Performance of RBF neural networks for array processing in impulsive noise environment

This paper addresses the array processing problems (mainly focuses on direction of arrival estimation and beamforming) of mobile communication system using linear antenna arrays in high impulsive noise environment. One possible way to simulate the impulsive noise is to introduce alpha-stable distribution as the noise model. In order to reduce the computational complexity, the problems of DOA and beamforming are approached as a nonlinear mapping which can be modeled using a suitable radial-basis function neural network (RBFNN) trained with input-output pairs. This paper discusses the application of a three-layer RBFNN to perform the DOA estimation and beamforming in presence of impulsive noise. The performance of the network is compared to that of the algorithms based fractional lower-order statistics. Simulations show that the RBFNN is appropriate to approach the DOA estimation and beamforming. At the same time, the RBFNN substantially reduces the computation complexity.

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