Enhanced Metaheuristic Algorithm-Based Load Balancing in a 5G Cloud Radio Access Network

Mobile operators must increase investments in network infrastructures due to the emergent growth of the internet and technological advancements. Mobile operators consider cloud-RAN and software defined networking to be developing technologies that can reduce costs and increase scalability for fifth-generation mobile communication networks (5G). A base station consists of two important components, namely baseband (BBU) and remote radio head (RRH) units. Unbalanced data traffic can arise, leading to call dropping and call blocking. When network traffic conditions start to vary, the performance of the system becomes suboptimal. Self-optimization of the network is necessary to reduce the load of overloaded eNode's with more call blocking, that increase the load of underloaded eNode's with less utilization of resources. The main objective of a self-organizing network is to reduce call blocking and optimize an unbalanced network. The proposed algorithm is an enhanced version of the cat swarm optimization algorithm performed by the host manager entity to select the best BBU-RRH combination after analyzing the quality-of-service (QoS) information from the remaining BBU-RRH configurations. Optimization is carried out on each user after a QoS analysis for every new BBU-RRH combination. The proposed algorithm is implemented in Matlab R2020a and evaluation is conducted in terms of blocking probability, response time, and throughput. The simulation results show that the proposed ECSO optimization algorithm reduces blocking probability by 10%, throughput is increased by 8%, and response time is reduced by 7% as compared with the existing PSO and CSO algorithms.

Automated summary

Mobile operators need to invest more in network infrastructures to keep up with the growth of the internet and technological advancements. Cloud-RAN and software defined networking are considered promising technologies for reducing costs and improving scalability in 5G networks. This study proposes an algorithm for self-optimizing networks by analyzing quality-of-service information to select the best combination of baseband and remote radio head units, reducing call blocking and optimizing network balance. Evaluation results show that the proposed algorithm outperforms existing algorithms in terms of blocking probability, throughput, and response time.