Abstract
As a core technology of cognitive radio, the spectrum sensing method requires accurate perform and fast sensing in low signal-to-noise ratio (SNR) environment. In this paper, an energy-based spectrum sensing method based on quantum-behaved particle swarm optimization (QPSO) and tri-stable stochastic resonance (TSR) is proposed. In this method, TSR was adopted to achieve greater SNR output than bi-stable stochastic resonance (BSR). To overcome the shortage that TSR can only set fixed parameters by experience, QPSO was adopted to adjust parameters of TSR adaptively when the noise power was fixed. Simulation results of SNR and spectrum show that the convergence, the output SNR in convergence status of QPSO–TSR performs better than QPSO–BSR under low input SNR, while QPSO–TSR has a slight delay due to its optimization searching process. The simulation result of receiver operating characteristic curves show that the proposed scheme has an excellent convergence performance and can improve the detection probability.
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