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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...
In decentralized cooperative sensing for cognitive radio, a few secondary users (SUs) sense the spectrum, process individual observation and then pass quantized data to the fusion center (FC), where the decision on signal present hypothesis or signal absent hypothesis is made. When the reporting channels between SUs and the FC are bandlimited and error prone, a quantization scheme was proposed recently...
In this paper, we consider decentralized detection where the transmission of local soft-decisions of the secondary users (SUs) to the fusion center (FC) is both rate-limited and error-prone. The quantized data to be sent from SUs should not only carry the local information but also need to be resilient to channel errors. With the assumption of independent and identical secondary user observations...
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