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

arXiv: https://arxiv.org/abs/2104.00189 · HTML: https://mlsanity.com/paper/2104.00189/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Study on Downlink CSI compression: Are Neural Networks the Only Solution?](https://mlsanity.com/paper/2502.17459/) (2025)

## Abstract (arXiv)

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.