Hard decision Cooperative Spectrum Sensing based on estimating the noise uncertainty factor

Spectrum Sensing (SS) comprises the most important component in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed as an effective approach to improve detection performance in fading environments. This paper introduces an efficient energy detection based hard decision CSS algorithm to alleviate the noise uncertainty effect. In the proposed algorithm, the decision threshold is dynamically switched between two levels based on a prior prediction of the Primary User (PU) activity. The two threshold levels are evaluated using an estimated value of the noise uncertainty factor to maximize the probability of detection and minimize the probability of false alarm. The proposed algorithm is studied theoretically to deduce the enhanced detection and false alarm probabilities. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high potency of the proposed scheme compared to the conventional CSS using different fusion rules.

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