By Topic

Neural computation approach for the maximum-likelihood sequence estimation of communications signal

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Ying Tan ; Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, China

A novel detection approach for signals in digital communications is proposed in this paper by using the NNTCTG (neural network with transient chaos and time-varying gain) developed by the author (1997, 1998). The maximum-likelihood signal detection problem can be always described as a complex optimization problem with so many local optima that conventional Hopfield-type neural networks cannot be applied. To amend the drawbacks of Hopfield-type networks, the NNTCTG is used to search for globally optimal or near-optimal solutions of the optimization problems with lots of local optima, since it has richer and more flexible dynamics than conventional networks with only point attractors. We established a neuro-based detection model for digital communication signals and analyzed its working procedure in detail. Two simulation experiments were conducted to illustrate the validity and effectiveness of the proposed approach

Published in:

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop  (Volume:2 )

Date of Conference: