Downlink Specific Linear Equalization for Frequency Selective CDMA Cellular Systems
Journal of VLSI Signal Processing Systems
COD: Diversity-Adaptive Subspace Processing for Multipath Separation and Signal Recovery
Journal of VLSI Signal Processing Systems
Semi-blind equalization at the symbol rate with application to OFDM
Signal Processing
Blind Channel Identification Based on Noisy Observation by Stochastic Approximation Method
Journal of Global Optimization
Blind linear channel estimation using genetic algorithm and SIMO model
Signal Processing
Projection minimization algorithm for blind channel equalizer
Signal Processing
Common factor estimation and two applications in signal processing
Signal Processing - Special issue on independent components analysis and beyond
Blind identification and equalization of two-channel FIR systems in unbalanced noise environments
Signal Processing - Content-based image and video retrieval
Identification of acoustic MIMO systems: challenges and opportunities
Signal Processing
Deterministic blind subspace MIMO equalization
EURASIP Journal on Applied Signal Processing
Subspace methods for multimicrophone speech dereverberation
EURASIP Journal on Applied Signal Processing
Joint multi-baseline SAR interferometry
EURASIP Journal on Applied Signal Processing
Underdetermined blind audio source separation using modal decomposition
EURASIP Journal on Audio, Speech, and Music Processing
Blind adaptive channel equalization with performance analysis
EURASIP Journal on Applied Signal Processing
Time delay estimation in room acoustic environments: an overview
EURASIP Journal on Applied Signal Processing
Estimation and direct equalization of doubly selective channels
EURASIP Journal on Applied Signal Processing
Multichannel blind seismic deconvolution using dynamic programming
Signal Processing
Fast communication: Blind SIMO channel identification using FFT/IFFT
Signal Processing
Fractionally-sampled blind channel estimation for a mobile communication system
Mobility '08 Proceedings of the International Conference on Mobile Technology, Applications, and Systems
Performance limits of alphabet diversities for FIR SISO channel identification
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
WTS'09 Proceedings of the 2009 conference on Wireless Telecommunications Symposium
Anomaly preserving l2,∞-optimal dimensionality reduction over a Grassmann manifold
IEEE Transactions on Signal Processing
Blind identification of multichannel systems driven by impulsive signals
Digital Signal Processing
Underdetermined convolutive blind source separation via time-frequency masking
IEEE Transactions on Audio, Speech, and Language Processing
Effective channel order estimation based on nullspace structure and exponential fit
IEEE Transactions on Signal Processing
Identification of LPTV systems in the frequency domain
Digital Signal Processing
Signal-based performance evaluation of dereverberation algorithms
Journal of Electrical and Computer Engineering
Fourth-Order cumulants and neural network approach for robust blind channel equalization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Blind system identification using precise and quantized observations
Automatica (Journal of IFAC)
Blind single channel identification based on signal intermittency and second-order statistics
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Operational modal parameter estimation of MIMO systems using transmissibility functions
Automatica (Journal of IFAC)
Structured Sparsity Models for Reverberant Speech Separation
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Hi-index | 35.70 |
Conventional blind channel identification algorithms are based on channel outputs and knowledge of the probabilistic model of channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. In this paper, we consider the system input to be an unknown deterministic signal and study the problem of blind identification of multichannel FIR systems without requiring the knowledge of the input statistical model. A new blind identification algorithm based solely on the system outputs is proposed. Necessary and sufficient identifiability conditions in terms of the multichannel systems and the deterministic input signal are also presented