Principles of Mobile Communication
Principles of Mobile Communication
Optimal and Adaptive Signal Processing
Optimal and Adaptive Signal Processing
OFDM and MC-CDMA for Broadband Multi-User Communications, WLANs and Broadcasting
OFDM and MC-CDMA for Broadband Multi-User Communications, WLANs and Broadcasting
Orthogonal Frequency Division Multiplexing for Wireless Communications (Signals and Communication Technology)
Reduced-rank adaptive filtering using Krylov subspace
EURASIP Journal on Applied Signal Processing
Linear Estimation and Detection in Krylov Subspaces
Linear Estimation and Detection in Krylov Subspaces
Reduced-rank adaptive filtering
IEEE Transactions on Signal Processing
Space-time adaptive reduced-rank multistage Wiener filtering for asynchronous DS-CDMA
IEEE Transactions on Signal Processing
Complexity Reduction of Iterative Receivers Using Low-Rank Equalization
IEEE Transactions on Signal Processing
Pilot-based channel estimation for OFDM systems by tracking the delay-subspace
IEEE Transactions on Wireless Communications
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
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We investigate a low-rank minimum mean-square error (MMSE) channel estimator in orthogonal frequency division multiplexing (OFDM) systems. The proposed estimator is derived by using the multi-stage nested Wiener filter (MSNWF) identified in the literature as a Krylov subspace approach for rank reduction. We describe the low-rank MMSE expressions for exploiting the time correlation function (TCF) of the channel path gains. The Krylov subspace technique requires neither eigenvalue decomposition (EVD) nor the inverse of the covariance matrices for parameter estimation. We show that the Krylov channel estimator can perform as well as the EVD estimator with a much smaller rank. Simulation results obtained confirm the superiority of the proposed Krylov low-rank channel estimator in approaching near full-rank MSE performance.