## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
#### 5.2.1 Resource Allocation in 5G Network Slicing  
In the case of legacy 5G networks realized without network slicing (presented in Figure 7), some users may demand all types of traffic, ranging from those with high QoS demands to those with low QoS demands. In such a configuration, the entire 5G network uses significantly more resources than necessary, since the network must be dimensioned according to the users demanding the highest QoS parameters. This leads to an increase in network energy consumption, while the amount of traffic passing through the network remains largely unchanged and a large amount of network resources actively operate at low utilization levels. Although individual network elements (NEs) in the 5G networks are generally more energy-efficient than the network elements of previous network generations (better power consumption per unit of transferred traffic), the densification and capacity increases in the installed 5G network elements result in an increase in the overall energy consumption of the 5G network when compared to networks of previous generations and this increase raises the OPEX of MNOs. For example, one macro 5G BS, although ensuring significantly larger capacities, requires nearly 70% more energy than a macro BS that, in the same rack, combines second-generation (2G), third-generation (3G), and 4G technology [40]. Although the possibilities of improving 5G networks' energy efficiency are limited, the use of network slicing in 5G networks can contribute to this improvement. One of the main approaches to improving 5G NS energy efficiency is 5G network resource allocation through the implementation of the 5G network slicing concept with the help of orchestrators, which are an integral part of the MANO entity (depicted in Figure 3). NSs can be created and orchestrated based on **soft or hard network slicing**. In soft slicing, NS can share some network resources with other NSs, while in hard slicing, NSs must be fully isolated from each other. Thus, two fundamental mechanisms used for adapting network resources are slice isolation and orchestration. For example, with hard slice isolation, slice B cannot use resources assigned to slice A, thus preventing an increase in energy consumption for slice A. Slice B may use the same physical network element where a network function is enabled for slice A, but how much energy each VNF on that physical element consumes for slice A and slice B must be precisely known. Slice orchestration can thus dynamically add to or reduce the resources of the NS(s) in a smart manner or an artificial intelligence (AI)-driven manner. By grouping users with similar traffic requirements and allocating appropriate resources only to specific types of traffic or service, along with the use of the mechanisms of slice isolation and orchestration, and by putting certain network elements into sleep mode through resource reallocation, network slicing can influence the dynamics of the usage of network elements and their corresponding capacities. As stated in the previous section, energy efficiency KPIs are defined as the ratio of network slice performance and consumed energy. Therefore, to increase these KPIs, increasing the NS performance and/or reducing energy consumption are possible ways to improve NS energy efficiency. The dynamic adaptation of network resources and resource reallocation primarily influence the increase or decrease in NS performance, while the smart sleep mode affects the reduction in consumed energy. The stated approaches to improving 5G NS energy efficiency are shown in the example illustrated in Figure 8, which represents two NS in a 5G network. Users are grouped in NSs according to the type of communication service they use. The network configuration of these slices is such that the required QoS of the services offered to the users must be provided.