学习在6G in-X子网络中最大化能量效率
Learning to Maximize Energy Efficiency in 6G in-X Subnetworks
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中文总结 AI 辅助
本文提出基于图神经网络的功率控制框架，在6G in-X子网络中通过三种能量效率公式优化发射功率，显著提升网络EE（最高1341%）和总速率（最高24.7%）。
中文摘要 AI 辅助
本文研究了6G in-X子网络中的能量高效功率控制问题。我们考虑了一种图神经网络（GNN）框架，该框架捕获子网络间干扰及底层无线拓扑以优化发射功率。研究了三种能量效率（EE）公式：（i）网络中心式，最大化总网络能量效率；（ii）子网络中心式，最大化每个子网络的平均能量效率；以及（iii）多目标方法，平衡能量效率与总速率性能。在采用3GPP信道模型的工业工厂环境中进行的大量仿真表明，GNN能有效学习干扰感知的功率分配策略，显著优于最大功率传输和现有基于GNN的功率控制方案。结果显示，相对于最大发射功率策略，根据所选优化公式和权衡设置，网络EE增益最高达1341%，每设备平均EE提升最高达1302%，总速率增强最高达24.7%。
英文摘要
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.
发表机构
- Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。
