Remote and Rural Connectivity: Infrastructure and Resource Sharing Principles

TL;DR

This paper introduces a resource sharing and energy-efficient infrastructure deployment strategy for rural mobile networks, enabling sustainable connectivity with comparable QoS to urban areas.

Contribution

It proposes a novel BS deployment and resource management approach that facilitates energy-efficient infrastructure sharing between operators in remote areas.

Findings

Achieves 51% energy savings with the proposed algorithm.

Simulation results show accurate energy and traffic load predictions.

Energy savings decrease as user count increases.

Abstract

As Mobile Networks (MNs) are advancing towards meeting mobile users requirements, the rural-urban divide still remains a major challenge. While areas within the urban space (metropolitan mobile space) are being developed, rural areas are left behind. Due to challenges of low population density, low income, difficult terrain, non-existent infrastructure, lack of power grid, remote areas have low digital penetration. This situation makes remote areas less attractive towards investments and to operate connectivity networks, thus failing to achieve universal access to the Internet. In addressing this issue, this paper proposes a new BS deployment and resource management method for remote and rural areas. Here, two MN operators share their resources towards the procurement and deployment of green energy-powered BSs equipped with computing capabilities. Then, the network infrastructure is shared between the mobile operators, with the main goal of enabling energy-efficient infrastructure sharing, i.e., BS and its co-located computing platform. Using this resource management strategy in rural communication sites guarantees a Quality of Service (QoS) comparable to that of urban communication sites. The performance evaluation conducted through simulations validates our analysis as the prediction variations observed shows greater accuracy between the harvested energy and the traffic load. Also, the energy savings decrease as the number of mobile users (50 users in our case) connected to the remote site increases. Lastly, the proposed algorithm achieves 51% energy savings when compared with the 43% obtained by our benchmark algorithm. The proposed method demonstrates superior performance over the benchmark algorithm as it uses foresighted optimization where the harvested energy and the expected load are predicted over a given short-term horizon.