Beamforming with phase transmittance complex valued RBF neural network for static and dynamic systems - Enriconi, Mateus Prauchner (2018)
In a world of insatiable demand for data, in a limited spectrum environment, wireless communications are increasingly operating under dynamic conditions, not only regarding information traffic parameters but also regarding the time varying conditions on the propagation channel that conveys the information between the transmitter (TX) and the receiver (RX). In this context, TX and RX need dynamically to adapt its operational parameters in order to obtain maximum data transmission efficiency. Smart antennas and beamforming techniques have an essential role on this dynamic operational environment. Such antennas are arranged on arrays and are based on adaptive systems, making them capable of generating any radiation pattern when the array comprises a sufficient number of electromagnetic irradiators. This thesis proposes the implementation of a novel beamforming technique, based on a complex radial basis function artificial neural network which presents phase transmittance between the input nodes and the output node (PT-RBF). The PT-RBF is capable of adaptively adjusting the radiation pattern of a smart antenna through a learning process based on the steepest descent algorithm. The proposed beamforming technique presents significantly superior results when compared with state-of-the-art algorithms presented in literature, making it possible to operate communication links under static scenarios on self-organizing wireless networks, and in dynamic scenarios with access in motion, both with multiple interferences, thus maximizing the throughput and the spectrum efficiency.