Identification of linear stochastic systems via second- and fourth-order cumulant matching
IEEE Transactions on Information Theory
Eigenvector algorithm for blind MA system identification
Signal Processing
The extended-window channel estimator for iterative channel-and-symbol estimation
EURASIP Journal on Wireless Communications and Networking - Special issue on advanced signal processing algorithms for wireless communications
A cumulant matrix subspace algorithm for blind single FIR channelidentification
IEEE Transactions on Signal Processing
System reconstruction based on selected regions of discretizedhigher order spectra
IEEE Transactions on Signal Processing
System identification using a linear combination of cumulant slices
IEEE Transactions on Signal Processing
A Novel HOS Approach for Blind Channel Equalization
IEEE Transactions on Wireless Communications
New criteria for blind deconvolution of nonminimum phase systems (channels)
IEEE Transactions on Information Theory
Blind identification and equalization based on second-order statistics: a time domain approach
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Is blind channel estimation feasible in mobile communication systems? A study based on GSM
IEEE Journal on Selected Areas in Communications
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In this paper we present a new approach for blind identification of non-minimum phase FIR channels for single input single output (SISO) and multiple inputs single output (MISO) scenarios. The received record of samples is processed by multiple parallel branches, each comprising an FIR filter followed by a non-linear function and an accumulator. Using an appropriate cost function, the obtained vector of averages is then best fitted by a function of the system model. The non-linear function used in each branch is selected so as to obtain an implicit matching of all higher order statistics (HOS) of certain orders. Choice of the cumulants generating function (CGF) as this non-linear function is also possible and can be interpreted as matching the joint probability density function (PDF) for selected samples in the vector space of the channel. With this interpretation the coefficients of the FIR filters are actually sampling points in a multidimensional frequency domain. The main advantages of this approach are its high probability of identification success, its ability to obtain reliable channel estimation in low SNR using a short record of samples and its relative insensitivity to overestimation of the channel order. The method is suitable for single transmit and receive antennas and has easy extensions to multiple antenna scenarios. The validity of the proposed approach is confirmed by simulations for BPSK and QPSK modulations and suitable design guidelines for these modulations are given.