A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks

2025-06-04

AI Summary

In 6G networks, dynamic traffic-capacity mismatches severely degrade energy efficiency. Method: We propose a model-data dual-driven resource allocation framework to maximize Integrated Relative Energy Efficiency (IREE). It employs Lyapunov queueing theory to model long-term mismatch accumulation and introduces GRAF—a robust graph neural network—to enable accurate spatiotemporal traffic forecasting under incomplete data conditions. Contribution/Results: Theoretical analysis, integrating Lyapunov optimization, universal approximation, and convergence guarantees, rigorously establishes feasibility and computational advantages. Experiments in high-mobility, high-visibility scenarios demonstrate a 23.7% IREE improvement over single-driven baselines and yield actionable capacity-traffic co-deployment strategies. This work pioneers the deep integration of queue-driven modeling and graph-Fourier-domain data-driven prediction, eliminating reliance on both precise system models and complete observational data.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Address traffic-capacity mismatch in 6G networks
• Maximize energy efficiency under dynamic traffic
• Reduce reliance on high-precision models and complete data

Innovation

• Model-data dual-driven resource allocation algorithm
• Lyapunov queue for historical mismatch information
• GRAF network for traffic variation prediction