Network Traffic Load Balance Using Fuzzy C-Mean Clustering in 6G Cellular Networks

The 6G networks will take all capabilities of 5G networks and add new applications such as Augmented Reality (AR) and Virtual Reality (VR) streaming, ehealth, automated vehicles, smart factories, smart cities and so on. The current cellular networks face the exploding network traffics due to the increasing number of high bandwidth applications. Since mobile users require different network resources and services, the mobile operators are troubled about the network traffic load balancing in their own networks. Thus, the network traffic load balance will be a key challenge to design and deploy 6G networks. The network traffic load balancing is about network traffic load re-distribution by detecting the imbalance of the network traffic load and changing the network parameters. Thus, it is helpful for preventing unexpected network traffics and maximizing the efficiency of the network resources. In this paper, we investigate the network traffic load balance problem and propose a new solution using Fuzzy C Mean (FCM) clustering. The network traffic load balance problem is formulated as a MINLP problem. Using the proposed network traffic load balance method, we obtained maximum 22% performance improvement under the given simulation configuration.