Matrix analysis
The theory and practice of modem design
The theory and practice of modem design
Performance analysis of the subspace method for blind channel identification
Signal Processing - Special issue on subspace methods, part I: array signal processing and subspace computations
Principles of Digital Transmission: With Wireless Applications
Principles of Digital Transmission: With Wireless Applications
Analytical blind channel identification
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
A simple proof of a known blind channel identifiability result
IEEE Transactions on Signal Processing
A statistical approach to subspace based blind identification
IEEE Transactions on Signal Processing
Blind channel identification: subspace tracking method without rankestimation
IEEE Transactions on Signal Processing
Second-order analysis of improper complex random vectors and processes
IEEE Transactions on Signal Processing
Single-channel blind equalization for GSM cellular systems
IEEE Journal on Selected Areas in Communications
Universal linear precoding for NBI-proof widely linear equalization in MC systems
EURASIP Journal on Wireless Communications and Networking - Multicarrier Systems
Performance limits of alphabet diversities for FIR SISO channel identification
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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In this paper, we consider the problem of blindly estimating the impulse response of a nonminimum phase single-input single-output channel, by resorting only to the second-order statistics (SOS) of the channel output. On the basis of a general treatment of the problem, we show that a SOS-based subspace procedure can be applied to the problem at hand if the transmitted signal is an improper process, exhibiting some additional properties. After characterizing and discussing these properties, we show that they are exhibited by many improper digital modulation schemes of practical interest. Moreover, based on our unifying framework, we devise subspace-based algorithms for blind channel identification, by addressing in particular the related identifiability issues. Finally, the theoretical expression of the mean-squared error of the channel estimate is derived, and numerical simulations are carried out for its validation and for comparing the performance of the proposed algorithm with that of a conventional subspace-based method.