This paper presents and analyzes low-rank channel estimators for orthogonal frequency-division multiplexing (OFDM) systems, focusing on the frequency correlation of the channel. The authors propose a low-complexity approximation to the frequency-based linear minimum mean-squared error (LMMSE) estimator using optimal rank reduction theory. This approach reduces the computational complexity while maintaining robust performance to changes in channel correlation and signal-to-noise ratio (SNR). The performance is evaluated in terms of uncoded symbol-error rate (SER) for a system using 16-quadrature amplitude modulation (QAM). The paper also discusses the design considerations and compares the proposed low-rank estimator with other estimators, including FIR Wiener filters and pilot-symbol assisted modulation (PSAM). The results show that the low-rank estimator achieves a significant reduction in complexity with only a small loss in performance, making it suitable for practical applications in OFDM systems.This paper presents and analyzes low-rank channel estimators for orthogonal frequency-division multiplexing (OFDM) systems, focusing on the frequency correlation of the channel. The authors propose a low-complexity approximation to the frequency-based linear minimum mean-squared error (LMMSE) estimator using optimal rank reduction theory. This approach reduces the computational complexity while maintaining robust performance to changes in channel correlation and signal-to-noise ratio (SNR). The performance is evaluated in terms of uncoded symbol-error rate (SER) for a system using 16-quadrature amplitude modulation (QAM). The paper also discusses the design considerations and compares the proposed low-rank estimator with other estimators, including FIR Wiener filters and pilot-symbol assisted modulation (PSAM). The results show that the low-rank estimator achieves a significant reduction in complexity with only a small loss in performance, making it suitable for practical applications in OFDM systems.