Reinforcement learning approaches and evaluation criteria for opportunistic spectrum access

Clement Robert, Christophe Moy, Cheng Xiang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper deals with the learning and decision making issue for cognitive radio (CR). Two reinforcement-learning algorithms proposed in the literature are compared for opportunistic spectrum access (OSA): Upper Confidence Bound (UCB) algorithm and Weight Driven (WD) algorithm. This paper also introduces two new metrics in order to evaluate the machine learning algorithm performance for CR: effective cumulative regret and percentage of successful trials. They provide a fair evaluation means for CR performance.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Communications, ICC 2014
PublisherIEEE
Pages1508-1513
Number of pages6
ISBN (Print)9781479920037
DOIs
Publication statusPublished - 2014
EventIEEE International Conference on Communications 2014 - Sydney, NSW, Australia
Duration: 10 Jun 201414 Jun 2014

Conference

ConferenceIEEE International Conference on Communications 2014
Abbreviated titleICC 2014
CountryAustralia
CitySydney, NSW
Period10/06/1414/06/14

Keywords

  • Cognitive radio
  • MAB
  • machine learning
  • opportunistic spectrum access
  • UCB

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Robert, C., Moy, C., & Wang, C. X. (2014). Reinforcement learning approaches and evaluation criteria for opportunistic spectrum access. In 2014 IEEE International Conference on Communications, ICC 2014 (pp. 1508-1513). IEEE. https://doi.org/10.1109/ICC.2014.6883535