支持MPTCP的混合LiFi与WiFi网络中面向资源分配的以用户为中心的机器学习
User-Centric Machine Learning for Resource Allocation in MPTCP-Enabled Hybrid LiFi and WiFi Networks
AI总结：
针对支持MPTCP的混合LiFi与WiFi网络的负载均衡难题，提出一种以用户为中心的机器学习方法，相比以网络为中心的学习方法及基于TCP的博弈论方法，性能更优，吞吐量最高提升40%。
摘要：
作为面向6G的异构网络（HetNets）的新兴范式，混合光保真（LiFi）与无线保真（WiFi）网络（HLWNets）有望挖掘光频谱与无线电频谱的互补优势。与其他原生协作型异构网络一样，由于接入点（AP）的异构性，HLWNets面临着关键的负载均衡（LB）问题。现有文献大多将该问题建模为接入点选择与资源分配（RA）的联合问题，假设每个用户设备（UE）在传统传输控制协议（TCP）约束下，每次仅由一个接入点提供服务。相比之下，允许同时使用多个接入点的多路径TCP（MPTCP）能够显著提升用户设备的吞吐量，并增强其网络韧性。然而，现有的基于TCP的负载均衡方法，尤其是机器学习辅助的方法，并不适用于MPTCP场景。本文探讨了在支持MPTCP的HLWNets中开发学习辅助负载均衡所面临的挑战，并提出了一种新颖的以用户为中心的学习模型来解决这一棘手问题。与传统的以网络为中心的学习方法不同，所提方法为单个目标用户设备确定负载均衡解决方案，在实际部署中具备低复杂度与高灵活性的特点。结果表明，所提出的以用户为中心的方法性能远优于以网络为中心的学习方法；相较于博弈论等基于TCP的负载均衡方法，该方法可将HLWNets的吞吐量提升最高达40%。
英文摘要：
As an emerging paradigm of heterogeneous networks (HetNets) towards 6G, the hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) have potential to explore the complementary advantages of the optical and radio spectra. Like other cooperation-native HetNets, HLWNets face a crucial load balancing (LB) problem due to the heterogeneity of access points (APs). The existing literature mostly formulates this problem as joint AP selection and resource allocation (RA), presuming that each user equipment (UE) is served by one AP at a time, under the constraint of the traditional transmission control protocol (TCP). In contrast, multipath TCP (MPTCP), which allows for the simultaneous use of multiple APs, can significantly boost the UE's throughput as well as enhancing its network resilience. However, the existing TCP-based LB methods, particularly those aided by machine learning, are not suitable for the MPTCP scenario. In this paper, we discuss the challenges when developing learning-aided LB in MPTCP-enabled HLWNets, and propose a novel user-centric learning model to tackle this tricky problem. Unlike the conventional network-centric learning methods, the proposed method determines the LB solution for a single target UE, rendering low complexity and high flexibility in practical implementations. Results show that the proposed user-centric approach can greatly outperform the network-centric learning method. Against the TCP-based LB method such as game theory, the proposed method can increase the throughput of HLWNets by up to 40\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
