Joint Position and Orientation Estimation in VCSEL-Based LiFi Networks: A Deep Learning Approach

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.