Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks

TL;DR

This paper presents a reinforcement learning framework for adaptive modulation and coding in 5G networks, enabling base stations to optimize spectral efficiency and error rates dynamically based on channel conditions.

Contribution

It introduces a Q-learning based RL approach for MCS selection in 5G, improving over traditional fixed schemes and outer loop adaptation.

Findings

Achieves higher spectral efficiency than fixed schemes.

Maintains low block error rate (BLER).

Outperforms conventional link adaptation methods.

Abstract

We design a self-exploratory reinforcement learning (RL) framework, based on the Q-learning algorithm, that enables the base station (BS) to choose a suitable modulation and coding scheme (MCS) that maximizes the spectral efficiency while maintaining a low block error rate (BLER). In this framework, the BS chooses the MCS based on the channel quality indicator (CQI) reported by the user equipment (UE). A transmission is made with the chosen MCS and the results of this transmission are converted by the BS into rewards that the BS uses to learn the suitable mapping from CQI to MCS. Comparing with a conventional fixed look-up table and the outer loop link adaptation, the proposed framework achieves superior performance in terms of spectral efficiency and BLER.