Joint Position and Orientation Estimation in VCSEL-Based LiFi Networks: A Deep Learning Approach
Joint Position and Orientation Estimation in VCSEL-Based LiFi Networks: A Deep Learning Approach
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DOI：
10.1109/globecom54140.2023.10436886
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Rizwana Ahmad;Hossein Kazemi;Elham Sarbazi;Harald Haas
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文献类型：
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作者：
Rizwana Ahmad;Hossein Kazemi;Elham Sarbazi;Harald Haas
为实现智能网络管理及各类6G智能服务，需要对用户位置和设备方向进行精确估计。基于垂直腔面发射激光器（VCSEL）的光保真（LiFi）技术，不仅能在超高数据速率、连接密度和区域容量等方面满足6G通信网络的需求，还能实现高精度的位置和方向估计。然而，联合位置与方向估计问题属于非凸优化问题。因此，本文设计了深度神经网络（DNN），用于基于VCSEL的LiFi接入网络中用户设备的联合位置与方向估计。仿真结果表明，所提框架通过显著降低位置和方向估计误差，性能优于现有先进方法，同时保持较低的复杂度。我们通过考虑分布式VCSEL和并置VCSEL这两种网络部署类型，说明了所提DNN解决方案的有效性。此外，我们还对所提学习框架进行了收敛性和复杂度分析。结果显示，与基线方法相比，所提DNN在位置和方向的平均估计误差方面分别至少提升了69%和27.9%。
To enable intelligent network management and various 6G smart services, the precise estimation of user location and device orientation is required. Light fidelity (LiFi) based on vertical cavity surface emitting lasers (VCSELs) can not only respond to the needs of 6G communication networks in terms of ultra-high data rate, connection density and area capacity, but also enable high precision position and orientation estimation. However, this problem of joint position and orientation estimation is a non-convex optimization problem. Therefore, in this paper, we design deep neural networks (DNNs) for joint position and orientation estimation of user devices in a VCSEL-based LiFi access network. Simulation results demonstrate that the proposed framework outperforms state-of-the-art methods by significantly reducing position and orientation estimation errors while maintaining a lower complexity. We illustrate the effectiveness of the proposed DNN solution by considering two types of network deployment including distributed VCSELs and collocated VCSELs. In addition, we present the convergence and complexity analysis for the proposed learning framework. It is shown that the proposed DNN provides at least 69% and 27.9% improvements in the mean estimation error for position and orientation, respectively, over the baseline method.