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.
With technological development, wireless communication has been one of the fastest growing fields of Computing and Engineering in recent years. This fact requires that new approaches be developed to ensure better performance and reliability in wireless communication. In this paper a new approach has been proposed as a solution to the problem of adaptive modulation and coding (AMC), through the development of an extension of the method naive Bayesian classifier, known as dynamic naive Bayesian classifier, to maximize spectral efficiency. The proposed approach exhibits a better performance than k-nearest neighbours algorithm and the traditional Look-Up table solution, with average classification error 2.85%, which represents approximately 10% with respect to the most similar method.
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