Conditional Generative Adversarial Networks-based Channel Estimation for ISAC-RIS System

LIU Yu; ZHENG Zelin; LIU Gang

doi:10.11999/JEIT251168

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LIU Yu, ZHENG Zelin, LIU Gang. Conditional Generative Adversarial Networks-based Channel Estimation for ISAC-RIS System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251168

Citation:

LIU Yu, ZHENG Zelin, LIU Gang. Conditional Generative Adversarial Networks-based Channel Estimation for ISAC-RIS System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251168

LIU Yu, ZHENG Zelin, LIU Gang. Conditional Generative Adversarial Networks-based Channel Estimation for ISAC-RIS System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251168

Citation:

LIU Yu, ZHENG Zelin, LIU Gang. Conditional Generative Adversarial Networks-based Channel Estimation for ISAC-RIS System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251168

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Conditional Generative Adversarial Networks-based Channel Estimation for ISAC-RIS System

doi: 10.11999/JEIT251168 cstr: 32379.14.JEIT251168

LIU Yu^{1, 2},
ZHENG Zelin³,
LIU Gang^{1, 4
,
,}

1.
Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System, Wuxi University, Wuxi 214105, China
2.
School of Electronics and Information Engineering, Wuxi University, Wuxi 214105, China
3.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
4.
School of Integrated Circuit Science and Engineering, Wuxi University, Wuxi 214105, China

Received Date: 2025-11-05
Accepted Date: 2026-01-22
Rev Recd Date: 2026-01-22

Available Online: 2026-02-03

Abstract

Abstract

Objective In RIS-assisted ISAC systems, accurate channel estimation is crucial to ensure reliable operation. Although traditional deep learning methods can partially address the channel estimation problem, their generalization ability and estimation accuracy remain limited in complex multi-user channel environments. To tackle these challenges, this paper proposes a two-stage channel estimation method based on Conditional Generative Adversarial Network(CGAN) for RIS-assisted multi-user ISAC systems, aiming to enhance both the accuracy and stability of channel estimation. Methods This paper proposes a two-stage channel estimation method based on CGAN for estimating the SAC channels in RIS-assisted multi-user ISAC systems. By adjusting the switching states of the RIS, the overall estimation problem is decomposed into subproblems, enabling sequential estimation of the direct and reflected channels. Within the proposed CGAN framework, the adversarial training between the generator and discriminator allows the model not only to learn the mapping relationship between the observed signals and the true channels but also to optimize the output according to the discriminator’s feedback, thereby effectively improving both training efficiency and estimation accuracy. Results and Discussions Extensive simulation experiments were conducted to verify the effectiveness of the proposed method. First, the estimation performance of the SAC channel under different SNR conditions was compared. The results demonstrate that the proposed CGAN-based method achieves significantly better NMSE performance than the LS benchmark and traditional models such as FNN and ELM (Fig. 4). Then, the impact of increasing the number of antennas and RIS elements on SAC channel estimation performance was investigated. Compared with the LS benchmark, the proposed CGAN method consistently maintains superior performance under various SNR conditions (Figs. 5 and 6). Conclusions This paper investigates the channel estimation problem in RIS-assisted multi-user ISAC systems and proposes a two-stage channel estimation method based on CGAN. By adjusting the switching states of the RIS and employing adversarial training between the generator and discriminator networks, the proposed method achieves accurate estimation of the SAC channel. Simulation results demonstrate that, under various SNR conditions and channel dimensions, the CGAN-based estimation method exhibits strong generalization capability and significantly outperforms the benchmark schemes in estimation accuracy. Therefore, it shows great potential as an effective solution for enhancing system stability and efficiency.

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