Conditional Generative Adversarial Networks-based Channel Estimation for ISAC-RIS System
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摘要: 通感一体化(Integrated Sensing and Communication, ISAC)技术作为未来无线通信发展的关键趋势，旨在通过频谱资源的高效利用，实现通信与感知功能的融合与协同。当智能反射表面(Reconfigurable Intelligent Surfaces, RIS)被引入ISAC系统后，可重构无线传播环境，从而显著提升通信质量及感知精度。然而，准确的信道估计对于保障可靠运行是至关重要的。尽管传统的深度学习方法在一定程度上能够应对信道估计问题，但在面对多用户复杂信道环境时，其泛化能力和估计精度仍存在不足。针对上述问题，本文对于RIS辅助多用户ISAC系统提出了一种基于条件生成对抗网络(Conditional Generative Adversarial Network, CGAN)的两阶段信道估计方法。该方法通过调整RIS的开关状态，分阶段完成对直射信道与反射信道的估计，以提高信道估计的准确性和稳定性。通过生成网络与判别网络的对抗训练，不仅能够学习观测信号与真实信道之间的映射关系，还能根据判别网络的反馈来不断优化输出，从而有效提升训练效率与估计精度。仿真结果表明，与传统深度学习方法相比，所提基于CGAN的方案在信道估计性能上均表现出显著优势。该结果验证了CGAN方法在RIS辅助ISAC系统下信道估计的应用潜力，并为实现更精准和可靠的系统部署奠定了基础。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 and6 ).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. -
表 1 CGAN网络的具体参数
模型 网络 层 大小 激活函数 CGAN 生成器 输入层 $ 2M{P}^{{{\mathrm{S}}_{l}}} $ - FFL 100 LeakyReLU FFL 200 LeakyReLU 输出层 $ 2{M}^{2} $ - 判别器 输入层 $ 2{M}^{2} $ - FFL 100 LeakyReLU FFL 200 LeakyReLU 输出层 1 - 
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表 2 两个阶段的子帧持续时间
时隙持续时间 时隙个数 子帧持续时间 子帧个数 两个阶段的总子帧持续时间 S1 TP=0.52 μs $ {P}^{{{\mathrm{S}}_{1}}} $=M+K=12 $ \begin{aligned}T_{\mathrm{F}}^{{\mathrm{S}}_{1}}&={T}_{\mathrm{P}}{P}^{{{\mathrm{S}}_{1}}}\\&=6.24 \;{\text{μs}}\end{aligned} $ $ {C}^{{{\mathrm{S}}_{1}}} $ $ \begin{aligned}{T}_{\mathrm{E}}&={C}^{{{\mathrm{S}}_{1}}}T_{\mathrm{F}}^{{\mathrm{S}}_{1}}+\left({C}^{{{\mathrm{S}}_{2}}}-{C}^{{{\mathrm{S}}_{1}}}\right)\\T_{\mathrm{F}}^{{\mathrm{S}}_{2}}&=99.84 \;{\text{μs}}\end{aligned} $ $ {\mathrm{S}}_{2} $ $ {T}_{\mathrm{P}}=0.52 \;{\text{μs}} $ $ {P}^{{{\mathrm{S}}_{2}}}=\max \left\{M,K\right\}=6 $ $ \begin{aligned}T_{\mathrm{F}}^{{\mathrm{S}}_{2}}&={T}_{\mathrm{P}}{P}^{{{\mathrm{S}}_{2}}}\\&=3.12 \;{\text{μs}}\end{aligned} $ $ {C}^{{{\mathrm{S}}_{2}}}-{C}^{{{\mathrm{S}}_{1}}} $
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表 3 SAC信道的路径损耗
距离 路径损耗指数 路径损耗 BS-目标-BS链路 $ {d}_{\mathrm{S}}=150\mathrm{m} $ $ {\beta }_{\mathrm{S}}=3 $ $ {\xi }_{\mathrm{S}}={\xi }_{0}{\left({d}_{\mathrm{S}}/{d}_{0}\right)}^{-{{\beta }_{\mathrm{S}}}} $ RIS-BS链路 $ {d}_{\mathrm{IB}}=50\mathrm{m} $ $ {\beta }_{\mathrm{IB}}=2.3 $ $ {\xi }_{\mathrm{IB}}={\xi }_{0}{\left({d}_{\mathrm{IB}}/{d}_{0}\right)}^{-{{\beta }_{\mathrm{IB}}}} $ $ {U}_{k} $-BS链路 $ {d}_{{{U}_{k}}\mathrm{B}}=50\mathrm{m} $ $ {\beta }_{{{U}_{k}}\mathrm{B}}=3.5 $ $ {\xi }_{{{U}_{k}}\mathrm{B}}={\xi }_{0}{\left({d}_{{{U}_{k}}\mathrm{B}}/{d}_{0}\right)}^{-{{\beta }_{{{U}_{k}}\mathrm{B}}}} $ BS-$ {D}_{j} $链路 $ {d}_{\mathrm{B}{{D}_{j}}}=50\mathrm{m} $ $ {\beta }_{\mathrm{B}{{D}_{j}}}=3.5 $ $ {\xi }_{\mathrm{B}{{D}_{j}}}={\xi }_{0}{\left({d}_{\mathrm{B}{{D}_{j}}}/{d}_{0}\right)}^{-{{\beta }_{\mathrm{B}{{D}_{j}}}}} $ $ {U}_{k} $-RIS链路 $ {d}_{{{U}_{k}}\mathrm{I}}=2\mathrm{m} $ $ {\beta }_{{{U}_{k}}\mathrm{I}}=2 $ $ {\xi }_{{{U}_{k}}\mathrm{I}}={\xi }_{0}{\left({d}_{{{U}_{k}}\mathrm{I}}/{d}_{0}\right)}^{-{{\beta }_{{{U}_{k}}\mathrm{I}}}} $ RIS-$ {D}_{j} $链路 $ {d}_{\mathrm{I}{{D}_{j}}}=2\mathrm{m} $ $ {\beta }_{\mathrm{I}{{D}_{j}}}=2 $ $ {\xi }_{\mathrm{I}{{D}_{j}}}={\xi }_{0}{\left({d}_{\mathrm{I}{{D}_{j}}}/{d}_{0}\right)}^{-{{\beta }_{\mathrm{I}{{D}_{j}}}}} $
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表 4 训练时间(s)
训练时间 ELM FNN CGAN ISAC BS $ {\mathrm{S}}_{1} $：$ \bf{A} $,$ {\bf{b}}_{k} $ 2.54 10.31 436.09 $ {\mathrm{S}}_{2} $：$ {\bf{B}}_{k} $ 14.76 485.26 618.24 下行$ {D}_{j} $ $ {\mathrm{S}}_{1} $：$ {\bf{d}}_{j} $ 2.38 3.94 493.52 $ {\mathrm{S}}_{2} $：$ {\bf{D}}_{j} $ 9.41 88.82 522.06
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