Conditional Diffusion Model-Driven Generative Channels for Double RIS-Aided Wireless Systems

TL;DR

This paper introduces a novel conditional diffusion model approach for generating wireless channels in double-RIS systems, reducing pilot overhead and improving accuracy over traditional methods.

Contribution

It presents a new channel generation technique using conditional diffusion models trained on synthetic data, enhancing environmental adaptability and model understanding.

Findings

Outperforms traditional methods in NMSE metrics

Reduces pilot overhead in channel acquisition

Demonstrates robustness in simulated environments

Abstract

With the development of the upcoming sixth-generation networks (6G), reconfigurable intelligent surfaces (RISs) have gained significant attention due to its ability of reconfiguring wireless channels via smart reflections. However, traditional channel state information (CSI) acquisition techniques for double-RIS systems face challenges (e.g., high pilot overhead or multipath interference). This paper proposes a new channel generation method in double-RIS communication systems based on the tool of conditional diffusion model (CDM). The CDM is trained on synthetic channel data to capture channel characteristics. It addresses the limitations of traditional CSI generation methods, such as insufficient model understanding capability and poor environmental adaptability. We provide a detailed analysis of the diffusion process for channel generation, and it is validated through simulations. The simulation results demonstrate that the proposed CDM based method outperforms traditional channel acquisition methods in terms of normalized mean squared error (NMSE). This method offers a new paradigm for channel acquisition in double-RIS systems, which is expected to improve the quality of channel acquisition with low pilot overhead.