Multi-Access Edge Computing (MEC) is a paradigm for handling delay sensitive services that require ultra-low latency at the access network. With it, computing and communications are performed within one Base Station (BS) site, where the computation resources are in the form of Virtual Machines (VMs) (computer emulators) in the MEC server. MEC and Energy Harvesting (EH) BSs, i.e., BSs equipped with EH equipments, are foreseen as a key towards next generation mobile networks. In fact, EH systems are expected to decrease the energy drained from the electricity grid and facilitate the deployment of BSs in remote places, extending network coverage and making energy self-sufficiency possible in remote/rural sites. In this paper, we propose an online optimization algorithm called ENergy Aware and Adaptive Management (ENAAM), for managing remote BS sites through foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 56% to 66% through the scaling of computing resources, and keeps the server utilization factor between 30% and 96% over time (with an average of 75%). Notable benefits are also found against heuristic energy management techniques.

The convergence of communication and computing has lead to the emergence of Multi-access Edge Computing (MEC), where computing resources (supported by Virtual Machines (VMs)) are distributed at the edge of the Mobile Network (MN), i.e., in Base Stations (BSs), with the aim of ensuring reliable and ultra-low latency services. Moreover, BSs equipped with Energy Harvesting (EH) systems can decrease the amount of energy drained from the power grid resulting into energetically self-sufficient MNs. The combination of these paradigms is considered here. Specifically, we propose an online optimization algorithm, called ENergy Aware and Adaptive Management (ENAAM), based on foresighted control policies exploiting (short-term) traffic load and harvested energy forecasts, where BSs and VMs are dynamically switched on/off towards energy savings and QoS provisioning. Our numerical results reveal that ENAAM achieves energy savings with respect to the case where no energy management is applied, ranging from 57% and 69%. Moreover, the extension of ENAAM within a cluster of BSs provides a further gain ranging from 9% to 16% in energy savings with respect to the optimization performed in isolation for each BS.

In virtualized computing platforms, energy consumption is related to the computing-plus-communication processes. However, most of the proposed energy consumption models and energy saving solutions found in literature consider only the active Virtual Machines (VMs), thus the overall operational energy expenditure is usually related to solely the computation process. To address this shortcoming, in this paper we consider a computing-plus-communication energy model, within the Multiaccess Edge Computing (MEC) paradigm, and then put forward a combination of a traffic engineering-and MEC Location Service-based online server management algorithm with Energy Harvesting (EH) capabilities, called Automated Resource Controller for Energy-aware Server (ARCES), for autoscaling and reconfiguring the computing-plus-communication resources. The main goal is to minimize the overall energy consumption, under hard per-task delay constraints (i.e., Quality of Service (QoS)). ARCES jointly performs (i) a short-term server demand and harvested solar energy forecasting, (ii) VM soft-scaling, workload and processing rate allocation and lastly, (iii) switching on/off of transmission drivers (i.e., fast tunable lasers) coupled with the location-aware traffic scheduling. Our numerical results reveal that ARCES achieves on average energy savings of 69%, and an energy consumption ranging from 31%-45%and from 21%-25% at different values of per-VM reconfiguration cost, with respect to the case where no energy management is applied.

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.

Abstract-Energy self-sufficiency is of prime importance for future mobile networks. The design of energy efficient and possibly self-sustainable base stations is key to reduce their impact on the environment, and diminish their operating expense. As a solution to this, we advocate base station deployments featuring energy harvesting and storage capabilities. Each base station can acquire energy from the environment, promptly use it to serve the local traffic or keep it in its storage for later use. In addition, a power packet grid (DC power lines and switches) is utilized to enable energy transfer (energy routing) across base stations, compensating for imbalance in the harvested energy or in the load. Most of the base stations are offgrid, i.e., they can only use the locally harvested energy and that transferred from other network elements, whereas some of them are ongrid, i.e., they can also purchase energy from the electrical grid. We formulate the optimal energy allocation and routing as a convex optimization problem with the goals of improving the energy self-sustainability of the network, while achieving high energy transfer efficiencies under dynamic load and energy harvesting processes. An optimal assignment based on the Hungarian method is also presented. Our numerical results reveal that the proposed convex policy: (i) substantially improves the energy self-sustainability of the system, (ii) decreases its outage probability to nearly zero, even when a small number of base stations are connected to the electrical grid, and (iii) the amount of energy purchased from the electrical grid per served user is respectively decreased of three and eight times with respect to using the Hungarian policy and a scenario where the energy exchange among base stations is not permitted.

Wireless Power Transfer (WPT) technology offers unprecedented opportunities to future cellular systems, making it possible to wirelessly recharge the mobile terminals as they get sufficiently close to the Base Stations (BSs). Here, we investigate the tradeoffs involved in the recharging process as multiple mobile users move across the cellular network, by systematically measuring the charging efficiency (i.e., amount of energy transferred as opposed to that transmitted) accounting for different mobility models, speeds, frequency range and inter-BS distance. We consider dense cellular deployments, where power is transferred to the mobile users through beamforming and scheduling techniques. At first, a genie is utilized to devise optimal charging schedules, where user locations and the residual energy in their batteries are exactly known by the controller. Hence, several heuristic policies are proposed and their performance is compared against that of the genie-based approach in terms of transfer efficiency and fraction of dead nodes (whose battery is completely depleted). Our numerical results reveal that: i) an even allocation of resources among users is inefficient, whereas even a rough estimate of their location allows heuristic policies to perform close to the genie-based approach, ii) mobility matters: group mobility leads to higher efficiencies and an increasing speed is also beneficial and iii) WPT can substantially reduce the number of dead nodes in the network, although this comes at the expense of constantly transmitting power and transfer efficiencies are very low under any scenario.

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical control channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. Towards this, we decode Downlink Control Information (DCI) messages carried within the LTE Physical Downlink Control CHannel (PDCCH). DCI messages are sent by the radio cell in clear text and, in this paper, are utilized to classify the applications and services executed at the connected mobile terminals. Two datasets are collected through a large measurement campaign: one labeled, used to train the classification algorithms, and one unlabeled, collected from four radio cells in the metropolitan area of Barcelona, in Spain. Among other approaches, our Convolutional Neural Network (CNN) classifier provides the highest classification accuracy of 98%. The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained decomposition of the traffic for the four monitored radio cells, in an online and unsupervised fashion.

The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in particular deformation Time Series (TS), enables advanced investigations for ground deformation 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.