## 1 Introduction  
There is no doubt that information and communication technology (ICT) dramatically changes our life. There are various eco-friendly aspects of ICT, such as a reduction in paper consumption, reduced transportation/traffic, global environmental monitoring and control, smart building and teleworking, and so on [1]. There are also various negative environmental aspects, such as the heat emission generated from data transmission and processing, garbage from manufacturing, disposal of PCs and smart phones, and so on. The ICT industry's greenhouse gas (GHG) emissions account for about 3% of global GHG emissions, and this value is growing rapidly [2]. Reducing environmental pollution from ICT is very important to the sustainability of Earth. Among various ICT related devices/equipment that cause a lot of environmental pollution, signal transmission systems such as a cellular base stations (BSs) are one of the most power hungry devices [3]. Currently, the amount of data transmission increases about ten times every five years [4; 5], and thus the energy efficiency (EE) increase of BSs is a vital research topic. Designing an energy efficient BS is particularly difficult, because the amount of signal transmissions is drastically increases day by day. Large-scale antenna systems (LSASs) that use a large number of transmitter (TX) antennas and serve a limited number of users, have a high possibility to increase the EE of the BS, and this has already been shown in several parts of the existing literature [6; 7; 8; 9; 10; 11; 12; 13]. In this paper, we show how to increase the EE of the BSs using technologies related to LSASs to reduce the carbon footprint. There are several important components that we should note in an LSAS. We overview each component of the LSAS and derive the expected EE gain from each component with an appropriate scheme. In current BS systems, the power amplifier (PA) consumes around 60\(\sim\)80% of total power consumption [14; 15]. Reducing PA power consumption is a main research topic for current signal transmission systems. One of the distinct characteristics of the LSAS is the possibility of low radiation power due to the beamforming effect. Moreover, because of a large number of TX antennas, the radiation power of each PA can be fairly small. Therefore, with LSASs, the status of PA power consumption could be different from current systems. Also, since an LSAS has a large number of TX antennas, each TX antenna needs to be connected to an RF component. The RF component typically consists of a digital-to-analog converter (DAC), mixer, and filter. Current BSs only use less than 10 TX antennas, so power consumption of RF components is not a problem because it is much less than the power consumption of the PA. However, in LSASs, the number of TX antennas can be hundreds or more, and thus reducing the power consumption of RF components could become a major research topic. Another component we can think of is baseband (BB) power consumption. Typically, BB power consumption is significantly less than PA and RF power consumption. However, BB processing is related to signal processing, and depending on the signal processing algorithm, it actually affects the spectral efficiency (SE) and/or data throughput (TP), which is a major parameter of EE. Also, BB processing is strongly related to the processing delay from high complexity signal manipulation, which should be prevented for low latency communication systems. Due to these reasons, BB processing is also considered as a factor that measures the EE of LSASs. Assuming the PAs/RFs are implemented properly, the problem of reference signal (RS) overhead and the accuracy of channel estimation also should be discussed. Since RS numbers increase as the number of transmitter (TX) antennas or the number of users increases, reducing RS overhead is crucial for the realization of LSASs [16]. However, RS overhead reduction almost always accompanies channel estimation errors; thus we also show how the channel estimation error affects EE gain.