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Published in:
01-10-2013
Spectrum Sensing for Cognitive Radio Using Binary Particle Swarm Optimization
Published in: Wireless Personal Communications | Issue 4/2013
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Abstract
Spectrum sensing techniques in cognitive radio are the most important issue to exploit the spectrum efficiently. Several techniques have been proposed recently to estimate the dimension of the received signal from which the vacant frequencies can be determined and made available to the secondary users. These techniques have difficulties in low signal to noise ratio and limited sensing interval cases. It is known that the Maximum Likelihood Estimation (MLE) has an outstanding performance in most practical scenarios. In this paper, we present a Maximum Likelihood Estimate (MLE) to detect the number of vacant channels in the spectrum. The resulting MLE estimate posses several minima and maxima, therefore it needs exhaustive search to be determined accurately. To solve the problem, an evolutionary algorithm called Binary Particle Swarm Optimization (BPSO) is proposed. Simulation results have shown significant improvement of the MLE-BPSO estimate over the conventional techniques by 3–5 dB.