Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

An approximate minimum BER approach to multiuser detection using recurrent neural networks

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
$31 $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

2 Author(s)
de Lamare, R.C. ; CETUC - PUC-RIO, Rio de Janeiro, Brazil ; Sampaio-Neto, R.

We investigate the use of an approximate minimum bit error rate (MBER) approach to multiuser detection using recurrent neural networks (RNN). We examine a stochastic gradient adaptive algorithm for approximating the MBER from training data using RNN structures. A comparative analysis of linear and neural multiuser receivers (MUD), employing minimum mean squared error (MMSE) and approximate MBER (AMBER) adaptive algorithms is carried out. Computer simulation experiments show that the neural MUD operating with a criterion similar to the AMBER algorithm outperforms neural receivers using the MMSE criterion via gradient-type algorithms and linear receivers with MMSE and MBER techniques.

Published in:

Personal, Indoor and Mobile Radio Communications, 2002. The 13th IEEE International Symposium on  (Volume:3 )

Date of Conference:

15-18 Sept. 2002