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
fDate :
4/3/2003 12:00:00 AM
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;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:20030378
