A Traffic Load Balancing Framework for Software-defined Radio Access Networks Powered by Hybrid Energy Sources

TL;DR

This paper proposes a traffic load balancing framework for energy-harvesting small cell networks that optimizes green energy use and network performance, reducing on-grid power consumption with minimal latency increase.

Contribution

It introduces a novel load balancing scheme leveraging SDN architecture to balance green energy utilization and traffic latency, with a distributed implementation reducing communication overhead.

Findings

Reduces on-grid power consumption by 30%.

Increases traffic latency by only 8%.

Provides an adjustable trade-off between energy use and latency.

Abstract

Dramatic mobile data traffic growth has spurred a dense deployment of small cell base stations (SCBSs). Small cells enhance the spectrum efficiency and thus enlarge the capacity of mobile networks. Although SCBSs consume much less power than macro BSs (MBSs) do, the overall power consumption of a large number of SCBSs is phenomenal. As the energy harvesting technology advances, base stations (BSs) can be powered by green energy to alleviate the on-grid power consumption. For mobile networks with high BS density, traffic load balancing is critical in order to exploit the capacity of SCBSs. To fully utilize harvested energy, it is desirable to incorporate the green energy utilization as a performance metric in traffic load balancing strategies. In this paper, we have proposed a traffic load balancing framework that strives a balance between network utilities, e.g., the average traffic delivery latency, and the green energy utilization. Various properties of the proposed framework have been derived. Leveraging the software-defined radio access network architecture, the proposed scheme is implemented as a virtually distributed algorithm, which significantly reduces the communication overheads between users and BSs. The simulation results show that the proposed traffic load balancing framework enables an adjustable trade-off between the on-grid power consumption and the average traffic delivery latency, and saves a considerable amount of on-grid power, e.g., 30%, at a cost of only a small increase, e.g., 8%, of the average traffic delivery latency.