25 March 1998 Neural-network-based beamforming for interference cancellation
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Abstract
A novel approach to the problem of finding the weights of an adaptive array is presented. In cellular and satellite mobile communications systems, desired as well as interfering signals are mobile. Therefore, a fast tracking system is needed to constantly estimate the directions of those users and then adapt the radiation pattern of the antenna to direct multiple beams to desired users and nulls to sources of interference. In this paper, the computation of the optimum weights is approached as a mapping problem which can be modeled using a suitable artificial neural network trained with input output pairs. A study of a three-layer Radial Basis Function Neural Network (RBFNN) is conducted. RBFNN were used due to their ability for data interpolation in higher dimensions. The network weights are modified using the normalized cumulative delta rule. The performance of this network is compared to the Wiener solution. It was found that networks implementing these functions were successful in tracking mobile users as they move across the antenna's field of view.
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Ahmed H. El Zooghby, Christos G. Christodoulou, Michael Georgiopoulos, "Neural-network-based beamforming for interference cancellation", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304832; https://doi.org/10.1117/12.304832
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