Threshold Optimization of a Finite Sample Based Cognitive Radio Network Using Energy Detector

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In this paper, the authors consider a cognitive radio network containing two Cognitive Radios (CRs) and one primary user. The CRs utilize finite number of received data samples for estimating the energy of the primary signals and forward these energy estimates to a Fusion Center (FC). The FC combines the energy estimates and utilizes a global threshold based on the exact knowledge of local thresholds of the CRs for determining the presence or absence of the primary signal. They propose selective and semi-selective soft combining schemes for this set-up. For the proposed schemes, they derive the total probability of error of detecting a spectrum hole.