Skip to Main Content
We develop a frequency-domain channel estimation algorithm for single-user orthogonal frequency division multiplex (OFDM) wireless systems in the presence of interference. The received measurement is correlated in space with a covariance matrix dependent on frequency. Hence, the commonly used least-squares algorithm is suboptimal. On the other hand, accurate estimation of the spatial covariance matrix in such a model using the multivariate analysis of variance (MANOVA) method would impose significant computational overhead, since it would require a large number of pilot symbols. To overcome these problems, we propose to model the covariance matrix using an a-priori known set of frequency-dependent functions of joint (global) parameters, resulting in a structured covariance matrix. We estimate the interference covariance parameters using a residual method of moments (RMM) estimator and the mean (user channel) parameters by maximum likelihood (ML) estimation. Since our RMM estimates are invariant to the mean, this approach yields simple non-iterative estimates of the covariance parameters while having optimal statistical efficiency. Therefore, our algorithm outperforms the least-squares method in accuracy, and requires a smaller number of pilots than the MANOVA method and thus has smaller overhead. Numerical results illustrate the applicability of the proposed algorithm.