Planning Solar in Energy-managed Cellular Networks

There has been a lot of interest recently on the energy efficiency and environmental impact of wireless networks. Given that the base stations are the network elements that use most of this energy, much research has dealt with ways to reduce the energy used by the base stations by turning them off during periods of low load. In addition to this, installing a solar harvesting sys- tem composed of solar panels, batteries, charge con- trollers and inverters is another way to further reduce the network environmental impact and some research has been dealing with this for individual base stations. In this paper, we show that both techniques are tightly coupled. We propose a mathematical model that captures the synergy between solar installation over a network and the dynamic operation of energy-managed base stations. We study the interactions between the two methods for networks of hundreds of base stations and show that the order in which each method is intro- duced into the system does make a difference in terms of cost and performance. We also show that installing solar is not always the best solution even when the unit cost of the solar energy is smaller than the grid cost. We conclude that planning the solar installation and energy management of the base stations have to be done jointly

Optimized energy-aware cellular network planning with random waypoint user mobility

In this work, we aim to plan an energy-efficient cellular network that can take into account minimizing the cost and energy of the whole network and minimizing the distance between user and base station. Also, it takes the mobility of users into account using the random waypoint method to represent the user mobility. The design strategy depends on turning off as many base stations as possible to reduce the consumed power while keeping full or partial coverage and quality of service over the serviced area. We have implemented all work by using Matlab software

Radio Planning and Management of Energy-Efficient Wireless Access Networks

RÉSUMÉ Dans les dernières années, le secteur des Technologies de l'Information et de la Communication (TIC) a transformé la façon dont nous vivons: il joue un rôle principal sur le développement économique et la productivité, en offrant des services innovants qui sont devenus partie intégrante de la vie quotidienne. En raison de ce phénomène, l'effet des technologies de l'information et de la communication sur le réchauffement climatique ne peut plus être ignoré. Le concept des TIC Vertes (ou Green ICT, en anglais) est né dans le but de stimuler la recherche vers des solutions respectueuses de l'environnement et économes en énergie. Étant une partie important des TIC, les réseaux de télécommunication connaissent une croissance en plein essor. Les contraintes de qualité de service et de capacité sont les principaux responsables de l'augmentation de la consommation d'énergie; en particulier, une grande partie de la facture d'électricité des opérateurs de réseaux est due aux exigences élevées de puissance des stations de base sans fil, qui ont été identifiées comme les composantes les plus énergivores des réseaux. Jusqu'à présent, l'industrie de la communication mobile s'est essentiellement concentrée sur le développement de terminaux mobiles à faible consommation d'énergie afin d'attirer un plus grand nombre de clients et, par conséquent, d'augmenter les profits des opérateurs; en revanche, le monde de la recherche étudie la question de l'efficacité énergétique d'un point de vue plus large. En plus des études sur dispositifs et protocoles économes en puissance, des travaux plus récents ont abordé la problème du design et du fonctionnement éco-énergétiques dans les infrastructures de réseaux câblés et sans fil. De nombreux aspects de la planification et gestion des réseaux verts ont été explorés. Cependant, les deux problèmes n'ont jamais été liés et abordés à la fois, en négligeant le fait que l'efficacité d'une gestion de réseau à faible consommation d'énergie dépend en grande partie des décisions prises dans la phase de design.----------ABSTRACT In the last years, the ICT sector has transformed the way we live. Consistently delivering innovative products and services, the ICT assumed a primary role on economic development and productivity, becoming an integral part of everyday life. However, due to their wide and constantly increasing diffusion, the effect of information and communication technologies on global warming can no longer be ignored. The concept of Green ICT has originated with the aim of building awareness of this, thus boosting the research toward environmentally sustainable, energy-efficient technologies and solutions. As an important part of the ICT, telecommunication networks are experiencing a booming growth. Capacity issues and quality of service constraints are some of the main concerns that contribute to raise the power consumption. In particular, a large portion of the electricity bill results from the high power requirements of wireless base stations, which have been proved to be the most energy-hungry network components. Up to now, the mobile communication industry has focused mostly on the development of power-efficient mobile terminals, so as to attract a higher number of customers and consequently increase the operators' profits; on the other hand, the research world has been investigating energy efficiency from a wider point of view. Besides studies on power-efficient devices and protocols, more recent works addressed the problem of energy-aware design and operation in wired and wireless network infrastructures. Many aspects and challenges of green network planning and management have been explored; nevertheless, the two problems have never been linked and tackled at the same time, neglecting the fact that an effective power-efficient network operation largely depends on the decisions taken in the design phase. The research presented in this doctoral thesis aims at filling this gap by developing an optimization framework that jointly considers the design and operation of wireless networks. The proposed joint planning and energy management problem (JPEM) strives to prove that, when cell sleeping is adopted as network management technique, the level of flexibility offered by the installed topology strongly improves the system capability to adapt to the varying traffic load. By minimizing the trade-off between capital expenditures (CapEx) related to the network deployment and operational expenditures (OpEx) calculated over the network lifetime, the model finds the most energy-efficient network topology while meeting the capital investment limitations imposed by the mobile operator

Scalable base station switching framework for green cellular networks

With the recent unprecedented growth in the wireless market, network operators are obliged not only to find new techniques including dense deployment of base stations (BSs) in order to support high data rate services and high user density, but also to reduce the operating costs and energy consumption of various network elements. To solve these challenges, powering down certain BSs during low-traffic periods, so-called BS sleeping, has emerged as an effective green communications paradigm. While BS sleeping offers the potential to significantly lower energy consumption, it also raises many challenges, since when a BS is switched off, this can lead to, for example, coverage holes, sudden degradation in quality of service (QoS), higher transmit power dissipation in off-cell mobile stations (MSs), an inability to rapidly power up/down equipment and finally, a failure to uphold regulatory requirements. In order to realise greener network designs which both maximise energy savings whilst guaranteeing QoS, innovative BS switching mechanisms need to be developed. This thesis presents a novel BS switching framework which improves energy efficiency (EE) in comparison with existing approaches, while guaranteeing the minimum QoS and seamless services. The major technical contributions in this framework are: i) a new BS to relay station (RS) switching model where certain BSs are switched to RS mode rather than being turned off, firstly using a fixed threshold based switching algorithm utilizing temporal traffic diversity, and ii) then subsequently by means of an adaptive threshold by exploiting the inherently asymmetric traffic profile between cells, i.e., by exploiting both the temporal and spatial traffic diversity; iii) a traffic-and-interference-aware BS switching strategy that considers the impact of inter-cell interference in the decision making process to dynamically determine the best BS set to be kept active for improved EE; and finally iv) a novel scalable multimode BS switching model which enables each BS to operate in different power modes i.e., macro/micro/sleep to explore energy savings potential even at higher traffic conditions. The thesis findings conclusively confirm this new BS switching framework provides significant EE improvements from both BS and MS perspectives, under diverse network conditions and represents a notable step towards greener communications

Planification et gestion de réseaux cellulaires avec énergie solaire

RÉSUMÉ Ce mémoire vise à étudier deux stratégies pour réduire les gaz à e˙et de serre dans les réseaux de télécommunications cellulaires : l’installation de panneaux solaires sur les stations de base et l’allocation dynamique des usagers aux stations. Les panneaux solaires permettent le remplacement énergétique par une énergie verte, ce qui réduira les gaz à effet de serre alors que l’allocation dynamique permet de mettre en veille certaines stations à certains moments de la journée, ce qui réduit la consommation énergétique. L’objectif principal de ce mémoire est de déterminer les interactions qu’il peut y avoir entre l’utilisation de l’énergie solaire et la gestion des stations de base. Pour répondre à cet objectif, deux modèles de réseau avec alimentation hybride ont été développés. Le premier modèle optimise l’énergie dans le réseau en considérant que la charge des usagers est constante à travers les années. La fonction objectif à minimiser est la somme du coût de capital des équipements solaires et du coût d’énergie pour l’opération du réseau. L’étude de ce modèle porte principalement sur l’interaction entre l’installation du solaire et la mise en veille dynamique des stations de base. On conclut qu’il y a une interaction marquée entre l’utilisation de l’énergie solaire et la gestion dynamique avec mode veille des stations du réseau. Plus particulièrement, l’ordre dans lequel chacune des méthode est introduite dans le réseau va avoir une influence sur les performances et son coût optimal. Le deuxième modèle permet, entre autres, d’avoir une croissance du trafic de données au fil des années. Ce modèle sert à étudier l’ajout de l’équipement solaire dans un réseau où il faut aussi rajouter des stations de base. On conclut avec ce deuxième modèle qu’il est important de repousser le plus tard possible l’installation de nouvelles stations peu importe qu’il y ait du solaire ou non.----------ABSTRACT The research done in this master’s thesis has the goal to diminish greenhouse gases in cellular telecommunications networks. This is done by adding solar equipment to dynamic networks where base stations can be turned o˙. Two mathematical models that capture the synergy between solar installation over a network and the dynamic operation of energy-managed base stations are presented. The first model optimizes the energy management in a network where the users’ load is constant through the years. The objective to be minimized is the sum of the capital cost needed for the solar equipment plus the cost of energy obtained from the electric grid. This model gives us insights on the synergy between planning of solar equipment in the network and switching o˙ the base stations. Notably, it is shown that the order in which these technologies are introduced makes a significant difference to the optimized objective function cost. Thus, this model emphasizes the fact that there is a strong correlation between the solar installation and the management of the base stations. Another result is that the solar equipment is not installed on every base station, even when the cost of solar energy is smaller than the cost of the grid. The second model optimizes a network where traÿc grows every year. This means that the model has to install new base stations and decide where to install them. Furthermore, the base stations now have different levels of transmission power instead of just being on or o˙. Finally, the functionality to install di˙erent kinds of solar equipment with different sizings is added. This complexity makes this model a lot more complex to solve and smaller networks are thus used. It is concluded that planning the installation of base stations throughout the years is much more important to reduce the total cost than installing solar