Learning to Maximize Energy Efficiency in 6G in-X Subnetworks

2026-09-21

AI Summary

本文通过图神经网络框架优化6G in-X子网的发射功率，解决能效问题，并在三种能效公式下展示了显著性能提升。

Abstract

This paper investigates energy-efficient power control in 6G in-X subnetworks. We consider a graph neural network (GNN) framework that captures inter-subnetwork interference and the underlying wireless topology to optimize transmit powers. Three energy efficiency (EE) formulations are studied: (i) network-centric, which maximizes total network energy efficiency; (ii) subnetwork-centric, which maximizes the average energy efficiency per subnetwork; and (iii) a multi-objective approach, which balances energy efficiency and sum-rate performance. Extensive simulations in industrial factory settings with 3GPP channel models demonstrate that the GNN effectively learns interference-aware power allocation policies, significantly outperforming maximum power transmission and existing GNN based power control solution. Results showed network EE gains of up to 1341%, average per-device EE improvements of up to 1302%, and sum-rate enhancements up to 24.7% relative to a maximum transmit power policy, depending on the chosen optimization formulation and trade-off settings.

Problem

• energy efficiency
• 6G in-X subnetworks
• power control

Innovation

• Graph Neural Network (GNN)
• Energy Efficiency (EE)
• Power Control
• Interference-aware
• 6G in-X Subnetworks