A Monte-Carlo implementation of the SAGE algorithm for joint soft multiuser and channel parameter estimation

E. Panayirci, A. Kocian, H. Vincent Poor, M. Ruggieri

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

Abstract

An efficient, joint transmission delay and channel parameter estimation algorithm is proposed for uplink asynchronous direct-sequence code-division multiple access (DS-CDMA) systems based on the space-alternating generalized expectation maximization (SAGE) framework. The marginal likelihood of the unknown parameters, averaged over the data sequence, as well as the expectation and maximization steps of the SAGE algorithm are derived analytically. To implement the proposed algorithm, a Markov Chain Monte Carlo (MCMC) technique, called Gibbs sampling, is employed to compute the a posteriori probabilities of data symbols in a computationally efficient way. Computer simulations show that the proposed algorithm has excellent estimation performance. This so-called MCMC-SAGE receiver is guaranteed to converge in likelihood.

Original languageEnglish (US)
Title of host publication2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009
Pages702-706
Number of pages5
DOIs
StatePublished - 2009
Event2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009 - Perugia, Italy
Duration: Jun 21 2009Jun 24 2009

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC

Other

Other2009 IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2009
Country/TerritoryItaly
CityPerugia
Period6/21/096/24/09

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Information Systems

Keywords

  • Asynchronous DS-CDMA
  • Gibbs sampling
  • Markov chain monte carlo (MCMC)
  • Space-alternating generalized expectation maximization(SAGE)

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