Enhancing MIMO Channel Estimation with Deep Learning: A Score-Based Generative Approach
Channel estimation is considered one of the major tasks in the area of digital communications that has a significant influence on the performance of the system. This study presents a new way of performing MIMO channel estimation using score-based generative models. As a cesimoc architecture, our approach employs a deep neural net aimed at estimating the log–prior gradient of wireless channels across high–dimensional space to address channel estimation through posterior sampling. We train a score matching neural network to realizations of channels sampled from CDL-D model with two antenna spacing’s and present results which enable almost similar and better performance in both scenarios of out-of-distribution and in-distribution of the GAN and CS approaches. The method introduces an improvement of 5 dB in the channel estimation error particularly at half antenna spacing in which GAN methods are efficiently used. The approach when evaluated on ‘never seen before’ CDL-C channels yields end-to-end cording performance advantage of up to 3dB over compressed sensing techniques and 0.5dB under ideal channel knowledge conditions.