Visible light positioning (VLP) systems can achieve high positioning precision. However, they are not compatible with visible light communication (VLC) systems. They require special positioning modules and could not reuse functional communication modules, while requiring more than two light emitting diodes (LEDs) to be deployed at user ends. In order to address the issues of weak compatibility and high complexity of VLP, we present a novel position estimation deep neural network (PE-DNN) and propose to add a PE-DNN aided module at the VLC receivers. The proposed module firstly learns features of the VLC channel from received pilot signals implicitly, then it can estimate receivers’ 2-dimension positions intelligently with a single LED. Accordingly, VLC systems can simultaneously provide positioning and information transmission services with only one LED and one photodiode (PD), thus the compatibility and the practicality are greatly improved. Simulation results show that the proposed system achieves a centimeter-level positioning accuracy, and can provide intelligent and practical positioning services for the users.

中文翻译：

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智能实用的可见光通信接收器深度学习辅助定位设计

可见光定位（VLP）系统可以实现高定位精度。但是，它们与可见光通信 (VLC) 系统不兼容。它们需要特殊的定位模块，不能重复使用功能通信模块，同时需要在用户端部署两个以上的发光二极管（LED）。为了解决 VLP 兼容性弱和复杂度高的问题，我们提出了一种新颖的位置估计深度神经网络 (PE-DNN)，并建议在 VLC 接收器上添加一个 PE-DNN 辅助模块。所提出的模块首先从接收到的导频信号中隐式地学习 VLC 信道的特征，然后它可以通过单个 LED 智能地估计接收器的二维位置。因此，VLC系统只需要一个LED和一个光电二极管（PD）就可以同时提供定位和信息传输服务，兼容性和实用性大大提高。仿真结果表明，该系统实现了厘米级定位精度，可为用户提供智能实用的定位服务。