# Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions
## 2 Energy Efficiency Solutions Available in the Market
Based on the application's sensitivity to delays, it establishes a maximum average time for a base station to rouse itself from its sleep. The crux of the matter is optimizing the \"hold time\" (T2)--the duration spent in the intermediate sleep mode, sleep mode 2 (SM2), before descending into the deepest sleep mode, sleep mode 3 (SM3). DCASM's ingenuity lies in its dynamic adaptability. During periods of low traffic, it encourages base stations to sleep longer, maximizing energy conservation. Conversely, as traffic surges, it ensures they remain in lighter sleep states or even fully awake, ready to respond promptly. This dynamic approach guarantees that average wake-up times stay within the predefined limits, making it ideal even for applications that demand swift responses. The brilliance of DCASM extends beyond theory. A closed-form expression enables the calculation of optimal sleep durations, streamlining its implementation in real 5G base stations. This practicality, combined with its ability to significantly reduce energy consumption while upholding performance guarantees, positions DCASM as a promising solution for a sustainable 5G future. In the intricate landscape of 5G dense small cell networks (SCNs), [PERSON] et al. (2019) envisioned a power-saving strategy that revolves around the concept of clustering. The goal is ambitious: to selectively switch off base stations, thus minimizing energy consumption, while ensuring that users continue to enjoy a satisfactory Quality of Service (QoS). Recognizing the computational challenge of finding the optimal on/off configuration for base stations (BSs), [PERSON] proposes a three-phase heuristic approach. The first phase involves partitioning the network into clusters based on BS load. Heavily burdened BSs are designated as the cores of these clusters. Next, within each cluster, an exhaustive search is conducted to identify the ideal combination of active and inactive BSs. This search aims to minimize the number of active BSs while still upholding the required QoS. Finally, the algorithm extends its reach to the boundaries between clusters, evaluating whether users served by BSs on the edges could be handed over to neighboring clusters, further reducing energy consumption. The clustering algorithm itself is a well-orchestrated process. It begins by calculating the load on each BS, taking into account the resource blocks (RBs) demanded by its usersand the total available RBs. BSs are then classified as underloaded, medium loaded, or overloaded using predefined thresholds. Heavily loaded BSs, or the two most loaded ones if none meet the \"heavily loaded\" criteria, are chosen as cluster cores. The remaining BSs then align themselves with the nearest core BS, forming clusters. Should any cluster become too large, it is split into two, with the two most loaded BSs within it assuming the role of new cores. Through simulations, this clustering-based scheme has proven its ability to significantly reduce power consumption without compromising QoS. Its performance shined particularly in scenarios where users were not uniformly distributed, outperforming existing approaches. In essence, this heuristic clustering algorithm serves as a powerful tool, effectively dividing a network into manageable subproblems, enabling the efficient on/off control of BSs within each cluster, and paving the way for a more energy-conscious 5G future. [PERSON] et al. [22] have proposed a novel energy management framework for 5G ultra-dense networks (UDNs) using graph theory. The framework is designed to address the increased energy demands of 5G networks due to the integration of small cells (SCs) alongside macro cells (MCs). The key idea is to dynamically control power-saving modes in the radio network by modeling the network as a graph and then using graph theory methods to determine the order in which nodes (cells) are switched off or on. The algorithm, called STAR5, prioritizes nodes for power-saving based on their traffic load and type (SC or MC).