5. Distributed Clustering of Cognitive Radio Networks: A Message-Passing Approach

  1. David Grace2 and
  2. Honggang Zhang3
  1. Kareem E. Baddour,
  2. Oktay Ureten and
  3. Tricia J. Willink

Published Online: 24 JUL 2012

DOI: 10.1002/9781118360316.ch5

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

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

How to Cite

Baddour, K. E., Ureten, O. and Willink, T. J. (2012) Distributed Clustering of Cognitive Radio Networks: A Message-Passing Approach, 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.ch5

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. Communications Research Centre, Ottawa, Ontario, Canada

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:

  • CRN distributed clustering, a message-passing approach;
  • ad hoc CRNs, and application scenarios;
  • coordinations for resilient crosslayer, and no excessive overhead;
  • spectrum, network's topology and multi-hop, for scalability issues;
  • cluster formation process, and network characteristics;
  • clustering in dynamic environments, abstracting network topology;
  • clustering approach, scalable/applicable to node densities and CRN sizes;
  • message-passing clustering based on AP clustering algorithm;
  • improving efficiency of cooperative spectrum sensing;
  • AP sensor selection, K-means algorithm and node locations

Summary

This chapter contains sections titled:

  • Introduction

  • Clustering Techniques for Cognitive Radio Networks

  • A Message-Passing Clustering Approach Based on Affinity Propagation

  • Case Studies

  • Implementation Challenges

  • Conclusions

  • References