DocumentCode
1183070
Title
Equalisation based on negentropy minimisation
Author
Choi, Sooyong ; Lee, Te-Won
Author_Institution
Inst. for Neural Comput., Univ. of California, La Jolla, CA, USA
Volume
39
Issue
7
fYear
2003
fDate
4/3/2003 12:00:00 AM
Firstpage
629
Lastpage
631
Abstract
An equalisation method based on negentropy minimisation is introduced and its characteristics are investigated. Negentropy includes higher order statistical information and its error minimisation provides improved convergence and performance. The bit error ratio of the proposed method has similar characteristics to the adaptive minimum bit error rate (AMBER) equaliser. The main advantage of the proposed equaliser is that it needs drastically fewer training iterations than the AMBER and the MMSE equalisers.
Keywords
adaptive equalisers; error statistics; higher order statistics; minimisation; minimum entropy methods; adaptive minimum bit error rate equaliser; bit error ratio; convergence; equalisation method; error minimisation; higher order statistical information; negentropy minimisation; training iterations;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:20030378
Filename
1194150
Link To Document