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.

翻译：混合光保真（LiFi）与无线保真（WiFi）网络因其光谱与射频物理特性的互补性，成为异构网络（HetNet）中极具前景的范式。然而，此类异构网络的发展目前主要受限于现有传输控制协议（TCP），该协议限制用户设备（UE）每次仅能连接一个接入点（AP）。尽管对多路径TCP（MPTCP）的持续研究能带来显著优势，但它使异构网络的拓扑结构复杂化，导致现有负载均衡（LB）学习模型效能降低。基于此，我们提出一种基于图神经网络（GNN）的模型，以解决支持MPTCP的异构网络（其形成部分网状拓扑）中的负载均衡问题。该拓扑可建模为图结构，其中信道状态信息与数据速率需求嵌入为节点特征，而负载均衡解则视为边标签。与传统深度神经网络（DNN）相比，所提出的GNN模型展现出两大关键优势：i）能更有效地解析复杂网络拓扑；ii）仅需单一训练模型即可处理不同数量的AP与UE。仿真结果表明，相较于传统优化方法，所提出的学习模型能以11.5%的差距实现接近最优的吞吐量，同时将推理时间降低4个数量级。与DNN模型相比，新方法在相近推理时间水平下，可将网络吞吐量提升高达21.7%。