## 4 Standardization of 5G Network Slicing Energy Efficiency KPIs
### Improving Energy Efficiency in the RAN Part of 5G Network Slices  
Considering that almost 70% of the 5G network energy is consumed in the RAN part of the network or, more specifically, by the BSs, the main focus of research on reducing the energy consumption of 5G networks should be on techniques that enable the energy-efficient allocation of RAN resources among NSs. According to [41], BS resources remain unused for 75-90% of their operation time, even though they can occasionally experience significant traffic loads. The field of improving the energy efficiency of the RAN part of different network generations (2G, 3G, 4G, and 5G) has been extensively analyzed in the literature [2; 38; 42; 43]. Thus, many technologies and techniques proposed or used for improving the energy efficiency of the RAN part of the 5G network can be adjusted for application in sliced 5G networks. In 5G sliced networks, the RAN part of the network can be dedicated to a specific NS or can be shared among different NSs. This means that BSs, as the major energy consumers in the RAN part of the network, can serve only one NS or can be shared among several different NSs. Although use cases of BSs serving multiple NSs will be dominant in practice, one NS being served by one BS is particularly interesting in the case of implementations of micro, pico, or femto BSs in private 5G networks acting as a standalone NS. With the introduction of the 5G new radio (NR) standard, complete BS or different BS components can enter sleep mode to achieve energy savings. The possibility of optimizing NSs energy consumption in the RAN part of the 5G network depends on the level of sleep state that BSs or specific components can achieve. the general sleep state depths of BSs or BS components are categorized as macro- and micro-sleep states and different BS components are more suitable for different sleep state depths (Table 4). The duration of micro-sleep states is in the order of microseconds or milliseconds, while the durations of macro-sleep mode are longer and can span up to hours or even days. A more practical means of achieving macro-sleep mode is using BS elements such as a remote radio head/unit (RRH/U), active antenna element (AAE), power amplifier, baseband unit (BBU), or complete BSs (Table 4). The BS components that are more suitable for the micro-sleep mode are carrier frequency blocks, subcarrier frequencies, wireless channels, and orthogonal frequency division multiplexing (OFDM) symbols. In general, the energy-saving process based on putting the complete BS or a BS component serving one or more NSs into sleep mode depends on three aspects: different triggers and scenarios for entering or exiting sleep mode, the duration of the sleep mode, and the depth of the sleep state. Thus, the optimization of NS energy consumption in 5G networks will require algorithms for adjusting BS parameters supporting NS(s) operation. Such algorithms need to enable BS or its component to frequently enter or exit sleep mode, while the duration of the sleep mode is impacted by the data and signaling traffic intensity of the NS served by a specific BS component or the whole BS. The depth of the sleep state (spanning from macro- to micro-sleep) is achieved by using different amounts of BSs components not serving any NS into sleep state. This can range from partial shutdown to the complete shutdown of certain BS components, with the possibility of restarting the BS components when necessary. Restating BS components from sleep state, however, takes longer if the depth of the sleep state is greater [44]. Thus, the trigger for the BS or BS component serving specific NSs to enter/exit the sleep state, and the depth and duration of the BS or BS component's sleep state (to achieve the required energy savings of the 5G network), can be directly influenced by the character of the NS service(s), its traffic and signaling intensity, and QoS constraints. In addition, energy savings in the RAN part of the 5G network can be achieved in the time, space, and frequency domains.