Orientational Beamforming via a Modified RBF Neural Network for Orientational UWB Interference Rejection

The performance of the recently proposed orientational beamforming (OBF) system for ultra-wideband (UWB) signals degrades seriously in multiuser situation. Although various techniques have been proposed in the literature to suppress multiuser interference in the time-hopping UWB system, there has been no research on interference suppression in the OBF UWB system. In this paper, we propose a nonlinear OBF system via a modified radial basis function (RBF) neural network, named the RBF-OBF system, to reject orientational UWB interferences caused by multiuser transmission in the OBF system. The RBF neural network is selected mainly because of its simple structure, convenient training process, and fast convergence speed. However, the conventional Euclidean-distance-based RBF neural network is not suitable for the training process used in this work, as the training samples are noiseless orientational steering vectors in frequency domain. Therefore, a modified RBF neural network is proposed in this paper, which evaluates the similarity between an input vector and the center vector of a hidden layer neuron by their signed complex correlation coefficient. Numerous simulations demonstrate that compared to the conventional OBF system, the proposed RBF-OBF system can significantly reduce the bit error rates (BERs) under different noise and interference situations. The BER reduction rate of the proposed RBF-OBF system is higher than 92% in additive white Gaussian noise channel and higher than 89% in line-of-sight multipath channel.