Title :
Bayesian MLSD for multipath Rayleigh fading channels
Author :
Roufarshbaf, Hossein ; Nelson, Jill K.
Author_Institution :
Dept. of Electr. & Comput. Eng., George Mason Univ., Fairfax, VA
fDate :
March 31 2008-April 4 2008
Abstract :
We propose a tree-search based Bayesian approach to blind maximum likelihood sequence detection (MLSD) of convolutionally encoded data transmitted over a multipath Rayleigh fading channel. In deriving the path metric for searching the channel-code tree, the proposed algorithm incorporates a forgetting factor matched to the time variation of the channel to generate accurate estimates of the correlation across the transmitted and received data. In addition, an augmented metric is presented to address the challenge of unknown channel order in time-varying systems. Simulation results show that the proposed algorithm can achieve significant improvement in bit error rate over competing schemes, even when channel order information is unavailable at the receiver.
Keywords :
Bayes methods; Rayleigh channels; convolutional codes; maximum likelihood detection; multipath channels; Bayesian approach; augmented metric; blind maximum likelihood sequence detection; channel code tree; convolutionally encoded data; forgetting factor; multipath Rayleigh fading channels; path metric; time varying system; tree search; unknown channel order; AWGN; Bandwidth; Bayesian methods; Channel estimation; Detectors; Fading; Intersymbol interference; Maximum likelihood detection; Maximum likelihood estimation; Time-varying channels; Maximum likelihood detection; equalizers; multipath channels; time-varying channels;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4518242