Energy sustainability of next generation cellular networks through learning techniques

The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks. In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand. However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version

SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes

In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems including these devices. The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions. Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics. Our tool has been developed using Matlab(TM) and is available under the GPL license at[1].Comment: Submitted to IEEE EnergyCon 201

An Extension of the ns-3 LTE Module to Simulate Fractional Frequency Reuse Algorithms

We developed an extension for the LTE module of the ns-3 simulator in order to allow the simulation of Fractional Frequency Reuse algorithms and the evaluation of their performance in an LTE scenario. In this paper, we describe the technical components of such extension, namely the new API for Fractional Frequency Reuse algorithms, the implementation of several state-of-the-art-algorithms based on such API, and the implementation of the LTE downlink and uplink power control functionality which are required by many of these algorithms. Additionally, we provide an overview of the test suites that are included with our extension in order to validate its correct functionality, and discuss some example scenarios illustrating how our extension can be used in an LTE simulation

Mobile Traffic Prediction at the Edge through Distributed and Transfer Learning

Traffic prediction represents one of the crucial tasks for smartly optimizing the mobile network. The research in this topic concentrated in making predictions in a centralized fashion, i.e., by collecting data from the different network elements. This translates to a considerable amount of energy for data transmission and processing. In this work, we propose a novel prediction framework based on edge computing which uses datasets obtained on the edge through a large measurement campaign. Two main Deep Learning architectures are designed, based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and tested under different training conditions. In addition, Knowledge Transfer Learning (KTL) techniques are employed to improve the performance of the models while reducing the required computational resources. Simulation results show that the CNN architectures outperform the RNNs. An estimation for the needed training energy is provided, highlighting KTL ability to reduce the energy footprint of the models of 60% and 90% for CNNs and RNNs, respectively. Finally, two cutting-edge explainable Artificial Intelligence techniques are employed to interpret the derived learning models.Comment: 12 pages, 9 figure

Miracle: the multi-interface cross-layer extension of ns2

We present Miracle, a novel framework which extends ns2 to facilitate the simulation and the design of beyond 4G networks. Miracle enhances ns2 by providing an efficient and embedded engine for handling cross-layer messages and, at the same time, enabling the coexistence of multiple modules within each layer of the protocol stack. We also present a novel framework developed as an extension of Miracle called Miracle PHY and MAC. This framework facilitates the development of more realistic Channel, PHY and MAC modules, considering features currently lacking in most state-of-the-art simulators, while at the same time giving a strong emphasis on code modularity, interoperability and reusability. Finally, we provide an overview of the wireless technologies implemented in Miracle, discussing in particular the models for the IEEE 802.11, UMTS and WiMAX standards and for Underwater Acoustic Networks. We observe that, thanks to Miracle and its extensions, it is possible to carefully simulate complex network architectures at all the OSI layers, from the physical reception model to standard applications and system management schemes. This allows to have a comprehensive view of all the interactions among network components, which play an important role in many research areas, such as cognitive networking and cross-layer design

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