Traffic Prediction in Cellular Networks using Graph Neural Networks

Abstract: Cellular networks are ubiquitous entities that provide major means of communication all over the world. One major challenge in cellular networks is a dynamic change in the number of users and their usage of telecommunication service which results in overloading at certain base stations. One class of solution to deal with this overloading issue is the deployment of drones that can act as temporary base stations and offload the traffic from the overloaded base station. There are two main challenges in the development of this solution. Firstly, the drone is expected to be present around the base station where an overload would occur in the future thus requiring a prediction of traffic overload. Secondly, drones are highly constrained in their resources and can only fly for a few minutes. If the affected base station is really far, drones can never reach there. This requires the initial placement of drones in sectors where overloading can occur thus again requiring a traffic forecast but at a different spatial scale. It must be noted that the spatial extent of the region that the problem poses and the extremely limited power resources available to the drone pose a great challenge that is hard to overcome without deploying the drones in strategic positions to reduce the time to fly to the required high-demand zone. Moreover, since drone fly at a finite speed, it is important that a predictive solution that can forecast traffic surges is adopted so that drones are available to offload the overload before it actually happens. Both these goals require analysis and forecast of cellular network traffic which is the main goal of this project