We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Efficient approximate-ML detection for MIMO spatial multiplexing systems by using a 1-D nearest neighbor search

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)
Seethaler, D. ; Inst. of Commun. & Radio-Frequency Eng., Vienna Univ. of Technol., Austria ; Artes, H. ; Hlawatsch, F.

It is known that suboptimal (equalization-based and ing-and-cancelling) detectors for MIMO spatial multiplexing systems cannot exploit all of the available diversity. Motivated by the insight that this behavior is mainly caused by poorly conditioned channel realizations, we propose the line-search detector (LSD) that is robust to poorly conditioned channels. The LSD uses a 1-D nearest neighbor search along the least significant singular vector of the channel matrix. It exhibits near-ML performance and has significantly lower complexity than the sphere-decoding algorithm for ML detection.

Published in:

Signal Processing and Information Technology, 2003. ISSPIT 2003. Proceedings of the 3rd IEEE International Symposium on

Date of Conference:

14-17 Dec. 2003