High-Diversity Cooperative Spectrum Sensing in Cognitive Radio Networks

Abstract

This paper develops a cooperative scheme among cognitive radios (CRs) to increase the spectrum sensing performance. CRs adaptively transmit local binary decisions to a fusion center (FC), which in turn uses a simple maximum-likelihood detector to decide the presence of absence of the primary user (PU). The diversity order of the probability of false alarm and miss-detection is studied under both Neyman-Pearson and minimum-error-probability criteria. In particular, it is shown that under the Neyman-Pearson criterion, the probability of miss- detection can achieve diversity order up to the number of CRs for a bounded probability of false alarm. Under the minimum-error-probability criterion, both the probability of false alarm and miss-detection can achieve diversity order up to the number of CRs. Compared to existing cooperative sensing approaches, this novel scheme is robust to fading effects in both PU-to-CR and CR-to-FC links. Simulated tests verify the analytical claims, showing considerable performance gains compared to non-cooperative and non-adaptive hard-decision schemes.