A Krylov subspace based low-rank channel estimation in OFDM systems
Signal Processing
MIMO: From Theory to Implementation
MIMO: From Theory to Implementation
Detection of number of sources via exploitation of centro-symmetryproperty
IEEE Transactions on Signal Processing
Blind channel identification and equalization in OFDM-based multiantenna systems
IEEE Transactions on Signal Processing
A semi-blind channel estimation method for multiuser multiantenna OFDM systems
IEEE Transactions on Signal Processing
Subspace-based blind and semi-blind channel estimation for OFDMsystems
IEEE Transactions on Signal Processing
Optimal training design for MIMO OFDM systems in mobile wireless channels
IEEE Transactions on Signal Processing
A novel approach for stabilizing recursive least squares filters
IEEE Transactions on Signal Processing
Subspace-based blind channel estimation for OFDM by exploiting virtual carriers
IEEE Transactions on Wireless Communications
Blind adaptive channel estimation in ofdm systems
IEEE Transactions on Wireless Communications
A comparison of the HIPERLAN/2 and IEEE 802.11a wireless LAN standards
IEEE Communications Magazine
IEEE Journal on Selected Areas in Communications
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In this paper, the blind subspace channel estimation using the block matrix scheme is proposed for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Based on the Toeplitz structure, the block matrix scheme collects a group of the received OFDM symbols into a vector, and then partitions it into a set of equivalent symbols. The number of equivalent symbols is about N times of OFDM symbols, where N is the size of FFT operation. With those equivalent symbols, the proposed blind subspace channel estimation can converge within a small amount of OFDM symbols. The identifiability of the proposed channel estimation is examined that the channel matrix is determined up to an ambiguity matrix. Besides, the semi-blind channel estimation is also investigated by combining few pilot sequences with the subspace method. Simulation results show that the proposed channel estimators perform very well even in a time-varying scenario. Especially the semi-blind methods achieve almost the same BERs as those by true channels.