5G multi-tier radio access network planning based on voronoi diagram. (31st March 2022) - Su, Jun; Beshley, Mykola; Przystupa, Krzysztof; Kochan, Orest; Rusyn, Bohdan; Stanisławski, Rafał; Yaremko, Oleg; Majka, Michał; Beshley, Halyna; Demydov, Ivan; Pyrih, Julia; Kahalo, Ihor
Highlights: Polarimetric Synthetic Aperture Radar (POLSAR) is a radar system with multi-channel and multi-parameter characteristics. Polarization SAR image classification is an important part of polarization Synthetic Aperture Radar (SAR) image processing. There is proposed POLSAR image classification method based on the support vector machine (SVM) and Radial basis function (RBF) neural network. The final classification result combines the support vector machine (SVM) and Radial basis function (RBF) neural network classifier. Abstract: Network planning of multi-layer heterogeneous mobile networks with complex topology is an important task. In this paper the spectral and energy efficiency of integrated LTE/Wi-Fi technologies for 5G are improved. A method of adaptive formation of the structure of radio access level (RAL) with the provision of the required quality of service (QoS) and the possibility of broadband data transmission is proposed. The Voronoi tessellation is used for designing the RAL of 5G mobile networks for the placement of base stations, which allowed optimum delimiting the coverage area for each base station and provide users the cell interface services. To minimize interference there was proposed a method of dynamic frequency reuse for different sizes of Voronoi cells. Modelling shows the developed method is effective both at low and at high network load, however, at high load there is a slightly smaller gain in energy efficiency than at low load.