NDLI: Model selection-based blind channel estimation method for parsimonious MIMO-COFDM systems

Content Provider	IEEE Xplore Digital Library
Author	El-Sallam, A.A.
Copyright Year	2008
Description	Author affiliation: Sch. of Electr., Univ. of Western Australia, Crawley, WA (El-Sallam, A.A.)
Abstract	The paper considers a blind channel estimation method combined with a model selection algorithm for the classification and estimation of significant channel parameters in coded orthogonal frequency division multiplexing (COFDM) systems with multiple receive antennas. Unlike conventional channel estimation methods, where channel parameters are estimated based on a known or an estimated length, we propose a blind-based method which will jointly classify and estimate only significant channel parameters and the channel length, i.e., parsimonious channel estimates. Firstly, by using only the received signal and userspsila spreading codes, a data model for the channel response is presented. Then, a blind channel estimation method based on eigenvalue value decomposition (EVD), incorporated with a hierarchical minimum description length (MDL) model selection method is used to estimate only the identified significant channel parameters. Simulation results show that, the proposed method is capable of identifying significant channel parameters with high probabilities and at low SNRs. In addition, the system performance based on the identified parameters is enhanced over conventional methods.
Starting Page	111
Ending Page	116
File Size	290181
Page Count	6
File Format	PDF
ISBN	9781424422944
DOI	10.1109/MMSP.2008.4665058
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-08
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Channel estimation Eigenvalues and eigenfunctions Computational modeling Antenna arrays Noise Receivers Receiving antennas
Content Type	Text
Resource Type	Article

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