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

Muhammad Karam Shehzad (1, 2) , Luca Rose (1) , Mohamad Assaad (2)

1 Nokia Bell Labs [Paris-Saclay]
2 L2S - Laboratoire des signaux et systèmes

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.

Domaines: Réseaux et télécommunications [cs.NI]

Muhammad Karam Shehzad, Luca Rose, Mohamad Assaad. Dealing with CSI Compression to Reduce Losses and Overhead: An Artificial Intelligence Approach. 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Jun 2021, Montreal, Canada. pp.1-6, ⟨10.1109/ICCWorkshops50388.2021.9473840⟩. ⟨hal-03502933⟩