Conditional Generative Adversarial Network-Based Channel Estimation for RIS-Assisted ISAC System

LIU Yu; ZHENG Zelin; LIU Gang

doi:10.11999/JEIT251168

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

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

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Conditional Generative Adversarial Network-Based Channel Estimation for RIS-Assisted ISAC 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
Rev Recd Date: 2026-01-22

Available Online: 2026-03-04

Abstract

Abstract

Objective Accurate channel estimation is essential for the reliable operation of RIS-assisted ISAC systems. Traditional deep learning methods provide partial solutions, but their generalization ability and estimation accuracy remain limited in complex multi-user channel environments. To address this issue, this study proposes a two-stage channel estimation method based on Conditional Generative Adversarial Network (CGAN) for RIS-assisted multi-user ISAC systems to improve estimation accuracy and stability. Methods A two-stage CGAN-based method is proposed for channel estimation in RIS-assisted multi-user ISAC systems. By adjusting the RIS switching states, the overall estimation task is divided into subproblems, which enables sequential estimation of the direct and reflected channels. Within the CGAN framework, adversarial training between the generator and discriminator is used to learn the mapping from observed signals to true channels. Feedback from the discriminator is further used to optimize the output, thereby improving training efficiency and estimation accuracy. Results and Discussions Extensive simulations are conducted to evaluate the effectiveness of the proposed method. Channel estimation performance is first assessed under different Signal-to-Noise Ratio (SNR) conditions. The CGAN-based approach achieves substantially better Normalized Mean Square Error (NMSE) performance than the Least Squares (LS) benchmark and conventional models such as FNN and ELM (Fig. 4). The effects of antenna number and RIS element count on channel estimation are then examined. Across different channel sizes and SNR conditions, the CGAN-based method consistently outperforms the LS benchmark (Figs. 5 and 6). Conclusions This study investigates channel estimation in RIS-assisted multi-user ISAC systems and proposes a two-stage CGAN-based method. By adjusting the RIS switching states and applying adversarial training between the generator and discriminator, accurate estimation of the direct and reflected channels is achieved. Simulation results show that the proposed method has strong generalization ability across different SNR levels and channel dimensions, and achieves substantially higher estimation accuracy than benchmark schemes. This method provides a promising solution for improving the accuracy and stability of channel estimation.

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