Energy sustainable paradigms and methods for future mobile networks: A survey

Abstract

Abstract-In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.

Unsupervised Learning of Representations from Solar Energy Data

“…We can normalize the effective irradiance, I eff with respect to the maximum radiation of 1 kW/m 2 , obtaining the radiation rate F (t, N ) = 0.001·I eff (t, N ). Then, we can compute the light-generated current for a single solar cells as i l (t, N ) = i sc F (t, N ) and obtain i out (t, N ) for a single solar cells using equation (5). Finally, the total current generated by the solar module is i M out (t, N ) = n p i out (t, N ).…”

“…Several works in the literature [3], [4], [5] discuss the integration of solar energy sources into communication networks as a way to reduce their energy consumption and conclude that such an integration is feasible only with the introduction of an intelligent control system able to manage the intermittent and erratic energy budget from renewable sources.…”

A Capacity-Enhanced Local Search for the 5G Cell Switch-off Problem

“…With these predictions, both public and private initiatives started to develop the fifth generation (5G) of cellular systems more than a decade ago. The design goals for such networks to clearly improve upon the existing technologies were quite ambitious [2,3], aiming, among others, at 1-10Gbps connections, 1 ms latency, 1000x bandwidth, 10-100x connections and, the one that targets this work, 90% of energy consumption to reduce the increasing carbon footprint of this newly 5G networks [18].…”

Online Supervisory Control and Resource Management for Energy Harvesting BS Sites Empowered with Computation Capabilities

“…The survey paper [23], presents a taxonomy of existing energy sustainable paradigms and methods to address energy savings in network elements (i.e., BSs) equipped with EH capabilities. Here, the authors discuss the shortcomings of previous studies related to efficient energy management procedures, the lack of relevant discussion related to the integration of EH into future networks, and lastly, energy self-sustainability in future networks.…”

“…Here, the authors discuss the shortcomings of previous studies related to efficient energy management procedures, the lack of relevant discussion related to the integration of EH into future networks, and lastly, energy self-sustainability in future networks. The current work is a technical contribution where we address some of the shortcomings that were identified in [23], also proposing the use of Machine Learning (ML) tools for pattern forecasting and adaptive control schemes for decision making. In addition, this work is in line with the research topics which can be found in our review paper [24].…”