Browsing by Subject "Generic optimization"

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    Optimal stochastic signal design and detector randomization in the Neyman-Pearson framework
    (IEEE, 2012-03) Dülek, Berkan; Gezici, Sinan
    Power constrained on-off keying communications systems are investigated in the presence of stochastic signaling and detector randomization. The joint optimal design of decision rules, stochastic signals, and detector randomization factors is performed. It is shown that the solution to the most generic optimization problem that employs both stochastic signaling and detector randomization can be obtained as the randomization among no more than three Neyman-Pearson (NP) decision rules corresponding to three deterministic signal vectors. Numerical examples are also presented. © 2012 IEEE.