Target 5G visible light positioning signal subcarrier extraction method using particle swarm optimization algorithm

With the explosive growth of demand for Internet of Things (IoT) applications and the increasing dependence of users on wireless connections, indoor location based service(LBS) under 5G-Public-Private Partnership (5G-PPP) using cases have received more attention and get rapid development. Thanks to the safty, security and customization of 5G network pointed by 5G forum white paper, indoor positioning systems using unified 5G New Radio (NR) signals have become the focus of the next generation of visible light positioning (VLP) systems. In 5G New Radio(NR) frame, totally 192 subcarriers are used to carry positioning reference signal(PRS). In order to improve the position accuracy, the aim of this study is to investigate the method of the 5G NR subcarrier selection. A certain number of PRSs are obtained from the signal source of 192 subcarriers, and the minimum positioning error(PE) is calculated by using particle swarm optimization(PSO) algorithm. Then the relationship between the PRS with the minimum PE and the position of the subcarrier is analyzed which achieved the subcarrier extraction. The experimental results show that the average PE of this scheme is below 6 cm, and the minimum PE can reach 0.05 cm. This approach is confirmed the feasibility of high accuracy in 5G indoor VLP application and has the potential to be extended to larger and more complex 5G-PPP scenarios.