6. Machine Learning Applied to Cognitive Communications

  1. David Grace2 and
  2. Honggang Zhang3
  1. Aimilia Bantouna,
  2. Kostas Tsagkaris,
  3. Vera Stavroulaki,
  4. Panagiotis Demestichas and
  5. Giorgos Poulios

Published Online: 24 JUL 2012

DOI: 10.1002/9781118360316.ch6

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

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

How to Cite

Bantouna, A., Tsagkaris, K., Stavroulaki, V., Demestichas, P. and Poulios, G. (2012) Machine Learning Applied to Cognitive Communications, 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.ch6

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. Department of Digital Systems, Telecommunication Networks and Integrated Services (TNS) Laboratory, University of Piraeus, Piraeus, Greece

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:

  • distributed artificial intelligence application;
  • machine learning in cognitive communications;
  • CR systems, raw network, service/user data in automated ways;
  • wireless communication networks and CRSs;
  • fuzzy logic and genetic algorithms;
  • neural networks and back propagation, flexibility for time-series;
  • conditional probabilities' update, and ‘distance’ of measurements;
  • SOMs, unsupervised learning techniques based on NNs;
  • learning capabilities, and cognitive systems;
  • machine learning in CRSs, data store minimization

Summary

This chapter contains sections titled:

  • Introduction

  • State of the Art

  • Learning Techniques

  • Advantages and Disadvantages of Applying Machine Learning to Cognitive Radio Networks

  • Conclusions

  • Acknowledgement

  • References