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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | El-Saleh, A.A. Ismail, M. Akbari, M. Manesh, M.R. Zavareh, S.A.R.T. |
| Copyright Year | 2012 |
| Description | Author affiliation: Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia (Ismail, M.) || Faculty of Engineering & Built Environment, University Kebangsaan Malaysia, Bangi, Malaysia (El-Saleh, A.A.; Akbari, M.; Manesh, M.R.; Zavareh, S.A.R.T.) |
| Abstract | Weighting the coefficients vector is the principal factor influencing the detection performance of cognitive radio networks that uses soft-detection fusion (SDF) based cooperative spectrum sensing. Maximal ratio combining- (MRC-), equal gain combining- (EGC-) and continuous genetic algorithm- (CGA-) based SDF are well suited for optimizing the detection performance and thus ensure safe access of spectrum by CR users. However the mentioned methods suffer from slow convergence and/or sub-optimality. In this paper, the use of particle swarm optimization (PSO) algorithm under MINI-MAX criterion is proposed to optimize the weighting coefficients vector so that the total probability of decision error is minimized. The performance of the PSO-based proposed method is examined and compared with GA-based technique as well as other conventional SDF schemes through computer simulations. Numerical results confirm the effectiveness of the proposed method. |
| Starting Page | 877 |
| Ending Page | 881 |
| File Size | 212102 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467304788 |
| e-ISBN | 9781467304795 |
| DOI | 10.1109/ICCCE.2012.6271342 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-07-03 |
| Publisher Place | Malaysia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | SDF FCC cooprative specrum sensing Vectors Sensors Cognitive radio CGA Particle swarm optimization PSO Convergence |
| Content Type | Text |
| Resource Type | Article |
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