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Optimal training sequence length for soft iterative channel estimation

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3 Author(s)
Hadj-Kacem, I. ; Lab. LETI, ENIS, Sfax, Tunisia ; Sellami, N. ; Kamoun, L.

In this paper, 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. The initial channel estimate based on the training sequence is iteratively improved using the expectation maximization (EM) algorithm. In order to "unbias" the EM estimates, a modified version of the EM estimator is used. 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.

Published in:

Electronics, Circuits, and Systems, 2009. ICECS 2009. 16th IEEE International Conference on

Date of Conference:

13-16 Dec. 2009