Locally optimum detection in heavy-tailed noise for spectrum sensing in cognitive radio

摘要: Cognitive radio today is considered to be the solution solving problem of spectrum scarcity. One most important features cognitive sensing. In sensing it sometimes necessary operate in a low SNR regime, which performance classical detectors decreases, especially when they have deal with imprecise knowledge noise characteristics. fact, practical applications, underlying often can not assumed Gaussian. this paper, we design locally optimum detector, assuming that follows Student's t-distribution, very suitable for modeling heavy-tailed noise. We also assume BPSK signals are used by primary users and flat fading channel. Simulation results show our proposed detector outperforms energy all pre-determined scenarios. It more robust dealing outliers than both based on assumption complex Gaussian