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Abstract：
A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals\' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions.
摘要：
混合LiFi和WiFi网络（HLWNet）集成了LightFidelity（LiFi）的快速数据传输功能和无线保真（WiFi）提供的广泛连接，为指定区域中的无线数据传输带来了巨大的好处。然而,由于电磁信号视距传输的特定特性，HLWNet的切换过程中的决策挑战变得更加复杂，与以前的异构网络相比，导致更高的复杂性。这项研究工作解决了混合LiFi和WiFi网络中的切换决策问题，并将其视为二元分类问题。因此,提出了一种基于深度神经网络(DNN)的切换方法。综合切换方案包含两组神经网络(ANN和DNN),它们利用诸如信道质量和用户移动性之类的输入因素来实现切换期间的明智决策。在使用带标签的数据集进行培训之后，基于神经网络的切换方法准确率超过95%。对所提出的方案与基准的比较分析表明，与基准人工神经网络（ANN）相比，所提出的方法将用户吞吐量大大提高了约18.58％至38.5％，同时将切换率降低了约55.21％至67.15％;此外，所提出的方法在面对用户移动性和信道条件的变化时具有鲁棒性。
