Joint Detection and Decoding in the Presence of Prior Information With Uncertainty

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Bayram S., DÜLEK B., Gezici S.

IEEE SIGNAL PROCESSING LETTERS, cilt.23, sa.11, ss.1602-1606, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 11
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1109/lsp.2016.2611650
  • Dergi Adı: IEEE SIGNAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1602-1606
  • Anahtar Kelimeler: Bayes, decoding, detection, Neyman-Pearson, NOISE
  • Hacettepe Üniversitesi Adresli: Evet

Özet

An optimal decision framework is proposed for joint detection and decoding when the prior information is available with some uncertainty. The proposed framework provides trade-offs between the average inclusive error probability (computed using estimated prior probabilities) and the worst case inclusive error probability according to the amount of uncertainty while satisfying constraints on the probability of false alarm and the maximum probability of miss-detection. Theoretical results that characterize the structure of the optimal decision rule according to the proposed criterion are obtained. The proposed decision rule reduces to some well-known detectors in the case of perfect prior information or when the constraints on the probabilities of miss-detection and false alarm are relaxed. Numerical examples are provided to illustrate the theoretical results.

An optimal decision framework is proposed for joint detection and decoding when the prior information is available with some uncertainty. The proposed framework provides tradeoffs between the average inclusive error probability (computed using estimated prior probabilities) and the worst case inclusive error probability according to the amount of uncertainty while satisfying constraints on the probability of false alarm and the maximum probability of miss-detection. Theoretical results that characterize the structure of the optimal decision rule according to the proposed criterion are obtained. The proposed decision rule reduces to some well-known detectors in the case of perfect prior information or when the constraints on the probabilities of miss-detection and false alarm are relaxed. Numerical examples are provided to illustrate the theoretical results.