Fifth-generation (5G) networks have been massively deployed in commerce. In addition, the new features introduced by 5G networks have been recognized to be beneficial to wireless localization. In this study, we investigate the performance of indoor localization with commercial 5G new radio (NR) signals, and the channel state information (CSI) is used for localization, which is acquired from the synchronization signal block (SSB) of the downlink physical channel. A hybrid indoor localization system named Hi-Loc has been developed. Initially, a feature enhancement module is designed to generate new features from the SSB CSI, and a data construction module is applied to prepare the dataset for subsequent localization algorithms. By introducing the dual-attention mechanism deep network with convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), a feature-based attention mechanism and a sample-based attention mechanism are designed to extract the implicit spatial and time information of CSI, respectively. Finally, in the online stage, by applying a fully connected neural network (FCNN), the 2-D coordinates are estimated based on the corresponding CSI features. We carry out indoor field tests in the typical office and corridor scenario. The results show that Hi-Loc achieves mean absolute errors of 0.65 and 1.03 m in the internal test cases of the office scenario and the corridor scenario, respectively, and 1.93 and 3.10 m in the external test cases of the office scenario and the corridor scenario, respectively, indicating its superiority over the compared CSI fingerprint-based methods in localization accuracy.

The fifth-generation (5G) networks have been massively deployed in commerce. The new features introduced by 5G networks are beneficial to wireless positioning. In this study, the performance of indoor positioning with commercial 5G new radio (NR) signals is investigated, and the channel state information (CSI) extracted from the downlink synchronization signal block is utilized. Considering the limited 5G NR base station (known as gNodeB) is hearable indoors, the fingerprint method is used, and an indoor positioning system termed iPos-5G is developed. The system consists of four components. First, a module of quality control is applied for CSI preprocessing. Second, an unsupervised deep-autoencoder network is utilized to reconstruct CSI features. Third, by supervised learning, a radial basis function is improved to optimize the probability model for similarity calculations. Finally, an amplitude-phase probability fusion function is proposed for positioning by weighting the coordinates of reference points. To verify the effectiveness of iPos-5G, indoor field tests are carried out in the scenarios of an office and a corridor. The test results show that iPos-5G achieves mean absolute errors of 2.14 and 2.81 m and standard deviation of the errors of 1.07 and 1.66 m, which outperforms the compared CSI fingerprint methods in terms of positioning accuracy and stability.

In this work, we investigate the property of the multi-beam of 5G new radio (NR) signals for indoor localization. Specifically, the 5G NR signals are firstly sampled by a self-developed software-defined receiver, and the multi-beam is extracted via detecting the multiple synchronization signal blocks (SSBs). Secondly, with the assistance of the pilots in the multiple SSBs, the reference signal received power (RSRP) and reference signal received quality (RSRQ) of the multi-beam are calculated. Thirdly, by stacking the RSRP and RSRQ of the multi-beam as the observables, a fingerprint database is constructed. With the aim to efficiently process the fingerprint features and improve the accuracy of indoor localization, a CatBoost-based algorithm is proposed, and the parameters are further optimized by tree-structured parzen estimator (TPE). To verify the effectiveness of the proposed method, indoor field tests are carried out in an office scenario, where real 5G signals are transmitted from a commercial 5G NR base station indoors. The field tests demonstrate that, by taking the advantages of the multi-beam of 5G NR, the localization accuracy can be able to achieve the accuracy of 1.06 m in the metric of root mean squared error (RMSE), even when only one base station is heard indoors. By comparison with the single-beam, the accuracy of multi-beam has improved 48%.

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 (IMPos) system based on 5G downlink multibeam signals, termed IMPos. In particular, this research only uses the multibeam 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.

Fifth-generation (5G) networks have been deployed to serve a massive number of users. The new properties of 5G, such as large bandwidth, high data rates, and extensive coverage, provide promising possibilities for positioning and navigation. In many commercial indoor scenarios, however, only one 5G base station (called gNB) is received by the receiver. This condition constrains the application of traditional geometric positioning methods, which typically rely on observations from multiple gNBs to a certain extent. In this work, we investigate the problem of indoor positioning with a single 5G gNB. We propose to utilize the multibeam property of 5G signals in the synchronization signal block of the downlink, while the fingerprinting method is used based on the multibeam channel state information. Field tests are conducted in different indoor scenarios to verify the effectiveness of the method. The tests show that the method is able to achieve mean absolute errors of positioning within 1.55 m by taking advantage of the multibeam property of 5G signals. Accuracy is improved by more than 54.7% compared with the single beam.