DocumentCode :
414927
Title :
Estimation of channel statistics for iterative detection of OFDM signals
Author :
Morelli, M. ; Sanguinetti, L.
Author_Institution :
Dept. of Inf. Eng., Pisa Univ., Italy
Volume :
2
fYear :
2004
fDate :
20-24 June 2004
Firstpage :
847
Abstract :
Maximum likelihood sequence detection for OFDM transmissions over unknown multipath fading channels is a challenging task for lack of efficient methods to maximize the likelihood function. A feasible solution to this problem based on the expectation-maximization (EM) algorithm has been recently proposed in the context of space-time block-coded OFDM. The resulting detector operates iteratively and exploits knowledge of the channel statistics and the operating signal-to-noise ratio (SNR). In this work we address the problem of estimating the above quantities in a recursive fashion. Simulations indicate that the EM detector employing the estimated SNR and channel statistics has better performance than other existing schemes that operate in a mismatched mode. Also, the performance loss with respect to a system with perfect channel knowledge is negligible at SNR values of practical interest.
Keywords :
OFDM modulation; block codes; channel coding; channel estimation; fading channels; iterative methods; maximum likelihood detection; multipath channels; recursive estimation; space-time codes; OFDM signals; channel estimation; expectation-maximization algorithm; iterative detection; maximum likelihood sequence detection; multipath fading channels; orthogonal frequency division multiplexing; recursive estimation; signal-to-noise ratio; space-time block-coded OFDM; Detectors; Fading; Iterative algorithms; Maximum likelihood detection; Maximum likelihood estimation; OFDM; Recursive estimation; Signal detection; Signal to noise ratio; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, 2004 IEEE International Conference on
Print_ISBN :
0-7803-8533-0
Type :
conf
DOI :
10.1109/ICC.2004.1312621
Filename :
1312621
Link To Document :
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