Phase Transmittance RBF Neural Network Beamforming for Static and Dynamic Channels

Abstract

This letter proposes a novel beamforming technique for self-organizing wireless networks, such as those encountered in fifth generation (5G) and the Internet of Things (IoT). The proposed beamformer is based on a complex radial basis function artificial neural network (ANN), which allows phase transmittance between the input and output nodes. The beamforming algorithm is applied to a six-element uniform circular array, considering the mutual coupling between elements. The proposed technique is evaluated over critical static and dynamic scenarios. The static scenario approaches a multiuser environment, highly polluted with electromagnetic interference and plenty of severe in-band undesired signals. The dynamic scenario emulates a close air support military operation, in which an aircraft moves around the ground station at a high velocity. In both scenarios, nonlinearities at the receiver analog RF front end with a –10 dB signal-to-interference ratio (SIR) are considered. Results show that the proposed technique presents significantly a superior performance when compared to other solutions. For the static scenario, the novel beamformer is able to focus the array radiation pattern in the direction of the desired signal, achieving a zero symbol error rate. For the dynamic scenario, the beamforming is able to track the moving station, achieving a low symbol error rate for a wide angular range.