Spectrum sensing algorithm based on stochastic resonance and non-central F-distribution - Yuanmeng FAN; Shunlan LIU (Dianxin kexue)
To solve the problem that the detection probability of the spectrum sensing algorithm is low and the number of samples required for detection is large at low signal-to-noise ratio (SNR), a spectrum sensing algorithm based on stochastic resonance and non-central F-distribution (SRNF) was proposed.By introducing direct-current stochastic resonance noise, the system model of SRNF was established, and the expression of test statistic, false alarm probability and detection probability, and the expression of decision threshold obeying non-central F-distribution were deduced, and the optimal stochastic resonance noise parameter was solved by numerical method.The simulation results show that the detection performance of the proposed SRNF algorithm is better than that of energy detection (ED) algorithm and blind spectrum sensing based on F-distribution (BSF) algorithm at a low SNR.When the false alarm probability is 5%, the SNR is -12 dB, and the number of samples is 200, the detection probability of the proposed algorithm is 95%, which is 34% and 67% higher than BSF algorithm and ED algorithm, respectively.When the SNR is -12 dB, and the detection probability reaches 95%, the number of samples required by the proposed algorithm is 210, which saves 340 samples compared to the BSF algorithm.Furthermore, the proposed algorithm is less affected by noise uncertainty than ED algorithm.