Visible light positioning (VLP) has drawn plenty of attention as a promising indoor positioning technique. However, in nonstationary environments, the performance of VLP is limited because of the highly time-varying channels. To improve the positioning accuracy and generalization capability in nonstationary environments, a cooperative VLP scheme based on federated learning (FL) is proposed in this paper. Exploiting the FL framework, a global model adaptive to environmental changes can be jointly trained by users without sharing private data of users. Moreover, a Cooperative Visible-light Positioning Network (CVPosNet) is proposed to accelerate the convergence rate and improve the positioning accuracy. Simulation results show that the proposed scheme outperforms the benchmark schemes, especially in nonstationary environments.

翻译：可见光定位（VLP）作为一种极具前景的室内定位技术已引起了广泛关注。然而，在非平稳环境中，由于信道具有高度时变性，VLP的性能受到限制。为提升非平稳环境下的定位精度与泛化能力，本文提出了一种基于联邦学习（FL）的协作式VLP方案。借助联邦学习框架，用户可在无需共享私有数据的前提下协同训练一个能适应环境变化的全局模型。此外，本文还提出了一种协作式可见光定位网络（CVPosNet），以加快收敛速度并提高定位精度。仿真结果表明，所提方案在非平稳环境下尤其优于基准方案。