Spectrum Sensing Algorithm Based on Real-time Likelihood Ratio in Cognitive Networks

In order to improve the spectrum sensing performance in the fading channel, a real-time likelihood ratio-based sensing algorithm is proposed. In the algorithm, we set the decision threshold according to the weighted factors of false alarm probability and detection probability. And then, we count the likelihood ratio of the cyclic spectrum peak of the signal received, which is based on the historical information of cognitive networks and the real-time information of the primary user and channel. We compare the likelihood ratio with the decision threshold to make the decision whether the primary user is present or absent. It overcomes the effect of channel fading on the spectrum sensing, and effectively trades off between the false alarm probability and detection probability of cognitive networks. Simulation results show that the algorithm proposed has about 3 dB signal-noise-ratio advantage over the conventional one in the fading channel.