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

TL;DR

This paper proposes an AI-based method to improve CSI compression and recovery at the base station, significantly reducing overhead and enhancing accuracy for reliable wireless communication.

Contribution

It introduces a scalable AI-assisted CSI acquisition scheme that enhances compression at the mobile terminal and achieves near-perfect recovery at the base station.

Findings

Nearly 100% CSI recovery observed in simulations

Lower overhead compared to benchmark schemes

Potential benefits for URLLC environments

Abstract

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 a scalable idea of adopting artificial intelligence (AI) aided CSI acquisition. The proposed scheme enhances the CSI compression, which is done at the mobile terminal (MT), along with accurate recovery of estimated CSI at the BS. Simulation-based results corroborate the validity of the proposed scheme. Numerically, nearly 100\% recovery of the estimated CSI is observed with relatively lower overhead than the 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.