A Novel Multi-Objective Genetic Algorithm for Cognitive Radio System

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 207)

Abstract

In this paper, we use genetic algorithm to realize adaptive algorithm chromium engine. On the basis of comparative analysis of chaotic, genetic algorithm and NSGA method and weighted-sum, we can dominate sort of pare to way to decide how to allocate the structure and parameters of the system and the level of CR resource allocation and avoid interference. In addition, 2d chromosomal structures is proposed and implemented to improve performance and the speed of the algorithm. Results the two algorithm comparison, makes a good looking for CR performance by evolution algorithm.

Keywords

Genetic algorithm Cognitive radio NSGA Adaptive cognitive radio engine 

References

  1. 1.
    Mitola J III, Maguire G Jr (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6(4):13–18CrossRefGoogle Scholar
  2. 2.
    Ldkail J (2002) Report of the spectrum efficiency working group. Fed Commun Commision 2:100–135Google Scholar
  3. 3.
    Broderson RW, Wolisz A, Cabric D, Mishra SM, Willkomm D (2004) A cognitive radio approach for usage of virtual unlicensed spectrum. In IST Mobile Summit 33:89–96Google Scholar
  4. 4.
    Newman TR (2007) Cognitive engine implementation for wireless multicarrier transceivers. Wirel Commun Mobile Comput 56:1129–1142CrossRefGoogle Scholar
  5. 5.
    Srinivas N, Dev K (1994) Multi-objective optimization using no dominated sorting in genetic algorithms. Evol Comput 2(3):221–248CrossRefGoogle Scholar
  6. 6.
    Tang KS (1996) Genetic algorithms and their applications. IEEE Sig Process Mag 43:110–125Google Scholar
  7. 7.
    Tang KS, Man KF, Chen G, Kwong S (2001) GA-optimized fuzzy PD+I controller for nonlinear systems. Ind Electron Soc 1:718–723Google Scholar
  8. 8.
    Perez-Neira AI, Bas J, Lagunas MA (2005) A neuro-fuzzy system for source location and tracking in wireless communications. Sig Commun Technol 77:119–148Google Scholar
  9. 9.
    Aliealiy M (2010) SEAMCAT Handbook. 77:39–70Google Scholar
  10. 10.
    Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: motivations, analysis, and first results. Clearing house Genet Algorithms 66:124–133Google Scholar
  11. 11.
    Coello CA (2000) An updated survey of ga-based multi-objective optimization techniques. ACM Comput Surv 32(2):220–300CrossRefGoogle Scholar
  12. 12.
    Chen S, Wyglinski AM (2009) Cognitive radio-enable distributed cross-layer optimization via genetic algorithms. : In proceeding of the 4th international conference on CROWNCOM 30:99–120Google Scholar
  13. 13.
    Sklar B (2001) Digital communications fundamentals and applications. Pract-Hall Int 24:155–182Google Scholar
  14. 14.
    Deb K, Pratap A, Agarwal S, Meyarivan TA (2002) Fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Changchun Institute of TechnologyChangchunChina

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