Training sequence length optimization for a turbo-detector using decision-directed channel estimation

Authors:
Imed Hadj Kacem;Noura Sellami;Inbar Fijalkow;Aline Roumy
Affiliations:
Laboratoire d'Electronique et des Technologies de l'Information (LETI), ENIS, Sfax, Tunisia and ETIS, UMR, CNRS, ENSEA, University Cergy-Pontoise, Cergy, France;Laboratoire d'Electronique et des Technologies de l'Information (LETI), ENIS, Sfax, Tunisia;ETIS, UMR, CNRS, ENSEA, University Cergy-Pontoise, Cergy, France;Institut National de Recherche en Informatique et Automatique (INRIA), Rennes Cedex, France
Venue:
Research Letters in Communications - Regular issue
Year:
2008

Citing 4
Cited 0

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A Proof of Convergence of the MAP Turbo-Detector to the AWGN Case

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Abstract

We consider the problem of optimization of the training sequence length when a turbo-detector composed of a maximum a posteriori (MAP) equalizer and a MAP decoder is used. At each iteration of the receiver, the channel is estimated using the hard decisions on the transmitted symbols at the output of the decoder. The optimal length of the training sequence is found by maximizing an effective signal-to-noise ratio (SNR) taking into account the data throughput loss due to the use of pilot symbols.