A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access

• Published in: IEEE transactions on cognitive communications and networking Vol. 5; no. 4; pp. 1125 - 1139
• Main Authors: Zhong, Chen, Lu, Ziyang, Gursoy, M. Cenk, Velipasalar, Senem
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actor-critic algorithms; Algorithm design and analysis; Algorithms; Channel estimation; channel switching patterns; Channels; Collision dynamics; Deep learning; deep Q-networks; deep reinforcement learning; dynamic channel access; Machine learning; Multiagent systems; Multichannel communication; Performance evaluation; Random access; Reinforcement learning; Switching
• ISSN: 2332-7731; 2332-7731
• DOI: 10.1109/TCCN.2019.2952909

To make efficient use of limited spectral resources, we in this work propose a deep actor-critic reinforcement learning based framework for dynamic multichannel access. We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously. We employ the proposed framework as a single agent in the single-user case, and extend it to a decentralized multi-agent framework in the multi-user scenario. In both cases, we develop algorithms for the actor-critic deep reinforcement learning and evaluate the proposed learning policies via experiments and numerical results. In the single-user model, in order to evaluate the performance of the proposed channel access policy and the framework's tolerance against uncertainty, we explore different channel switching patterns and different switching probabilities. In the case of multiple users, we analyze the probabilities of each user accessing channels with favorable channel conditions and the probability of collision. We also address a time-varying environment to identify the adaptive ability of the proposed framework. Additionally, we provide comparisons (in terms of both the average reward and time efficiency) between the proposed actor-critic deep reinforcement learning framework, Deep-Q network (DQN) based approach, random access, and the optimal policy when the channel dynamics are known.