Title

A neural-network-based linearly constrained minimum variance beamformer

Authors

Authors

A. H. El Zooghby; C. G. Christodoulou;M. Georgiopoulos

Comments

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Abbreviated Journal Title

Microw. Opt. Technol. Lett.

Keywords

adaptive array antennas; adaptive beamforming; neural networks; wireless; communications; interference cancellation; ANTENNA-ARRAYS; ADAPTIVE ARRAY; PERFORMANCE; Engineering, Electrical & Electronic; Optics

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 hacking mobile users in real time as they move across the antenna's field of view (C) 1999 John Wiley & Sons, Inc.

Journal Title

Microwave and Optical Technology Letters

Volume

21

Issue/Number

6

Publication Date

1-1-1999

Document Type

Article

DOI Link

http://dx.doi.org/10.1002/(sici)1098-2760(19990620)21:6<451::aid-mop15>3.3.co;2-d

Language

English

First Page

451

Last Page

455

WOS Identifier

WOS:000080627500015

ISSN

0895-2477

Recommended Citation

"A neural-network-based linearly constrained minimum variance beamformer" (1999). Faculty Bibliography 1990s. 2615.
https://stars.library.ucf.edu/facultybib1990/2615