Scheduled System Maintenance:
On May 6th, system maintenance will take place from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). During this time, there may be intermittent impact on performance. We apologize for the inconvenience.
By Topic

Optimization of Power Allocation for Interference Cancellation With Particle Swarm Optimization

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

4 Author(s)
Zielinski, K. ; Inst. for Electromagn. Theor. & Microelectron. (ITEM), Univ. of Bremen, Bremen ; Weitkemper, P. ; Laur, R. ; Kammeyer, K.-D.

In code division multiple access (CDMA) systems a significant degradation in detection performance due to multiuser interference can be avoided by the utilization of interference cancellation methods. Further enhancement can be obtained by optimizing the power allocation of the users. The resulting constrained single-objective optimization problem is solved here by means of particle swarm optimization (PSO). It is shown that the maximum number of users for a CDMA system can be increased significantly if an optimized power profile is employed. Furthermore, an extensive study of PSO control parameter settings using three different neighborhood topologies is performed on the basis of the power allocation problem, and two constraint-handling techniques are evaluated. Results from the parameter study are compared with examinations from the literature. It is shown that the von-Neumann neighborhood topology performs consistently better than gbest and lbest. However, strong interaction effects and conflicting recommendations for parameter settings are found that emphasize the need for adaptive approaches.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:13 ,  Issue: 1 )