The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

翻译：随着6G技术的出现，无线网络容量与流量需求的快速剧烈波动加剧了流量-容量失配问题，引发了人们对无线网络能耗的担忧。为应对这一挑战，我们提出了一种模型-数据双驱动资源分配算法，旨在动态流量条件下最大化综合相对能效指标。与传统的模型驱动或数据驱动方案不同，所提出的MDDRA框架采用模型驱动的Lyapunov队列来累积长期历史失配信息，并利用数据驱动的图径向基傅里叶网络来预测不完整数据下的流量变化，从而消除了对高精度模型和完整时空流量数据的依赖。我们建立了所提GRAF网络的通用逼近特性，并对MDDRA算法进行了收敛性和复杂度分析。数值实验验证了通过数据驱动和模型驱动组件所实现的性能增益。通过分析不同流量条件下的IREE和EE曲线，我们建议网络运营商应投入更多精力平衡流量需求与网络容量分布，以确保网络性能，尤其是在速度限制较大和驾驶能见度较高的场景中。