Stochastic expectation maximization algorithm for long-memory fast-fading 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


In this paper, we develop a novel statistical detection algorithm following similar principles to that of expectation maximization (EM) algorithm. Our goal is to develop an iterative algorithm for joint channel estimation and data detection in channels that have a long memory and are fast varying in time. At each iteration, starting with an estimate of the channel, we combine a Markov Chain Monte Carlo (MCMC) algorithm for data detection, and an adaptive algorithm for channel tracking, to develop a statistical search procedure that finds joint important samples of possible transmitted data and channel impulse responses. The result of this step, which may be thought as E-step of the proposed algorithm, is used in an M-step that refines the channel estimate, for the next iteration. Excellent behavior of the proposed algorithm is presented by examining it on real data from underwater acoustic communication channels.

Original languageEnglish (US)
Title of host publication2010 IEEE Global Telecommunications Conference, GLOBECOM 2010
StatePublished - 2010
Event53rd IEEE Global Communications Conference, GLOBECOM 2010 - Miami, FL, United States
Duration: Dec 6 2010Dec 10 2010

Publication series

NameGLOBECOM - IEEE Global Telecommunications Conference


Other53rd IEEE Global Communications Conference, GLOBECOM 2010
Country/TerritoryUnited States
CityMiami, FL


  • Markov chain Monte Carlo techniques
  • Turbo equalization
  • Underwater acoustic channels

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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