This paper studies the problem of developing an efficient signal sampling framework for distributed blind spectrum sensing, together with the corresponding local cognitive radio (CR) decision and fusion rules. A novel collaborative sampling and binary output (CSBO) generating scheme is proposed to overcome the disadvantage of traditional energy detectors for blind spectrum sensing, in which the signal-to-noise ratio (SNR) and level of noise power at the CRs are unknown and thus the decision threshold of local energy detectors cannot be specified. In designing the fusion rule in conjunction with the proposed CSBO scheme, both fixed-sample-size tests and sequential tests are considered. It is theoretically shown that the proposed CSBO scheme outperforms traditional blind cooperative energy detectors with uniform sampling schemes in terms of its ability to increase the amount of information acquired under a given number of samples. Simulation results confirm the theoretical analyses, and demonstrate that, compared with the fixed-sample-size test, the sequential test requires fewer observations on average to achieve comparable detection performance. The reduction in the expected sample size needed in the sequential CSBO test when compared to the fixed-sample-size CSBO can reach 50% when primary signal is present.

In this paper, we study the problem of blind spectrum sensing by exploring signal sampling at each cognitive radio (CR) in a distributed cognitive radio network. Specifically, a new cooperative sampling scheme is proposed to deal with the challenge of unknown signal-to-noise ratio and level of noise power at each CR, which is a more realistic assumption in distributed spectrum sensing. We prove that the observations acquired by the proposed collaborative sampling are informative from an information theoretic perspective. Simulation results also demonstrate better receiver operating characteristics (ROC) of the proposed sampling scheme than the traditional uniform sampling scheme.