AI-Driven Energy Optimization in 6G Networks: A Multi-Objective Approach
Dr. Hanna Bogucka, Prof. H. Vincent Poor
Poznan University of Technology / Princeton University
Abstract
This paper addresses the critical challenge of energy efficiency in 6G networks through a multi-objective AI optimization framework. Our approach uses evolutionary neural networks to simultaneously optimize energy consumption, spectral efficiency, and user QoS. Deployed on a testbed with 20 base stations, the system achieves 40% energy reduction during low-traffic periods while maintaining 99.9% QoS satisfaction, representing a significant step toward carbon-neutral 6G networks.
AI Summary
- Multi-objective AI framework optimizing energy, spectrum, and QoS simultaneously.
- 40% energy reduction during low-traffic while maintaining 99.9% QoS.
- Validated on a 20 base station testbed.
- Uses evolutionary neural networks for Pareto-optimal solutions.
Key Findings
- 1Multi-objective optimization outperforms single-objective energy minimization.
- 2Traffic prediction enables proactive base station sleep scheduling.
- 3The approach adapts to seasonal and event-driven traffic patterns.
Industry Implications
Critical for achieving carbon-neutral 6G networks by 2030.
Reduces operational expenditure for network operators.
Demonstrates that energy savings and QoS are not mutually exclusive.
Read the Original Paper
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