By Topic

Polynomial constrained detection for MIMO systems using penalty function

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

2 Author(s)
Tao Cui ; Dept. of Electr. & Comput. Eng., Alberta Univ., Edmonton, Alta., Canada ; C. Tellambura

In this paper, we develop a family of approximate maximum likelihood (ML) detectors for multiple-input multiple-output (MlMO) systems by relaxing the ML detection problem. Polynomial constraints are formulated for any signal constellation. The resulting relaxed constrained optimization problem is solved using a penalty function approach. Moreover, to escape from the local minima and to improve the performance of detection, a probabilistic restart algorithm based on noise statistics is proposed. Simulation results show that our polynomial constrained detectors perform better than several existing detectors.

Published in:

PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.

Date of Conference:

24-26 Aug. 2005