Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications

TL;DR

This paper introduces a diffusion model-based MIMO-OFDM receiver that leverages prior channel knowledge and adapts to noise, significantly improving channel reconstruction especially in low-pilot scenarios.

Contribution

It proposes a novel diffusion model approach for MIMO-OFDM receivers, integrating traditional signal estimation with generative modeling to enhance robustness and reduce training costs.

Findings

Reduces channel-reconstruction error by up to 2x in low-pilot regimes.

Effectively adapts to varying noise levels and pilot schemes.

Improves performance with larger imagination sizes at higher computational cost.

Abstract

This paper focuses on wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplex (OFDM) receivers. Traditional wireless receivers have relied on mathematical modeling and Bayesian inference, achieving remarkable success in most areas but falling short in their ability to characterize channel matrices. Neural networks (NNs) have demonstrated significant potential in this aspect. Nevertheless, integrating traditional inference methods with NNs presents challenges, particularly in tracking the error progression. Given the inevitable presence of noise in wireless systems, generative models that are more resilient to noise are garnering increased attention. In this paper, we propose re-evaluating the MIMO-OFDM receiver using diffusion models, which is a common generative approach. With diffusion models, we can effectively leverage prior knowledge of channel matrices and incorporate traditional signal estimation components. Specifically, we explore the diffusion system and introduce an imagination-screening strategy to guide the diffusion process. Furthermore, diffusion models enable adaptation to varying noise levels and pilot schemes using the same NN, significantly reducing training and deployment costs. Simulated results reveal that, for pilot densities ranging from 4-6 pilots per 64-subcarrier block and signal-to-noise ratios (SNRs) from -4 dB to 0 dB, our proposed receiver reduces channel-reconstruction error by up to two times compared to leading deep-learning models, with the most pronounced improvements observed in low-pilot conditions. Additionally, performance enhancements can be achieved with a larger imagination size, despite increased computational complexity.