We propose a complex-valued multilayer perception (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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Suksmono, A. B., & Hirose, A. (2003). Adaptive beamforming by using complex-valued multi layer perceptron. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 959–966. https://doi.org/10.1007/3-540-44989-2_114
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