EACE-DM: Environment-Aware Channel Estimation via Transformer-Empowered Conditional Diffusion Model

Channel estimation in a fading environment can be regarded as a typical statistical estimation problem. Its optimal performance relies on the prior distribution of the channel coefficients, which are environment-specific. However, the conventional channel estimators, such as the least square (LS) and linear minimum mean square error (LMMSE) estimators, do not fully exploit the prior channel distribution law. To address this limitation, we propose to use the conditional diffusion model (DM) to achieve environment-aware channel estimation, referred to as EACE-DM. In this framework, the environment information is incorporated as the condition to guide the EACE-DM in learning the hidden features of channels from various environments. The trained EACE-DM is functionally decomposed into two components, i.e., the environment identification module and the channel estimation module. The environment identification module first uses the DM’s forward process to diffuse LS channel estimation into a noisy sample. Then it executes the DM’s reverse denoising process conditioned on candidate environments to recover the LS estimation from the noisy channel. The environment is then identified via maximum a posteriori (MAP) estimation by comparing these recovered estimations with the ground-truth LS estimation. Finally, the identified environment is utilized to guide the channel estimation module, denoising the LS estimation. Numerical simulations demonstrate that the proposed EACE-DM significantly decreases normalized mean square errors (NMSEs) of channel estimation across diverse environments while incurring a moderate increase in computational complexity compared to conventional estimators and existing DM-based approaches.