OFDM channel estimation in the presence of interference

We develop a frequency-domain channel estimation algorithm for single-user multiantenna orthogonal frequency division multiplexing (OFDM) wireless systems in the presence of synchronous interference. In this case, the synchronous interferer's signal on each OFDM subcarrier is correlated in spac...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2004-12, Vol.52 (12), p.3429-3439
Main Authors: Jeremic, A., Thomas, T.A., Nehorai, A.
Format: Article
Language:English
Subjects:
Applied sciences
Channel estimation
Covariance matrix
Detection, estimation, filtering, equalization, prediction
Econometrics
Exact sciences and technology
Fading
Frequency estimation
Information, signal and communications theory
Intersymbol interference
Maximum likelihood estimation
Multiplexing
OFDM
Parameter estimation
Signal and communications theory
Signal, noise
Streaming media
Studies
Telecommunications and information theory
Yield estimation
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Summary:We develop a frequency-domain channel estimation algorithm for single-user multiantenna orthogonal frequency division multiplexing (OFDM) wireless systems in the presence of synchronous interference. In this case, the synchronous interferer's signal on each OFDM subcarrier is correlated in space with a rank one spatial covariance matrix. In addition, the interferer's spatial covariance matrix is correlated in frequency based on the delay spread of the interferer's channel. To reduce the number of unknown parameters we develop a structured covariance model that accounts for the structure resulting from the synchronous interference. To further reduce the number of unknown parameters, we model the covariance matrix using a priori known set of frequency-dependent functions of joint (global) parameters. We estimate the interference covariance parameters using a residual method of moments (RMM) estimator and the channel parameters by maximum likelihood (ML) estimation. Since our RMM estimates are invariant to the mean, this approach yields simple noniterative estimates of the covariance parameters while having optimal statistical efficiency. Hence, our algorithm outperforms existing channel estimators that do not account for the interference, and at the same time requires smaller number of pilots than the MANOVA method and thus has smaller overhead. Numerical results illustrate the applicability of the proposed algorithm.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2004.837440