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

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.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure

InSAR deformation time series classification using a convolutional neural network

Temporal analysis of deformations Time Series (TS) provides detailed information of various natural and humanmade displacements. Interferometric Synthetic Aperture Radar (InSAR) generates millimetre-scale products, indicating the chronicle behaviour of detected targets via TS products. Deep Learning (DL) can handle a massive load of InSAR TS to categorize significant movements from non-moving targets. To this end, we employed a supervised Convolutional Neural Network (CNN) model to distinguish five deformations trends, including Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error (PUE). Considering several arguments in a CNN model, we trained numerous combinations to explore the most accurate combination from 5000 samples extracted from a Persistent Scatterer Interferometry (PSI) technique and Sentinel-1 images over the Granada region, Spain. The model overall accuracy exceeds 92%. Deformations of three cases of landslides were also detected over the same area, including the Cortijo de Lorenzo, El Arrecife, and Rules Viaduct areas.Peer ReviewedPostprint (published version

Supervised machine learning algorithms for ground motion time series classification from InSAR data

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the genera- tion of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deforma- tion identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/ AEI/10.13039/501100011033. Additionally, it has been supported by the European Regional Devel- opment Fund (ERDF) through the project “RISKCOAST” (SOE3/P4/E0868) of the Interreg SUDOE Programme. Additionally, this work has been co-funded by the European Union Civil Protection through the H2020 project RASTOOL (UCPM-2021-PP-101048474).Peer ReviewedPostprint (published version

Energy Management Strategies for Sustainable 5G Mobile Networks

The massive use of Information and Communications Technology (ICT) is increasing the amount of energy drained by the telecommunication infrastructure and its footprint on the environment. With the advent of the smartphone, mobile traffic is massively growing driven by both the rising number of user subscriptions and an increasing average data volume per subscription. This is putting a lot of pressure on the mobile network operators side, which are enforced to boost their infrastructure capacity by densifying the network with more Base Stations (BSs) and resources, which translates to a growth in the energy consumption and related costs. Hence, any future development in the ICT sector and its infrastructure has definitely to cope with their environmental and economical sustainability, where energy management is essential. In this thesis, we discuss the role of energy in the design of eco-friendly cost-effective sustainable mobile networks and, in particular, we elaborate on the use of Energy Harvesting (EH) hardware as a means to decrease the environmental footprint of the 5G network. Specifically, we investigate energy management strategies in 5G mobile networks with the main goals of: (i) improving the energy balance across base stations and other network elements, (ii) understanding how the energy can be exchanged either among network elements and the electrical grid, and (iii) investigating how renewable energy sources can be utilized within network elements to maximize the utility for the overall network in terms of better performance for the users (e.g., throughput, coverage, etc.), and lower energy consumption (i.e., carbon footprint) for the 5G network infrastructure. Therefore, we address, formulate and solve some of the problems related to the energy management in different scenarios within the 5G mobile network. The main covered topics are: (i) Wireless Energy Transfer where we investigate the tradeoffs involved in the recharging process from base stations to end users; (ii) Energy Cooperation in Mobile Networks where we target deployments featuring BSs with EH capabilities, i.e., equipped with solar panels and energy storage units, that are able to transfer energy among them; (iii) Energy Trading with the Electrical Grid where energy management schemes to diminish the cost incurred in the energy purchases from the electrical grid are pursued; and (iv) Energy Harvesting and Edge Computing Resource Management where EH and Mobile Edge Computing (MEC) paradigms are combined within a multi-operator infrastructure sharing scenario with the goal of maximizing the exploitation of the network resources while decreasing monetary costs. Online learning techniques, such as Gaussian Processes and Machine Learning Neural Networks, and adaptive control tools, like Model Predictive Control, are put together to tackle these challenges with remarkable results in decreasing costs related to energy purchases from the electrical grid and energy efficiency among network elements

Energy Management Strategies for Sustainable 5G Mobile Networks

The massive use of Information and Communications Technology (ICT) is increasing the amount of energy drained by the telecommunication infrastructure and its footprint on the environment. With the advent of the smartphone, mobile traffic is massively growing driven by both the rising number of user subscriptions and an increasing average data volume per subscription. This is putting a lot of pressure on the mobile network operators side, which are enforced to boost their infrastructure capacity by densifying the network with more Base Stations (BSs) and resources, which translates to a growth in the energy consumption and related costs. Hence, any future development in the ICT sector and its infrastructure has definitely to cope with their environmental and economical sustainability, where energy management is essential. In this thesis, we discuss the role of energy in the design of eco-friendly cost-effective sustainable mobile networks and, in particular, we elaborate on the use of Energy Harvesting (EH) hardware as a means to decrease the environmental footprint of the 5G network. Specifically, we investigate energy management strategies in 5G mobile networks with the main goals of: (i) improving the energy balance across base stations and other network elements, (ii) understanding how the energy can be exchanged either among network elements and the electrical grid, and (iii) investigating how renewable energy sources can be utilized within network elements to maximize the utility for the overall network in terms of better performance for the users (e.g., throughput, coverage, etc.), and lower energy consumption (i.e., carbon footprint) for the 5G network infrastructure. Therefore, we address, formulate and solve some of the problems related to the energy management in different scenarios within the 5G mobile network. The main covered topics are: (i) Wireless Energy Transfer where we investigate the tradeoffs involved in the recharging process from base stations to end users; (ii) Energy Cooperation in Mobile Networks where we target deployments featuring BSs with EH capabilities, i.e., equipped with solar panels and energy storage units, that are able to transfer energy among them; (iii) Energy Trading with the Electrical Grid where energy management schemes to diminish the cost incurred in the energy purchases from the electrical grid are pursued; and (iv) Energy Harvesting and Edge Computing Resource Management where EH and Mobile Edge Computing (MEC) paradigms are combined within a multi-operator infrastructure sharing scenario with the goal of maximizing the exploitation of the network resources while decreasing monetary costs. Online learning techniques, such as Gaussian Processes and Machine Learning Neural Networks, and adaptive control tools, like Model Predictive Control, are put together to tackle these challenges with remarkable results in decreasing costs related to energy purchases from the electrical grid and energy efficiency among network elements

A Sharing Framework for Energy and Computing Resources in Multi-Operator Mobile Networks

EH and MEC are here combined to build energy-sustainable mobile networks. We consider an edge infrastructure shared among several mobile operators and equipped with a solar EH farm for energy efficiency purposes together with an edge MEC server for low-latency computation, where two main goals are pursued: (i) to maximally and fairly exploit the available resources at the edge, allotting them among BS belonging to different operators; and (ii) to decrease the monetary cost incurred by energy purchases from the power grid. To do so, we devise an online framework combining ANN-based pattern forecasting that learns energy harvesting and traffic load profiles over time, and MPC-based adaptive algorithms. Numerical results, obtained with real-world harvested energy, traffic load, and energy price traces, show that our proposal effectively reduces the amount of purchased energy from the electrical grid by more than 50% with respect to the case where no EH is considered, and by about 30% with respect to the case where the optimization is performed disregarding future energy and traffic load forecasts. Moreover, it is capable of reducing the energy consumption related to edge computation by about 20% with respect to two benchmark policies

Smart Energy Policies for Sustainable Mobile Networks via Forecasting and Adaptive Control

The design of sustainable mobile networks is key to reduce their impact on the environment, and to diminish their operating cost. As a solution to this, we advocate Energy Harvesting (EH) Base Stations (BSs) that collect energy from the environment, use it to serve the local traffic and/or store it in a battery for later use. Moreover, whenever the amount of energy harvested is insufficient to serve their traffic load, BSs purchase energy from the power grid. Within this setup, a smart energy management strategy is devised with the goal of diminishing the cost incurred in the energy purchases. This is achieved by intelligently controlling the amount of energy that BSs buy from the electrical grid over time, by accounting for the harvested energy, the traffic load, and hourly energy prices. The proposed optimization framework combines pattern forecasting and adaptive control. In a first stage, harvested energy and traffic load processes are modeled through a Long Short-Term Memory (LSTM) neural network, allowing each BS to independently predict future energy and load patterns. LSTM-based forecasts are then fed into an adaptive control block, where foresighted optimization is performed using Model Predictive Control (MPC). Numerical results, obtained with real-world energy and load signals, show cost savings close to 20% and reductions in the amount of energy purchased from the electrical grid of about 24%, with respect to a heuristic scheme where future system states are not taken into account