Dealing with CSI Compression to Reduce Losses and Overhead: An Artificial Intelligence Approach

Motivated by the issue of inaccurate channel state information (CSI) at the base station (BS), which is commonly due to feedback/processing delays and compression problems, in this paper, we introduce scalable idea of using artificial intelligence (AI). The proposed scheme not only deals with the CSI compression, which is done at the mobile terminal (MT), but also with the accurate recovery of estimated CSI at the BS. Simulation-based results corroborate the validity of proposed scheme. Numerically, nearly 100% recovery of the estimated CSI is observed with relatively lower overhead as compared to benchmark scheme. The proposed idea can bring potential benefits in the wireless communication environment, e.g., ultra-reliable and low latency communication (URLLC), where imperfect CSI and overhead is intolerable.