Bounds on SIMO and MIMO Channel Estimation and Equalization with Side Information
Journal of VLSI Signal Processing Systems
Modeling MPEG Coded Video Traffic by Markov-Modulated Self-Similar Processes
Journal of VLSI Signal Processing Systems
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
Blind channel estimation and detection for space--time coded CDMA in ISI channels
Digital Signal Processing
Blind adaptive channel equalization with performance analysis
EURASIP Journal on Applied Signal Processing
Blind identification of FIR channels in the presence of unknown noise
EURASIP Journal on Applied Signal Processing
Blind deconvolution in nonminimum phase systems using cascade structure
EURASIP Journal on Applied Signal Processing
Blind identification of multichannel systems driven by impulsive signals
Digital Signal Processing
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)
Hi-index | 35.69 |
This paper develops a fast maximum likelihood method for estimating the impulse responses of multiple FIR channels driven by an arbitrary unknown input. The resulting method consists of two iterative steps, where each step minimizes a quadratic function. The two-step maximum likelihood (TSML) method is shown to be high-SNR efficient, i.e., attaining the Cramer-Rao lower bound (CRB) at high SNR. The TSML method exploits a novel orthogonal complement matrix of the generalized Sylvester matrix. Simulations show that the TSML, method significantly outperforms the cross-relation (CR) method and the subspace (SS) method and attains the CRB over a wide range of SNR. This paper also studies a Fisher information (FI) matrix to reveal the identifiability of the M-channel system. A strong connection between the FI-based identifiability and the CR-based identifiability is established