Conditional Denoising Diffusion for ISAC Enhanced Channel Estimation in Cell-Free 6G

TL;DR

This paper introduces a novel Conditional Denoising Diffusion Model combined with a Multimodal Transformer to significantly improve channel estimation in cell-free 6G ISAC systems, enhancing accuracy and robustness against noise and pilot contamination.

Contribution

It proposes a new framework integrating sensing data into diffusion models for channel estimation, outperforming traditional estimators and previous diffusion models in accuracy and robustness.

Findings

Achieves 8-9 dB NMSE improvement over LS and MMSE estimators.

Attains 27.8% NMSE improvement over traditional denoising diffusion models.

Demonstrates robustness against pilot contamination and low SNR conditions.

Abstract

Cell-free Integrated Sensing and Communication (ISAC) aims to revolutionize 6th Generation (6G) networks. By combining distributed access points with ISAC capabilities, it boosts spectral efficiency, situational awareness, and communication reliability. Channel estimation is a critical step in cell-free ISAC systems to ensure reliable communication, but its performance is usually limited by challenges such as pilot contamination and noisy channel estimates. This paper presents a novel framework leveraging sensing information as a key input within a Conditional Denoising Diffusion Model (CDDM). In this framework, we integrate CDDM with a Multimodal Transformer (MMT) to enhance channel estimation in ISAC-enabled cell-free systems. The MMT encoder effectively captures inter-modal relationships between sensing and location data, enabling the CDDM to iteratively denoise and refine channel estimates. Simulation results demonstrate that the proposed approach achieves significant performance gains. As compared with Least Squares (LS) and Minimum Mean Squared Error (MMSE) estimators, the proposed model achieves normalized mean squared error (NMSE) improvements of 8 dB and 9 dB, respectively. Moreover, we achieve a 27.8% NMSE improvement compared to the traditional denoising diffusion model (TDDM), which does not incorporate sensing channel information. Additionally, the model exhibits higher robustness against pilot contamination and maintains high accuracy under challenging conditions, such as low signal-to-noise ratios (SNRs). According to the simulation results, the model performs well for users near sensing targets by leveraging the correlation between sensing and communication channels.