By Topic

Partial Parallel Interference Cancellation Multiuser Detection using Recurrent Neural Network Based on Hebb Learning Rule

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

3 Author(s)
Yanping Li ; Dept. of Inf. Eng., Taiyuan Univ. of Technol. ; Yongbo Zhang ; Huakui Wang

In CDMA communication systems, in order to decrease the influence on reception performance resulted from incorrect decision of the interference users' information bits in parallel interference cancellation (PIC) process, a recurrent neural network based on Hebb learning rule is designed and applied to adjusting interference cancellation factors (ICF) in partial parallel interference cancellation (PPIC) multiuser detection. Simulation results show that the proposed Hebb-PPIC detection has strong anti-MAI ability and its performance of bit error rate (BER) is improved on the basis of conventional PIC in both conditions of ideal power control and "near-far" scenario

Published in:

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on  (Volume:1 )

Date of Conference:

0-0 0