用图神经网络解决多路径WiFi+LiFi网络的负载均衡问题。
Learning Load Balancing with GNN in MPTCP-Enabled Heterogeneous Networks
- 用图神经网络建模异构网络拓扑，节点含信道状态和速率需求。
- 相比传统优化方法，吞吐量接近最优且推理时间缩短一万倍。
- 可适配不同数量接入点和终端，适合动态异构网络场景。
混合光保真（LiFi）与无线保真（WiFi）网络是具有互补物理特性的异构网络（HetNet）有前景的范式。然而，当前此类网络的发展大多受限于现有传输控制协议（TCP），限制用户设备（UE）一次只能连接一个接入点（AP）。尽管多路径TCP（MPTCP）的研究能带来显著优势，但其使异构网络拓扑更复杂，导致现有负载均衡（LB）学习模型效果下降。为此，我们提出一种基于图神经网络（GNN）的负载均衡模型，用于处理启用MPTCP的异构网络，形成部分网状拓扑。该拓扑可建模为图结构，其中信道状态信息和数据速率需求作为节点特征，负载均衡方案视为边标签。相比传统深度神经网络（DNN），所提GNN模型具备两大优势：一是能更好解析复杂网络拓扑；二是可使用单一训练模型应对不同数量的AP与UE。仿真结果表明，相较于传统优化方法，该学习模型在11.5%的差距内实现近似最优吞吐量，同时推理时间减少四个数量级；相较于DNN模型，可提升网络吞吐量达21.7%，且推理时间相当。
原文摘要 · Abstract (English)
Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks are a promising paradigm of heterogeneous network (HetNet), attributed to the complementary physical properties of optical spectra and radio frequency. However, the current development of such HetNets is mostly bottlenecked by the existing transmission control protocol (TCP), which restricts the user equipment (UE) to connecting one access point (AP) at a time. While the ongoing investigation on multipath TCP (MPTCP) can bring significant benefits, it complicates the network topology of HetNets, making the existing load balancing (LB) learning models less effective. Driven by this, we propose a graph neural network (GNN)-based model to tackle the LB problem for MPTCP-enabled HetNets, which results in a partial mesh topology. Such a topology can be modeled as a graph, with the channel state information and data rate requirement embedded as node features, while the LB solutions are deemed as edge labels. Compared to the conventional deep neural network (DNN), the proposed GNN-based model exhibits two key strengths: i) it can better interpret a complex network topology; and ii) it can handle various numbers of APs and UEs with a single trained model. Simulation results show that against the traditional optimisation method, the proposed learning model can achieve near-optimal throughput within a gap of 11.5%, while reducing the inference time by 4 orders of magnitude. In contrast to the DNN model, the new method can improve the network throughput by up to 21.7%, at a similar inference time level.
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