Range-free positioning for Industrial Internet of Things in a mixed public-private midband and mmWave 5G deployment

The design of positioning solutions for the Industrial Internet of Things (IIoT) faces several open challenges, particularly in complex indoor environments typical of industrial settings: first and foremost, the limited availability of experimental datasets from realistic scenarios, in particular in relation to private 5G networks.This paper investigates range-free positioning for IIoT in indoor environments using a combination of public and private 5G networks operating at both midband (3.7 GHz) and mmWave (26 GHz) frequencies. The study introduces one of the first publicly available datasets collected in a realistic industrial setting, utilizing synchronization signals from 5G networks. A Weighted k-Nearest Neighbors (WkNN) algorithm is used to perform fingerprinting-based positioning using the experimental data, comparing the achievable accuracy in public vs. private deployments. The results show that private mmWave deployments significantly outperform public networks in terms of positioning accuracy, achieving errors as low as 2.16 meters. Additionally, the study highlights the discriminative power of time-based features such as Time of Arrival (ToA) in enhancing range-free positioning accuracy.