A neural-network-based linearly constrained minimum variance beamformer

被引：0
|
作者：
El Zooghby, AH [1 ]
Christodoulou, CG
Georgiopoulos, M
机构：
[1] Univ Cent Florida, Dept Elect & Comp Engn, Orlando, FL 32816 USA
[2] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
来源：
MICROWAVE AND OPTICAL TECHNOLOGY LETTERS | 1999年 / 21卷 / 06期
关键词：
adaptive array antennas; adaptive beamforming; neural networks; wireless communications; interference cancellation;
D O I：
10.1002/(SICI)1098-2760(19990620)21:6<451::AID-MOP15>3.0.CO;2-M
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：451 / 455
页数：5
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