Sampling-Free Diffusion Transformers for Low-Complexity MIMO Channel Estimation

TL;DR

This paper introduces SF-DiT-CE, a sampling-free diffusion transformer for low-complexity MIMO channel estimation that directly predicts channels in a single step, reducing computational load.

Contribution

It proposes a novel sampling-free diffusion transformer that leverages channel sparsity for efficient, accurate MIMO channel estimation without iterative sampling.

Findings

Achieves higher estimation accuracy than existing methods.

Demonstrates robustness with significantly lower computational complexity.

Operates effectively in a single forward pass during inference.

Abstract

Diffusion model-based channel estimators have shown impressive performance but suffer from high computational complexity because they rely on iterative reverse sampling. This paper proposes a sampling-free diffusion transformer (DiT) for low-complexity MIMO channel estimation, termed SF-DiT-CE. Exploiting angular-domain sparsity of MIMO channels, we train a lightweight DiT to directly predict the clean channels from their perturbed observations and noise levels. At inference, the least square (LS) estimate and estimation noise condition the DiT to recover the channel in a single forward pass, eliminating iterative sampling. Numerical results demonstrate that our method achieves superior estimation accuracy and robustness with significantly lower complexity than state-of-the-art baselines.