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Powerful spectrum sensing schemes enable cognitive radios (CRs) to findtransmission opportunities in spectral resources allocated exclusively to theprimary users. In this paper, maximizing the average throughput of a secondaryuser by optimizing its spectrum sensing time is formulated assuming that aprior knowledge of the presence and absence probabilities of the primary usersis available. The energy consumed for finding a transmission opportunity isevaluated and a discussion on the impact of the number of the primary users onthe secondary user throughput and consumed energy is presented. In order toavoid the challenges associated with the analytical method, as a secondsolution, a systematic neural network-based sensing time optimization approachis also proposed in this paper. The proposed adaptive scheme is able to findthe optimum value of the channel sensing time without any prior knowledge orassumption about the wireless environment. The structure, performance, andcooperation of the artificial neural networks used in the proposed method aredisclosed in detail and a set of illustrative simulation results is presentedto validate the analytical results as well as the performance of the proposedlearning-based optimization scheme.

Analytical and Learning-Based Spectrum Sensing Time Optimization in Cognitive Radio Systems