Enhancement and performance analysis for 3D beamforming systems

Abstract

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis thesis is about the researching for 5th generation (5G) communication system, which focus on the improvement of 3D beamforming technology in the antenna array using in the Full Dimension Multiple-Input Multiple-Output (FD-MIMO) system and Millimeter-wave (mm-wave) system. When the 3D beamforming technology has been used in 5G communication system, the beam needs a weighting matrix to direct the beam to cover the UEs, but some compromises should be considered. If the narrow beams are used to transmit signals, then more energy is focused in the desired direction, but this has a restricted coverage area to a single or few User Equipments (UEs). If the BS covers multiple UEs, then multiple beams need to be steered towards more groups of UEs, but there is more interference between these beams from their side lobes when they are transmitted at same time. These challenges are waiting to be solved, which are about interference between each beam when the 3D beamforming technology is used. Therefore, there needs to be one method to decrease the generated interference between each beam through directing the side lobe beams and nulls to minimize interference in the 3D beamforming system. Simultaneously, energy needs to be directed towards the desired direction. If it has been decided that one beam should covera cluster of UEs, then there will be a range of received Signal to Interference plus Noise Ratio (SINR) depending on the location of the UEs relative to the direction of the main beam. If the beam is directed towards a group of UEs then there needs be a clustering method to cluster the UEs. In order to cover multiple UEs, an improved K-means clustering algorithm is used to cluster the multiple UEs into different groups, which is based on the cosine distance. Itcan decrease the number of beams when multiple UEs need be covered by multiple beams at same time. Moreover, a new method has been developed to calculate the weighting matrix for beamforming. It can adjust the values of weighting matrix according to the UEs’ location and direct the main beam in a desired direction whilst minimizing its side lobes in other undesired directions. Then the minimum side lobe beamforming system only needs to know the UEs’ location and can be used to estimate the Channel State Information (CSI) of UEs. Therefore, the scheme also shows lower complexity when compared to the beamforming methods with pre-coding. In order to test the improved K-means clustering algorithm and the new weighting method that can enhance the performance for 3D beamforming system, the two simulation systems are simulated to show the results such as 3D beamforming LTE system and mm-wave system

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