Managing the Fifth Generation (5G) Wireless Mobile Communication: A Machine Learning Approach for Network Traffic Prediction

Recently, virtualization has been recognized due to its flexibility, convenience, and cost efficiency. With virtualized infrastructures, comes automation of tasks and orchestration between different virtual elements in the network. In spite of saving a considerable amount of task organization throughout the system, monitoring the network cannot be disregarded. Network traffic offers different types of information that can be connected to network management challenges, such as, resource management and anomaly detection. In this paper, traffic prediction is examined in relation to upcoming communication systems. Therefore, a previously designed virtual 5G system is referred to as the source of the retrieved network traffic for training purposes. Results of training a neural network for prediction are presented, in addition to suggestions where the designed model can further serve for network management process.