用图注意力网络优化5G用户接入，降低能耗
Graph Attention Network for Optimal User Association in Wireless Networks
- 基于图注意力网络建模基站与用户关系
- 相比传统方法能耗降低18.7%，支持小区休眠
- 适合5G网络节能优化研究者参考
随着5G部署增加，网络密集化程度空前提高，以满足指数级增长的吞吐量需求。然而，这导致能耗显著上升，运营商运营支出（OpEx）压力加大，迫切需要提升网络节能（NES）水平。在蜂窝网络中，用户关联（UA）策略是影响运行效率的关键因素，直接关系到频谱效率、负载均衡等，进而影响整体能耗。鉴于蜂窝网络拓扑天然适合图结构抽象，图模型在网路优化中日益重要。本文提出一种基于图抽象的优化方法，用于蜂窝网络中的用户关联，旨在通过智能决策激活如小区休眠等节能机制，实现网络节能。实验对比表明，该方法优于传统方案。
原文摘要 · Abstract (English)
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.
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