Cooperative spectrum sensing optimization under different system constraints

This paper proposes three optimization solutions for hard cooperative sensing by taking into account different system constraints. Considering the global detection performance of the cooperative system, different methods for optimizing the detection are provided. These methods depend on both local and global false alarm probabilities but they also require the knowledge of individual local detection probabilities. A second part of this paper is therefore dedicated to a novel SNR prediction method, which is necessary for estimating the local detection probabilities used in the optimization process. This work clearly shows that depending on different local or global detection constraints, the result of the optimization is different.