Joint spectrum sensing for detection of primary users using cognitive relays with evolutionary computing

The spectrum sensing is one of the most challenging research fields in cognitive radio networks (CRN). Joint spectrum sensing not only improves the detection performance of the system, but also increases overall agility of the CRN. This study presents a centralised relay-based spectrum sensing technique using hybrid nature based algorithm that detects the number of active primary users and jointly estimates (amplitude, carrier frequency and direction of arrival of far-field sources impinging on uniform linear arrays (ULA) mounted on each relay. The fitness function is a sum of mean square error and the correlation error. All the individual decisions from each relay are passed on to the fusion centre that provides the final decision on the values of the aforementioned parameters. The validity of the proposed scheme is investigated for various SNR levels along with different sizes of ULAs on each relay.