5G1M: Indoor Fingerprint Positioning Using a Single 5G Module

Many critical applications necessitate indoor positioning, and the implementation of indoor positioning cannot be separated from various wireless signals. Among these, 5G-based indoor positioning is gaining increasing attention due to the wide coverage of 5G base stations and the stability of 5G signals. However, current positioning algorithms based on 5G signals require specialized measuring tools or information from telecommunication operators. Therefore, this paper proposes an indoor positioning algorithm, called 5G1M, that uses only a single 5G module and several commercial base stations already built outdoors. Besides these, 5G1M does not need any further assistance. We design 5G1M based on fingerprint positioning, which is less dependent on the environment and devices. First, 5G1M incorporates a Siamese network model with Ghost modules and squeeze-and-excitation blocks to reduce reliance on the scale of the fingerprint database and computational power while maintaining accuracy. Then, transfer learning is used to adapt to long-term environmental changes, and a trajectory-fitting method is employed to mitigate instantaneous signal fluctuations. The experimental result shows that in such a simplified positioning platform, 5G1M achieves a positioning error of 2.53 meters, improving accuracy by 29.7% compared to existing algorithms, and it also demonstrates faster convergence with fewer model parameters.