Carrier Phase Ranging for Indoor Positioning with 5G NR Signals
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Abstract (arXiv)
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 paper, 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 receiver (SDR) is developed for indoor positioning. With our SDR indoor positioning system, the 5G NR signals are firstly 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-σ error interval is about 0.5 m, while in the pedestrian mobile environment, the probability of range accuracy within 0.8 m is 95%.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Dec 2021.
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
- 2024Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Imagescited by 1
- 2024Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeBcited by 0
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges24 Jan 2025
- 2024Model-driven deep neural network for enhanced direction finding with commodity 5G gNodeB14 Dec 2024
- 2024Dynamic Indoor Fingerprinting Localization based on Few-Shot Meta-Learning with CSI Images11 Jan 2024