Throughput and Collision Analysis of Multichannel Multistage Spectrum Sensing Algorithms

Multistage sensing is a novel concept that refers to a general class of spectrum sensing algorithms that divide the sensing process into a number of sequential stages. The number of sensing stages and the sensing technique per stage can be used to optimize the performance with respect to secondary user (SU) throughput and the collision probability between primary user (PU) and SU. So far, the impact of multistage sensing on network throughput and collision probability for a realistic network model has been relatively unexplored. Therefore, we present the first analytical framework that enables the performance evaluation of different multichannel multistage spectrum sensing algorithms for opportunistic spectrum access (OSA) networks. The contribution of this paper lies in studying the effect of the following parameters on performance: number of sensing stages, physical layer sensing techniques and durations per each stage, single and parallel channel sensing and access, number of available channels, PU and SU traffic, buffering of incoming SU traffic, and medium access control layer sensing algorithms. The analyzed performance metrics include the average SU throughput and the average collision probability between PU and SU. Our results show that when the probability of PU misdetection is constrained, the performance of multistage sensing is, in most cases, superior to the single-stage sensing counterpart. In addition, prolonged channel observation at the first stage of sensing considerably decreases the collision probability while keeping the throughput at an acceptable level. Finally, in realistic PU traffic scenarios, using two stages of sensing provides a good balance between SU throughput and collision probability while meeting successful detection constraints subjected by OSA communication.