Title
Neural-Network-Based Linearly Constrained Minimum Variance Beamformer
Abstract
This paper presents a neural network approach for beam-forming and interference cancellation. A three-layer radial basis function neural network is trained with input-output pairs. The results obtained from this network are in excellent agreement with the Wiener solution. It was found that networks implementing these functions are successful in tracking mobile users in real time as they move across the antenna's field of view.
Publication Date
6-20-1999
Publication Title
Microwave and Optical Technology Letters
Volume
21
Issue
6
Number of Pages
451-455
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1002/(SICI)1098-2760(19990620)21:6<451::AID-MOP15>3.0.CO;2-M
Copyright Status
Unknown
Socpus ID
0344771061 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0344771061
STARS Citation
El Zooghby, A. H.; Christodoulou, C. G.; and Georgiopoulos, M., "Neural-Network-Based Linearly Constrained Minimum Variance Beamformer" (1999). Scopus Export 1990s. 4133.
https://stars.library.ucf.edu/scopus1990/4133