Soft-decision based distributed detection over correlated sensing channels

This paper proposes a new framework that performs spectrum sensing in soft decision based distributed detection systems while considering correlated sensors' readings. The main contribution is formulating the problem into a nonlinear integer programming problem for which the genetic algorithm is further applied to find a suboptimal solution. This framework is able to handle both soft and hard decisions while assuming correlation for any number of sensors, and hence largely extends the scenarios being considered in previous works. The results show that our scheme outperforms previous schemes and approaches the centralized detection scheme performance in terms of probability of error.

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