Conditional Generative Adversarial Networks for Channel Estimation in RIS-Assisted ISAC Systems

TL;DR

This paper introduces a novel CGAN-based approach for channel estimation in RIS-assisted ISAC systems, significantly enhancing accuracy over traditional deep learning methods by leveraging adversarial training.

Contribution

The paper presents the first application of conditional GANs for channel estimation in RIS-assisted ISAC systems, improving estimation accuracy and training efficiency.

Findings

CGAN-based method outperforms conventional DL techniques in estimation accuracy

Numerical simulations validate the effectiveness of the proposed approach

Potential to enable more reliable and precise ISAC deployments

Abstract

Integrated sensing and communication (ISAC) technology has been explored as a potential advancement for future wireless networks, striving to effectively use spectral resources for both communication and sensing. The integration of reconfigurable intelligent surfaces (RIS) with ISAC further enhances this capability by optimizing the propagation environment, thereby improving both the sensing accuracy and communication quality. Within this domain, accurate channel estimation is crucial to ensure a reliable deployment. Traditional deep learning (DL) approaches, while effective, can impose performance limitations in modeling the complex dynamics of wireless channels. This paper proposes a novel application of conditional generative adversarial networks (CGANs) to solve the channel estimation problem of an RIS-assisted ISAC system. The CGAN framework adversarially trains two DL networks, enabling the generator network to not only learn the mapping relationship from observed data to real channel conditions but also to improve its output based on the discriminator network feedback, thus effectively optimizing the training process and estimation accuracy. The numerical simulations demonstrate that the proposed CGAN-based method improves the estimation performance effectively compared to conventional DL techniques. The results highlight the CGAN's potential to revolutionize channel estimation, paving the way for more accurate and reliable ISAC deployments.