Beamweight Prediction Using Complex Valued Convolutional Neural Networks

Abstract

The advanced antenna systems used for telecommunication today utilize antenna array structures. These structures consist of grids of antenna elements divided into smaller subarrays of elements where each subarray is dynamically controllable, independently from the others. Each subarray is typically mapped to a baseband port, and the amplitude and phase of the signal on each of this baseband port can be controlled independently, which is called excitation vectors or beamweights. The main aim of this thesis was to investigate and develop a machine learning model that can predict beamweights for unseen antenna arrays such that the radiated far field of the antenna follows a specific pattern. The model was evaluated and compared to a more common method, stochastic optimization. Furthermore, the model was tested to see how stable the outputs are against noise and if the model is scalable to antenna arrays of different sizes. The main conclusion from the thesis is that it is possible to train a complex valued convolutional neural network (CV-CNN), a type of machine learning model, to predict the desired beamweights. The results also show that the beamweights generated from the two different methods result in far fields which favour different performance metrics. An important difference in the models is the time it takes to generate beamweights. A pre-trained CV-CNN generates beamweights in a fraction of the time it takes to generate beamweights using the more common method. The conclusion from the comparison is that the method should be chosen case by case. The stability analysis shows that the model is more sensitive to noise in the phase than noise in the amplitude of the input. The reason for this might come from an approximation made while processing the data but a possible solution to this is presented. Finally, the results show promise of scalability of the model. The model must however be optimized for each size of antenna array in order to get satisfactory results.