Blind ZF equalization with controlled delay robust to order over estimation
Signal Processing - From signal processing theory to implementation
A blind MIMO channel estimation method robust to order overestimation
Signal Processing - Special issue on independent components analysis and beyond
Global convergence of a blind multichannel identification algorithm
Signal Processing
Blind estimation of MIMO channels with an upper bound for channel orders
Signal Processing
Self-tuning blind identification and equalization of IIR channels
EURASIP Journal on Applied Signal Processing
Fast adaptive blind MMSE equalizer for multichannel FIR systems
EURASIP Journal on Applied Signal Processing
Blind identification of FIR channels in the presence of unknown noise
EURASIP Journal on Applied Signal Processing
Fast communication: Blind SIMO channel identification using FFT/IFFT
Signal Processing
Estimation of basis frequencies for time-varying SIMO channels: a second-order method
IEEE Transactions on Signal Processing
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Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations