Denoising Diffusion Model-Based Channel Estimation in IRS-Assisted ISAC Systems

In Integrated Sensing and Communications (ISAC) systems assisted by Intelligent Reflecting Surfaces (IRS), precise channel estimation is crucial for optimal system performance and maximizing the potential benefits. The estimation problem, however, becomes intricate due to complex channel coupling and specific environmental conditions. This paper addresses these challenges by formulating channel estimation as a denoising problem and proposing a novel diffusion-based framework. This approach mitigates the impact of ill-posedness, which is typically problematic for general neural network (NN)-based techniques. By modeling the joint distribution and sampling from the learned posterior distribution, we accurately recover the channel coefficients from noisy pilot-based observations. Simulation results demonstrate significant performance improvements of the proposed method compared to both least-squares and NN-based benchmarks. Moreover, thanks to the carefully designed architecture and residual prediction strategy, the number of sampling steps in the diffusion reverse process is greatly reduced, enabling real-time CSI acquisition.