Design of Elastic Call Admission Control in Tidal Load Scenario for Cloud-based Cellular Networks

Abstract

Cellular networks have witnessed an unprecedented expansion represented by the number of subscribers in addition to the unlimited number of applications. In the era of IoT, connections may involve machines working side by side with humans on the same network. Tidal load is one of the key challenges in mobile networks, which represents a temporary phenomenon resulting from increasing traffic volumes at certain times of the day in particular places. This leads to the problem of overutilization of some base stations, particularly in fixed resources allocation systems such as 4G LTE architecture, which contains physically separated base stations. This results in increasing the dropping and blocking probability for the incoming calls in an overloaded area, while others suffer from underutilization or might be almost idle for a particular time. In this paper, an Elastic Call Admission Control (ECAC) approach is proposed to reduce the probability of blocking and dropping to improve the network performance in the tidal load scenario. This is achieved using the concept of gain multiplexing technique and cloud-based cellular architecture for utilizing the shared available recourses of the co-located virtual base stations in the cloud. A Fuzzy Type 2 (FT2) system is used to maintain the switching decision for resource sharing mode, the network load is predicted in advance using Gaussian Process Regression (GPR) model. Performance evaluation is carried out using the cloud-based Fog-Radio Access Network (F-RAN) platform. Compared to legacy LTE architecture, the results illustrate a noticeable enhancement in terms of minimizing blocking probability, enhancing overall network performance represented by network data throughput, and network utilization.