Bayesian sparse channel estimation and tracking

It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure and make it more feasible for practical applications, this article investigates sparse channel estimation for OFDM from the perspect...

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Bibliographic Details
Published in2012 IEEE Statistical Signal Processing Workshop (SSP) pp. 472 - 475
Main Authors Chulong Chen, Zoltowski, M. D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2012
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Summary:It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure and make it more feasible for practical applications, this article investigates sparse channel estimation for OFDM from the perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. In addition, the time-varying channel can be tracked naturally by iteratively updating the maximum likelihood function of the channel impulse response. Simulation studies show a significant improvement in channel estimation and promising performance for channel tracking with reduced the number of pilot tones.
ISBN:9781467301824
1467301825
ISSN:2373-0803
2693-3551
DOI:10.1109/SSP.2012.6319735