Browsing by Subject "Detector randomization"

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    Detector randomization and stochastic signaling for minimum probability of error receivers
    (Institute of Electrical and Electronics Engineers, 2012) Dulek, B.; Gezici, Sinan
    Optimal receiver design is studied for a communications system in which both detector randomization and stochastic signaling can be performed. First, it is proven that stochastic signaling without detector randomization cannot achieve a smaller average probability of error than detector randomization with deterministic signaling for the same average power constraint and noise statistics. Then, it is shown that the optimal receiver design results in a randomization between at most two maximum a-posteriori probability (MAP) detectors corresponding to two deterministic signal vectors. Numerical examples are provided to explain the results.
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    Optimal detector randomization in cognitive radio systems in the presence of imperfect sensing decisions
    (2014) Sezer, A. D.; Gezici, Sinan; Gursoy, M. C.
    In this study, optimal detector randomization is developed for secondary users in a cognitive radio system in the presence of imperfect spectrum sensing decisions. It is shown that the minimum average probability of error can be achieved by employing no more than four maximum a-posteriori probability (MAP) detectors at the secondary receiver. Optimal MAP detectors and generic expressions for their average probability of error are derived in the presence of possible sensing errors. Also, sufficient conditions are presented related to improvements due to optimal detector randomization.
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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.