This paper presents and analyzes low-rank channel estimators for orthogonal frequency-division multiplexing (OFDM) systems using the frequency correlation of the channel. Low-rank approximations based on the discrete Fourier transform (DFT) have been proposed, but these suffer from poor performance when the channel is not sample spaced. The paper applies the theory of optimal rank-reduction to linear minimum mean-squared error (LMMSE) estimators and shows that these estimators, when using a fixed design, are robust to changes in channel correlation and signal-to-noise ratio (SNR). The performance is presented in terms of uncoded symbol-error rate (SER) for a system using 16-quadrature amplitude modulation (QAM). The paper discusses the use of singular value decomposition (SVD) for optimal rank reduction in the LMMSE estimator. The SVD of the channel autocovariance matrix is used to derive the optimal rank-p estimator. The paper also discusses the use of time correlation in the channel estimation process and compares the performance of low-rank estimators with other estimators such as FIR Wiener filters. The paper concludes that low-rank estimators offer a good balance between complexity and performance, and that they can be used in a variety of OFDM systems. The paper also discusses the use of pilot-symbol assisted modulation (PSAM) in conjunction with low-rank estimators. The paper shows that the low-rank estimator can be used in PSAM and that it performs well in this scenario. The paper also discusses the use of time correlation in the channel estimation process and shows that the two-dimensional LMMSE estimator can be simplified using the same technique with rank reduction as described here. However, it is shown that such an estimator gives an inferior performance for a fixed complexity. Hence, it seems that separating the use of frequency correlation and time correlation is the most efficient way of estimating the channel. The paper also discusses other approaches to exploit the time correlation, such as decision-directed schemes and FIR filters. The former can be used in a slow-fading environment, where it offers good performance for a small complexity, whereas the latter is preferred in a fast-fading environment. The paper concludes that low-rank estimators are a good choice for OFDM systems and that they can be used in a variety of scenarios. The paper also discusses the use of low-rank estimators in PSAM and shows that they perform well in this scenario. The paper also discusses the use of time correlation in the channel estimation process and shows that the two-dimensional LMMSE estimator can be simplified using the same technique with rank reduction as described here. However, it is shown that such an estimator gives an inferior performance for a fixed complexity. Hence, it seems that separating the use of frequency correlation and time correlation is the most efficient way of estimating the channel. The paper also discusses other approaches to exploit the time correlation, such as decision-directed schemes andThis paper presents and analyzes low-rank channel estimators for orthogonal frequency-division multiplexing (OFDM) systems using the frequency correlation of the channel. Low-rank approximations based on the discrete Fourier transform (DFT) have been proposed, but these suffer from poor performance when the channel is not sample spaced. The paper applies the theory of optimal rank-reduction to linear minimum mean-squared error (LMMSE) estimators and shows that these estimators, when using a fixed design, are robust to changes in channel correlation and signal-to-noise ratio (SNR). The performance is presented in terms of uncoded symbol-error rate (SER) for a system using 16-quadrature amplitude modulation (QAM). The paper discusses the use of singular value decomposition (SVD) for optimal rank reduction in the LMMSE estimator. The SVD of the channel autocovariance matrix is used to derive the optimal rank-p estimator. The paper also discusses the use of time correlation in the channel estimation process and compares the performance of low-rank estimators with other estimators such as FIR Wiener filters. The paper concludes that low-rank estimators offer a good balance between complexity and performance, and that they can be used in a variety of OFDM systems. The paper also discusses the use of pilot-symbol assisted modulation (PSAM) in conjunction with low-rank estimators. The paper shows that the low-rank estimator can be used in PSAM and that it performs well in this scenario. The paper also discusses the use of time correlation in the channel estimation process and shows that the two-dimensional LMMSE estimator can be simplified using the same technique with rank reduction as described here. However, it is shown that such an estimator gives an inferior performance for a fixed complexity. Hence, it seems that separating the use of frequency correlation and time correlation is the most efficient way of estimating the channel. The paper also discusses other approaches to exploit the time correlation, such as decision-directed schemes and FIR filters. The former can be used in a slow-fading environment, where it offers good performance for a small complexity, whereas the latter is preferred in a fast-fading environment. The paper concludes that low-rank estimators are a good choice for OFDM systems and that they can be used in a variety of scenarios. The paper also discusses the use of low-rank estimators in PSAM and shows that they perform well in this scenario. The paper also discusses the use of time correlation in the channel estimation process and shows that the two-dimensional LMMSE estimator can be simplified using the same technique with rank reduction as described here. However, it is shown that such an estimator gives an inferior performance for a fixed complexity. Hence, it seems that separating the use of frequency correlation and time correlation is the most efficient way of estimating the channel. The paper also discusses other approaches to exploit the time correlation, such as decision-directed schemes and