HANDOVER OPTIMIZATION IN 5G NETWORKS USING ANFIS

Abstract

The Corona Virus Disease (COVID-19) pandemic has upended the lives of people all over the world, and people have realized that the demand for new technologies, such as Internet of Things (IoT)-based devices, Vehicle Adhoc Networks (VANETS), and other upcoming technologies is increasing nowadays. These technologies are mostly based on controlling objects from a remote location, and some of them avoid close contact with others. So, a 5G network with more bandwidth is beneficial but the higher frequency signals will have more collisions with obstacles in the air, and thus it tends to reduce its energy more quickly. As a result, 5G networks are segregated into small cells to alleviate this issue but at the same time it is difficult to manage the handover process in small cells. To optimize the handover process, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is adopted in this research. The structure, as well as the input and output variables have been developed, and the rule base mathematical model, which describes all the possible handover scenarios based on these parameters, has been evaluated. A MATLAB simulation was used to test the effectiveness of the proposed handover ANFIS.