## 3 Energy-Efficiency Solutions and Technologies  
To overcome the energy consumption challenges of 5G network technologies, researchers have been tackling various angles of the relevant topics, such as:  
* The dynamic power management (DPM) techniques that usually turn off the base stations during low-traffic periods, which can significantly reduce the overall energy consumption [21]. Figure 2 illustrates a power state machine representing a microprocessor with three power states: RUN, IDLE, and SLEEP [22]. CMOS technology induces more power consumption due to the dynamic electronic switching and the static electronic leakage components; here, the aim of DPM is to reduce static power consumption, whereas the aim of DVFS is to minimize dynamic power consumption [23]. * Energy-efficient hardware components--such as advanced power amplifiers [24], small cells [25], low-power modems [26], edge computing [27], processors [28], cooling systems [29, 30], and AI-powered network management [31, 32] (Figure 3)--can all significantly contribute to energy savings in 5G networks. Figure 2: An example of a power state machine for a Strong\(-\)ARM SA\(-\)1100 processor. * Efficient spectrum management strategies can also reduce energy consumption by optimizing the usage of different resources [33; 34]. For example, massive MIMO (multiple input-multiple output) technologies are vital for 5G and beyond (Figure 3); these employ a large number of antennas at a given base station. Within the same frequency band, this allows the station to serve multiple users. Thus, massive MIMO technologies can reduce energy consumption by adjusting the overall transmission power with optimal coverage [35]. * The dense deployment of base stations and interconnected equipment and devices is well-suited for integrating renewable energy sources [36; 37], such as wind and solar energy sources; this is especially the case in regions where power grid extension is not feasible. Industries that corporate renewable energy sources into their 5G networks may importantly reduce their dependence on fossil fuels in addition to reducing their overall carbon footprints. Many researchers have been developing several advanced modeling and simulation methods in order to better quantify the energy consumption of 5G networks under different conditions. [PERSON] et al., 2021 [38], addressed resource allocation and the management of dynamic power via multiple strategies such as small cell deployment and the integration of scheduling algorithms [39]. This will provide telecommunication operators and network designers with tools that can enhance overall energy efficiency. For the operators of 5G networks, the objective was to create a balance between the demands for faster speeds, massive connectivity, and lower latency, all accompanied by the aim of minimizing overall operational costs. These primary modeling and simulation works [38; 39] provide significant insights for future energy-efficient new-generation network infrastructures. Today, the ease of access to big data from diverse sources allow telecommunication and network designers and operators to better integrate artificial intelligence (AI) tools in their practices, enabling them to better enhance energy consumption while satisfying network reliability and ensuring correct functioning. Such AI-data-driven methods are vital for constituting energy-efficient 5G and future 6G networks. Figure 3: AI energy-saving-based techniques for 5G base stations [32]. The complexity of 5G networks, with denser deployment of small cells (e.g., advanced antenna equipment and higher-frequency bands) contribute to larger power demands compared to previous G-networks. To overcome these challenges, researchers have recently been trying to develop different advanced energy-efficient methods [25, 40, 41].