The Prediction Analysis of Cellular Radio Access Network Traffic: From Entropy Theory to Networking Practice

Although the research on traffic prediction is an established field, most existing works have been carried out on traditional wired broadband networks and rarely shed light on cellular radio access networks (CRANs). However, with the explosively growing demand for radio access, there is an urgent need to design a traffic-aware energy-efficient network architecture. In order to realize such a design, it becomes increasingly important to model the traffic predictability theoretically and discuss the traffic-aware networking practice technically. In light of that perspective, we first exploit entropy theory to analyze the traffic predictability in CRANs and demonstrate the practical prediction performance with the state-of-the-art methods. We then propose a blueprint for a traffic-based software-defined cellular radio access network (SDCRAN) architecture and address the potential applications of predicted traffic knowledge into this envisioned architecture.