In the industrial environments of the future, robots, sensors, and other industrial devices will have to communicate autonomously and in a robust and efficient manner with each other, relying on a large extent on wireless communication links, which will expand and supplement the existing wired/Ethernet connections. The wireless communication links suffer from various channel impairments, such as attenuations due to path losses, random fluctuations due to shadowing and fading effects over the channel and the non line-of-sight (NLoS) due to obstacles on the communication path. Several channel models exist to model the industrial environments in indoor, urban, or rural areas, but a comprehensive comparison of their characteristics is still missing from the current literature. Moreover, several IoT technologies are already on the market, many competing with each other for future possible services and applications in Industrial IoT (IIoT) environments. This paper aims at giving a survey of existing wireless channel models applicable to the IIoT context and to compare them for the first time in terms of worst-case, median-case, and best-case predictive behaviors. Performance metrics, such as cell radius, spectral efficiency, and outage probability, are investigated with a focus on three long-range IoT technologies, one medium-range, and one short-range IoT technology as selected case studies. A summary of popular IoT technologies and their applicability to industrial scenarios is addressed as well.

The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station.

Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.

This is a data descriptor paper for a set of raw GNSS signals collected via roof antennas and Spectracom simulator for general-purpose uses. We give one example of possible data use in the context of Radio Frequency Fingerprinting (RFF) studies for signal-type identification based on front-end hardware characteristics at transmitter or receiver side. Examples are given in this paper of achievable classification accuracy of six of the collected signal classes. The RFF is one of the state-of-the-art, promising methods to identify GNSS transmitters and receivers, and can find future applicability in anti-spoofing and anti-jamming solutions for example. The uses of the provided raw data are not limited to RFF studies, but can extend to uses such as testing GNSS acquisition and tracking, antenna array experiments, and so forth.

Radio Frequency fingerprinting (RFF) methods are gaining popularity as physical-layer identification or authentication methods in various navigation and communication applications. Traditionally, RFF has been used in terrestrial communications to identify the genuine transmitters from spoofers and jammers. In recent literature, RFF has gained attention also in the context of satellite navigation and Low Earth Orbit (LEO) satellite communications, though this research area is still in an incipient phase. RFF studies in the context of satellite transmitters (or transceivers) are typically hindered by the challenges in acquiring high-quality raw measurement data. In this paper, we analyze via RFF methods, in both pre-correlation and post-correlation domain, the raw GNSS data collected at three locations: Tampere (Finland), Nottingham (UK), and Nuremberg (Germany). The datasets for the first two scenarios have been collected by the authors, while the third dataset is available in open access. We show that we are able to reach average classification probabilities of spoofer versus GNSS up to 99.99% (i.e., Nuremberg measurements) with pre-correlation data and up to 87.72 % (i.e., Nottingham measurements) with post-correlation data. We also discuss the challenges and limitations of RFF in the context of GNSS.

In this paper, the spectrum efficiency for four scenarios: baseline, cell split, fixed relay and dedicated mobile relay on vehicle are analyzed and compared in terms of unified spectrum efficiency bps/Hz/km2. The theoretical expression is derived and the capacity is calculated by numerical integration based on the parameters for 3GPP LTE-advanced system. The deployment costs for different scenarios are also estimated based on the current real network. This comparison can clearly show the benefit and cost of different solutions, and this is helpful for the strategy making.