6G networks will play a crucial role in Industry 4.0 advancing further smart manufacturing and trustworthy communication. This paper introduces a reinforcement learning scheduler that considers environmental knowledge designed to improve reliability and security aspects of trustworthiness by favoring to serve nodes when in line-of-sight. We evaluate the performance in a factory floor scenario for two different heights of remote-radio heads and various blockage densities. The results show that the proposed method outperforms traditional schedulers such as round robin and proportional fair in terms of reliability, availability and fairness enhancing thus reliability and security aspects of trustworthiness when the environment is prone to a mixture of line-of-sight and non-line-of-sight.

Wireless Sensor Networks (WSNs) play a pivotal role in diverse domains such as environmental sensing, industrial monitoring, and entertainment. This paper focuses on modeling the connectivity of the link considering the quality of service (QoS) criterion. This is a baseline for considering the probability density function (PDF) of the node’s distance from the base station (BS) to form a multi-hop wireless sensor networks (WSNs) structure. We delve into modeling the connectivity behavior in multi-hop networks, addressing scenarios involving non-homogeneous Poison Point Process (NHPPP) and homogeneous Poison Point Process (HPPP). Furthermore, we introduce closed-form expressions with reduced computational complexity. This contribution establishes a comprehensive framework for understanding and enhancing the performance of multi-hop wireless sensor networks.

Nowadays Internet of Things and Industry 4.0 devices are often connected wirelessly. Current wireless sensor network (WSN) deployments are relying in most cases on the industrial, scientific, and medical (ISM) bands without centralized resource scheduling. Thus, each device is a potential source of interference to other devices, both within its own WSN but also to devices in other collocated WSNs. If the transmission behavior of devices from other WSNs is not random, we are able to find patterns in the time domain in their channel access. This is, for example, possible for periodic channel access, which is quite common for WSNs with demanding low-power and reliability requirements. The main goal of this work is to detect multiple sources of periodic interference in time slotted signal-level measurements and estimate the time windows of future transmissions. This gives a WSN a certain understanding of the radio surrounding and can be used to adapt the transmission behavior to thus avoid collisions. For this, the multihypothesis tracking algorithm is adapted and used together with timeslot-based interference measurements on low-cost sensor nodes. The applicability of the algorithm is shown with extensive simulations and the performance is demonstrated with measurements on a time-division multiple access-based WSN built upon the Bluetooth low-energy physical layer.

Minimized and nearly deterministic end-to-end latency facilitates real-time data acquisition and actuator control. In addition, defined latency is an integral part of quality-oriented service in order to get closer to the reliability of wired networks and at the same time takes advantage of wireless networking. This article introduces a QoS routing protocol capable of balancing power consumption between wireless sensor and actuator nodes while minimizing end-to-end latency. We introduce a time-division multiple access scheme in the routed wireless network to enable defined latency, and in addition, it improves the energy efficiency by avoiding collisions, which eliminates time and energy consuming retries. Our novel routing method allows latency and round-trip times to be calculated in advance. We implement a demonstrator and show experimental results of a wireless sensor network with our proposed routing scheme.

For Cyper-Physical Production Systems (CPPS), localization is becoming increasingly important as wireless and mobile devices are considered an integral part. While localizing targets in a wireless communication system based on the Received Signal Strength Indicators (RSSIs) is a usual solution, it is limited by sensor quality. We consider the scenario of a car moving in and out of a chamber and propose to use a particle filter for sensor fusion, allowing us to incorporate non-idealities in our model and achieve a high-quality position estimate. Then, we use Machine Learning (ML) to classify the vehicle position. Our results show that the location output of the particle filter is a better input to the classifiers than the raw RSSI data, and we achieve improved accuracy while simultaneously reducing the number of features that the ML has to consider. We also compare the performance of multiple ML algorithms and show that SVMs provide the overall best performance for the given task.

Industrial Internet-of-Things (IIoT) applications require reliable and efficient wireless communication. Assuming dense wireless sensor networks (WSNs) operating in a harsh environment, a concept of a time-division multiple access (TDMA)-based WSN enriched with electronically steerable parasitic array radiator (ESPAR) antennas is proposed and examined in this work. The utilized antenna provides one omnidirectional and 12 directional radiation patterns that can be electronically switched by the sensor node. We introduce a relay discovery algorithm, which selects those sensor nodes with an ESPAR antenna capable to act as relay. The selection of the relay nodes is based on a certain link quality threshold that algorithm uses as input. The outcome is a reduction in the number of layers or hops with a guaranteed Quality of Service (QoS). To emphasize the physical aspect of the wireless propagation, we introduce the measured antenna radiation patterns and consider two different path-loss propagation models representing blockage-free and blockage-prone industrial environments. A number of network simulations were performed and signal-to-noise ratio (SNR) as a link quality measure was examined with respect to the network density and different measured radiation pattern settings. The main outcomes show a tradeoff between SNR per link and the percentage of nodes that can serve as relays. As a result, we propose network design guidelines that take under consideration the QoS range with respect to SNR together with an optimal number of antenna radiation patterns that should be selected as a tradeoff between latency, energy consumption, and reliability in a network.

Numerical simulation of wireless communications systems is an important means within the process of development and evolution of mobile communications specifications. We offer a software suite to the academic society for free under an academic use license that enables the community to perform simulations of relevant scenarios for 5G and beyond in a reproducible manner. To cover a variety of potential scenarios for future mobile communications systems, we offer the Vienna 5G Link Level (LL) Simulator and the Vienna 5G System Level (SL) Simulator. In this contribution, we perform a verification of our LL and SL simulators which allows us to claim simulation results to be valid. As the Vienna 5G SL Simulator relies on link performance simulation results carried out with the Vienna 5G LL Simulator, we further consider their interaction in the context of verification.

For real-world performance evaluation, channel models should be accurate in reflecting a realistic behavior between transmitter and receiver. A major concern with the actual standardized channel models, is that they do not consider the time evolution and are relevant only for drop based simulations. The need for spatial consistency of these channel models has been also acknowledged by standardization bodies, e.g., 3GPP, and alternative models are under discussion. In this paper, we compare the statistics generated with the 3GPP 3D channel model to those of ray tracing simulations and validate our method to achieve spatial consistency. Moreover, we estimate the correlation parameters such that the results obtained with our method match the ray-tracing results.

We propose a method to estimate doubly-selective channels based on the time and frequency correlation of scattered pilots. To reduce the interference at the pilot and data positions, we apply an iterative interference cancellation scheme. Our method is applicable to arbitrary linear modulation techniques, with Orthogonal Frequency Division Multiplexing (OFDM) and Filter Bank Multicarrier Modulation (FBMC), being special cases. Simulations over doubly-selective channels show that our channel estimation method comes close to having perfect channel knowledge available. A downloadable Matlab code supports reproducibility.

Massive MIMO, 3D beamforming and beam tracking strategies for mobile users are among the key strategies for the 5th generation of mobile cellular networks. Their investigation requires channel models that are spatially consistent and evolve smoothly over time. State-of-the-art channel models, such as, 3GPP SCM, WINNER and the 3GPP 3D channel model, do not consider the time evolution and are relevant only for drop based simulations. In this paper we propose two methods to model spatially correlated channels; first, by introducing spatial correlation to LOS/NLOS propagation and indoor/outdoor state of a user, and second, by introducing spatial correlation to the small scale parameters that represent short-term fading characteristics. Our results exhibit realistic behavior of users that move through the network, in terms of LOS/NLOS and indoor/outdoor states and preserve the statistics of the channel model.