A deep reinforcement learning approach to fair distributed dynamic spectrum access

Syed Qaisar Jalil, Mubashir Husain Rehmani, Stephan Chalup

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper investigates the task how to achieve fairness in distributed dynamic spectrum access (DSA). Specifically, we consider a cognitive radio network scenario with multiple primary users (PUs) and secondary users (SUs). Each PU operates in a licensed channel. We assume that there is no coordination between PUs and SUs, and no coordination among SUs. The key challenges for SUs are to: (1) avoid collisions with PUs, (2) avoid collisions with other SUs, (3) fair access of spectrum resources in an uncoordinated system, (4) deal with different PU activity patterns, (5) deal with spectrum sensing errors. To address these challenges, we propose a deep reinforcement learning (DRL) approach and an associated reward function to achieve fair access to spectrum resources. Specifically, we use the method of Dueling Double Deep Q-Networks with Prioritised Experience Replay (D3QN-PER) as DRL algorithm for each SU. In our simulation experiments, we demonstrate that the proposed approach performs better than existing DRL methods.

Original languageEnglish
Title of host publicationProceedings of the 17th EAI International Conference on Mobile and Ubiquitous Systems
Subtitle of host publicationComputing, Networking and Services, MobiQuitous 2020
PublisherAssociation for Computing Machinery (ACM)
Pages236-244
Number of pages9
ISBN (Electronic)9781450388405
DOIs
Publication statusPublished - 07 Dec 2020
Externally publishedYes
Event17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2020 - Virtual, Online, Germany
Duration: 07 Dec 202009 Dec 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference17th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2020
Country/TerritoryGermany
CityVirtual, Online
Period07/12/202009/12/2020

Keywords

  • Cognitive radio
  • Distributed dynamic spectrum access
  • Multi-agent deep reinforcement learning

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