Enhancing MIMO Channel Estimation: A Denoising Diffusion Approach

As accurate channel state information (CSI) is essential for unlocking the full potential of Multiple-Input Multiple-Output (MIMO) architectures, which are foundational to modern wireless communication systems, channel estimation (CE) becomes an important task. While traditional methods like least squares and compressed sensing leverage channel sparsity, they struggle with noise variability, and recent machine learning techniques often fail to account for communication-specific structures like antenna layouts, exhibiting poor performance in low Signal-to-Noise Ratio (SNR) conditions. This paper proposes a novel MIMO CE framework that advances the Denoising Diffusion Implicit Model (DDIM) through domain-specific innovations. We introduce two key contributions: (1) a sampling strategy that enhances performance under low SNR scenarios, and (2) a communication-specific network architecture that integrates antenna array layout into the noise estimation network. Simulations validate that these enhancements significantly improve the robustness and accuracy of channel estimation, particularly in challenging low SNR environments.