We present an analytical framework which enables performance evaluation of different multi-channel multi-stage spectrum sensing algorithms for Opportunistic Spectrum Access networks. The analytical framework models the following : number of sensing stages, physical layer sensing techniques  , single and parallel channel access, primary and secondary user traffic, as well as buffering of incoming secondary user traffic  . Analyzed performance metrics include the average secondary user throughput and the average collision probability between primary and secondary users. Our results show that when the probability of primary user mis-detection is constrained, the performance of multi-stage sensing is  in most cases  superior to the single stage sensing counterpart. Besides, prolonged channel observation at the first stage of sensing decreases the collision probability considerably, while keeping the throughput at an acceptable level. Finally, in realistic primary user traffic scenarios, using two stages of sensing maximizes throughput while meeting constraints subjected by Opportunistic Spectrum Access communication.
Multi-stage 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 performance with respect to secondary user throughput and the collision probability between primary and secondary users. So far, the impact of multi-stage sensing on network throughput and collision probability for a realistic network model is relatively unexplored. Therefore, we present the first analytical framework which enables performance evaluation of different multi-channel multi-stage spectrum sensing algorithms for Opportunistic Spectrum Access networks. The contribution of our work 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, primary and secondary user traffic, buffering of incoming secondary user traffic , as well as MAC layer sensing algorithms . Analyzed performance metrics include the average secondary user throughput and the average collision probability between primary and secondary users. Our results show that when the probability of primary user mis-detection is constrained, the performance of multi-stage sensing is , in most cases , superior to the single stage sensing counterpart. Besides, prolonged channel observation at the first stage of sensing decreases the collision probability considerably, while keeping the throughput at an acceptable level. Finally, in realistic primary user traffic scenarios, using two stages of sensing provides a good balance between secondary users throughput and collision probability while meeting successful detection constraints subjected by Opportunistic Spectrum Access communication.
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We present an
Multi-stage 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 performance with respect to secondary user throughput and the collision probability between primary and secondary users. So far, the impact of multi-stage sensing on network throughput and collision probability for a realistic network model is relatively unexplored. Therefore, we present the first
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analytical framework models the following
contribution of our work lies in studying the effect of the following parameters on performance
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and durations per each stage
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access,
sensing and access, number of available channels,
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as well as
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, as well as MAC layer sensing algorithms
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,
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,
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maximizes throughput while meeting
provides a good balance between secondary users throughput and collision probability while meeting successful detection