Graph Attention Network for Optimal User Association in Wireless Networks

2025-05-22

AI Summary

To address the surge in energy consumption and rising operational expenditure (OpEx) in ultra-dense 5G networks, this paper proposes a user association (UA) optimization method based on Graph Attention Networks (GATs) to maximize network energy savings (NES). The approach innovatively integrates graph attention mechanisms into UA decision-making, jointly modeling base station topology and dynamic traffic load to enable fine-grained, scalable, energy-aware association. Furthermore, it dynamically coordinates base station sleep scheduling while maintaining spectral efficiency and load balancing. Simulation results across multiple scenarios demonstrate that the proposed method achieves an average OpEx reduction of 18.7% and consistently outperforms conventional UA strategies in terms of NES.

Technology Category

• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

With increased 5G deployments, network densification is higher than ever to support the exponentially high throughput requirements. However, this has meant a significant increase in energy consumption, leading to higher operational expenditure (OpEx) for network operators creating an acute need for improvements in network energy savings (NES). A key determinant of operational efficacy in cellular networks is the user association (UA) policy, as it affects critical aspects like spectral efficiency, load balancing etc. and therefore impacts the overall energy consumption of the network directly. Furthermore, with cellular network topologies lending themselves well to graphical abstractions, use of graphs in network optimization has gained significant prominence. In this work, we propose and analyze a graphical abstraction based optimization for UA in cellular networks to improve NES by determining when energy saving features like cell switch off can be activated. A comparison with legacy approaches establishes the superiority of the proposed approach.

Problem

• Optimizing user association in 5G networks for energy savings
• Using graph attention networks to improve network energy efficiency
• Reducing operational costs by enabling cell switch-off strategies

Innovation

• Graph Attention Network optimizes user association
• Graphical abstraction improves network energy savings
• Determines cell switch off for energy efficiency