A Parallel Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio networks, spectrum sensing is a crucial component in the discovery of spectrum opportunities for secondary systems (or unlicensed systems). The performance of spectrum sensing is characterized by both accuracy and efficiency. Currently, significant research effort has been made on improving the sensing accuracy. Several exemplary techniques include energy detectors, feature detectors, and cooperative sensing. In these schemes, either one or multiple secondary users (SUs) perform sensing on a single and the same channel during each sensing period. This strategy on simultaneously sensing a single channel by several SUs may limit the sensing efficiency to a large extent. In this paper, we propose a new parallel spectrum sensing. In this scheme, several SUs are optimally selected to perform sensing. During a sensing period, each of the selected SUs senses a different channel. As a consequence, multiple channels can be simultaneously sensed in one sensing period, and the sensing efficiency is envisioned to improve significantly. An analytical model is presented to investigate the tradeoff between the transmitted data and the sensing overhead. A throughput maximization problem is formulated to find key design parameters: the number of SUs that perform parallel sensing and the threshold in stopping the sensing. Both saturation and nonsaturation situations are investigated with respect to throughput, transmission gain, overhead, and delay. Numerical examples demonstrate that our proposed scheme is able to achieve substantially higher throughput and lower delay, as compared with existing mechanisms.