Coordination of Macro Base Stations for 5G Network with User Clustering

Area of Science:

• Telecommunications Engineering
• Computer Science
• Environmental Science

Background:

• Fifth-generation mobile communication networks (5G) face significant energy consumption challenges from increased terminal equipment and higher communication quality demands.
• The escalating energy usage of 5G base stations (BSs) leads to higher operational costs for telecom operators and increased environmental burden.
• Existing energy management strategies may not be sufficient to address the scale of energy consumption in dense 5G networks.

Purpose of the Study:

• To propose a novel, two-step energy management method for coordinating 5G macro base stations.
• To reduce both energy consumption and electricity costs in 5G networks through intelligent coordination and user clustering.
• To enhance the computational efficiency of the energy management process.

Main Methods:

• A two-step energy management strategy is developed, coordinating communication and standard equipment within 5G macro BSs.
• A novel user clustering method is introduced to optimize resource allocation and energy usage.
• Benders decomposition is employed to accelerate the solving process of the energy management problem.

Main Results:

• The proposed method demonstrates computational efficiency, achieving near-optimal performance.
• Significant reductions in energy consumption for 5G base stations were observed.
• Electricity costs for telecom operators were effectively lowered compared to conventional schemes.

Conclusions:

• The developed energy management method offers an effective solution for reducing the environmental impact and operational costs of 5G networks.
• The combination of user clustering and Benders decomposition provides a computationally efficient and near-optimal approach.
• This strategy is crucial for sustainable development and economic viability in the era of 5G expansion.