# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 8 Discussion on Performed Analyses and Obtained Results  
The obtained results presented in Figures 2-4 show a significant impact on the coverage and data EE metrics of DV increase in the 5G HetNets. The results presented in Figure 2 reveal the reverse influence of increased DVs on the coverage and data EE metrics. These adverse changes of data and coverage EE metrics are noticed for all approaches which use deployment and operation policies that include an adaptation of BSs resources according to DV increase. While an increase of DV in the area of analyzed device density classes has an impact on the decrease of the coverage EE metrics (Figures 2 and 4), it also has an impact  
Figure 4: Impact of DV increase on coverage EE metrics and 5G network yearly energy consumption during the 2020s for (**a**) indoor-hotspot, (**b**) dense-urban, (**c**) urban and (**d**) rural device density class. on the increase of the data EE metrics (Figures 2 and 3). Due to such contrary changes in trends of data and coverage EE metrics, there is no optimal DV that, transferred by the network, will ensure 5G network operation with the best possible combination of data and coverage EE metrics. To simultaneously improve both data and coverage EE metrics, the best approach is to implement 5G BSs deployment and operation strategies that have the highest level of adaptation of installed BSs resources for accommodating the increasing DV trend in any device density class. Based on the results presented in Figure 3, for any device density class, approaches that will have better data EE metric are those which have the lower instant power consumption for the same DVs that must be transferred in the same network area. Those approaches are deployment and operation Approaches 4 and 5 (Table 3), which, in comparison with Approaches 1 and 2, have lower instant power consumption for transfer of the same DVs (Figure 3). Additionally, based on the results presented in Figure 4, it can be noticed that, for any device density class, approaches that have higher coverage EE metric are also approaches that have lower yearly energy consumption for the case when the same DV must be transferred in the same area of device density class. Obviously, approaches that have the lowest energy consumption (Figure 4) will also have the lowest instant power consumption (Figure 3). Therefore, only optimization of network instant power consumption (and consequently energy consumption) will result in simultaneous improvement of both data and coverage EE metrics. Hence, those BSs' deployment and operation strategies that enable the best possible adaptation of BSs' resources according to the increase of network DV will result in the optimized power consumption, which contributes to the simultaneous increase of both EE metrics. This observation further raises the necessity of implementing 5G BSs deployment and operation concepts that will ensure the transfer of constantly increasing DVs, while keeping both data and coverage 5G HetNets EE metrics at higher values as much as possible. The further discussion concerns the values of the coverage and data EE metrics shown in Figures 2-4. It is necessary to highlight that these values of EE metrics were obtained for the specific device density classes (Table 1), which consist of versatile numbers of small and macro BSs (Figure 1). Hence, every analyzed device density class has been defined in terms of the number of macro and small BSs, with the goal of satisfying the expected DV increase in the area of specific device density classes during the 2020s. However, different real-life deployments of 5G HetNets may vary in terms of the dynamics of DV increase through time and in the type and number of installed small and macro BSs. For that reason, versatile practical deployments of HetNets can differ in absolute values of coverage and data EE metrics when compared with those presented in this work.