Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
Digital processing of random signals: theory and methods
Digital processing of random signals: theory and methods
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
Subspace-based blind channel identification of SISO-FIR systems with improper random inputs
Signal Processing - Signal processing in communications
Efficiency of subspace-based DOA estimators
Signal Processing
Analytical blind channel identification
IEEE Transactions on Signal Processing
Connections between the least-squares and the subspace approachesto blind channel estimation
IEEE Transactions on Signal Processing
A least-squares approach to blind channel identification
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Asymptotic performance of second-order algorithms
IEEE Transactions on Signal Processing
Blind channel estimation using the second-order statistics: asymptotic performance and limitations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On subspace methods for blind identification of single-inputmultiple-output FIR systems
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Information Theory
Single-channel blind equalization for GSM cellular systems
IEEE Journal on Selected Areas in Communications
| Hi-index | 35.68 |
Finite impulse responses (FIR) of single-input single-output (SISO) channels can be blindly identified from second-order statistics of transformed data, for instance when the channel is excited by binary phase shift keying (BPSK), minimum shift keying (MSK), or quadrature phase shift keying (QPSK) inputs. Identifiability conditions are derived by considering that noncircularity induces diversity. Theoretical performance issues are addressed to evaluate the robustness of standard subspace-based estimators with respect to these identifiability conditions. Then benchmarks such as asymptotically minimum variance (AMV) bounds based on various statistics are presented. Some illustrative examples are eventually given where Monte Carlo experiments are compared to theoretical performances. These comparisons allow to quantify limits to the use of the alphabet diversities for the identification of FIR SISO channels, and to demonstrate the robustness of algorithms based on high-order statistics.