Adaptive beamforming by using complex-valued multi layer perceptron

Authors:
Andriyan Bayu Suksmono;Akira Hirose
Affiliations:
Dept. of Electrical Engineering, Institut Teknologi Bandung, Bandung, Indonesia;Grad. School of Frontier Sciences, University of Tokyo, Tokyo, Japan
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

We propose a complex-valued multilayer perceptron (CVMLP) neural network for adaptive beamforming. The complex-valued backpropagation algorithm (CVBPA) has been used to train the network. Experiments for a narrowband signal with multiple beam pointings and multiple nulls steering has been conducted. By using a 7-2-1 CVMLP topology and linear activation function, it is demonstrated that the beamforming by using CVMLP outperforms beamforming using complex-valued least mean square (CLMS) algorithm in terms of faster learning convergence and better interferences suppressions.