Reinforcement-based data transmission in temporally-correlated fading channels: Partial CSIT scenario

TL;DR

This paper applies reinforcement algorithms to wireless data transmission over temporally-correlated fading channels with partial CSIT, demonstrating significant performance improvements without increasing feedback or complexity.

Contribution

It introduces reinforcement-based transmission models tailored for temporally-correlated fading channels with partial CSIT, enhancing performance while maintaining existing feedback levels.

Findings

Reinforcement algorithms significantly improve transmission performance.

Performance gains achieved without additional feedback or complexity.

Applicable to temporally-correlated fading channels with partial CSIT.

Abstract

Reinforcement algorithms refer to the schemes where the results of the previous trials and a reward-punishment rule are used for parameter setting in the next steps. In this paper, we use the concept of reinforcement algorithms to develop different data transmission models in wireless networks. Considering temporally-correlated fading channels, the results are presented for the cases with partial channel state information at the transmitter (CSIT). As demonstrated, the implementation of reinforcement algorithms improves the performance of communication setups remarkably, with the same feedback load/complexity as in the state-of-the-art schemes.