 

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3GPP TSG RAN WG1 #120-bis	             			           	R1-2503150
Wuhan, China, April 7th – 11th, 2025

Agenda Item: 		9.7.1
Source: 		Moderator (AT&T)
Title: 	 Email summary on Post-120bis-ISAC-01
Document for: 	    Discussion and Decision

Conclusions

The moderator would like to thank all of the companies contributing to the constructive discussion on ISAC channel model calibration assumptions. Based on the email discussions, proposals, and comments in the above sections, the following proposals are stable and can be considered endorsed:

Proposal 4-2-2

Proposal 4-2-2:  For the purposes of large scale calibrations for Automotive sensing targets, the following parameters are updated below in Table x based on the agreements in RAN1#120-bis. 


Table x. Simulation assumptions for large scale calibration for Automotive sensing targets



Proposal 4-3-2

Proposal 4-3-2:  For the purposes of full calibrations for Automotive sensing targets, the following parameters are proposed below in Table x. 

Table x. Simulation assumptions for full calibration for Automotive sensing targets


Proposal 5-3-1 

Proposal 5-3-1:  For the purposes of large scale calibration for Human (indoor and outdoor) sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for large scale calibration for Human sensing targets





Proposal 5-4-2

Proposal 5-4-2:  For the purposes of full calibration for Human sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for full calibration for Human sensing targets




Proposal 6-2-1 

Proposal 6-2-1:  For the purposes of large scale calibration for AGV sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for large scale calibration for AGV indoor sensing targets (full calibration)



Proposal 6-3-2

Proposal 6-3-2:  For the purposes of full calibration for AGV sensing targets, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for full calibration for AGV indoor sensing targets (full calibration)



Proposal 8.1.1.1

Proposal 8-1-1-1:  RAN1 may calibrate EO Type-2 for ISAC in Rel-19. Interested companies can provide results. For the purposes of EO Type-2 calibration, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for calibration of EO type-2



Proposal 8-1-2 

Proposal 8-1-2:  RAN1 may calibrate spatial consistency for ISAC in Rel-19. Interested companies can provide results. For the purposes of spatial consistency calibration, the following calibration parameters are proposed below in Table x. 

Table x. Simulation assumptions for calibration of spatial consistency



Proposal 8-3 

Proposal 8-3:  For the purposes of full calibration for UAV sensing targets, the following update is proposed below based on the agreement from RAN1#120-bis for Coupling loss for target channel:

3GPP TSG RAN WG1 #120-bis	             			           	R1-2503151
Wuhan, China, April 7th – 11th, 2025

Agenda Item: 		9.7.1
Source: 		Moderator (AT&T)
Title: 	 ISAC Calibration Templates 
Document for: 	    Discussion and Decision

TDoc file conclusion not found