Bayesian Inference, Stochastic Simulation and Their Applications in Wireless Communication Systems

  • Flávio Rainho Ávila Electrical Engineering Program (PEL), Rio de Janeiro State University (UERJ)
  • Michel Pompeu Tcheou Electrical Engineering Program (PEL), Rio de Janeiro State University (UERJ)
Keywords: Bayesian inference, Markov Chain Monte Carlo, Gibbs sampling, Metropolis-Hastings, Wireless communications, Symbol detection.

Abstract

Bayesian inference has been successfully applied in fields as varied as anti-spam filtering, DNA sequencing, war codebreaking and election forecasting. Founded on the apparently simple Bayes’ theorem, which relates the previous distribution of a parameter with its distribution after evidence is collected, Bayesian tools allow for incorporating all existing knowledge about the phenomenon under study in order to improve parameter estimation. Because of the stochastic nature of the wireless channel, Bayesian inference is particularly well suited to the problem of symbol detection in many modern digital communication systems. When combined with Markov Chain Monte Carlo (MCMC) techniques, Bayesian receivers are capable of achieving minimum Bit Error Rate (BER) while avoiding the prohibitively high computational complexity associated with standard Maximum Likelihood (ML) or Maximum A Posteriori (MAP) estimators. In addition, such receivers are capable of numerically integrating out channel coefficients and noise variance, thus avoiding the need to use sub-optimal estimates of these parameters. This tutorial presents the rudiments of Bayesian statistics and MCMC in general, and discusses their applications in wireless communications in particular. The paper also details the design of Bayesian MCMC receiver in a system employing BPSK and ubject frequency-selective fading and Gaussian noise. Afterwards, recent advances in Bayesian receivers are surveyed for several important practical wireless transmission schemes, including MIMO, CDMA and OFDM. In addition, the paper addresses the application of Bayesian tools in challenging channelconditions — namely, nonlinear, non-Gaussian, underwater and fast fading channels.

Author Biographies

Flávio Rainho Ávila, Electrical Engineering Program (PEL), Rio de Janeiro State University (UERJ)
Flávio Ávila received the D.Sc. degree in Electrical Engineering from the Federal University of Rio de Janeiro (UFRJ), Brazil, in 2012. Since 2013, he has been a professor at the Rio de Janeiro State University (UERJ). His main research interest is on applications of statistical signal processing to  Audio and Speech Processing and Wireless Communications. He is a member of the IEEE and the Audio Engineering Society (AES).
Michel Pompeu Tcheou, Electrical Engineering Program (PEL), Rio de Janeiro State University (UERJ)
Michel P. Tcheou was born in Rio de Janeiro, Brazil. He received the Engineering degree in electronics from Federal University of Rio de Janeiro (UFRJ) in 2003, the M.Sc. and D.Sc. degrees in Electrical Engineering from COPPE/UFRJ in 2005 and 2011, respectively. He has worked at the Electric Power Research Center (Eletrobras Cepel) in Rio de Janeiro, Brazil, from 2006 to 2011. Since 2012 he has been with the Department of Electronics and Communications Engineering (the undergraduate dept.) at Rio de Janeiro State University (UERJ). He has also been with the Postgraduate in Electronics Program. His research interests are in signal processing, communications, data compression and numerical optimization.
Published
30-11-2016
How to Cite
Ávila, F., & Tcheou, M. (2016). Bayesian Inference, Stochastic Simulation and Their Applications in Wireless Communication Systems. Journal of Communication and Information Systems, 31(1). https://doi.org/10.14209/jcis.2016.27
Section
Tutorial Papers