Joint Load Control and Energy Sharing for Renewable Powered Small Base Stations: A Machine Learning Approach

Abstract

The deployment of dense networks of small base stations represents one of the most promising solutions for future mobile networks to meet the foreseen increasing traffic demands. However, such an infrastructure consumes a considerable amount of energy, which, in turn, may represent an issue for the environment and the operational expenses of the mobile operators. The use of renewable energy to supply the small base stations has been recently considered as a mean to reduce the energy footprint of the mobile networks. In this paper, we consider a hierarchical structure in which part of the base stations are powered exclusively by solar panels and batteries. Base stations are grouped in clusters and connected in a micro-grid. A central controller enables base station sleep mode and energy sharing among the base stations based on the available energy budget and the traffic demands. We propose three different implementations of the controller through Machine Learning models, namely Imitation Learning, Q-Learning and Deep Q-Learning, capable of learning optimal sleep mode and energy sharing policies. We provide an exhaustive discussion on the achieved performance, complexity and feasibility of the proposed models together with the energy and cost savings attained.

A Scoping Review of Renewable Energy, Sustainability and the Environment

“…Surprisingly, sustainable human-centric healthcare and distant education systems [72,73], which intuitively may inspire scientists, due to the recent pandemic situation, in the last two years, give way to the logistics-related problems. Data science techniques and artificial intelligence can support ecological design, which accelerates the transition towards a regenerative approach [74] or can support energy management and sharing among base stations [75].…”

An Analysis of Renewable Energy Usage by Mobile Data Network Operators

“…The use of renewable energy is also gaining popularity in a dense network of small base stations [24]. To cope with the expected traffic requirements of mobile networks, a network of small base stations is being immensely deployed.…”

Renewable Energy-Enabled Cellular Networks

“…3) Techniques: Data size will continue to increase in the form of massive and distributed data and machine learning with big data training will be the most innovative technique for designing a 6G autonomous system [173]. Machine learning was already exploited for RE-based HetNets [73], [174], [175]. The authors [73] maximized energy efficiency through traffic offloading, and power allocation, whereas the work in [174] obtained the optimal BS sleep mode control and the energy sharing mechanism for the minimum grid energy consumption.…”

“…Machine learning was already exploited for RE-based HetNets [73], [174], [175]. The authors [73] maximized energy efficiency through traffic offloading, and power allocation, whereas the work in [174] obtained the optimal BS sleep mode control and the energy sharing mechanism for the minimum grid energy consumption. In the case of [175], the work addressed user scheduling and resource allocation to maximize energy efficiency.…”