8. Reinforcement Learning-Based Cognitive Radio for Open Spectrum Access

  1. David Grace2 and
  2. Honggang Zhang3
  1. Tao Jiang1 and
  2. David Grace1,2

Published Online: 24 JUL 2012

DOI: 10.1002/9781118360316.ch8

Cognitive Communications: Distributed Artificial Intelligence (DAI), Regulatory Policy & Economics, Implementation

Cognitive Communications: Distributed Artificial Intelligence (DAI), Regulatory Policy & Economics, Implementation

How to Cite

Jiang, T. and Grace, D. (2012) Reinforcement Learning-Based Cognitive Radio for Open Spectrum Access, in Cognitive Communications: Distributed Artificial Intelligence (DAI), Regulatory Policy & Economics, Implementation (eds D. Grace and H. Zhang), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9781118360316.ch8

Editor Information

  1. 2

    Department of Electronics, University of York, UK

  2. 3

    Department of Information Science and Electronic Engineering, Zhejiang University, China

Author Information

  1. 1

    Department of Electronics, University of York, Heslington, UK

  2. 2

    Department of Electronics, University of York, UK

Publication History

  1. Published Online: 24 JUL 2012
  2. Published Print: 31 AUG 2012

ISBN Information

Print ISBN: 9781119951506

Online ISBN: 9781118360316

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Keywords:

  • reinforcement learning-based CR for open spectrum access;
  • open spectrum access and wireless communications;
  • learning-based CRs and behaviour, in open spectrum scenario;
  • RL-based spectrum sharing in open spectrum bands;
  • CR, learning agent, communication resource access with value function;
  • RL-based spectrum sharing algorithm;
  • exploration control techniques for CRs;
  • RL to CR, for users in avoiding hidden terminals;
  • CDF of system blocking probability of RL-based;
  • efficient autonomous spectrum partitioning, in open spectrum scenario

Summary

This chapter contains sections titled:

  • Open Spectrum Access

  • Reinforcement Learning-Based Spectrum Sharing in Open Spectrum Bands

  • Exploration Control and Efficient Exploration for Reinforcement Learning-Based Cognitive Radio

  • Conclusion

  • References