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An Unsupervised LLR Estimation with unknown Noise Distribution

Authors
  • Mestrah, Yasser1
  • Savard, Anne2
  • Goupil, Alban3
  • Gellé, Guillaume3
  • Clavier, Laurent2
  • 1 IMT Lille Douai, IRCICA - USR 3380, University of Reims Champagne-Ardenne, CReSTIC - EA 3804, 50 Avenue Halley, Lille, 59650, France , Lille (France)
  • 2 IMT Lille Douai, Univ. Lille, CNRS, UMR 8520 - IEMN, F-59000, 50 Avenue Halley, Lille, 59650, France , Lille (France)
  • 3 CReSTIC - EA 3804, University of Reims Champagne-Ardenne, Moulin de la Housse, Reims, 51100, France , Reims (France)
Type
Published Article
Journal
EURASIP Journal on Wireless Communications and Networking
Publisher
Springer International Publishing
Publication Date
Jan 29, 2020
Volume
2020
Issue
1
Identifiers
DOI: 10.1186/s13638-019-1608-9
Source
Springer Nature
Keywords
License
Green

Abstract

Many decoding schemes rely on the log-likelihood ratio (LLR) whose derivation depends on the knowledge of the noise distribution. In dense and heterogeneous network settings, this knowledge can be difficult to obtain from channel outputs. Besides, when interference exhibits an impulsive behavior, the LLR becomes highly non-linear and, consequently, computationally prohibitive. In this paper, we directly estimate the LLR, without relying on the interference plus noise knowledge. We propose to select the LLR in a parametric family of functions, flexible enough to be able to represent many different communication contexts. It allows limiting the number of parameters to be estimated. Furthermore, we propose an unsupervised estimation approach, avoiding the need of a training sequence. Our estimation method is shown to be efficient in large variety of noises and the receiver exhibits a near-optimal performance.

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