## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
### General Artificial Intelligence-Based Process for Optimizing the Energy Efficiency of 5G Sliced RAN  
The possible strategies for energy savings in 5G sliced networks in the time, spatial, and frequency domains presented in the previous sections can be realized only with advanced management and orchestration strategies for 5G RAN. To achieve such strategies, the use of artificial intelligence (AI) emerges as a potential approach for reducing the energy consumption of 5G NSs in the RAN. An overview of the general procedure of intelligent energy-saving strategies based on AI in the RAN network is presented in Figure 18[45]. The AI-based procedure for improving the energy efficiency of 5G NS(s) in the RAN network is composed of several phases. The first phase is dedicated to obtaining relevant data about the performance of the BSs in an RAN serving NSs through control mechanisms (Figure 18). After data collection, the data are processed through filtering and adapted to a format that is applicable for further processing. Such data are fed to the AI software, which creates the AI algorithm(s) and generates an operation scheme (scenario) for a specific NS, with the aim of optimizing the network energy consumption. Based on the goals that need to be achieved, in this phase, energy-saving KPIs and thresholds are also set (Figure 18). Following this, in phase 5, using historical data, the AI predicts the type and amount of traffic that will be present in a specific period of NS operation, and determines the energy-saving technique(s) and the timeframe for their application (Figure 18). In step 6, the chosen energy-saving strategy, which can be time-, space-, or frequency-based (or a combination of these), for a specific NS is sent to the control system as VNFs and executed utilizing the SDN technique (Figure 18). After implementing the selected strategy, in the final phase, phase 7, the performance of NSs is monitored to assess whether the energy efficiency goals set for a specific NS have been achieved (Figure 18). This AI-based procedure for energy consumption reductions in RAN needs to be implemented in a closed-loop system, which includes continuous adjustments of the energy efficiency KPIs and thresholds for a specific NS (as indicated in step 4). From the presented AI procedure (Figure 18), it can be seen that specific RAN resources of the entire 5G system are allocated to each NS. The management of these resources, with the aim of optimizing energy consumption, is handled in the same manner through the exploitation of AI-based algorithms. It is important to emphasize that the AI-based procedures for energy consumption reductions can be applied separately to the RAN subnet of each NS. Also, if more NSs served by the same BS are comprised at the same time  
Figure 18: AI procedures for energy consumption reductions in RAN serving NS(s) [45]. with the energy-saving scheme selected by the AI algorithm, the entire BS can be in the energy-saving mode of operation. Resource Provisioning in Network Slicing Based on Artificial Intelligence  
The main challenge in the practical realization of the described AI-based procedure for improving network energy efficiency (Figure 18) is the accomplishment of phases 5 and 6. Phase 5 is related to the prediction of the type and amount of traffic that will be present in the NS for a specific time period, while in phase 6, the resources that will offer appropriate energy savings have to be selected. These traffic prediction and energy-saving techniques are directly related to the NS resource allocation that needs to be provisioned to ensure an appropriate QoS and, consequently, impacts the NS energy consumption. In [46], the authors study two migration policies of virtual router selection in sliced networks, defined as local traffic prediction based on only local knowledge of the traffic profiles of a specific network segment, and global traffic prediction based on the knowledge of the complete daily traffic profile of an operator.