A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network

Md Kamal Hossain, Ayman A. El-Saleh, Mahamod Ismail

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The main obstacle for a cognitive radio (CR) is to detect the presence of primary users (PUs) reliably in order to reduce the interference to licensed communications. Genetic algorithms (GAs) are well suited for CR optimization problems to improve spectrum utilization by manipulating its unused portions and offer a solution to the apparent spectrum underutilization problem. In this paper, we present binary genetic algorithm (BGA) and continuous genetic algorithm (CGA)-based soft decision fusion (SDF) scheme for cooperative spectrum optimization in cognitive radio network (CRN). Then BGA-based optimization engine is implemented at the fusion center (FC) of a linear SDF scheme to optimize the linear coefficient vector with other conventional methods. The comparison between BGA and CGA shows that BGA performs better than CGA. Then the results and analysis confirm that BGA is efficient and stable and it outperforms conventional natural deflection coefficient- (NDC), modified deflection coefficient- (MDC-), maximal ratio combining- (MRC-) and equal gain combining- (EGC-) based SDF schemes as well as the OR-rule based hard decision fusion (HDF) scheme. It also verifies that the computation complexity of the BGA method meets the real time requirements of cognitive radio spectrum sensing.

Original languageEnglish
Title of host publicationProceedings - 2011 IEEE Student Conference on Research and Development, SCOReD 2011
Pages259-264
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 IEEE Student Conference on Research and Development, SCOReD 2011 - Cyberjaya
Duration: 19 Dec 201120 Dec 2011

Other

Other2011 IEEE Student Conference on Research and Development, SCOReD 2011
CityCyberjaya
Period19/12/1120/12/11

Fingerprint

Cognitive radio
Genetic algorithm
Fusion
Coefficients
Communication
Optimization problem
Rule-based
Interference

Keywords

  • Binary Genetic Algorithm
  • Cognitive Radio
  • Continuous Genetic Algorithm
  • Soft Decision Fusion
  • Spectrum Sensing

ASJC Scopus subject areas

  • Management Science and Operations Research

Cite this

Hossain, M. K., El-Saleh, A. A., & Ismail, M. (2011). A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network. In Proceedings - 2011 IEEE Student Conference on Research and Development, SCOReD 2011 (pp. 259-264). [6148747] https://doi.org/10.1109/SCOReD.2011.6148747

A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network. / Hossain, Md Kamal; El-Saleh, Ayman A.; Ismail, Mahamod.

Proceedings - 2011 IEEE Student Conference on Research and Development, SCOReD 2011. 2011. p. 259-264 6148747.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hossain, MK, El-Saleh, AA & Ismail, M 2011, A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network. in Proceedings - 2011 IEEE Student Conference on Research and Development, SCOReD 2011., 6148747, pp. 259-264, 2011 IEEE Student Conference on Research and Development, SCOReD 2011, Cyberjaya, 19/12/11. https://doi.org/10.1109/SCOReD.2011.6148747
Hossain MK, El-Saleh AA, Ismail M. A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network. In Proceedings - 2011 IEEE Student Conference on Research and Development, SCOReD 2011. 2011. p. 259-264. 6148747 https://doi.org/10.1109/SCOReD.2011.6148747
Hossain, Md Kamal ; El-Saleh, Ayman A. ; Ismail, Mahamod. / A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network. Proceedings - 2011 IEEE Student Conference on Research and Development, SCOReD 2011. 2011. pp. 259-264
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