Successive Interference Cancellation-aided Diffusion Models for Joint Channel Estimation and Data Detection in Low Rank Channel Scenarios

TL;DR

This paper introduces a SIC-aided diffusion model for joint channel estimation and data detection that excels in low-rank channel scenarios, outperforming existing methods especially when user numbers exceed antennas.

Contribution

It presents a novel score-based diffusion algorithm that effectively estimates channels and data in low-rank scenarios, addressing limitations of prior full-rank focused approaches.

Findings

Outperforms baseline methods in NMSE and SER

Effective in low-rank and full-rank scenarios

Significant improvements at various SNR levels

Abstract

This paper proposes a novel joint channel-estimation and source-detection algorithm using successive interference cancellation (SIC)-aided generative score-based diffusion models. Prior work in this area focuses on massive MIMO scenarios, which are typically characterized by full-rank channels, and fail in low-rank channel scenarios. The proposed algorithm outperforms existing methods in joint source-channel estimation, especially in low-rank scenarios where the number of users exceeds the number of antennas at the access point (AP). The proposed score-based iterative diffusion process estimates the gradient of the prior distribution on partial channels, and recursively updates the estimated channel parts as well as the source. Extensive simulation results show that the proposed method outperforms the baseline methods in terms of normalized mean squared error (NMSE) and symbol error rate (SER) in both full-rank and low-rank channel scenarios, while having a more dominant effect in the latter, at various signal-to-noise ratios (SNR).