Optimal Policies of Advanced Sleep Modes for Energy-Efficient 5G networks

We study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G networks. ASM correspond to different levels of sleep modes ranging from deactivation of some components of the base station for several micro-seconds to switching off of almost all of them for one second or more. ASMs are made possible in 5G networks thanks to the definition of so-called lean carrier radio access which allows for configurable signaling periodicities. We model such a system using Markov Decision Processes (MDP) and find optimal sleep policy in terms of a trade-off between saved power consumption versus additional incurred delay for user traffic which has to wait for the network components to be woken-up and serve it. Eventually, for the system not to oscillate between sleep levels, we add a switching component in the cost function and show its impact on the energy reduction versus delay trade-off.Comment: The 18th IEEE International Symposium on Network Computing and Applications (NCA 2019) 26-28 September 2019 Cambridge, MA US

Gestion des modes de veille avancés pour des réseaux 5G économes en énergie

The energy consumption of mobile networks has been an active research direction in the last decade for both environmental and economic concerns. With the tremendous growth in the traffic as well as the proliferation of devices and new services and the expansion of Machine to Machine communications, the need to reduce the energy consumption became more and more urgent and was emphasized by the expected 5G roll out. Knowing that the radio access network is the most energy consumer in the network, particularly the base station which consumes around 80% of the global energy consumption, we direct our study towards this component. The energy consumption of the base stations is composed of two parts: a fixed one that does not vary with the load, and a second part which is load-dependent. Our target is to find efficient solutions enabling to reduce the fixed component. One promising solution is to put the base station, or some of its components, into sleep mode whenever there is no traffic to serve. We study in this thesis an Advanced Sleep Mode technique enabling to shut down the base station's components in a gradual manner depending on the time needed for each of them to deactivate and reactivate again. This introduces different possible levels of sleep. Going from one level to a deeper one will help us make more energy savings as we deactivate more components but can also incur a larger delay for the users who request a service when the base station is in sleep mode. The network operator has to find efficient management solutions that can handle this tradeoff between energy consumption reduction that can be achieved by the sleep modes, and the minimization of the corresponding induced delay. To this aim, we propose in this work management solutions based especially on learning techniques, namely Markov Decision Processes and Q-learning, which enable us to find the optimal policy to follow depending on the priorities given to both metrics: energy consumption and delay. Our solutions show that the energy savings can reach 90% in low traffic when priority is given to energy reduction. The more we care about delay, the more energy saving decreases, as is the case for the induced delay. We show that even when we have a strict constraint on the delay, we can still achieve high energy savings (around 50%) while the added delay by the sleep mode is negligible.La consommation énergétique des réseaux radiomobiles a été une direction de recherche très active au cours de la dernière décennie et ce, pour des raisons économiques ainsi qu'environnementales. Avec l'énorme croissance du trafic ainsi que la multiplication des équipements mobiles, des nouveaux services et des communications Machine à Machine, la nécessité de réduire la consommation énergétique est devenue de plus en plus urgente surtout avec le déploiement des réseaux 5G. Sachant que le réseau d'accès est le plus grand consommateur dans le réseau mobile, en particulier les stations de base qui consomment environ 80% de la consommation totale, nous nous focalisons dans cette thèse sur ce composant. La consommation énergétique des stations de base est composée de deux parties: une partie fixe qui ne varie pas en fonction du trafic et une partie qui en dépend. Notre objectif est de trouver des solutions efficaces permettant de réduire la partie fixe de cette consommation. Une solution prometteuse consiste à mettre la station de base, ou certains de ses composants, en mode veille s'il n'y a pas de trafic à servir. Nous étudions dans cette thèse une technique dite "modes de veille avancés" qui permet d'éteindre les composants de la station de base de manière progressive en fonction du temps nécessaire pour chacun pour se désactiver et se réactiver de nouveau. Ceci introduit différent niveaux de sommeil possibles. En allant d'un niveau à un autre plus profond nous pouvons réaliser plus d'économies d'énergie puisqu'il y a plus de composants qui sont en veille, mais nous introduisons plus d'impact sur le délai s'il y a des demandes de services qui arrivent pendant cette période de veille. L'opérateur doit trouver alors des politiques de gestion efficaces pour gérer ce compromis entre la réduction d'énergie et la minimisation de l'impact sur le délai. Dans ce contexte, nous proposons des méthodes de gestion des modes de veille avancés basées spécifiquement sur des modèles d'apprentissage, à savoir les processus de décision Markoviens et le Q-learning, qui nous permettent de trouver la politique optimale à suivre en fonction des priorités accordées aux deux métriques : la consommation d'énergie et le délai. Nos solutions montrent que les économies d'énergie peuvent atteindre 90% dans le cas d'un faible trafic lorsque la priorité est accordée à la réduction d'énergie. Plus on augmente la contrainte imposée sur le délai, plus la réduction d'énergie diminue, et aussi l'impact sur le délai. Nos résultats montrent que même si la contrainte sur le délai est très forte, nous pouvons aussi avoir des économies d'énergie élevées (environ 50%) alors que l'impact sur le délai devient négligeable

Energy consumption optimization in 5G networks using multilevel beamforming and large scale antenna systems

International audienceCellular networks are witnessing an exponential traffic growth leading to an increase in Energy Consumption (EC), and having both environmental and economic impact. Recently, different approaches have been studied to build Green cellular networks focusing mainly on the Base Stations (BSs) as the access network represents 80% of the total wireless network consumption [1] [2]. One of the promising solutions for increasing throughput and reducing EC is the deployment of large antenna arrays, known as Large Scale Antenna Systems (LSAS) [3] [4] [5] [6], that can transmit highly focused beams. In the present work, we focus on the use of an advanced BS power model developed within the GreenTouch project [7] [8]. This model allows to quantify the power consumption of a reference scenario comprising multiple sites with standard BS antennas, and then compare it to a LSAS solution implementing multilevel beamforming. We then exploit the LSAS merits to get greener deployment with less BSs, some of which can be turned off as a function of the traffic demand. The coverage areas of each cell can be modified, by updating the codebook of beams. We finally investigate different network configurations which represent distinct trade-offs between EC and capacity. We propose a methodology to represent and design green policies for managing the network which select the desired operating points. Detailed simulation results illustrate the proposed methodology

Optimal policies of advanced sleep modes for energy-efficient 5G networks

International audienceWe study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G networks. ASM correspond to different levels of sleep modes ranging from deactivation of some components of the base station for several micro-seconds to switching off of almost all of them for one second or more. ASMs are made possible in 5G networks thanks to the definition of so-called lean carrier radio access which allows for configurable signaling periodicities. We model such a system using Markov Decision Processes (MDP) and find optimal sleep policy in terms of a trade-off between saved power consumption versus additional incurred delay for user traffic which has to wait for the network components to be woken-up and serve it. Eventually, for the system not to oscillate between sleep levels, we add a switching component in the cost function and show its impact on the energy reduction versus delay trade-off

Reinforcement learning approach for Advanced Sleep Modes management in 5G networks

International audienceAdvanced Sleep Modes (ASMs) correspond to a gradual deactivation of the Base Station (BS)'s components in order to reduce its Energy Consumption (EC). Different levels of Sleep Modes (SMs) can be considered according to the transition time (deactivation and activation durations) of each component. We propose in this paper a management solution for ASMs based on Q-learning approach. The target is to find the optimal durations for each SM level according to the requirements of the network operator in terms of EC reduction and delay constraints. The proposed solution shows that even with a high constraint on the delay, we can achieve high energy savings (almost 57% of EC reduction) without inducing any impact on the delay. When the delay constraint is relaxed, we can achieve up to almost 90% of energy saving

Green mobile networks for 5G and beyond

International audienceThe heated 5G network deployment race has already begun with the rapid progress in standardization efforts, backed by the current market availability of 5G-enabled network equipment, ongoing 5G spectrum auctions, early launching of non-standalone 5G network services in a few countries, among others. In this paper, we study current and future wireless networks from the viewpoint of energy efficiency (EE) and sustainability to meet the planned network and service evolution toward, along, and beyond 5G, as also inspired by the findings of the EU Celtic-Plus SooGREEN Project. We highlight the opportunities seized by the project efforts to enable and enrich this green nature of the network as compared to existing technologies. In specific, we present innovative means proposed in SooGREEN to monitor and evaluate EE in 5G networks and beyond. Further solutions are presented to reduce energy consumption and carbon footprint in the different network segments. The latter spans proposed virtualized/cloud architectures, efficient polar coding for fronthauling, mobile network powering via renewable energy and smart grid integration, passive cooling, smart sleeping modes in indoor systems, among others. Finally, we shed light on the open opportunities yet to be investigated and leveraged in future developments

Key technologies to accelerate the ICT Green evolution An operator's point of view

The exponential growth in networks' traffic accompanied by the multiplication of new services like those promised by the 5G led to a huge increase in the infrastructures' energy consumption. All over the world, many telecom operators are facing the problem of energy consumption and Green networking since many years and they all convey today that it turned from sustainable development initiative to an OPEX issue. Therefore, the challenge to make the ICT sector more energy-efficient and environment-friendly has become a fundamental objective not only to green networks but also in the domain of green services that enable the ICT sectors to help other industrial sector to clean their own energy consumption. The present paper is a point of view of a European telecom operator regarding green networking. We address some technological advancements that would enable to accelerate this ICT green evolution after more than 15 years of field experience and international collaborative research projects. Basically, the paper is a global survey of the evolution of the ICT industry in green networks including optical and wireless networks and from hardware improvement to the software era as well as the green orchestration