In order to quantify the energy efficiency of a wireless network, the power consumption of the entire system needs to be captured. In this paper, the necessary extensions with respect to existing performance evaluation frameworks are discussed. The most important addendums of the proposed energy efficiency evaluation framework (E 3 F) are a sophisticated power model for various base station (BS) types, as well as large-scale long-term traffic models. The BS power model maps the RF output power radiated at the antenna elements to the total supply power of a BS site.The proposed traffic model emulates the spatial distribution of the traffic demands over large geographical regions, including urban and rural areas, as well as temporal variations between peak and off-peak hours. Finally, the E 3 F is applied to quantify the energy efficiency of the downlink of a 3GPP LTE radio access network. Index TermsEnergy efficiency, green radio, power & traffic model, system level energy efficiency simulations, energy aware radio and network technologies (EARTH)

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The global mobile communication industry is growing rapidly. Today there are already more than 4 billion mobile phone subscribers worldwide [1], more than half the entire population of the planet. Obviously, this growth is accompanied by an increased energy consumption of mobile networks. Global warming and heightened concerns for the environment of the planet require a special focus on the energy efficiency of these systems [2]. The EARTH 1 project [3] is a concerted effort to achieve this goal and as part of its objectives, a holistic framework is developed to evaluate and compare the energy efficiency of several design approaches of wireless cellular communication networks.For the quantification of energy savings in wireless networks, the power consumption of the entire system needs to be captured and an appropriate energy efficiency evaluation framework (E 3 F) is to be defined. The EARTH E 3 F presented in Section 1.2 provides the key levers to facilitate the assessment of the overall energy efficiency of cellular networks over a whole country. The E 3 F primarily builds on well-established methodology for radio network performance evaluation developed in 3GPP; the most important addendums, introduced in Sections 1.3 and 1.4, are to add a sophisticated power model of the base stations 1 EU funded research project EARTH (Energy Aware Radio and neTwork tecHnologies), FP7-

Abstract-In order to quantify the energy savings in wireless networks, the power consumption of the entire system needs to be captured and an appropriate energy efficiency evaluation framework must be defined. In this paper, the necessary enhancements over existing performance evaluation frameworks are discussed, such that the energy efficiency of the entire network comprising component, node and network level contributions can be quantified. The most important addendums over existing frameworks include a sophisticated power model for various base station (BS) types, which maps the RF output power radiated at the antenna elements to the total supply power of a BS site. We also consider an approach to quantify the energy efficiency of large geographical areas by using the existing small scale deployment models along with long term traffic models. Finally, the proposed evaluation framework is applied to quantify the energy efficiency of the downlink of a 3GPP LTE radio access network.Index Terms-energy efficiency, green radio, power & traffic model, system level energy efficiency simulations, energy aware radio and network technologies (EARTH)

Abstract-With the explosion of wireless communications in number of users and data rates, the reduction of network power consumption becomes more and more critical. This is especially true for base stations which represent a dominant share of the total power in cellular networks. In order to study power reduction techniques, a convenient power model is required, providing estimates of the power consumption in different scenarios. This paper proposes such a model, accurate but simple to use. It evaluates the base station power consumption for different types of cells supporting the 3GPP LTE standard. It is flexible enough to enable comparisons between state-of-the-art and advanced configurations, and an easy adaptation to various scenarios. The model is based on a combination of base station components and sub-components as well as power scaling rules as functions of the main system parameters.

This paper discusses how energy consumption can be significantly reduced in mobile networks by introducing discontinuous transmission (DTX) on the base station side. By introducing DTX on the downlink, or cell DTX, we show that it is possible to achieve significant energy reductions in an LTE network. Cell DTX is most efficient when the traffic load is low in a cell but even when realistic traffic statistics are considered the gains are impressive. The technology potential for a metropolitan area is shown to be 90% reduced energy consumption compared to no use of cell DTX. The paper also discusses different drives for the increased focus on energy efficient network operation and also provides insights on the impact of cell DTX from a life cycle assessment perspective.

This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London and Hong Kong. By comparing whole cities typical patterns, we provide insights on how cultural, technological and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity.

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