Generative Diffusion Models for High Dimensional Channel Estimation

TL;DR

This paper introduces a novel diffusion model-based method for high-dimensional wireless channel estimation, achieving high accuracy, reduced latency, and lower pilot overhead, suitable for real-time ultra-massive MIMO systems.

Contribution

It develops a deep generative prior using diffusion models for MIMO channel estimation, including low-resolution and noisy measurement scenarios, with unsupervised learning capabilities.

Findings

Achieves 10x reduction in estimation latency.

Reduces pilot overhead by 50%.

Outperforms existing estimators in accuracy.

Abstract

Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by this trend, we investigate the application of the advanced diffusion models (DMs), a representative class of generative AI models, to high dimensional wireless channel estimation. By capturing the structure of multiple-input multiple-output (MIMO) wireless channels via a deep generative prior encoded by DMs, we develop a novel posterior inference method for channel reconstruction. We further adapt the proposed method to recover channel information from low-resolution quantized measurements. Additionally, to enhance the over-the-air viability, we integrate the DM with the unsupervised Stein's unbiased risk estimator to enable learning from noisy observations and circumvent the requirements for ground truth channel data that is hardly available in practice. Results reveal that the proposed estimator achieves high-fidelity channel recovery while reducing estimation latency by a factor of 10 compared to state-of-the-art schemes, facilitating real-time implementation. Moreover, our method outperforms existing estimators while reducing the pilot overhead by half, showcasing its scalability to ultra-massive antenna arrays.