Autoencoder Guided Low Complexity Adaptive Beamforming

Adaptive Beamforming is an array signal processing problem in which a beam pattern is generated in the direction of desired signal and nulls are placed in the directions of undesired signals. Neural networks are widely employed for adaptive beamforming problems. In this paper, a autoencoder(AE) guided radial basis function(RBF) neural network(NN) is proposed. The proposed AE guided RBF neural network is also named as hybrid neural network since it is a combination of AE and RBF. Feature extraction and data compression are achieved by using the encoder part of AE in the input and first hidden layer of the hybrid neural network. The proposed hybrid neural network model is compared with a classical 3-layered RBF neural network according to Signal to Interferance Ratio(SIR) performance, Half Power Beamwidth(HPBW) and processing load. Simulation results show that the proposed hybrid neural network reduces the processing load without decreasing the SIR performance.