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

Chen Zhong, Ziyang Lu, M. Cenk Gursoy, Senem Velipasalar

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number8896945
Pages (from-to)1125-1139
Number of pages15
JournalIEEE Transactions on Cognitive Communications and Networking
Volume5
Issue number4
DOIs
StatePublished - Dec 2019

Keywords

  • Actor-critic algorithms
  • channel switching patterns
  • deep Q-networks
  • deep reinforcement learning
  • dynamic channel access

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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