## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
These standardized KIPs are presented in relations (1) and (2) to express whether the energy efficiency of the 5G network can be directly implemented if only one E2E NS is realized in the 5G network. However, in most real implementation cases, NSs are created in the 5G network in such a way that more than one NS uses the common network element or communication link, and thus the 3 GPP defines the adopted KPI to express the  
Figure 6: Standardized interfaces in the 5G network architecture [33]. energy efficiency of NSs. According to the 3 GPP standard [35], the KPI for expressing the energy efficiency of a generic network slice (\(EE\)\(KPI_{GNS}\)) is defined as follows:  
\[EE\)\(KPI_{GNS}=\frac{P_{ns}}{EC_{ns}} \tag{3}\]  
In Equation (3), \(P_{ns}\) represents the performance indicator of the NS, and its value depends on the key performance factor selected for analyses of that slice. For example, depending on the use case of NS, for some slices, the key factor can be throughput, while for other NSs it can be latency, etc. The unit of measurement varies depending on the slice, i.e., the key characteristics by which it is defined. Depending on the selected performance parameter, for example, for the eMBB NS (Table 2), the unit of measurement is bit/J, while the unit of measurement for the mIoT NS is user/J or user equipment/J. In relation (3), the \(EC_{ns}\) is the consumed energy of the NS. The 3 GPP standard [35] defines NS energy consumption KPI, expressed in Joules (J), as follows:  
\[EC_{ns}=\sum_{NF}EC_{NF}\quad(J) \tag{4}\]  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**KPI Name** & **Description of KPI Calculation** & \begin{tabular}{c} **Performance** \\ **Indicator** \\ \end{tabular}  & **Unit (Remark)** \\ \hline Energy efficiency of eMBB network slice (SST type 1): \(EE_{eMBB,DV}\) & This KPI is obtained by dividing the \(\mathrm{N}\)\(\mathrm{N}  
According to relation (4), the consumed energy of the NS is calculated as the sum of all energies consumed by each network function (\(EC_{NF}\)) executed in the NS. If this is an E2E NS, it is clear that it involves the network functions of entities from Figure 1 (except for UE) and that \(EC_{ns}\) in (4) is equal to \(EC_{ns}\) in (3). Additionally, the energy consumed by each network function \(EC_{NF}\) of NS is calculated as the sum of all physical network functions and virtual network functions of NS and is expressed as follows:  
\[EC_{NF}=\sum_{PNF}EC_{PNF\ measured}+\sum_{VNF}EC_{VNFestimated}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
namic allocation of network resources, and smart sleeping [37]. These three approaches can be applied in 5G network slicing separately or combined to a greater or lesser extent. This work will not analyze **reengineering**, as this is beyond the scope of this paper since it involves the use of energy-efficient materials and components for developing more energy-efficient equipment hardware and the reduction in device complexity, which tends to ensure that the same performance can be achieved with fewer electronic elements and thus a reduced energy consumption. However, the **dynamic adaptation of network resource allocation and smart sleeping,** as prominent approaches to improving the energy efficiency of 5G NSs, will be analyzed in detail in the remaining sections of this work. **The dynamic allocation of network resources** involves the scaling of NS(s) resources in such a way that NS(s) resources that are not necessary for service delivery will not be utilized. In the **smart sleeping** approach, specific NS elements are completely shut down to achieve energy savings and they are restarted as needed [38; 39]. Also, key enablers of network slicing include SDN, VNF, and cloud computing. The SDN separates the control plane from the physical network elements, which results in a reduced energy consumption by the physical network elements.