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

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