# Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions
## 1 Introduction  
According to the GSMA [1], the telecom industry is responsible for 2-3% of global energy consumption, and power costs constitute 15-40% of an operator's operating expenses (OPEX). Consequently, major telecom providers like Deutsche Telekom, Vodafone, Verizon, AT&T, and Orange have established ambitious targets for reducing power consumption and carbon emissions in the near future [2, 3, 4, 5, 6].  
Networks consume 90% of a telecom operator's total power/electricity, with 60-80% attributed to the Radio Access Network (RAN), as per GSMA data [7]. The 3 GPP [8] defines network energy efficiency as the amount of data transmitted per unit of energy consumed, measured in bits per Joule (bit/J). A higher bit/J value signifies greater energy efficiency.  
5G RAN, depicted in Figure 1, has substantial potential for energy savings and has become a focal point for research. Various approaches have been proposed, including the following:  
* Integrating renewable energy sources with traffic offloading and Advanced Sleep Modes [9];
* Implementing mechanisms to deactivate underutilized small cells based on predicted user mobility [10, 11];
* Utilizing machine learning to control cell sleep modes efficiently based on traffic, interference, and buffer status [12, 13];
* Developing heuristic solutions that consider energy efficiency and power allocation by switching off base stations [14];* Proposing algorithms to dynamically adjust cell coverage and base station activity based on traffic load, maximize energy efficiency, and balance grid and solar power consumption [15];
* Employing genetic algorithms to deactivate underutilized base stations [16];
* Investigating dynamic switching between base station backhauling technologies [17];
* Optimizing Advanced Sleep Mode (ASM) parameters to balance energy savings and delay constraints, addressing a new aspect of Quality of Service (QoS) [18];
* Proposing a clustering-based energy-saving scheme to minimize active base stations while ensuring QoS for all User Equipment (UE) [19];
* Developing a centralized dynamic sleep method using a genetic algorithm and clustering to enhance energy savings through coordinated sleep decisions [20];
* Dynamically updating the sleep mode status of Radio Units (RUs) based on the network state to ensure that only necessary RUs remain active, meeting demand and maintaining QoS [21];
* Dynamically controlling power-saving modes using graph theory to determine the order of cell activation/deactivation, maximizing power savings while maintaining coverage and minimizing signaling [22].  
The need for user connectivity and communication capabilities in any geographical area where 5G or traditional mobile technologies cannot be made available is continuously rising. That is the reason why advanced network architectures like Satellite-Terrestrial Integrated Networks (STINs), depicted in Figure 2, and Integrated Terrestrial-Aerial Networks (ITANs), depicted in Figure 3, have begun to attract more and more interest. The key difference between STINs and ITANs lies in ITANs' integration of the aerial platform and utilization of RISs (reconfigurable intelligent surfaces), while STINs are provided by terrestrial and satellite networks with a common scope of delivering ubiquitous communication improving security and energy consumption.  
Figure 1: 5G network.  
Various approaches to achieve energy efficiency for beyond 5G new networks have been taken:  
* Hybrid beamforming design in [23; 24];
* Robust beamforming design in [25].  
Besides these, other approaches look at overall energy efficiency solutions by focusing on computational load, indirectly obtaining 5G networks' energy efficiency [26; 27].  
Section 2 of this paper will provide a detailed analysis of these solutions, establishing criteria for comparison and identifying their strengths and weaknesses, which will be discussed in Section 3. Section 4 will conclude with potential enhancements.  
Figure 3: System model of ITAN with multi-layer RIS.  
Figure 2: System model of the considered STIN.