Blind Channel Estimation for MIMO OFDM Systems via Nonredundant Linear Precoding
IEEE Transactions on Signal Processing
Identification of Matrices Having a Sparse Representation
IEEE Transactions on Signal Processing
Optimal training design for MIMO OFDM systems in mobile wireless channels
IEEE Transactions on Signal Processing
A Semiblind Channel Estimation Approach for MIMO–OFDM Systems
IEEE Transactions on Signal Processing - Part I
Parametric Channel Estimation for Pseudo-Random Tile-Allocation in Uplink OFDMA
IEEE Transactions on Signal Processing
Sinusoidal Modeling and Adaptive Channel Prediction in Mobile OFDM Systems
IEEE Transactions on Signal Processing
Subspace-based blind channel estimation for OFDM by exploiting virtual carriers
IEEE Transactions on Wireless Communications
Pilot-based channel estimation for OFDM systems by tracking the delay-subspace
IEEE Transactions on Wireless Communications
Sparse Channel Estimation with Zero Tap Detection
IEEE Transactions on Wireless Communications
Iterative estimation of the time-varying underwater acoustic channel using basis expansion models
Proceedings of the Eighth ACM International Conference on Underwater Networks and Systems
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In this paper, a very efficient semi-blind approach for the detection of most significant taps (MSTs) in sparse orthogonal frequency-division multiplexing (OFDM) channel estimation is developed. The least square (LS) estimation problem of sparse OFDM channels is first formulated, showing that the key to sparse channel estimation lies in the detection of the MSTs. An in-depth study of the second-order statistics of the signal received through a noise-free sparse OFDM channel reveals the sparsity and other properties of the correlation functions of the received signal. These properties lead to a direct relationship between the positions of the MSTs of the sparse channel and the most significant lags of the correlation functions, which is then used in conjunction with a pilot-assisted LS estimation to detect the MSTs in a semi-blind fashion. It os also shown that the new MST detection algorithm can be extended for the estimation of multiple-input-multiple-output (MIMO)-OFDM channels. A number of computer-simulation-based experiments for various sparse channels are carried out to confirm the effectiveness of the proposed semi-blind approach.