Diffusion Inpainting MIMO-OFDM Channels with Limited Noisy Observations

TL;DR

This paper introduces a diffusion-based inpainting method for MIMO-OFDM channel estimation using limited noisy pilot observations, achieving high accuracy and efficiency.

Contribution

It proposes a novel Conditional Diffusion Transformer framework with specialized encoding and attention mechanisms for robust channel inpainting from sparse noisy data.

Findings

Achieves over 5 dB performance gain compared to baselines.

Maintains robust channel estimation at a pilot density of 1/32.

Generates high-quality channel matrices within 10 inference steps.

Abstract

Acquiring the channel state information from limited and noisy observations at pilot positions is critical for wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. In this paper, we view this process as a conditional generative task in which the partial noisy channel estimates at the pilots are utilized as a ``prompt'' to guide the diffusion ``inpainting'' of the underlying channel. To this end, we resort to a general Conditional Diffusion Transformer (CDiT) framework with a well-designed network architecture and update rule. In particular, we design a dedicated embedding strategy to encode and adapt to different pilot patterns and noise levels, and utilize a special cross-attention mechanism to align the partial raw channel observations with the denoised channel at each time step of the generation process. This architecture effectively anchors the diffusion process, enabling the model to accurately recover full channel details from limited noisy observations. Comprehensive experimental results show that, the proposed approach achieves a performance gain of over 5 dB compared to the baselines under varying noise conditions, and provides robust channel acquisition even under a sparse pilot density of 1/32 without significant performance loss compared to the denser pilot cases. Moreover, it is capable of generating high-quality channel matrices within just 10 inference steps, effectively balancing estimation accuracy with computational efficiency and inference speed. Ablation studies demonstrate the rationality of the model design and the necessity of its modules.