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Abstract: Multi-stage sensing is a novel concept that refers to a general class ofspectrum sensing algorithms that divide the sensing process into a number ofsequential stages. The number of sensing stages and the sensing technique perstage can be used to optimize performance with respect to secondary userthroughput and the collision probability between primary and secondary users.So far, the impact of multi-stage sensing on network throughput and collisionprobability for a realistic network model is relatively unexplored. Therefore,we present the first analytical framework which enables performance evaluationof different multi-channel multi-stage spectrum sensing algorithms forOpportunistic Spectrum Access networks. The contribution of our work lies instudying the effect of the following parameters on performance: number ofsensing stages, physical layer sensing techniques and durations per each stage,single and parallel channel sensing and access, number of available channels,primary and secondary user traffic, buffering of incoming secondary usertraffic, as well as MAC layer sensing algorithms. Analyzed performance metricsinclude the average secondary user throughput and the average collisionprobability between primary and secondary users. Our results show that when theprobability of primary user mis-detection is constrained, the performance ofmulti-stage sensing is, in most cases, superior to the single stage sensingcounterpart. Besides, prolonged channel observation at the first stage ofsensing decreases the collision probability considerably, while keeping thethroughput at an acceptable level. Finally, in realistic primary user trafficscenarios, using two stages of sensing provides a good balance betweensecondary users throughput and collision probability while meeting successfuldetection constraints subjected by Opportunistic Spectrum Access communication.



Author: Wesam Gabran, Przemysław Pawełczak, Danijela Čabrić

Source: https://arxiv.org/







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