Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions

Area of Science:

• Telecommunications Engineering
• Computer Science
• Network Optimization

Background:

• Energy efficiency is critical for 5G New Radio (NR) and future networks.
• It impacts User Equipment (UE) battery life and base station operational costs.
• Balancing energy efficiency with latency, throughput, and reliability is essential.

Purpose of the Study:

• To conduct a comprehensive review of power-saving research for 5G and beyond networks.
• To elucidate the advantages, disadvantages, and characteristics of various energy-saving techniques.
• To identify limitations and propose future research directions for enhanced network efficiency.

Main Methods:

• Review of existing literature on power-saving techniques in 5G and beyond.
• Analysis of algorithms including reinforcement learning, heuristic algorithms, genetic algorithms, and Markov Decision Processes.
• Examination of hybridized standard algorithms used in 5G and 5G NR.

Main Results:

• Identified key limitations such as computational expense, deployment complexity, and scalability constraints.
• Evaluated the pros and cons of diverse power-saving methodologies.
• Highlighted the potential of specific algorithms and their hybridizations.

Conclusions:

• Future research should explore online learning, base station clustering, and hard handover (HO) for reduced network consumption.
• These advancements can help mobile network operators achieve targets for lowering carbon emissions and operational expenditures (OPEX).
• Optimizing energy efficiency is crucial for sustainable and cost-effective mobile networks.