# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 4 Device Density Classes for Assessment of Energy Efficiency  
The method for EE evaluation of 5G RAN was presented in ETSI standard (ES) 203 228 [9]. According to this method, when the measurement of parameters needed for EE assessment of complete MN on the large scale is not feasible, the total network can be divided into a set of smaller sub-networks that are classified according to device densities of a specific sub-network. For EE assessment of 5G RAN presented in this work, analysis was performed by using sub-networks categorized by device density classes for an area of one square kilometer (1 km\({}^{2}\)). Figure 1 visualizes analyzed 5G HetNet deployments for different device density classes with the maximal number of installed small and macro BSs per square kilometer area.  
The impact of DVs on different EE metrics can be analyzed for different device density classes which are defined in ETSI standards [9; 10]. They can range from the rarely populated rural device density class having up to hundreds of users per square kilometer (km\({}^{2}\)) to the  
Figure 1: Visualization of analyzed 5G HetNet deployments for different device density classes with the maximal number of installed small and macro BSs per square kilometer area.  
urban, dense-urban and densely populated indoor-hotspot device density classes, having up to a few hundreds of thousands of users per km\({}^{2}\). The maximal densities of user devices in specific device density classes for the analyzed area of one square kilometer are presented in Table 1. Hence, the device density classes presented in Table 1 are classified depending on the maximal user density of the square kilometer area (users/km\({}^{2}\)).  
As an example, Figure 1 visualizes analyzed device density classes of the 5G RAN deployment in terms of the number of allocated macro and small BSs. Analyzed HetNet 5G RAN deployment architectures are, for each of the device density classes, composed of a few macro BS(s) and a large number of small BSs (Figure 1). Figure 1 presents a maximal number of macro BSs allocated in each device density class. In the analyses, it is assumed that the number of macro BSs is constant during the 2020s and corresponds to the number of BSs allocated during the initial deployment of the 5G network. This approach to deployments of macro BSs is frequent in practice, since MNOs deploy macro 5G BSs to ensure initial coverage and capacity demands of a specific area. In areas expecting larger DVs, a larger number of macro BSs will be initially deployed, and this explains the differences in the maximal number of macro BSs deployed in different device density classes in Figure 1. Moreover, a number of small BSs will be deployed according to different deployment and operation approaches described and analyzed in further sections. Since the deployment of the number of small 5G BSs in some analyzed areas depends on different MNO deployment and operation approaches, in Figure 1, the span of small BSs that can be deployed is indicated in the range from minimal to the maximal number of small BSs that are considered for analyzes in different approaches. Therefore, Figure 1 illustrates basic MNO principles of RAN deployment analyzed in this work, according to which a few macro BSs are deployed for ensuring basic signal coverage and capacity of the analyzed area and a larger number of deployed small BSs are dedicated to satisfying capacity demands at locations of traffic hotspots.