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Gaussian approximation based mixture reduction for joint channel estimation and detection in MIMO systems

Research output: Contribution to journalArticle (Academic Journal)

Original languageEnglish
Pages (from-to)2384 - 2389
Number of pages6
JournalIEEE Transactions on Wireless Communications
Issue number77
DatePublished - Jul 2007


A novel Gaussian approximation based mixture reduction algorithm is proposed for semi-blind joint channel tracking and symbol detection for spatial multiplexing multiple-input multiple-output (MIMO) systems with frequency-flat time-selective channels. The proposed algorithm is based on a modified sequential Gaussian approximation detector (SGA) which takes into account channel uncertainty, and the first order generalized pseudo-Bayesian (GPB1) channel estimator. Simulation results show that the proposed algorithm performs better than the conventional and computationally expensive decision-directed method with Kalman filter based channel estimation and a posteriori probability (APP) symbol detection.

Additional information

Publisher: Institute of Electrical and Electronics Engineers (IEEE) Rose publication type: Journal article Sponsorship: This work was supported by Toshiba Research Europe Ltd (Bristol), UK. Terms of use: Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Wireless Communications. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bristol's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

    Research areas

  • joint estimation and detection, MIMO systems, multiple model estimation, multiuser detection, time-varying channels

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