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A neural‐network‐based linearly constrained minimum variance beamformer
Author(s) -
El Zooghby A. H.,
Christodoulou C. G.,
Georgiopoulos M.
Publication year - 1999
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/(sici)1098-2760(19990620)21:6<451::aid-mop15>3.0.co;2-m
Subject(s) - artificial neural network , beamforming , minimum variance unbiased estimator , antenna (radio) , tracking (education) , microwave , function (biology) , interference (communication) , engineering , computer science , electronic engineering , algorithm , telecommunications , mathematics , artificial intelligence , channel (broadcasting) , statistics , mean squared error , evolutionary biology , biology , psychology , pedagogy
This paper presents a neural network approach for beamforming 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. ©1999 John Wiley & Sons, Inc. Microwave Opt Technol Lett 21: 451–455, 1999.