Future wireless communication systems are foreseen to provide powerful enhancements such as high-precision localization, allowing the radio environment to be characterized with high spatial resolution. On the other hand, future industrial ultra-reliable low-latency communications (URLLC) scenarios will place extreme reliability requirements on communication systems. In this work, we present the idea of using high-resolution radio environment maps (REMs) that contain received power levels per location as well as information about spatially occurring fading patterns for the trajectory optimization of mobile robotics to proactively increase communications reliability. To find the optimal trajectory, we show how to apply the Dijkstra path-finding algorithm to a two-dimensional REM. Simulations using a high-resolution REM from a previous measurement campaign indicate that small trajectory adaptations may increase the minimum received power (and hence the reliability) by orders of magnitude. By penalizing trajectory adaptations during optimization, we analyze the tradeoff between the number of lane adaptations and the communications reliability. Appropriate penalty parameters enable to drastically reduce the number of path adaptations without compromising reliability. Similar results are found for a ray-tracing-based REM of the same environment which suggests the use of synthetic REMs for effective investigation of other environments. This encourages follow-up investigations on reliability gains for radio propagation conditions in other environments.

Achieving high reliability in the presence of fading is particularly challenging under latency constraints, because the usual way of error mitigation by repetition becomes unfavorable. On the other hand, multi-connectivity does improve reliability without adding latency, but multiplies the required bandwidth per link and does not scale to a large number of users. There is hence a need for frequency diversity in a spectrum-efficient way. In this work, we investigate multi-user resource allocation schemes both without and with knowledge of each user’s channel state. We evaluate the allocation-dependent reliability in terms of outage rate and outage duration based on simulations of automated guided vehicles in an industrial environment. To increase validity and ensure real-world correlation between vehicles, we draw channel states from high-resolution channel measurements at a factory floor. For channel-aware allocation, we propose a near-optimal low-complexity algorithm using different quality functions based on channel state preference lists. Since accurate channel information per user and resource incurs signaling overhead, we also evaluate the algorithm’s sensitivity to the number and bandwidth of resources as well as to outdated channel information. In conclusion, channel-aware allocation offers significant reliability improvements over static allocation and emerges as a key enabler to realize ultra-reliable low-latency communications on a larger scale.

When employing wireless connectivity in industrial applications, reliability is indispensable. However, due to the real-time requirements of said scenarios, time diversity is not feasible. Instead, frequency diversity has to be utilized by transmitting on frequencies with different fading characteristics. But as increasing bandwidth requirements for the individual user does not scale well for many users in the presence of a limited system bandwidth, the available resources have to be allocated efficiently. In this work, we compare multi-user resource allocation algorithms utilizing each user’s channel state information. The performance of the allocators is evaluated with regard to the achievable outage rate for different scenarios and different sensitivity analyses are performed. We also evaluate the algorithmic complexity of the allocators, both empirically and analytically. For evaluation, we employ analytical, spectrally correlated Rayleigh fading in order to emulate different environments. For a number of existing allocators, we propose modifications to improve the performance in regard to reliability and algorithmic complexity. The simulation results show, that the utilized allocators can be employed for a multitude of scenarios as they are suited for a large variety of application areas. Additionally, our results show that the allocation complexity of existing allocators can be reduced by up to a factor of 10 in the investigated scenario with a modified approach without sacrificing reliability.

Knowledge about the current and future states of a radio channel takes the reliability of a communications system to a new level. A Radio Environment Map (REM) contains information about the channel state in the spatial domain for a given environment. Given a known user trajectory, this information can be used for channel prediction. In this work, we investigated the two primary limitations to this approach: the required spatio-temporal stationarity of the channel and the high localization accuracy of the user. The channel stationarity is quantified by repeated channel measurements. The high measurable consistency indicates the value of REMs in non-changing environments. Based on a high-resolution REM that we measured in an office environment, we quantify the impact of one- and two-dimensional localization errors on the resulting prediction error. With the results shown, localization accuracy requirements can be derived given the target channel prediction accuracy. Also, the results help to determine the required spatial resolution of REM measurements in practice.

Packet repetitions aim at increasing the communication reliability of vehicle-to-vehicle (V2V) communication by exploiting the wireless channel diversity. Congestion-awareness is essential to limit the channel load increase, leading to packet loss and rising access delays. Previously, channel busy ratio (CBR) thresholds have been derived under static load conditions, the investigation of time-varying load conditions remained open. This work provides the first investigation on threshold-based repetition allocation under time-varying load conditions, focusing on the accuracy and timeliness of the CBR observation. Therefore, we evaluate the impact on the achievable transmission range and end-to-end delay. The results show a trade-off between responsiveness to load changes achieved by short, and high accuracy achieved for long observation windows. While short observations allow to quickly adapt to load changes, which avoid performance degradation due to temporarily channel congestion, the observation underlies severe fluctuations leading misallocations causing performance loss. In contrast, long observations allow to avoid wrong allocations, instead they tend to oscillations. Based on the results, the dynamic adaptation of the observation time based on the load history is proposed. It shows that the adaptation using a simple threshold-based detection of load changes does not only quickly converges to the optimal allocation, but also reduces false and oscillating allocations.

Reliable V2V communications is key to enable energy-efficient and accident-free road mobility. Packet repetitions as proposed for IEEE 802.11bd allow to increase the transmission reliability by combining redundant messages at the cost of increased channel load, leading to packet loss and rising access delays. As so far no investigations on this reliability trade-off are available, we present the first comprehensive analysis on repetitions for the new technology. Therefore, we quantify the combining gain in link-level simulations, which show a benefit of frequency diversity. Next, we model the repetition reliability trade-off in dependency of the vehicle density on system-level. The results show a severe performance degradation with increasing density caused by the added transmissions. Based on the results, we elaborate on how to design an optimal distributed repetition algorithm. It shows that defining static channel load thresholds is not sufficient to maximize the transmission reliability, as it does not correctly considers the impact of the allocation. Finally, we propose to employ multi-channel repetitions to mitigate potential channel congestion, meanwhile maximizing the combining gain.

Ultra-Reliable Low-Latency Communications (URLLC) is a prerequisite for advancing industrial automation. To verify whether communications systems meet these stringent requirements, physical layer simulations are a powerful tool. Such simulations rely on a large number of channel realizations to obtain statistically significant results. Using exclusively real-world measured channels is challenging, e.g., due to the measurement time needed. In this work, we propose how to derive a channel model that mimics not only the mean but equally the temporal behavior of data from an industrial channel measurement campaign. The approach considers time variation on a large scale, modeled through a Markov process, as well as fading of individual channel components, achieved through Doppler-filtered random processes based on empirical distributions. The model is validated in link-level simulations by comparing the synthesized channels to the original measured data by means of performance metrics relevant to URLLC, including mean and maximum outage durations. Our validations show that the model performs well and especially the outage durations, caused by consecutive packet losses, match the real channel characteristics very well. In the future, we can use the model to investigate how extensive measurements need to be in order to make statements about the performance of communications systems.

To realize the next generation of ultra-reliable low-latency communications (URLLC) with less radio resource consumption, methods relying solely on the addition of redundancy do not suffice. An alternative is to monitor the wireless channel and prevent outages due to small scale fading by allocating users to suitable radio resources based on the channel gain. To overcome monitoring delays, predicted channel information is utilized. In this paper we are analyzing the outage probability of a max-min radio resource allocation (RRA) approach when Gaussian errors are present in the channel state information (CSI). As a communications channel, a Rayleigh fast fading channel is assumed. The outage probability is lower and upper bounded to get analytical performance approximations. Furthermore, the performance is also empirically evaluated by means of extensive Monte-Carlo simulations. The analysis shows, that the achievable outage probability mostly depends on the size of the resource pool and the quality of the CSI.

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.

Multi-connectivity is a key enabler for realtime applications demanding high reliability such as connected vehicles. Employing macro-diversity with distributed transceivers has the advantage of mitigating large-scale losses such as shadowing, but may incur time offsets between packets, requiring the receiver to synchronize to each packet individually. Since packet detection is prerequisite for any downstream receiver processing, synchronization can become a bottleneck to achieving high reliability. In this paper, we propose a concept to improve receiver performance in macro-diversity multi-connectivity networks in case of time offsets between packets, for instance, due to loose synchronization of distributed transmitters. By buffering the inputs of parallel receiver paths and allowing for iterative synchronization, successfully detected packets can serve as extended correlation sequence to detect previously undetected packets which thereby become available to diversity combining. Taking link-level simulations of IEEE 802.11 (WLAN) as an example, we demonstrate the efficacy of such Joint Synchronization (JS) and provide first numerical results. We see an SNR gain of about 1 dB in the mid-SNR range, which is equivalent to a packet error rate reduction by an order of magnitude for four-fold diversity. With power consumption in mind, we consider the trade-off between implementation complexity and the gain of JS. We conclude that JS is a viable backwards-compatible approach to improve diversity combining of delayed packets in multi-connectivity networks.

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industrie 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements. This paper proposes representative use cases, which have been examined in the German Tactile Internet 4.0 (TACNET 4.0) research project. In order to analyze these use cases, this paper identifies the main challenges and requirements of communication networks in Industrie 4.0 and discusses the applicability of 5th generation wireless communication systems (5G).