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Wireless Personal Communications

, Volume 57, Issue 1, pp 73–87 | Cite as

Cognitive Radio with Reinforcement Learning Applied to Multicast Downlink Transmission with Power Adjustment

  • Mengfei YangEmail author
  • David Grace
Article

Abstract

This paper shows how channel assignment in multicast terrestrial communication systems with distributed channel occupancy detection can be improved using intelligence based on reinforcement learning and transmitter power adjustment. It is shown how such schemes greatly reduce the number of reassignments and improve the dropping probability, at the expense of increased blocking. It is found that using different minimum quality of service threshold percentages can partly control and improve the performance, in place of the more traditional SINR threshold levels. The paper also shows how a power adjustment technique is developed which significantly reduces the level of overlap between adjacent base stations, and further reduces interference and transmitter power.

Keywords

Cognitive radio Reinforcement learning Multicast Distributed sensing Power adjustment 

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References

  1. 1.
    Mitola J. III, Maguire G. Q. Jr. (1999) Cognitive radio: Making software radios more personal. IEEE Personal Communications 6(4): 13–18 (see also IEEE Wireless Communications)CrossRefGoogle Scholar
  2. 2.
    Alsarhan, & Agarwal, A. (2009, August). Cluster based spectrum management using cognitive radios in wireless mesh network. In COGCOM 2009. San Francisco, USA.Google Scholar
  3. 3.
    Fette B.A. (2006). Cognitive Radio Technology. Elsevier Science and Technology Books, Jordan Hill, OxfordGoogle Scholar
  4. 4.
    Katzela I., Naghshineh M. (1996) Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey. IEEE Personal Communications 3: 10–31CrossRefGoogle Scholar
  5. 5.
    Berlemann, L., Mangold, S., Hiertz, G., & Walke, B. (2006, April). Policy defined spectrum sharing and medium access for cognitive radios. Journal of communications, 1(1), 1–12.Google Scholar
  6. 6.
    Cheng M. M.-L., Chuang J. C.-I. (1996) Performance evaluation of distributed measurement-based dynamic channel assignment in local wireless communications. IEEE Journal on Selected Areas in Communications 14: 698–710CrossRefGoogle Scholar
  7. 7.
    Peha J. M. (2005) Approaches to spectrum sharing. IEEE Communications Magazine 43: 10–12Google Scholar
  8. 8.
    Yang, M., & Grace, D. (2008). Cognitive radio based spectrum assignment for heterogeneous multicast terrestrial communication systems with different transmission rate requirements, Cognitive Radio and Software Defined Radios: Technologies and Techniques, IET Seminar on 18th Sep. 2008.Google Scholar
  9. 9.
    Yang, M., & Grace, D. (2009, June). Cognitive radio with reinforcement learning applied to multicast terrestrial communication systems. In CROWNCOM. Hannover, German.Google Scholar
  10. 10.
    Zhao, J., Zheng, H., & Yang, G. H. (2005). Distributed coordination in dynamic spectrum allocation networks. In IEEE DySPAN (pp. 259–268).Google Scholar
  11. 11.
    Weiss, T. A., Hillenbrand, J., Krohn, A., & Jondral, F. K. (2003). Efficient signaling of spectral resources in spectrum pooling systems. In 10th Symposium on Communications Vehicular Technology (SCVT).Google Scholar
  12. 12.
    Saunders, S. R. (reprinted in August, 2004). Antennas and Propagation for wireless communication systems. University of Surrey, Guilford, UK, John Wiley & Son Ltd.Google Scholar
  13. 13.
    Harrold, T. J., Faris, P. C., & Beach, M. A. (2008). Distributed Spectrum Detection Algorithms for Cognitive, Cognitive Radio and Software Defined Radios: Technologies and Techniques, IET Seminar on 18th Sep. 2008.Google Scholar
  14. 14.
    Kaelbling L. P., Littman M. L., Moore A. W. (1996) Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4: 237–285Google Scholar
  15. 15.
    Sutton R. S., Barto A. G. (1998) Reinforcement learning: An introduction. The MIT Press, CambridgeGoogle Scholar
  16. 16.
    Mitchell T. M. (1997) Machine learning. McGraw-Hill, New YorkzbMATHGoogle Scholar
  17. 17.
    Yang, M., & Grace, D. (2009, August). Cognitive radio with reinforcement learning applied to multicast downlink transmission and distributed occupancy detection. In COGCOM. San Francisco, USA.Google Scholar
  18. 18.
    Jiang, T., Grace, D., & Liu, Y. (2008). Cognitive radio spectrum sharing schemes with reduced spectrum sensing requirements, IET Seminar on 18th Sep. 2008.Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  1. 1.Communication Research Group, Department of ElectronicsUniversity of YorkYorkUK

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