# Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions
## 3 Identifying Pros and Cons of Available Solutions  
The algorithms designed to enhance 5G and beyond 5G networks' energy efficiency analyze several key features:  
* Mobility prediction: anticipating user movement to optimize resource allocation;
* Traffic offloading: shifting users from congested base stations to less busy ones;
* Sleep modes: putting base stations into low-power states during periods of inactivity;
* Renewable energy: utilizing solar or wind power to supplement the network's energy supply;
* Clustering: grouping base stations based on load or cooperation factors to simplify algorithm complexity;
* Hybridization of the solutions: using multiple algorithms, obtaining the value of their benefits while lowering limitations;
* Optimization of the solution computational load: reducing the number of operations in the solution transaction. These features are integrated into various algorithms, often employing reinforcement learning, genetic algorithms, or heuristic solutions, to achieve not only energy efficiency but also to maintain Quality of Service (QoS) for users and the security of communication. Table 1, Features of energy efficiency studies of 5G networks, summarizes the different algorithms, highlighting that most leverage sleep modes, a standard 5G power-saving mechanism. A few solutions also explore alternative approaches like adjusting backhaul technologies based on network load or simply switching base stations on/off. Table 2, Features of studies of beyond 5G new networks, compares different approaches of new network architectures to improve energy efficiency through hybrid beamforming design while providing communication capabilities similar to cellular networks in any geographical area. Further enhancements have been incorporated to improve QoS, reduce algorithm complexity, or further boost energy efficiency. These include the following:  
* Renewable energy integration: supplementing power supply during peak load times with solar energy;
* Clustering: simplifying algorithms by grouping base stations;* Traffic offloading: ensuring user QoS while improving efficiency by moving users from low-traffic to high-traffic base stations;
* Mobility prediction: predicting traffic load to optimize base station sleep modes. Implementing any of these energy-efficient algorithms can lead to significant energy savings ranging from 10% to 80%. The actual savings depend on factors like peak hours, network density, algorithm parameters, coordination between base stations and the central controller, network topology, and latency. In Tables 3 and 4, we have analyzed the advantages and limitations of each studied solution, and motivated by our findings, we propose a hybrid solution of the studied ones in order to achieve better energy efficiency overall. In future works, we aim to measure the results of our solution against a data set of data records tracking resources available for cells of a 2G, 4G, and 5G real network. Similarly, we aim to benchmark our proposed solution against the studied ones in this article having the same data set. Our main contributions are summarized below:  
* Examining the literature and identifying available solutions in the last five years;
* Identifying the advantages and limitations of available solutions;
* Identifying a theoretical solution to improve the energy efficiency of communication networks through the hybridization of available solutions, highlighting unexplored areas that will be measured in future studies.