Abstract
In this paper, collaborate spectrum sensing with incomplete information is considered, which fully exploits the sparsity of active radios. In the traditional collaborate spectrum sensing, the fusion center is applied to determine the locations of idle channels and a lot of sensing information is required to make decision. Too much information is the bottleneck of the collaborative spectrum sensing applications. Here, two novel efficient algorithms based on matching pursuit are presented. Fusion center is also adopted, but the proposed methods can greatly reduce the quantity of necessary sensing information and obtain better detection performance. Simulations have shown that one has much faster sensing speed and the other obtains better detection accuracy. For 20% primary users are active, the detection probability based on the first algorithm can reach 100% only requiring 64% measurements of the traditional collaborative spectrum sensing.
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Jiao, L.C., Chen, J., Wu, J. et al. Efficient Collaborative Spectrum Sensing with Low Sample Rate. Wireless Pers Commun 67, 923–936 (2012). https://doi.org/10.1007/s11277-011-0419-z
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DOI: https://doi.org/10.1007/s11277-011-0419-z