Distributed Cooperative Spectrum Sensing in Mixture of Large and Small Scale Fading Channels

In this paper, we study distributed energy detectors for spectrum sensing in cognitive radio networks. We treat the primary signal as unknown and deterministic with a known power and the channels as random with a Nakagami-Lognormal mixture distribution. In other words, the channel gains are modeled as product of two terms corresponding to the small and large-scale fadings. The small-scale terms are all Nakagami and independent for all sensors. Whereas for large-scale fading, sensors in each cluster are assumed to have identical Lognormal terms. We also assume that different clusters have independent large-scale fading. Assuming that the total energies from clusters are received, we investigate four distributed detection strategies: 1) Equal Gain Combining detector (EGCD), 2) Neyman-Pearson detector (NPD) or Likelihood Ratio detector (LRD) for known channel gains, 3) Generalized Likelihood Ratio detector (GLRD) for unknown gains, and 4) Average Likelihood Ratio detector (ALRD) given a priori distribution of channel gains. The simulation results show that the effects of both small and large-scale fadings on sensing performance should be considered simultaneously and confirm that a priori statistical information about large and small-scale fading used in ALRD result in performance improvement compared to EGCD and GLRD.