Adaptive Modulation and Coding Techniques Based on Reinforcement Learning

Traditional Adaptive Modulation and Coding (AMC) techniques rely on real-time and accurate Signal-to-Noise Ratio (SNR) estimates, but often resulting in suboptimal data transmission performance in scenarios with significant link transmission delays and rapid channel changes. This paper introduces a reinforcement learning (RL) algorithm to enhance traditional AMC techniques. By applying Q-learning principles to define the RL framework, including state space, action space, and reward, this study proposes a Q-learning-based AMC technique. Leveraging RL's robust perception and decision-making capabilities in uncertain environments, this method mitigates the shortcomings of traditional AMC techniques, such as coarse channel state partitioning and mismatched state lookup tables, especially in dynamic channel conditions, thereby reducing resource inefficiencies and enhancing system throughput. Evaluation of spectral efficiency and bit error rate under conditions of rapid channel changes and SNR estimation errors due to feedback delays reveals that the Q-learning-based AMC technique achieves superior performance compared to threshold-based AMC methods.