# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
## 1 Introduction  
During the last few years, the number of mobile devices, as well as the capacity required by applications running on them, has grown. Consequently, the data traffic has increased and will increase. The work in [1] forecasts that mobile data traffic will grow at a compound annual growth rate of 47% until 2021. Because of this trend, the communication network has expanded, as well, in terms of size and of provided capacity to guarantee the required quality of service. This has an impact on the network energy consumption. In 2008, wired and wireless communication networks were already responsible for 15% of the electricity consumption of ICT (Information and Communication Technology). Within communication networks, wireless access networks, through which end users access telecommunication services, are large power consumers. Up to 80% of the energy consumption in 3G and LTE (Long-Term Evolution) is caused by the base stations that compose those networks. In this way, access networks contribute to the general increase of the power consumption of these years, caused by ICT [2]. However, most of the energy we use today comes from fossil fuels, as mentioned in [3]. Fossil fuels are not renewable. This means that in the future, we will have a possible depletion if we continue to use them as we do today. Furthermore, it is supposed that politicalinstability in oil-producing countries can result in severe oil price shocks, with devastating effects for economies based on this. Today, we have already noticed a significant increase in energy cost. Besides the possible future depletion and the increase of energy cost, burning fossil fuels also causes the emission of carbon dioxide, which directly contributes to climate change. For the reasons mentioned above, in the literature, much effort has been put toward designing energy-efficient wireless access networks. In [4], a capacity-based wireless access network is analyzed: the network responds to the instantaneous bit rate requested by the active users, switching part of the active base stations to sleep mode. Furthermore, it is demonstrated that the presence of different types of base stations results in a power consumption reduction. In this case, the wireless access network is composed by both macrocell base stations and microcell base stations, which provide a coverage area and a power consumption smaller than the macrocell base stations, as described in [5]. Moreover, wireless access networks powered with renewable energy sources (i.e., with solar and wind energy) are proposed. The work in [6] considers a cellular network where a part of the base stations is supplied by renewable energy only, while others are only connected to the traditional power grid. A base station sleep policy is used in order to save energy, based on energy and traffic predictions. A simpler heuristic algorithm is also proposed. It defines the number of active renewable powered base stations, based on traffic demand. The deactivation of grid-powered base stations is left to a dynamic programming algorithm. The work in [7] proposes a cloud based on heterogeneous cellular networks with hybrid energy sources. An optimization problem is formulated to reduce the energy consumption and to balance the load among base stations. The work in [8] investigates a portion of a mobile access network where base stations, equipped with Photovoltaic (PV) panels and energy storage, can be powered either by solar energy or by the power grid. A base station switching on/off policy operates in a centralized way to reduce energy consumption. It makes the decision depending on the actual traffic load, guaranteeing a minimum capacity for satisfying the current user demand, considering real location data about traffic and solar irradiation. Optimal system dimensioning is also investigated. In [9], the authors study the problem of energy efficiency in cellular heterogeneous networks using radio resource and power management combined with a BS switching on/off approach.