Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks
This addresses network optimization for 5G communication systems, representing an incremental improvement over existing approaches.
The authors tackled the problem of selecting optimal modulation and coding schemes in 5G networks by developing a reinforcement learning framework based on Q-learning, which improved spectral efficiency and reduced block error rate compared to conventional methods.
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.