Markov chain Monte Carlo detection for underwater acoustic channels

Hong Wan, Rong Rong Chen, Jun Won Choi, Andrew Singer, James Preisig, Behrouz Farhang-Boroujeny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we develop novel statistical detectors to combat intersymbol interference for frequency selective channels based on Markov Chain Monte Carlo (MCMC) techniques. While the optimal maximum a posteriori (MAP) detector has a complexity that grows exponentially with the constellation size and the memory of the channel, the MCMC detector can achieve near optimal performance with a complexity that grows linearly. This makes the MCMC detector particularly attractive for underwater acoustic channels with long delay spread. We examine the effectiveness of the MCMC detector using actual data collected from underwater experiments. When combined with adaptive least mean square (LMS) channel estimation, the MCMC detector achieves superior performance over the direct adaptation LMS turbo equalizers (LMS-TEQ) for a majority of data sets transmitted over distances from 60 meters to 1000 meters.

Original languageEnglish (US)
Title of host publication2010 Information Theory and Applications Workshop, ITA 2010 - Conference Proceedings
Pages44-48
Number of pages5
DOIs
StatePublished - 2010
Event2010 Information Theory and Applications Workshop, ITA 2010 - San Diego, CA, United States
Duration: Jan 31 2010Feb 5 2010

Publication series

Name2010 Information Theory and Applications Workshop, ITA 2010 - Conference Proceedings

Other

Other2010 Information Theory and Applications Workshop, ITA 2010
Country/TerritoryUnited States
CitySan Diego, CA
Period1/31/102/5/10

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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