NDLI: Markov Chain Monte Carlo detection over time-varying frequency-selective channels

Content Provider	IEEE Xplore Digital Library
Author	Hong Wan Rong-Rong Chen Jun Won Choi Preisig, J. Farhang-Boroujeny, B.
Copyright Year	2010
Description	Author affiliation: University of Utah, USA (Hong Wan; Rong-Rong Chen; Farhang-Boroujeny, B.) \|\| Woods Hole Oceanographic Institution, USA (Preisig, J.) \|\| Qualcomm Inc., USA (Jun Won Choi)
Abstract	In this paper, we develop a novel list channel refinement (LCR) based Markov Chain Monte Carlo (MCMC) detector for time-varying frequency-selective channels. Information fed back from the channel decoder is exploited for both data detection and channel estimation. The LCR-MCMC detector adopts parallel Gibbs samplers to find a list of mostly likely transmitted sequences as well as matching channel impulse response (CIR). It then computes the log-likelihood ratio (LLR) of the transmitted bits based on this list. Furthermore, the estimated CIR is refined based on the LLRs to facilitate iterative joint data detection and channel estimation. A bidirectional channel estimation is performed among data segments to provide initial channel estimates. The LCR-MCMC provides a low-complexity means to approximate the optimal maximum a posterior (MAP) detection in a statistical fashion and is applicable to channels with long memory. Excellent behavior of the LCR-MCMC is presented using both synthetic channels and real data collected from actual underwater acoustic experiments.
Starting Page	502
Ending Page	506
File Size	491803
Page Count	5
File Format	PDF
ISBN	9781424482153
e-ISBN	9781424482160
DOI	10.1109/ALLERTON.2010.5706948
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Channel estimation Detectors Markov processes Decoding Joints Iterative decoding
Content Type	Text
Resource Type	Article

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