Title
Performance analysis and adaptive Newton algorithms of multimodulus blind equalization criterion
Abstract
This paper studies the stationary points of the multimodulus blind equalization criterion in a noiseless communication channel and proposes two adaptive Newton algorithms. It is shown in this paper that the stationary points of the multimodulus criterion can be grouped into two categories, according to the power of equalizer output. The stationary points having the same equalizer output power with that of the transmitted symbols are desirable global minima, while the stationary points having less equalizer output power than that of the transmitted symbols are saddle points. A pseudo Newton learning algorithm and a full Newton learning algorithm minimizing the multimodulus criterion are proposed. By using the matrix inversion lemma, both Newton algorithms can be efficiently implemented with a computational complexity of O(N^2), where N is the tap length of equalizer. Computer experiment results are presented. It is found that the full Newton algorithm performs well in both static and time-varying communication channels, while the pseudo Newton algorithm performs well only in static communication channels.
Year
DOI
Venue
2009
10.1016/j.sigpro.2009.05.003
Signal Processing
Keywords
Field
DocType
performance analysis,full newton,multimodulus criterion,adaptive newton algorithm,transmitted symbol,full newton algorithm,pseudo newton,newton algorithm,equalizer output power,pseudo newton algorithm,stationary point,multimodulus blind equalization criterion,computer experiment,communication channels,blind equalization,saddle point,computational complexity
Computer experiment,Mathematical optimization,Control theory,Algorithm,Maxima and minima,Woodbury matrix identity,Stationary point,Adaptive algorithm,Blind equalization,Mathematics,Newton's method,Computational complexity theory
Journal
Volume
Issue
ISSN
89
11
Signal Processing
Citations 
PageRank 
References 
8
0.51
12
Authors
2
Name
Order
Citations
PageRank
Xi-Lin Li154734.85
wenjun zeng22029220.14