Bayesian sparse channel estimation and data detection for OFDM communication systems

Guan Gui, Abolfazl Mehbod Niya, Fumiyuki Adachi

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

9 Citations (Scopus)

Abstract

Channel state information (CSI) is required at receiver in orthogonal frequency division modulation (OFDM) communication systems due to the fact that frequency-selective fading channel leads to inter-symbol interference (ISI) over data transmission. Broadband channel model is often described by very few dominant channel taps and they can be probed by sparse channel estimation (SCE) methods, e.g., subspace pursuit (SP) algorithm, can take the advantage of sparse structure effectively in broadband channels as for prior information. However, these developed methods are vulnerable to both noise, interference and column coherence of training signal matrix. In other words, the primary objective of these conventional methods is to catch the dominant channel taps without a report of posterior channel uncertainty. To improve the estimation performance, we proposed a Bayesian sparse channel estimation (BSCE) method which not only exploits the channel sparsity but also mitigates the unexpected channel uncertainty. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. Computer simulations show that our technique can improve the estimation performance with comparable computational complexity when comparing with conventional SCE methods.

Original languageEnglish
Title of host publication2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013 - Las Vegas, NV, United States
Duration: 2013 Sep 22013 Sep 5

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252

Other

Other2013 IEEE 78th Vehicular Technology Conference, VTC Fall 2013
CountryUnited States
CityLas Vegas, NV
Period13/9/213/9/5

Keywords

  • Bayesian sparse channel estimation (BSCE)
  • Data detection
  • Ofdm system
  • Sparse channel representation (SCE)

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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