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Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning
Insoo Sohn1, Huaping Liu2, Nirwan Ansari3
1Division of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul, Republic of Korea.
Plos One
|July 14, 2015
Summary
This study optimizes green cellular network energy use. A novel algorithm using game theory reduces power consumption and carbon footprint for sustainable mobile networks.
Area of Science:
- Telecommunications Engineering
- Computer Science
- Applied Mathematics
Background:
- Rising energy costs and carbon footprint in cellular networks necessitate sustainable solutions.
- Renewable energy integration in cellular networks is crucial for reducing carbon emissions from base station energy consumption.
- Game theory offers a framework for optimizing systems with competing objectives in cellular networks.
Purpose of the Study:
- To address the challenge of energy cost and carbon footprint in cellular infrastructure.
- To optimize green energy utilization and power consumption in renewable energy-powered cellular networks.
- To develop a novel distributed algorithm for efficient resource management.
Main Methods:
- Modeling the green energy utilization and power consumption problem as a pilot power selection strategic game.
- Proposing a novel distributed algorithm based on strategic learning methods.
- Utilizing game theory to analyze and optimize the cellular network system.
Main Results:
- The proposed algorithm achieves correlated equilibrium in the pilot power selection game.
- Demonstrated optimum green energy utilization.
- Achieved significant power consumption reduction in cellular networks.
Conclusions:
- The developed strategic learning algorithm effectively optimizes energy utilization and power consumption in green cellular networks.
- The findings contribute to the development of more sustainable and cost-effective mobile network infrastructure.
- This approach offers a viable solution for reducing the environmental impact of the expanding cellular industry.
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