LAUSR

Location-based services (LBSs) provide necessary infrastructure for daily life, from bicycle sharing to nursing care. In contrast to traditional positioning methods such as Wi-Fi, Bluetooth, and ultra-wideband (UWB), fifth-generation (5G) networking is defined as a paradigm of integrated sensing and communication (ISAC). With its advantages of wide-range coverage and indoor-outdoor integration, 5G is promising for high-precision positioning in indoor and urban canyon environments. However, 5G location studies face great obstacles due to the lack of commercialized 5G ISAC base stations that support positioning functions as well as publicly available datasets. In this paper, we first propose a dataset generation method, the Multilevel Feature Synthesis Method (Multilevel-FSM), to obtain positioning features. In particular, the features of a multiple-input multiple-output (MIMO) channel are flattened into a single image to increase the information density and improve feature expression, and data augmentation is performed to provide stronger robustness to noise. Subsequently, we devise a specially designed deep learning positioning method, Multipath Res-Inception (MPRI), trained on the proposed dataset to enhance positioning accuracy. Finally, the results of extensive experiments conducted in two typical 5G scenarios (indoors and urban canyon) show that Multilevel-FSM and MPRI outperform state-of-the-art works in accuracy, time overhead and robustness to noise.