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VLSISP
2002

Monte Carlo Bayesian Signal Processing for Wireless Communications

8 years 9 months ago
Monte Carlo Bayesian Signal Processing for Wireless Communications
Abstract. 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 ...
Xiaodong Wang, Rong Chen, Jun S. Liu
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where VLSISP
Authors Xiaodong Wang, Rong Chen, Jun S. Liu
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