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arxiv: 2408.11295 · v1 · submitted 2024-08-21 · 📡 eess.SP

Channel Modeling Framework for Both Communications and Bistatic Sensing Under 3GPP Standard

Chenhao Luo , Aimin Tang , Fei Gao , Jianguo Liu , Xudong Wang This is my paper

Pith reviewed 2026-05-23 21:52 UTC · model grok-4.3

classification 📡 eess.SP
keywords ISACbistatic sensingchannel modeling3GPPintegrated sensing and communicationsray tracingtarget modelspatial coherence
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The pith

A bistatic ISAC channel modeling framework extends the 3GPP standard by adding weaker clusters and target reflection models while preserving full compatibility with communication models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper develops a channel modeling framework for bistatic integrated sensing and communications that builds directly on the existing 3GPP standard. The framework adds support for sensing by generating more clusters with weaker power to represent potential targets, allowing either deterministic or statistical descriptions of those targets. For statistical targets it incorporates reflection models that preserve spatial coherence among rays. A sympathetic reader would care because accurate joint modeling of communication and sensing channels is required to design and evaluate future networks that combine both functions. The work validates the approach through ray-tracing simulations and real experiments while confirming compatibility with pure communication models.

Core claim

The proposed channel modeling framework extends the current 3GPP channel modeling framework and ensures the compatibility with the communication channel model. To support the bistatic sensing function, several key features for sensing are added. First, more clusters with weaker power are generated and retained to characterize the potential sensing targets. Second, the target model can be either deterministic or statistical, based on different sensing scenarios. Furthermore, for the statistical case, different reflection models are employed in the generation of rays, taking into account spatial coherence. The effectiveness of the proposed bistatic ISAC channel model framework is validated by,

What carries the argument

Extended 3GPP geometry-based stochastic channel model with added weak-power clusters, deterministic or statistical target models, and spatial-coherence reflection models for rays.

If this is right

  • The same simulation engine can now evaluate both communication link quality and bistatic sensing accuracy without separate model implementations.
  • Standard 3GPP communication traces and parameters remain directly usable, so existing network simulators require only incremental extensions.
  • Deterministic target models enable precise evaluation when object locations are known while statistical models support uncertain environments.
  • Spatial coherence in the reflection models produces correlated sensing observations at different receiver locations, matching physical bistatic geometry.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could be applied to frequency bands or high-mobility scenarios beyond those used in the paper's ray-tracing and experiment validation.
  • The increase in retained weak clusters may raise the computational cost of large-scale network-level simulations.
  • A natural next step is to combine this bistatic extension with existing monostatic sensing models to produce a unified ISAC channel description.

Load-bearing premise

Generating and retaining additional weaker clusters plus employing specific reflection models for statistical targets will sufficiently and accurately characterize potential sensing targets in bistatic scenarios without introducing inconsistencies with the underlying 3GPP communication model.

What would settle it

A side-by-side comparison in which the extended model yields sensing performance metrics that differ from measurements obtained in a controlled bistatic experiment or that deviate from the output of an unmodified 3GPP communication simulation under identical geometry.

Figures

Figures reproduced from arXiv: 2408.11295 by Aimin Tang, Chenhao Luo, Fei Gao, Jianguo Liu, Xudong Wang.

Figure 1
Figure 1. Figure 1: Diagram of the two sensing modes in the ISAC system. based stochastic channel model at the frequency from 0.5 GHz to 100 GHz for 5G systems. These models are primarily designed for communication systems. When wireless sensing is further considered, new channel models are required for ISAC channels. Currently, ISAC has already been consid￾ered in 3GPP standard with feasibility study [6] and service requirem… view at source ↗
Figure 2
Figure 2. Figure 2: Framework of bistatic ISAC channel modeling. Other general parameters in step 1 are generated following the 3GPP procedure [5]. The following step 2 to step 4 can also directly follow the standard procedure in [5] to generate necessary general parameters. 2) Small Scale Parameters Generation: As is shown in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Three types of reflection model for sensing rays in a target cluster. target is the last scatter, we can extract the azimuth angle of arrival (AOA) and zenith angle of arrival (ZOA) information (or possible relative arrival velocity) from the sensing channel. However, if the sensing target is neither the first scatter nor the last scatter, we can hardly extract sensing information from the sensing channel.… view at source ↗
Figure 4
Figure 4. Figure 4: Ray tracing simulation in an indoor office. III. VERIFICATION OF THE BISTATIC ISAC CHANNEL MODEL A. Validation by Ray Tracing Simulations 1) Simulation setup: Ray tracing is a method of simulating the behavior of light or electromagnetic waves as they propa￾gate through a given environment by computing the Maxwell equations. Ray tracing can provide information regarding the propagation paths, attenuation, … view at source ↗
Figure 5
Figure 5. Figure 5: A slice of the ray tracing simulation of a person sitting-down. 3) Deterministic cluster modeling: In the second simulation, deterministic modeling for target cluster is studied. More specifically, two human behaviors, namely standing-up and sitting-down, are simulated by ray tracing method. In this simulation, the transmitter and receiver are separately placed and a human model is placed in front of the t… view at source ↗
Figure 7
Figure 7. Figure 7: Experiment environment. 0 5 10 15 20 25 Range (m) 0 0.2 0.4 0.6 0.8 1 Normalized Amplitude 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 RD Spectrum Amplitude 10-4 Raw CIR Processed CIR Target location LoS range [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The raw CIR and radar processed CIR for the weak target. for the two cases are shown in [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 10
Figure 10. Figure 10: Sensing evaluation study under statistic modeling: (a) detection probability, (b) range estimation error. adopted. The radar processing gain is about 30 dB. We change the total power of the ISAC channel, i.e., the channel SNR, and evaluate the sensing performance of detection rate and range estimation error. For a fixed SNR, we run 1000 times with random noise generation to collect the results. The simula… view at source ↗
read the original abstract

Integrated sensing and communications (ISAC) is considered a promising technology in the B5G/6G networks. The channel model is essential for an ISAC system to evaluate the communication and sensing performance. Most existing channel modeling studies focus on the monostatic ISAC channel. In this paper, the channel modeling framework for bistatic ISAC is considered. The proposed channel modeling framework extends the current 3GPP channel modeling framework and ensures the compatibility with the communication channel model. To support the bistatic sensing function, several key features for sensing are added. First, more clusters with weaker power are generated and retained to characterize the potential sensing targets. Second, the target model can be either deterministic or statistical, based on different sensing scenarios. Furthermore, for the statistical case, different reflection models are employed in the generation of rays, taking into account spatial coherence. The effectiveness of the proposed bistatic ISAC channel model framework is validated by both ray tracing simulations and experiment studies. The compatibility with the 3GPP communication channel model and how to use this framework for sensing evaluation are also demonstrated.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a channel modeling framework for bistatic integrated sensing and communications (ISAC) that extends the existing 3GPP channel model while claiming full compatibility with its communication statistics. Key extensions include generating and retaining additional clusters with weaker power to represent sensing targets, support for either deterministic or statistical target models, and reflection models that incorporate spatial coherence for the statistical case. Effectiveness is asserted via ray-tracing simulations and experiments, together with explicit demonstrations of 3GPP compatibility and sensing evaluation usage.

Significance. If the compatibility claim is substantiated by an explicit normalization procedure that leaves 3GPP power-delay profiles, delay spreads, and angular spreads unchanged, the framework would supply a practical, standards-aligned tool for joint communication-sensing performance evaluation in B5G/6G networks. The dual validation route (ray tracing plus measurements) and the reuse of an established standard are concrete strengths that would increase adoption potential.

major comments (2)
  1. [Abstract] Abstract: the central compatibility claim is load-bearing yet unsupported by any derivation or normalization step. Adding and retaining extra weaker clusters necessarily changes cluster count and power allocation; without an explicit procedure (e.g., re-normalization of cluster powers or conditional generation) that restores the original 3GPP delay-spread and angular-spread statistics exactly, communication-link evaluations become inconsistent with the standard.
  2. [Framework description] Framework description (cluster-generation procedure): the post-hoc retention of weaker clusters for sensing targets risks altering the underlying 3GPP cluster statistics. The manuscript must supply a quantitative check (e.g., Table comparing RMS delay spread, angular spread, and cluster power distribution before and after the sensing additions) to confirm that the communication model remains numerically identical.
minor comments (2)
  1. Clarify the precise mathematical definition of the reflection models used for statistical targets and how spatial coherence is enforced (e.g., correlation function or phase-screen parameters).
  2. Figure captions for the ray-tracing and measurement results should explicitly state which 3GPP parameters are held constant and which are modified by the sensing extensions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting the need to explicitly substantiate the compatibility claim. We agree that an explicit normalization procedure and quantitative validation are required to ensure the communication statistics remain unchanged, and we will incorporate these in the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central compatibility claim is load-bearing yet unsupported by any derivation or normalization step. Adding and retaining extra weaker clusters necessarily changes cluster count and power allocation; without an explicit procedure (e.g., re-normalization of cluster powers or conditional generation) that restores the original 3GPP delay-spread and angular-spread statistics exactly, communication-link evaluations become inconsistent with the standard.

    Authors: We acknowledge that the compatibility claim in the abstract requires explicit support. In the revision, we will add a dedicated subsection detailing the normalization procedure: after generating and retaining the additional weaker clusters for sensing, the powers of all clusters (including the original 3GPP ones) are re-normalized such that the total power and the resulting power-delay profile, RMS delay spread, and angular spreads exactly match the original 3GPP statistics. This conditional generation approach ensures communication evaluations remain fully consistent with the standard. revision: yes

  2. Referee: [Framework description] Framework description (cluster-generation procedure): the post-hoc retention of weaker clusters for sensing targets risks altering the underlying 3GPP cluster statistics. The manuscript must supply a quantitative check (e.g., Table comparing RMS delay spread, angular spread, and cluster power distribution before and after the sensing additions) to confirm that the communication model remains numerically identical.

    Authors: We agree that a quantitative check is necessary. The revised manuscript will include a new table (and associated text) that reports the RMS delay spread, angular spread, and cluster power distribution computed from the standard 3GPP generation procedure versus the same procedure after adding and retaining the weaker sensing clusters (both before and after the normalization step). This will numerically confirm that the communication statistics are identical. revision: yes

Circularity Check

0 steps flagged

No circularity; extension of external 3GPP standard remains independent

full rationale

The paper describes an extension of the external 3GPP channel model by adding weaker clusters, deterministic/statistical target models, and reflection models with spatial coherence. No quoted equations or steps reduce any claimed prediction or compatibility result to a fitted parameter, self-citation chain, or input by construction. Validation relies on ray-tracing simulations and experiments external to the model generation procedure itself, so the central claim of compatibility does not collapse into the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the framework extends the existing 3GPP model without detailing new fitted quantities or postulates.

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Reference graph

Works this paper leans on

55 extracted references · 55 canonical work pages · 1 internal anchor

  1. [1]

    is not fully compatible with the pure communication channel, since more sensing clusters are directly added to t he communication channel. Moreover, such an approach cannot well reflect the weak environment clutters for sensing, sinc e the communication channel modeling in the 3GPP standard only retains relatively strong clusters. Thus, how to model the bi...

    work page

  2. [2]

    In the extended modeling framework, a few communication clusters are converted to sensing clusters, with either statistical or deterministic modeli ng method

    A bistatic ISAC channel modeling framework is proposed in this paper, which extends the current 3GPP channel modeling framework [5] for both communications and bistatic sensing. In the extended modeling framework, a few communication clusters are converted to sensing clusters, with either statistical or deterministic modeli ng method. The proposed ISAC ch...

    work page

  3. [3]

    Several key features for bistatic ISAC channel modeling are revealed, including: 1) the communication scenarios and sensing scenarios should be jointly considered; 2) more weak clusters should be included in the bistatic ISAC channel than that in communication channel; 3) both spatial and time coherence should be ensured for sensing clusters, either in st...

    work page

  4. [4]

    The key features for bistatic sensing channel requirements are verified

    Extensive ray tracing simulations and experiment mea- surements are carried out to validate the effectiveness of our proposed framework. The key features for bistatic sensing channel requirements are verified. Furthermore, the compatibility with the 3GPP communication channel model and how to use this framework for sensing evalu- ation are also demonstrate...

    work page

  5. [5]

    monostatic no statistical communication channel consists of backward scattering components from sensing

    work page

  6. [6]

    monostatic yes statistical communication and sensing share some clusters; birth and death process is proposed for communication clust ers to sensing clusters

    work page

  7. [7]

    Simulation and experiment validation of our proposed channel model are carried out in III

    monostatic yes statistical communication and sensing share some clusters; shared degree is defined and introduced [19] monostatic and bistatic yes statistical communication channel is utilized as target unrelated envi ronment channel; targeted related sensing channel is separately generated a nd added [20] monostatic and bistatic yes hybrid communication c...

    work page

  8. [8]

    First, paths with very lo w energy in [5] are omitted due to their little contributions t o communication performance

    cannot be directly applied to the sensing function, due to the following two-fold reasons. First, paths with very lo w energy in [5] are omitted due to their little contributions t o communication performance. However, these low energy path s may be crucial for target sensing. In radar sensing processi ng, a large radar coherent processing gain can be ach...

    work page

  9. [9]

    2, the generation of general parameters is from step 1 to step

    General Parameters Generation : As is shown in Fig. 2, the generation of general parameters is from step 1 to step

    work page

  10. [10]

    For the ISAC channel, in addition to the commu- nication scenario, the sensing scenario is further assigne d

    In the 3GPP model, a communication scenario such as urban micro (UMi) and urban macro (UMa) is assigned at the first step. For the ISAC channel, in addition to the commu- nication scenario, the sensing scenario is further assigne d. A communication scenario and a sensing scenario together for m an ISAC scenario. Some typical examples of sensing scenario s ...

    work page

  11. [11]

    2, the generation of small scale parameters is from step 5 to step 9

    Small Scale Parameters Generation : As is shown in Fig. 2, the generation of small scale parameters is from step 5 to step 9. In Step 5, clusters are generated with different dela ys. To support wireless sensing for weak targets, more clusters are required for an ISAC channel, compared to that for a communication channel. Thus, the number of clusters N in...

    work page

  12. [12]

    For the target clusters, either sta tistical modeling or deterministic modeling can be applied to genera te rays for each sensing cluster, based on the ISAC scenario

    is used to generate the arrival and departure angles for each cluster and perform random coupling of rays in step 8e.1 and 8e.2, respectively. For the target clusters, either sta tistical modeling or deterministic modeling can be applied to genera te rays for each sensing cluster, based on the ISAC scenario. St a- tistical modeling is suitable for sensing...

    work page

  13. [13]

    Generate departure angles θn,ZOD, φn,AOD for each target cluster n following the 3GPP procedure

    work page

  14. [14]

    Calculate the 3D location Rn of the equivalent reflection point of the cluster n. Rn is the intersection between 6 the ray emitted from the transmitter of the direction of (θn,ZOD, φn,AOD) and the ellipse with Tx and Rx as the focus and τn · c as the major axis ( c is the speed of light)

    work page

  15. [15]

    Parameters for type 1 rays are first generated. For point target model, Rn is treated as the only reflection point for the target; for extended target model, multiple reflection points Rn,m are randomly generated around the cluster central reflection point Rn with an angle spread factor. Each reflection point Rn,m leads to one ray. For each ray, calculate its ...

    work page

  16. [16]

    In type 2 reflection model, the target is the second scatterer, and its reflection points Rn,m for the second bounce are inherited from the type 1 ray

    Type 2 rays are extended from type 1 rays. In type 2 reflection model, the target is the second scatterer, and its reflection points Rn,m for the second bounce are inherited from the type 1 ray. The first reflection point Rn,m,env of the environment scatterer is randomly generated for ray (n, m). The time delay τn,m is determined by the path of Tx - Rn,m,env ...

    work page

  17. [17]

    While in the position-priority method, the position of the reflection point is generated first

    The procedure for the generation of type 3 rays is similar to that for type 2 rays, except that the target is the first scatterer and the arrival angle for each ray is regenerated. While in the position-priority method, the position of the reflection point is generated first. The procedures are illus trated as follows:

    work page

  18. [18]

    For each cluster, randomly choose a point Rn to be the equivalent reflection point on the ellipse with Tx and Rx as the focus and τn · c as the major axis ( c is the speed of light)

    work page

  19. [19]

    The initial position is more random in option 2, while it is constrained by the angle information in option 1

    The remaining steps are the same as described in step 3) - step 5) in the angle-priority methods As we can see, the only difference between these two options is the generation of the initial 3D target location. The initial position is more random in option 2, while it is constrained by the angle information in option 1. The select ion of the option depend...

    work page

  20. [20]

    2, the coeffi- cient generation includes the final three steps

    Coefficient Generation : As is shown in Fig. 2, the coeffi- cient generation includes the final three steps. Random init ial phases Φθθ n,m, Φθφ n,m, Φφθ n,m, and Φφφ n,m are generated in step 10 for each ray of all clusters. The channel coefficients are generated in step 11 to get the small-scale channel impulse response (CIR), where the sensing cluster requ...

    work page

  21. [21]

    Ray tracing can provide information regarding t he propagation paths, attenuation, and angles of electromagn etic signals in the spatial domain

    Simulation setup : Ray tracing is a method of simulating the behavior of light or electromagnetic waves as they propa - gate through a given environment by computing the Maxwell equations. Ray tracing can provide information regarding t he propagation paths, attenuation, and angles of electromagn etic signals in the spatial domain. The ray tracing simulat...

    work page

  22. [22]

    4(a) is illustrated in Fig

    Power of target cluster : The CIR for the environment set in Fig. 4(a) is illustrated in Fig. 4(b). The power of LoS path is −78.9 dBm, while the rays related to the target cluster are within the power range from −122 dBm to −132 dBm. Other environment echos are within the power range from −90 dBm to −134 dBm. The result shows that the target clusters can...

    work page

  23. [23]

    More specifically, two human behaviors, namely standing-up and sitting-down, are simulated by ray tracing method

    Deterministic cluster modeling : In the second simulation, deterministic modeling for target cluster is studied. More specifically, two human behaviors, namely standing-up and sitting-down, are simulated by ray tracing method. In this simulation, the transmitter and receiver are separately pl aced and a human model is placed in front of the transmitter and...

    work page

  24. [24]

    7) to verify a few key features of the proposed channel modeling framework

    Experiment setup : Some experiments are carried out in an indoor office (shown in Fig. 7) to verify a few key features of the proposed channel modeling framework. The system is operating on the central frequency of 28 GHz with 500 MHz bandwidth. Particularly, it utilizes OFDM waveform with 18 24 subcarriers and a carrier interval of 270 kHz. The transmiss ...

    work page

  25. [25]

    Power of target cluster : In the first experiment, a person stands in the room as a sensing target as in Fig. 7. With the collected data, the raw CIR and also the processed CIR with radar sensing processing are extracted. Particularly , the raw CIR is obtained by an inverse Fourier transform of the 0.5 1 1.5 2 2.5 3 Time (s) -100 -50 0 50 100 Doppler shift...

    work page

  26. [26]

    More specifically, the CIRs from experiment results of behavior recognition for two behaviors are utilized in this validati on

    Deterministic cluster modeling : In the second experiment, deterministic modeling for target cluster is studied. More specifically, the CIRs from experiment results of behavior recognition for two behaviors are utilized in this validati on. The CIRs from the standing-up and sitting-down behavior are first extracted from the experiments and then substituted ...

    work page

  27. [27]

    The generation of the standard communicatio n channel follows the procedure in 3GPP TR 38.901, while our bistatic ISAC channel generation follows our proposed procedure

    Simulation setup : To verify the compatibility of our proposed ISAC channel with communication evaluation, we compare the communication bit-error-rate (BER) performan ce between our channel model and the standard communication channel model. The generation of the standard communicatio n channel follows the procedure in 3GPP TR 38.901, while our bistatic I...

    work page

  28. [28]

    Communication performance : The BER results of the proposed ISAC channel and the 3GPP communication channel for different modulations are illustrated in Fig. 9. The res ult depicts that the BER performance of the ISAC channel aligns closely with that of the 3GPP communication channel, which indicates that the ISAC channel is capable of evaluating the comm...

    work page

  29. [29]

    More specifically, we use the above described simulation setup in this subsection to generate an ISAC channel

    Sensing evaluation via statistical cluster modeling : In this simulation, a case study of sensing performance evaluation under the proposed bistatic ISAC channel model framework is provided. More specifically, we use the above described simulation setup in this subsection to generate an ISAC channel. In this case study, the following detailed setup is used...

    work page

  30. [30]

    Channel model ing framework for bistatic ISAC under 3GPP standard,

    C. Luo, A. Tang, F. Gao, J. Liu, and X. Wang, “Channel model ing framework for bistatic ISAC under 3GPP standard,” in Proc. IEEE VTC- Spring, 2024

    work page 2024

  31. [31]

    Integrated sensing and communications: Toward dual-func tional wire- less networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, an d S. Buzzi, “Integrated sensing and communications: Toward dual-func tional wire- less networks for 6G and beyond,” IEEE J. Sel. Areas Commun. , vol. 40, no. 6, pp. 1728–1767, 2022

    work page 2022

  32. [32]

    ICT-317669-METIS/D1.4, METIS channel model , 2015

    work page 2015

  33. [33]

    3GPP TR 36.873 version 12.7.0, Study on 3D channel model for LTE , 2018

    work page 2018

  34. [34]

    3GPP TR 38.901 version 17.0.0, Study on channel model for frequencies from 0.5 to 100 GHz , Mar. 2022

    work page 2022

  35. [35]

    3GPP TR 22.837 version 19.2.1, Feasibility study on integrated sensing and communication, Feb. 2024

    work page 2024

  36. [36]

    3GPP TS 22.137 version 19.0.0, Service requirements for integrated sensing and communication , Dec. 2023

    work page 2023

  37. [37]

    IEEE 802.11 ad-based radar: An approach to joint vehicular communicati on-radar system,

    P . Kumari, J. Choi, N. González-Prelcic, and R. W. Heath, “IEEE 802.11 ad-based radar: An approach to joint vehicular communicati on-radar system,” IEEE Trans. V eh. Tech., vol. 67, no. 4, pp. 3012–3027, 2017

    work page 2017

  38. [38]

    Reference signal design an d power optimization for energy-efficient 5G V2X integrated sensin g and com- munications,

    Q. Zhao, A. Tang, and X. Wang, “Reference signal design an d power optimization for energy-efficient 5G V2X integrated sensin g and com- munications,” IEEE Trans. Green Commun. Netw. , vol. 7, no. 1, pp. 379–392, 2023

    work page 2023

  39. [39]

    A novel 6G ISAC channel model combining forward and backward scatte ring,

    R. Y ang, C. Wang, J. Huang, E.-H. M. Aggoune, and Y . Hao, “ A novel 6G ISAC channel model combining forward and backward scatte ring,” IEEE Trans. Wireless Commun. , vol. 22, no. 11, pp. 8050–8065, 2023

    work page 2023

  40. [40]

    A novel 3D non- stationary localization-assisted ISAC channel model,

    R. Y ang, Y . Wu, J. Huang, and C.-X. Wang, “A novel 3D non- stationary localization-assisted ISAC channel model,” in Proc. IEEE WCNC. IEEE, 2023, pp. 1–6

    work page 2023

  41. [41]

    A general channel model for integrated sensing and communication sce narios,

    Z. Zhang, R. He, B. Ai, M. Y ang, C. Li, H. Mi, and Z. Zhang, “ A general channel model for integrated sensing and communication sce narios,” IEEE Commun. Mag. , vol. 61, no. 5, pp. 68–74, 2023

    work page 2023

  42. [42]

    A cluster-based statistical channel model for integ rated sensing and communication channels,

    Z. Zhang, R. He, B. Ai, M. Y ang, Y . Niu, Z. Zhong, Y . Li, X. Z hang, and J. Li, “A cluster-based statistical channel model for integ rated sensing and communication channels,” arXiv preprint arXiv:2403.00505 , 2024

    work page arXiv 2024

  43. [43]

    A shared multipath components evolution model fo r inte- grated sensing and communication channels,

    Z. Zhang, R. He, B. Ai, M. Y ang, X. Zhang, R. Chen, H. Zhang , and Z. Zhong, “A shared multipath components evolution model fo r inte- grated sensing and communication channels,” IEEE Antennas Wireless Propag. Lett., 2023

    work page 2023

  44. [44]

    Integrated sensing and communication channel: Measureme nts, char- acteristics, and modeling,

    J. Zhang, J. Wang, Y . Zhang, Y . Liu, Z. Chai, G. Liu, and T. Jiang, “Integrated sensing and communication channel: Measureme nts, char- acteristics, and modeling,” IEEE Commun. Mag. , pp. 1–7, 2023

    work page 2023

  45. [45]

    A shared c luster-based stochastic channel model for integrated sensing and commun ication systems,

    Y . Liu, J. Zhang, Y . Zhang, Z. Y uan, and G. Liu, “A shared c luster-based stochastic channel model for integrated sensing and commun ication systems,” IEEE Trans. V eh. Tech., 2023

    work page 2023

  46. [46]

    How to extend 3D GBSM to integrated sensing and communication chan nel with sharing feature?

    Y . Liu, J. Zhang, Y . Zhang, H. Gong, T. Jiang, and G. Liu, “ How to extend 3D GBSM to integrated sensing and communication chan nel with sharing feature?” IEEE Wireless Commun. Lett. , 2024

    work page 2024

  47. [47]

    Channel modeling for heterogeneous vehicular isac system with shared clusters,

    B. Xiong, Z. Zhang, Y . Ge, H. Wang, H. Jiang, L. Wu, and Z. Z hang, “Channel modeling for heterogeneous vehicular isac system with shared clusters,” in Proc. IEEE VTC2023-Fall . IEEE, 2023, pp. 1–6

    work page 2023

  48. [48]

    A unified channel model for both communication and sensing in i ntegrated sensing and communication systems,

    J. Lou, R. Liu, C. Jiang, X. Han, Z. Han, Q. Y ang, and Z. Wan g, “A unified channel model for both communication and sensing in i ntegrated sensing and communication systems,” in Proc. IEEE VTC-Fall , 2023, pp. 1–6

    work page 2023

  49. [49]

    Integrated sensing and communication c hannel modeling and measurements: Requirements and methodologie s toward 6G standardization,

    W. Y ang, Y . Chen, N. Cardona, Y . Zhang, Z. Y u, M. Zhang, J. Li, Y . Chen, and P . Zhu, “Integrated sensing and communication c hannel modeling and measurements: Requirements and methodologie s toward 6G standardization,” IEEE V ehicular Technology Magazine, 2024. 11

    work page 2024

  50. [50]

    Multi-s cattering centers extraction and modeling for isac channel modeling,

    Y . Chen, Z. Y u, J. He, W. Y ang, J. Li, and G. Wang, “Multi-s cattering centers extraction and modeling for isac channel modeling, ” in 2024 18th European Conference on Antennas and Propagation (EuCAP) . IEEE, 2024, pp. 1–5

    work page 2024

  51. [51]

    Signal processing for passive radar using OFDM waveforms,

    C. R. Berger, B. Demissie, J. Heckenbach, P . Willett, an d S. Zhou, “Signal processing for passive radar using OFDM waveforms, ” IEEE J. Select. Topics Signal Process. , vol. 4, no. 1, pp. 226–238, 2010

    work page 2010

  52. [52]

    Wireless insite

    Remcom, “Wireless insite.” [Online]. Available: https://www.remcom.com/wireless-insite-em-propagation-software

    work page

  53. [53]

    Radar sensing for healthcare,

    D. F. Fioranelli, D. S. A. Shah, H. Li1, A. Shrestha, D. S. Y ang, and D. J. L. Kernec, “Radar sensing for healthcare,” Electronics Letters , vol. 55, no. 19, pp. 1022–1024, 2019

    work page 2019

  54. [54]

    I ntegrating passive bistatic sensing into mmwave B5G/6G networks: Desi gn and experiment measurement,

    S. Li, C. Luo, A. Tang, X. Wang, C. Xu, F. Gao, and L. Cai, “I ntegrating passive bistatic sensing into mmwave B5G/6G networks: Desi gn and experiment measurement,” in Proc. IEEE ICC , 2023, pp. 2952–2957

    work page 2023

  55. [55]

    Channel Modeling Framework for Both Communications and Bistatic Sensing Under 3GPP Standard

    K. M. Braun, “OFDM radar algorithms in mobile communica tion networks,” Ph.D. dissertation, Karlsruher Institut für Te chnologie (KIT), Karlsruhe, Germany, 2014. Chenhao Luo received the B.S. degree in elec- trical and computer engineering from Shanghai Jiao T ong University , Shanghai, China, in 2022. He is currently pursuing the master’s degree in infor...

    work page internal anchor Pith review Pith/arXiv arXiv 2014