Monte Carlo Bayesian Signal Processing for Wireless Communications

  • Xiaodong Wang
  • Rong Chen
  • Jun S. Liu


Many statistical signal processing problems found in wireless communications involves making inference about the transmitted information data based on the received signals in the presence of various unknown channel distortions. The optimal solutions to these problems are often too computationally complex to implement by conventional signal processing methods. The recently emerged Bayesian Monte Carlo signal processing methods, the relatively simple yet extremely powerful numerical techniques for Bayesian computation, offer a novel paradigm for tackling wireless signal processing problems. These methods fall into two categories, namely, Markov chain Monte Carlo (MCMC) methods for batch signal processing and sequential Monte Carlo (SMC) methods for adaptive signal processing. We provide an overview of the theories underlying both the MCMC and the SMC. Two signal processing examples in wireless communications, the blind turbo multiuser detection in CDMA systems and the adaptive detection in fading channels, are provided to illustrate the applications of MCMC and SMC respectively.


Markov Chain Monte Carlo Prior Distribution Fading Channel Gibbs Sampler Multiuser Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Xiaodong Wang
    • 1
  • Rong Chen
    • 2
  • Jun S. Liu
    • 3
  1. 1.Electrical Engineering DepartmentTexas A&M UniversityCollege StationUSA
  2. 2.Information and Decision Science DepartmentUniversity of Illinois at ChicagoChicagoUSA
  3. 3.Statistics DepartmentHarvard UniversityCambridgeUSA

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