Location-based services are increasingly vital to urban digital transformation, with indoor positioning technologies enabling applications such as intelligent transportation and smart logistics. Leveraging the comprehensive indoor coverage of 5G cellular networks, this paper introduces FIVPos, a novel multi-beam fusion indoor positioning system designed for single-base-station scenarios. Unlike conventional approaches, FIVPos relies solely on transmission signals from a single commercial 5G base station, allowing simultaneous extraction of energy and distance information through a single receiving antenna. The proposed system builds on a multi-beam collaborative ranging model that combines carrier phase measurements with Reference Signal Received Power (RSRP). A lightweight stacked IMPos fingerprinting method is then employed to enhance dynamic positioning. To further improve robustness, a particle filter–based fusion framework integrates beam-based ranging with fingerprint-based matching, ensuring accurate and resilient positioning in complex indoor environments. Evaluation conducted in two representative indoor environments shows that FIVPos achieves sub-1.5-meter Root Mean Square Error (RMSE) in dynamic scenarios, while delivering over 33% reduction in Maximum Error (MAXE) compared with conventional fingerprint-only approaches. These results confirm the effectiveness of multi-beam fusion as a practical and scalable solution for high-precision indoor positioning in 5G networks.

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.

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%.

Location-based service in the indoor environment is playing a crucial role in different application scenarios. The introduction of technologies, such as ultradense network and massive multiple-input multiple-output enables fifth-generation (5G) cellular signals, as a new generation of cellular network signals, to show unique advantages in indoor positioning. This article describes 5G reference signal structures that can be used for navigation. A high-precision time-of-arrival estimation method based on 5G downlink signal is proposed that can be realized by edge computing. A software-defined receiver (SDR) based on machine learning to extract navigation observations from 5G signals is then developed. In simulation, the error sources of SDR in additive white gaussian noise channel and multipath channel were analyzed, and the possible ranging accuracy achieved by 5G signals in the developed SDR was evaluated. In field experiments, commercial 5G signals deployed by operators were collected, and the performance of SDR in practical applications was evaluated. The feasibility in practical applications of the proposed SDR is demonstrated, and high pseudorange measurement accuracy can be achieved.

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.

Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial intelligence (AI) and is expected to play a significant role in the upcoming era of AI. However, affected by the complexity of indoor environments, it is still highly challenging to achieve continuous and reliable indoor positioning. Currently, 5G cellular networks are being deployed worldwide, the new technologies of which have brought the approaches for improving the performance of wireless indoor positioning. In this article, we investigate the indoor positioning under the 5G new radio (NR), which has been standardized and being commercially operated in massive markets. Specifically, a solution is proposed and a software-defined radio (SDR) receiver is developed for indoor positioning. With our SDR indoor positioning system, the 5G NR signals are first sampled by universal software radio peripheral (USRP), and then, coarse synchronization is achieved via detecting the start of the synchronization signal block (SSB). Then, with the assistance of the pilots transmitted on the physical broadcasting channel (PBCH), multipath acquisition and delay tracking are sequentially carried out to estimate the Time of Arrival (ToA) of received signals. Furthermore, to improve the ToA ranging accuracy, the carrier phase of the first arrived path is estimated. Finally, to quantify the accuracy of our ToA estimation method, indoor field tests are carried out in an office environment, where a 5G NR base station (known as gNB) is installed for commercial use. Our test results show that, in the static test scenarios, the ToA accuracy measured by the 1-$\sigma $error interval is about 0.5 m, while in the pedestrian mobile environment, the probability of range accuracy within 0.8 m is 95%.