ML modulation classification in presence of unreliable observations

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DÜLEK B.

ELECTRONICS LETTERS, cilt.52, sa.18, ss.1569-1570, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52 Sayı: 18
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1049/el.2016.1611
  • Dergi Adı: ELECTRONICS LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1569-1570
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Joint detection and maximum-likelihood (ML) classification of linear modulations based on observations collected over an unknown flat-fading additive Gaussian noise channel is considered. It is assumed that some of the observations are subject to data failures, in which case the receiver acquires only noise. Expectation-maximisation algorithm is employed to compute the ML estimates of the unknown channel parameters, which are then substituted into the corresponding likelihood expressions to perform hypothesis testing. Numerical simulations indicate that a suboptimal classifier, which is ignorant to data failures, exhibits extremely poor performance in the presence of high failure rates. On the other hand, the proposed classifier demonstrates comparable performance with that of the clairvoyant classifier which is assumed to have a priori knowledge of the channel parameters and data failures.

Joint detection and maximum-likelihood (ML) classification of linear modulations based on observations collected over an unknown flat-fading additive Gaussian noise channel is considered. It is assumed that some of the observations are subject to data failures, in which case the receiver acquires only noise. Expectation–maximisation algorithm
is employed to compute the ML estimates of the unknown channel parameters, which are then substituted into the corresponding likelihood expressions to perform hypothesis testing. Numerical simulations indicate that a suboptimal classifier, which is ignorant to data failures, exhibits extremely poor performance in the presence of high failure rates. On the other hand, the proposed classifier demonstrates comparable performance
with that of the clairvoyant classifier which is assumed to have a priori knowledge of the channel parameters and data failures.