# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 3 Review of NS3 Modules
The main feature of this approach is the existence of a notification that displays when the standard QCN is overloaded. Another option for researching the NS3 network simulator usage is module integration with a novel technology (which has no implementation yet). So the development group added FD-MIMO technology to the 5G NR module [29]. Analyzing both the situation and the behavior of the added technology has shown that FD-MIMO overcomes several practical problems such as interference and hardware constraints. Besides the introduction of new modules, the scientific community analyze existing tools. This way, while analyzing the algorithm for separating a 5G mobile network based on priority, it was noticed that the goals of this approach, in particular improving performance, security, flexibility and monitoring of networks, are achieved by dramatically increasing latency and reducing work efficiency [30]. The obtained using the simulator results show the need to modernize the existing algorithms. The increasing attention to neural networks attracts scientists to implement and process data while working with network simulators. Therefore, NS3 is most often used as a traffic generator or a tool for downloading large volumes of data, which is a viable option for neural network models training. For instance, the article [31] analyzes using machine learning to predict channels of the 5G network of the module mmWave. The presented results show that it is possible to detect a higher percentage of congestion situations using machine learning, opening up opportunities to modernize protocols for future standards. The project using adaptive transmission of 3D-video when exchanging data in 5G mobile networks [32] uses an analogous principle working with the NS3. The developers note that the simulator has a ready-made module and affords a convenient way to generate data planned to process by neural networks in the future. Additionally, Support Vector Machine applies to coordinate self-organizing networks towards optimization and analysis of throughput in mobile networks [33; 34]. As in the previous, this project uses the network simulator as a data generator. The exception is in analyzing both 4G and 5G networks by comparative between performance and efficiency. When different networks types interact with each other, the analysis of the behavior of their models implemented with the simulator is also used. For instance, the project to performance evaluation of 5G access technologies and the SDN transport network [35] analyses traffic carried out during transmission in mobile networks of the 4 th and 5 th generations to the transport SDN network. The estimation results showed that eMBB traffic transmitted by 5G networks is more suitable for interacting with SDN than 4G networks, and, also, the transition to a new mobile network generation makes absolute sense from the point of this issue view. A common enough solution is the issue of virtualization and containerization of services in 5 th generation mobile networks. For instance, the analysis and improvement of the existing functionality to implement the container LTE network in 5G through incipient modules is in several projects all at once [36; 37; 38]. Additionally, the process of integrating the algorithms for the implementation of virtual small base stations with the possibility of dynamic selection of the more suitable one occurs in the projects. Estimating the behavior of these manipulations in all projects became possible using ready-made modules and scripts of the NS3 network simulator. There are difficulties with the load on protocol interaction for sending packets when the simulator is used as receiver traffic of 5G mobile networks. It is due to a continuing synchronous increase in the number of intelligent applications and user traffic. In this regard, the development group presented a module with a virtualized and programmable EPC architecture (NOS-EPC) [39; 40].