IMPos: Indoor Mobile Positioning With 5G Multibeam Signals From a Single Base Station
Xin Zhou, Liang Chen, Yanlin Ruan, Tao Zhou, Ruizhi Chen
Abstract
With the widespread deployment of the fifth-generation (5G) network indoors, commercial 5G signals are highly attractive in the field of indoor positioning because of their ubiquity. Considering the user equipment (UE) requirements for user privacy protection, low computational resource consumption, and the need for location services in mobile conditions, this study developed a low-cost indoor mobile positioning system based on 5G downlink multi-beam signals, termed IMPos. In particular, this research only uses the multi-beam reference signal received power as data source, which is derived from a single commercially deployed base station (BS) and received by a single receiving antenna. Based on this data source, a machine learning method is first proposed for floor-level recognition. Thereafter, a G2Bi network based on stacked recurrent neural networks is designed to achieve UE mobile self-positioning. To evaluate the performance of IMPos, field tests are carried out in different floor scenarios. Results show that even with just one BS, IMPos achieves a floor-level recognition accuracy exceeding 95% and a mobile positioning root-mean-square error of below 1.5 m in various scenarios.