A Deep Reinforcement Learning Approach to Dynamic Channel Selection
TL;DR
A deep reinforcement learning-based system to perform dynamic channel selection is suggested, which provides the formulation of the problem as a Markov Decision Process and uses a Deep Q-Network to train the optimal long-term policies of spectrum access.
Abstract
Channel selection is a dynamic and challenging issue in the present wireless network that is shared and suffers interference in a spectrum that is not fully controlled. The traditional heuristic and greedy methods depend on immediate channel measurements and they are not always adapted to non-stationary environments resulting in poor throughput, high packet error rates and unpredictable channel switching behaviour. As a remedy to these shortcomings, this paper will suggest a deep reinforcement learning-based system to perform dynamic channel selection, which provides the formulation of the problem as a Markov Decision Process and uses a Deep Q-Network to train the optimal long-term policies of spectrum access. The given strategy combines spectrum sensing, contextual state representation, and reward-based learning to make adaptive and stable choices to select the channel. The reward function explicitly includes a switching cost in order to strike a balance between the adaptability and the stability. Numerous simulation-based tests have shown that the given technique replicates the best results in terms of mean throughput, packet error rate, and frequency of channel switching, compared to the random, greedy and bandit-based baseline schemes. The findings validate the outcomes of deep reinforcement learning in learning temporal spectrum dynamics and maximizing the long-term communication performance in non-stationary wireless conditions.