Open Access

A Low-Complexity KL Expansion-Based Channel Estimator for OFDM Systems

EURASIP Journal on Wireless Communications and Networking20052005:172340

https://doi.org/10.1155/WCN.2005.163

Received: 23 April 2004

Published: 28 April 2005

Abstract

This paper first proposes a computationally efficient, pilot-aided linear minimum mean square error (MMSE) batch channel estimation algorithm for OFDM systems in unknown wireless fading channels. The proposed approach employs a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve (KL) orthogonal expansion and finds MMSE estimates of the uncorrelated KL series expansion coefficients. Based on such an expansion, no matrix inversion is required in the proposed MMSE estimator. Moreover, optimal rank reduction is achieved by exploiting the optimal truncation property of the KL expansion resulting in a smaller computational load on the estimation algorithm. The performance of the proposed approach is studied through analytical and experimental results. We then consider the stochastic Cramér-Rao bound and derive the closed-form expression for the random KL coefficients and consequently exploit the performance of the MMSE channel estimator based on the evaluation of minimum Bayesian MSE. We also analyze the effect of a modelling mismatch on the estimator performance. To further reduce the complexity, we extend the batch linear MMSE to the sequential linear MMSE estimator. With the fast convergence property and the simple structure, the sequential linear MMSE estimator provides an attractive alternative to the implementation of channel estimator.

Keywords

channel estimationOFDM systemsMMSE estimation

Authors’ Affiliations

(1)
Department of Computer Engineering, Kadir Has University
(2)
Department of Electrical-Electronics Engineering, Istanbul University
(3)
Department of Electronics Enginering, IŞik University

Copyright

© Şenol et al. 2005

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.