Load Balancing in 5G C-RAN Based on Dynamic BBU-RRH Mapping Supporting IoT Communications

Abstract

The Internet of Things (IoT) recent widespread popularity has sparked the research to implement communication networks that achieve better performance, flexibility, scalability and provide efficient resource management. 5G cellular networks provide a reliable framework for IoT devices to communicate effectively by relying on cloud technology known as Cloud Radio Access Network (C-RAN). However, the tremendous increase in wireless IoT network traffic leads to an imbalance in IoT network traffic, large number of blocked connections, and degradation in Quality of Service (QoS) of the IoT network. This paper aims to propose a solution for improving the QoS based on predicted load and exploiting the network re-mapping capability of C-RANs. It proposes to re-configure logical connections of Base Band Units (BBU) and Radio Resource Heads (RRH) based on predicting IoT network traffic and results in balanced load among the IoT network. The traffic load is predicted by employing Markov Model based on current users location for each cell. Ant Colony Optimization (ACO) technique is employed to search optimized mapping between BBU and RRH. The balanced traffic load leads to the minimization of blockage connections in the IoT network and hence improves the QoS. The obtained results indicate that the proposed technique ensures the maximization of QoS by minimizing the number of blockage connections as well as handovers which leads to more balanced IoT network traffic.