Future industrial shopfloors will feature a large number of autonomous, mobile devices that communicate wirelessly. In such controlled environments, collecting channel state information (CSI) by location and reusing it over time may reduce the overhead of channel sounding and CSI feedback that is required for channel-aware radio resource allocation. However, even if the trajectories of devices repeat over time, temporary variations of the environment put a question mark behind the usability of radio environment maps (REMs) from the past. To address this important question, we designed and carried out a channel measurement campaign in an industrial-like area, we evaluated the data regarding the consistency of the radio channel in a time-varying environment, and we conducted simulations of a multi-user communications scenario based on measured data to present a potential application of channel information reuse. The measurement results show a high similarity of the REM over time and a spatially limited impact of the temporary variations caused by a metallic object on the REM. Our simulation results show up to 75% savings in CSI feedback overhead while the reliability of the communications system remains almost unaffected. In the future, we will elaborate on the estimation of the REM in a time-varying environment.

The operation of future radio systems will benefit from any available information about the radio environment, e.g., to better allocate radio resources, and to predict the radio conditions of users based on their locations. Thereby, radio environment maps (REMs), i.e., the information about the radio channel per location, can assist future radio systems. However, measuring large-area REMs with high spatial resolution results in enormous effort and it is more efficient to estimate REMs from sparse observations. In this work, we present a deep neural network (DNN)-based interpolation technique that is capable of recovering spatial fading patterns through interpolation by extracting position-dependent channel correlations. Our approach solely relies on the sparsely sampled REM that is to be interpolated. By systematically studying DNN structures and input features, we extract a favorable structure for the spatial interpolation of anisotropic environments. Based on a simulated indoor REM with varying fading structures, we demonstrate that our approach is superior to conventional methods in recovering spatial fading structures. Using reconstructed REMs for radio applications in future work will yield application-specific metrics to further assess the reconstruction quality.

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.

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

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.

In the future, mobility use cases will depend on precise predictions, with Quality of Service (QoS) prediction being a prominent example. This paper presents realistic measurements from today’s vehicles to support robust QoS prediction in the future. Based on a dedicated and controlled measurement campaign, we highlight aspects of the wireless environment and the device characteristics, like the sampling rates, that influence the collected datasets. If not properly handled, such characteristics might hinder the performance of Artificial Intelligence-based algorithms for QoS prediction. Therefore, we also provide insights on dataset characteristics that should be further used to enable easier adoption of AI-based algorithms. New AI-based algorithms should be able to operate in very diverse radio environments with data captured from different devices. We provide several examples that highlight the importance of thoroughly understanding the datasets and their dynamics.

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.