Title
Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays
Keywords
Adaptive arrays; Antenna arrays; Beamforming; Neural network applications; Tracking
Abstract
In this letter, we present a neural network approach to the problem of finding the weights of one- (1-D) and two-dimensional (2-1)1 adaptive arrays. In modern cellular satellite mobile communications systems and in global positioning systems (GPS's), both desired and interfering signals change their directions continuously. Therefore, a fast tracking system is needed to constantly track the users and then adapt the radiation pattern of the antenna to direct multiple narrow beams to desired users and nulls interfering sources. In the approach suggested in this paper, the computation of the optimum weights is accomplished using three-layer radial basis function neural networks (RBFNN). The results obtained from this network are in excellent agreement with the Wiener solution. © 1999 IEEE.
Publication Date
12-1-1998
Publication Title
IEEE Transactions on Antennas and Propagation
Volume
46
Issue
12
Number of Pages
1891-1893
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/8.743843
Copyright Status
Unknown
Socpus ID
0001617975 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/0001617975
STARS Citation
El Zooghby, A. H.; Christodoulou, C. G.; and Georgiopoulos, M., "Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays" (1998). Scopus Export 1990s. 3757.
https://stars.library.ucf.edu/scopus1990/3757