By scheduling users to resources that are operational rather than on a best effort basis, the overall resource consumption of ultra-reliable low-latency communications (URLLC) can be reduced while maintaining a desired quality of service (QoS). To overcome the time delay between monitoring the channel state and the actual payload transmission, predictive methods which are tailored to the needs of URLLC become indispensable. In this paper we extend our Wiener filter based Rayleigh fading outage predictor to the Rician fading case. Compared to Rayleigh fading, additional estimators for the line of sight (LOS) parameters are presented. Our results show that the overall outage prediction performance increases significantly with increasing power of the LOS component compared to the Rayleigh fading case. The resource utilization for a particular user equipment (UE) rises to more than 99% in the investigated scenario for small prediction horizons and a Rician K-factor of K = 10 while achieving effective outage probabilities of 10−5. By comparing with the case of perfect parameter estimation, we show that the influence of the introduced estimators on the outage prediction performance is within acceptable limits.

Production processes coming up with Industry 4.0 will demand a high degree of flexibility since customers request more individual products. Employing wireless communication is a key enabler to meet these requirements. In order to deploy wireless systems for emerging industrial use cases in a way that both low latency as well as high reliability is guaranteed, knowledge about the radio channel is crucial. This requires representative channel measurements considering a specific use case. With this in mind, we propose a novel channel measurement approach representing industrial Device-to-Device (D2D) communication between moving Automated Guided Vehicles (AGVs), and present the obtained results. We study how obstacles affect the channel by modifying the test environment with metallic obstacles. For reproducibility, we automate the movement during the measurements using an AGV. We capture impulse responses each millisecond to resolve the time variation of the channel. It turns out that even under Non-Line-of-Sight (NLOS) conditions, the loss of receive power over the whole bandwidth is moderate due to a large number of reflections. We conclude that exploiting frequency and spatial diversity is a promising way to improve the communication reliability in industrial scenarios. We also infer that modeling the time-varying nature of channel parameters in industrial environments is feasible based on measurement data.

Wireless closed-loop control is of major significance for future industrial manufacturing. However, control applications pose stringent quality of service requirements for reliable operation. Contrary to traditional ultra-reliable low-latency communications design goals such as low packet loss rates and low latency, research results in the domain of networked control systems (NCS) state that depending on the sampling period, control applications inherently tolerate a few consecutive packet losses. This translates into a better-suited metric to capture control application requirements and therefore a more conclusive design goal for wireless networks: ensuring a maximum age of information (AoI). With a Markov modeling approach, we propose to exploit the tolerance through a novel dynamic multi-connectivity scheme that we term state-aware resource allocation (SARA), which temporally negatively correlates packet losses, thus avoiding long packet loss sequences. Through statistical multiplexing, SARA enables a mean time to failure (MTTF) in the order of years while keeping the per-agent average channel usage close to one, also in a multi-agent setting with competition for resources. Compared with static dual-connectivity, the MTTF can be increased 100-fold whereas the number of required channels reduces by 40%. Our approach also statistically guarantees system-wide AoI distributions, which aid to ensure control performance.

Wireless closed-loop control is of major significance for different application areas, such as future industrial manufacturing, and ultra-reliable low-latency communications (URLLC) are designed to enable such systems. Static multi-connectivity, in which a number of independent parallel channels are allocated for each service, is a possible solution to achieve URLLC requirements, but this increases resource usage significantly, which becomes an issue particularly in multi-user systems. Building upon a control-communications codesign (CoCoCo) approach, a control-application optimized state-aware resource allocation (SARA) scheme was developed, which exploits the control cycle's inherent capability of tolerating a limited number of consecutive packet losses before ultimately failing. In essence, SARA negatively correlates packet losses through dynamic channel allocation in order to yield extraordinary availability values while keeping the average resource consumption low. This article develops a multi-user system representation of SARA with competition for limited resources using a Markov chain approach and subsequently evaluates the mean time to failure, demonstrating that SARA scales better than static multi-connectivity, fully supporting the maximum system availability at fewer channels per agent.

Control communications co-design enables robust and scalable wireless closed-loop control system design. We study the metric “control-communications availability” that allows consecutive errors until the control application is deemed dysfunctional. Multi-connectivity helps increasing the network availability, but there is a lack of dynamic and resource efficient link management. Thus, we propose the “state-aware resource allocation” scheme, whereby parallel links can be assigned adaptively to a given connection, depending on the number of previously, consecutively lost packets. We develop a Markov chain that captures the novel resource allocation approach suited for closed-loop wireless control applications. Our approach outperforms static dual connectivity by two orders of magnitude in terms of control-communications availability while reducing the amount of required resources to approximately half.

Increased usage of wireless technologies in unlicensed frequency bands inevitably increases the co-channel interference. Hence, for applications such as ultra-reliable-low-latency-communications (URLLC) in factory automation, the interference should be avoided. An intelligent coexistence management entity, which dynamically distributes the time and frequency resources, has been shown to be greatly beneficial in boosting efficiency and avoiding crippling interruptions of the wireless medium. This entity also supports multi-connectivity schemes, which are crucial for industry-level reliability requirements. The proposed governing technique of the coexistence management is a deep reinforcement learning (DRL) method, which is a model-free framework and channel allocation decisions are learned merely by interactions with the environment. The simulation results have shown that the employed method can greatly increase the reliability of the wireless network, when compared with legacy methods.

Realizing wireless mission-critical applications in industry, such as closed-loop control, necessitates ultra-reliable low latency communications (URLLC) to achieve error-free message transmission with hard real-time requirements. Recently, multi-connectivity (MC) has been introduced as a promising scheme to ensure URLLC in Industry 4.0. However, implementing MC in mobile industrial communications rises multiple technical challenges, such as avoiding degradation in reliability due to fading and the shadowing effect, and managing multiple links in parallel which increases signaling overhead dramatically. To deal with these challenges, this paper investigates the gains and limitations of implementing MC in industrial wireless communications. It studies conflicting optimization problems using MC based on different radio parameters. Also, a link management scheme is introduced for MC to reduce the signaling overhead based on different radio parameters. The simulation results demonstrate gains and limitations of using MC and the selected parameters (frequency reuse factor, number of users, and frequency band) on the reliability and the signaling overhead in industrial communications.

Ultra-Reliable Low-Latency Communications (URLLC) are considered as one of the key services of the upcoming fifth generation (5G) of wireless communications systems. Enabling URLLC is especially challenging due to the strict requirements in terms of latency and reliability. Multi-connectivity is a powerful approach to increase reliability. However, most of the current research is restricted to single-user scenarios, neglecting the challenges of multi-cellular, multi-user systems, i.e., interference and the competition for limited resources. In this article, we develop analytic comparisons of different connectivity approaches, showing that multi-connectivity may not always be optimal in the considered scenario. Moreover, we propose and evaluate novel resource allocation approaches based on stable matching theory to enable wireless URLLC. We extend the pure many-to-one stable matching procedure by utilizing the optimal connectivity approach for each user, optimizing the maximum number of matched resources, and providing a resource reservation mechanism for users suffering from bad channel conditions. System-level simulations demonstrate that the proposed algorithm outperforms baseline resource allocation approaches in outage probability by up to three orders of magnitude. Even in a highly loaded system, an outage probability in the range of ${10^{-5}}$ is achieved.

With the fifth generation (5G) of mobile networks the challenging field of ultra-reliable low latency communications (URLLC) is envisioned. In this area of research, reliability requirements are commonly formulated only in terms of packet loss rates. However, sparse packet loss can usually be tolerated by an application. Furthermore, the terms reliability and availability are usually loosely defined and not sufficiently separated in the wireless communications field. This paper applies fundamental metrics from dependability theory to wireless communications, providing more powerful key performance indicators (KPIs) for URLLC. In particular, we derive analytic expressions which reflect the up- and downtime distributions as well as the mission availability in a wireless multi-connectivity scenario with selection combining and Rayleigh fading. Further, we propose an appropriate approximation for the computationally complex expression of the mission availability.

Wireless system design on the physical layer is usually evaluated using comprehensive channel models. However, there is still a lack of publicly available stochastic channel models tailored to industrial use cases, which are recently considered more frequently. This paper presents the derivation of such a channel model for the 5 GHz ISM band and its parametrization. The frequency-selective behaviour is modeled by the Saleh-Valenzuela model. Based on a measurement campaign, the parameters of this model for a factory environment are determined and published the first time for the 5 GHz ISM band. Spatial correlation is modeled by the Kronecker model. The temporal variation of the channel is based on a theoretically derived Doppler spectrum assuming Laplacian distributed angle of arrivals. In addition to the description of the model components, key issues and common mistakes while constructing a channel model for industrial applications are discussed in order to advance the design and the deployment of future wireless industrial communications systems. The derived channel model is used in IEEE 802.11ax link layer simulations. It is shown that for industrial use cases specially tailored channel models are needed.

Wireless communication systems have been evolving since the first generation. With the fifth generation of wireless systems, not only the evolutionary aspect of increased data rates is tackled but also the revolutionary aspect. Here, emerging use cases such as massive machine-type communication and ultrareliable low-latency communication will play a crucial role. Within this context, applications with stringent latency and reliability requirements are emerging. Wireless reliability is understood as successfully transmitting the desired amount of data within a given time. Diversity techniques, such as multiconnectivity, are potential solutions to achieve stringent reliability requirements. However, in a multiuser scenario, in which resources are shared, this might not always be possible. In this paper, we discuss the feasibility of various multiconnectivity approaches and propose a matching theory-based algorithm together with a novel scheduler aiming to guarantee the reliability requirements of as many users as possible in a multicellular, multiuser system. System-level simulations demonstrate that the proposed approach achieves 100% reliability for the fifth-percentile users in a highly loaded system. The maximum gain of fifth-percentile user throughput as compared to a static multiconnectivity approach is 150%.

The fifth-generation (5G) of mobile networks are supposed to address radically new types of applications that have not been possible with previous generations of wireless technologies. Ultra-Reliable Low-Latency Communication (URLLC) is one 5G use case category imposing strict requirements on the availability, reliability, and latency together with zero mobility interruption. Aiming for a common understanding, we put forward a comprehensive description of performance metrics based on reliability theory focusing on availability and reliability metrics as a function of time. We establish a link to wireless communications by studying Rayleigh fading channels in a multi-connectivity system. The reliability metric definitions and analytic models presented in this paper contribute to enable a systematic and accurate performance analysis for URLLC. Numerical evaluations demonstrate the importance of considering not only availability alone but combinations of availability and time-based reliability statistics jointly.

Enhancing the connectivity reliability is one of the most challenging requirements for the design of future wireless communications systems. The scope of this paper is to leverage the existing tool set of reliability theory for enabling reliable communication in wireless systems. Definitions, concepts, and methods of reliability theory are applied and extended to wireless communications networks, which are modeled as a repairable system. The steady-state and transient system behaviour are considered. Two new key performance indicators (KPIs) for the reliability analysis of wireless communications systems are introduced, namely mean time to first failure (MTTFF) and interval reliability (IR), and a closed form expression is derived for the MTTFF. By evaluating an exemplary scenario, the trade-off between availability, reliability and throughput is discussed.