A neural adaptive beamforming system to reduce interfering signals in mobile communications

This work proposes a neural adaptive beamforming system combining a feedforward artificial neural network with a backpropagation learning algorithm and linear, planar and circular antenna arrays. It intends to reduce interfering signals of the type for multi-path, co-channel interference or near-far...

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Bibliographic Details
Main Authors: Arruda Filho, E.J.M., Cavalcante, G.P.S.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive arrays
Adaptive systems
Antenna feeds
Antenna radiation patterns
Array signal processing
Artificial neural networks
Backpropagation algorithms
Interchannel interference
Linear antenna arrays
Position control
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Summary:This work proposes a neural adaptive beamforming system combining a feedforward artificial neural network with a backpropagation learning algorithm and linear, planar and circular antenna arrays. It intends to reduce interfering signals of the type for multi-path, co-channel interference or near-far field problems in communication systems. The system as a whole maximizes the signal/interference rate (SIR) by the orientation control of the radiation pattern of the arrays. The array individual element excitation allows the creation of a radiation pattern orienting the beam in different directions, pointing out a null (or minimum) for the interfering signal, and a maximum for the desired one.
DOI:10.1109/IMOC.1999.866266