Title
Blind identification/equalization using deterministic maximum likelihood and a partial prior on the input
Abstract
A (semi)deterministic maximum likelihood (DML) approach is presented to solve the joint blind channel identification and blind symbol estimation problem for single-input multiple-output systems. A partial prior on the symbols is incorporated into the criterion which improves the estimation accuracy and brings robustness toward poor channel diversity conditions. At the same time, this method introduces fewer local minima than the use of a full prior (statistical) ML. In the absence of noise, the proposed batch algorithm estimates perfectly the channel and symbols with a finite number of samples. Based on these considerations, an adaptive implementation of this algorithm is proposed. It presents some desirable properties including low complexity, robustness to channel overestimation, and high convergence rate.
Year
DOI
Venue
2006
10.1109/TSP.2005.861787
IEEE Transactions on Signal Processing
Keywords
Field
DocType
estimation accuracy,fewer local minimum,index terms—adaptive algorithm,deter- ministic maximum likelihood method,poor channel diversity condition,proposed batch algorithm,prior knowledge.,desirable property,deterministic maximum likelihood,joint estimation,blind identification,finite number,blind symbol estimation problem,joint blind channel identification,adaptive implementation,blind equalization,convergence rate,maximum likelihood,adaptive signal processing,local minima,maximum likelihood estimation,indexing terms,maximum likelihood method
Mathematical optimization,Communication channel,Robustness (computer science),Adaptive filter,Rate of convergence,Adaptive algorithm,System identification,Deterministic system (philosophy),Blind equalization,Mathematics
Journal
Volume
Issue
ISSN
54
2
1053-587X
Citations 
PageRank 
References 
2
0.36
19
Authors
3
Name
Order
Citations
PageRank
Florence Alberge110015.66
Mila Nikolova21792105.71
P. Duhamel314618.79