Reliable communication is critical for robotic teleoperation in Search and Rescue missions, particularly in challenging environments such as street canyons, underground garages, and industrial halls. While mesh networks, operating within unlicensed frequencies, extend range through multi-hop connectivity, they face interference, frequency competition, and reduced data rates per hop. Cellular technologies offer a promising alternative by leveraging dedicated frequency bands. However, standard-compliant Integrated Access and Backhaul (IAB) solutions are limited by hardware compatibility restrictions that result in vendor lock-in. This paper introduces LANTERN, an open multi-radio relay system that builds on the established SEAMLESS Multi-Link protocol, enabling interoperability across diverse communication technologies, including cellular and mesh networks. This architecture ensures reliable operation even in complex environments by combining established systems with modern ones without requiring substantial infrastructure changes. Initial validation experiments using a homogeneous 5G over-the-top relay demonstrate seamless transitions between the umbrella and relay networks, enabling uninterrupted immersive robotic teleoperation during mission-critical operations. In addition, a second case study demonstrates the functionality of heterogeneous relaying between a cellular umbrella and a mesh network, while highlighting potential future improvements to enhance handover and link usage when dealing with heterogeneous technologies.

The advancement of wireless communication technologies, especially in the context of Millimeter-Wave (mmWave) frequencies, is facilitating novel advancements in industrial applications. This study investigates the energy efficiency of mobile mmWave end devices deployed within industrial settings. Utilizing and extending an established laboratory setup, an experimental framework is developed to create device-specific energy profiles and conduct precise mobility testing. Measurements based on robotic evaluation scenarios are performed to analyze the interplay between energy consumption and performance under varying radio conditions caused by mobility, alignment, and Line-of-Sight (LOS) availability. The findings highlight improvement opportunities by optimizing end device orientation and incorporating additional network hardware to minimize Non-LOS (NLOS) conditions via Beyond-LOS (BLOS) links, which in turn enhance the overall energy efficiency. The optimization techniques implemented lead to a notable stabilization of the end device’s throughput alongside a 19.9 % decrease in power consumption resulting in a 29.1 % higher energy efficiency. These results are crucial for extending the operational duration of mobile applications while minimizing charging needs and offer significant insights for future research aimed at advancing mmWave-enabled devices for industrial robotics.

Intelligent reflecting surfaces (IRSs) will be key for efficient ubiquitous 6G millimeter-wave (mmWave) connectivity. Geometry-defined static intelligent reflecting surfaces (IRSs) following the HELIOS architecture could be adopted in the short term owing to scalability and compatibility with current generation networks. They need to be custom-tailored to the deployment scenario, however, reliance on electromagnetic (EM) simulations is a time-consuming bottleneck during the geometry optimization stage. Therefore, this letter introduces and validates an analytical reflection model based on physical optics (PO). Our findings demonstrate that the reflection pattern is accurately estimated, particularly within and around the main lobe, while significantly reducing computation time.

Future mobile use cases such as teleoperation rely on highly available mobile networks. Due to the nature of the mobile access channel and the inherent competition, the availability may be restricted in certain initially unknown areas or timespans. We automated mobile network data acquisition using a smartphone application and dedicated hardware to address this challenge, providing detailed connectivity insights. DoNext, a massive dataset of 4G and 5G mobile network data and active measurements, was collected over two years in Dortmund, Germany. To the best of our knowledge, it is the most extensive openly available mobile dataset. Machine learning methods were applied to the data to demonstrate its utility in key performance indicator prediction. Radio environmental maps facilitating key performance indicator predictions and application planning across different locations are generated through spatial aggregation for in-advance predictions. We also showcase signal strength modeling with transfer learning for arbitrary locations in individual mobile network cells, covering private and restricted areas. By openly providing the dataset, we aim to enable other researchers to develop and evaluate their machine-learning methods without conducting extensive measurement campaigns.

6G millimeter-wave (mmWave) networks are expected to provide widespread multi-Gbit/s connectivity. However, increased sensitivity to obstacle blockage leads to underconnected shadow regions, motivating the introduction of intelligent reflecting surfaces (IRSs) for efficient smart radio environments that are dynamically illuminated by anomalous reflections. Against this background, this article first presents a comprehensive analysis of the current state of IRS implementations and experimentation. We observe a scarcity of large-scale reconfigurable mmWave IRSs and insufficient experimental insights using communications equipment outside laboratory conditions. To address this gap, we develop R-HELIOS, a mechatronically reconfigurable IRS based on our geometry-based passive HELIOS IRS, which has previously been validated as a large static IRS in field studies with mmWave modems. It is complemented by a remote-control operation center that systematically orchestrates reflection behavior. The IRS research platform is then integrated into a private mmWave network environment utilizing commercial user equipments (UEs) for 6G-relevant experimentation. In an indoor factory-like scenario, we investigate an IRS beam search mechanism to improve connectivity for shadowed UEs in safety cages for machinery. Serving both UEs with a multi-armed reflection configuration maximizes the cell throughput to 2.1 Gbit/s, constituting an increase by 173 %. In a larger-scale indoor-to-outdoor setup covering an approximately 30 m2 shadowed study area, the IRS facilitates a mean downlink throughput improvement of 0.9 Gbit/s. Our study thus underscores the high potential of mmWave IRSs for static UEs. Additional measurements with a mobile user highlight the necessity for fine-grained angular beam tracking to mitigate fades at intermediate positions.

Robot competitions are a valuable platform in robotics research for evaluating various designs in situations and environments of interest, such as challenging terrain or complex manipulation tasks. Since challenging network conditions are expected in the targeted scenarios, integrating them into competitions is necessary to evaluate the performance of robotic designs in network-constrained environments and to foster communications considerations during the design of robotic systems. This paper proposes and demonstrates an integration approach for custom network degradation in robotic competitions. The competitions attended yielded performance evaluations of the robots under network degradation and highlighted optimization potential, which in one case even led to network setup enhancements and raised the robot's overall performance by 40%. One aspect of the carried out enhancements is demonstrated in an experiment to explain the observed performance increase. The results showcase the impact of network degradation on the evaluated robots' performance and illustrates potential gains achievable through optimization of the data transmission pipeline. Demonstration video available online at: https://tiny.cc/ RadioDegradationChallenge.

The analysis of the physical layer in the frequency spectrum is subject to vigorous research in the last couple of years. From localization tasks to anomaly detection, the research is starting to incorporate more artificial intelligence-based solutions. In case of anomaly detection, the looming problem is that there are many different and unquantifiable types of anomalous effects. Hence trying to find a model that predicts anomaly itself is not feasible. Therefore, we propose an approach of successfully detecting all known signals in a given signal range, which implicitly leads to finding possible anomalies. This is done by collecting Power Spectral Density waterfall diagrams and segmenting them with a Convolutional Neural Network named U-Net. The results are compared against a knowledge base of the scanned bandwidth and an informed decision on the validity of the observed signal is made. We are able to provide a working concept for the distributed monitoring and stress test system STING for detecting anomalies in private 5G networks. The system is able to achieve an accuracy up to 90%, while providing a false negative rate of 2.37%. We aim to supply full coverage of a given industrial workplace, through the distribution of software defined radios over the STING-system itself and thus are able to detect anomalies over the complete industrial facility in the future.

Wireless communication plays an increasingly large role in modern industrial environments, enabling highly flexible manufacturing processes. While Wi-Fi is still a dominant technology, private 5G networks promise to provide a sophisticated, more reliable solution in licensed frequency bands. However, it remains uncertain in practice which level of end-to-end performance can actually be achieved. To this end, this contribution focuses on a technology-independent performance evaluation with distributed test devices based on the STING concept (Spatially Distributed Traffic and Interference Generation). We present a comparative analysis of 5G and Wi-Fi technologies for private industrial networks under realistic network load scenarios, focusing on latency to determine their suitability within industrial contexts. It can be seen that the current Wi-Fi 6 technology can keep critical applications reliable in low to medium load scenarios, but struggles when multiple stations have critical Quality of Service (QoS) requirements. In contrast, it is shown that 5G technology maintains lower latency times and has fewer outlier effects that are considered unacceptable in functional safety applications, especially in scenarios with medium to high network utilization. In order to maintain the advantage of simple Wi-Fi technology while increasing latency stability, we have optimized channel access (EDCA) in a further step on the basis of an open source Wi-Fi stack. It is shown that Wi-Fi technology with suitable modifications beyond the standard can also be able to reliably serve several stations with high QoS requirements.

The teleoperation of robotic systems relies heavily on the perception of the robot's environment and, therefore, expects reliability and low latency from wireless networks. The convergence of digital and physical worlds envisioned by the upcoming 6G standard is expected to enable use cases that provide immersive sensory experiences. This work focuses on one such case: immersive situational awareness for first responders. It proposes a system architecture based on the multi-link concept that combines multiple network links into a single link, thus combining their strengths to meet the reliability and responsiveness requirements of the application. The proposed architecture is implemented in a demonstration scenario. The results show improved connectivity robustness and confirm the importance of multi-X communications for future communication standards. Video Abstract-Demonstration video available online at: https://tiny.cc/6GEMRescueRoboticsDemo.

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

The use of higher frequency bands beyond 6 GHz is considered to be key for future 6G networks. Yet, although generally available for licensing in an increasing number of countries, the 5G FR2 band has not seen much deployment for private networks so far in Europe. The main challenge for these higher frequency bands is proper beam management which allows to react flexibly to the radio conditions. This paper presents a systematic experimental evaluation of a commercial Millimeter-Wave (mmWave) indoor multi-user deployment in various reproducible channel conditions, in particular depending on Line-of-Sight (LOS) availability. We show that the beam management of the analyzed deployment can cope well with multi-user scenarios. When obstructing the LOS, alternative propagation paths are discovered and leveraged to avoid performance degradation. Only in extreme situations, such as in the case of an industry-typical safety cage that provides strong electromagnetic shielding, a significant performance decrease of the mmWave link is noted. To resolve such situations, we demonstrate that our passive Intelligent Reflecting Surface (IRS) solution HELIOS artificially introduces a qualitative reflection path that is seamlessly employed by the network to realize high-capacity communication in challenging radio environments utilizing a Beyond Line-of-Sight (BLOS) link. All-in-all, the evaluation demonstrates the promising potential of mmWave for real-life indoor factory deployments.

Private networks represent a key innovation in current 5G and future 6G networks, offering significant benefits, particularly for vertical industries with mission-critical industrial applications. Compared to public networks, the deployment of numerous potential private networks demands automated network planning while simultaneously meeting higher performance requirements for targeted applications. Emerging approaches have successfully utilized AI-based methodologies as a basis for automated network planning in greenfield deployments within licensed but purely private frequency bands. However, these approaches fail to include and extend brownfield implementations in public mobile networks, which would be crucial for private networks running a shared operator model. Thus, this paper presents an AI-based automated network planning methodology augmented by our recently introduced and thoroughly validated data-driven channel modeling approach, DRaGon. Further, this combined AI-based planning methodology is extended to provide automated network planning solutions for shared private networks within public macro networks. The overall planning accuracy was successfully validated with only minor deviations using public network deployments as ground truth. As a key result, we demonstrate that the performance of the presented AI-based planning method can reliably and accurately plan demand-driven network expansions for professional applications with the highest quality requirements.

Wireless 5G connectivity in private operation is rapidly evolving into a necessity component for business-critical services within professional industries, such as those related to manufacturing and logistics. The resulting large number of private 5G networks requires reliable methods for network planning and operation, for which current research is already presenting approaches for efficient AI-based automated network planning. However, there is often a significant gap between current research findings and their practical application in industry, resulting in insufficient delays for the utilization of research outcomes by industrial stakeholders. Hence, this paper introduces a solution that provides industry partners with free and open access to the latest research findings on automated network planning. As primary contribution, we present a web-based planning tool facilitating intuitive network planning within a few simple steps. As a result, radio environmental maps are generated using deep learning propagation modeling as well as AI-driven network planning outcomes are evaluated, balancing Quality of Service (QoS) levels against the requisite number of base stations.

Future 6G private networks rely on millimeter-wave (mmWave) cells for ultra-high local connectivity, but non-line-of-sight (NLOS) scenarios demand costly dense deployments. Instead, a new approach gaining much attention in research introduces intelligent reflecting surfaces (IRSs) to increase coverage with fewer full-blown active antennas. In an earlier work, we introduced and validated a new passive IRS architecture leveraging additive manufacturing to provide reflections tailored exactly to the needs of a specific scenario. The design goal for so-called HELIOS reflectors is to procure a geometry realizing high power gains for mobile devices in a well-defined service area. This contribution introduces a design process coupling electromagnetic (EM) simulations with differential evolution-based optimization to custom-tailor the reflector shape. Our indoor measurements confirm the specified reflection characteristics and find that up to, on average, 41.2 % higher gain in receive power is attained over a broad angular range compared with a narrow-reflecting IRS. By increasing the IRS size, the peak gain of 20 dB can be matched. Moreover, it is found that the designed HELIOS reflectors support the entire mmWave spectrum. An additional benefit is given by the fact that the sub-6 GHz anchor link attains power gains of up to 12 dB from the same reflectors.

Especially in emergencies, disaster management and logistics domains share similar challenges for applying robot systems. These include dynamic and complex environments with large operational areas and unstable network conditions. Within these environments, diverse operators must effectively coordinate multiple robot and software systems that do not contain automated emergency procedures. This leads to a high complexity of emergency handling systems. The lack of a unified framework that addresses the above-mentioned challenges to minimize emergency handling system complexity increases safety risks and operation inefficiencies in both domains. So far, existing works on MRS frameworks only cover parts of the mentioned challenges and the key requirements posed to MRSs in disaster response, emphasizing the need for a new holistic framework. This paper proposes the cross-domain human-enhanced robot orchestration framework HERO. HERO leverages concepts and approaches from disaster management and logistics so that both domains can mutually benefit from each other's functionalities. We define three main components: (1) Application and Network Orchestration Module, (2) Human-Robot-Team Module, and (3) Emergency Handling Module, and demonstrate as well as evaluate the framework in exemplary use cases. We thereby show that HERO meets all requirements for MRS frameworks in disaster response and addresses the challenges of disaster management and logistics by solving emergency handling system complexity, improving operation efficiency, and mitigating safety hazards. Further, by emphasizing the parallels between the two domains, HERO shows how we can develop robust and versatile robotic systems applicable to disaster management and logistics operations, solving mutual challenges with one solution.

Private cellular network deployments are increasingly adopted globally to connect machines on industrial campuses. Millimeter-wave (mmWave) cells need to be utilized when high wireless traffic volumes are expected, e.g., owing to high application requirements in conjunction with large number of devices. However, mmWave connectivity is highly sensible to non-line-of-sight (NLOS) conditions. For cost- and energy-efficiency reasons, 6G aims to illuminate under-connected shadow regions within the targeted service area using intelligent reflecting surfaces (IRSs). We present promising indoor measurement results for the attained performance boost using commercial equipment, where the introduction of a custom-designed passive IRS seamlessly restores throughput to 2 Gbit/s with the new propagation path increasing the link margin by more than 10 dB.

Joint Communications and Sensing (JCS) requires innovative new technologies and solutions in both the HW and SW domains. The key factors for JCS are very high frequencies (GHz/THz) with large bandwidths, the agility of the 6G infrastructure spectrum, and the ability to process radio signals for multiple purposes. The Innovation potential in this area is huge, but technological challenges to be solved are also demanding. In the component and hardware area, frequency-agile RF components are subject of the development to support very high bandwidths with the lowest possible nonlinearity and power consumption. The common hardware and algorithmic challenge is to ensure coherent processing of signals, sensor fusion and in particular common waveform design, novel baseband solutions, JCS supporting packet structures and efficient edge-based processing. This article provides an overview of JCS from the perspective of the 6GEM project.

The 6G standard aims to be an integral part of the future economy by providing high-performance communication and sensing services. At terahertz (THz) frequencies, indoor campus networks can offer the highest sensing quality. Health monitoring in hospitals is expected to be an application site for these. This work outlines a monostatic phase-based system for breathing rate monitoring. Our feasibility study observes motion measurement accuracy down to the micrometer level. However, we also find that the patient's pose needs to be considered for generalized applicability. Thus, a solution that leverages multiple propagation paths and beam orientations is proposed.

State-of-the-art robot deployments for rescue robotics typically involve more than one robot, each potentially equipped with multiple radio access technologies for redundancy. Evaluating and benchmarking such deployments with numerous robots and network communication links requires tools that scale well across these dimensions. This work builds on previous work that presented vSTING as a solution for emulating challenging network environments for single robots. The current work extends vSTING to include multi-robot and multi-link support, making it a pure software solution but distributed, eliminating the need for dedicated hardware. Furthermore, the resulting solution can be used in real-world applications to manage or limit network resources. In the first evaluation step, we validate the multi-robot and multi-link feature. Then, an exemplary validation of a basic multi-link solution in its early development stage is conducted by replaying a network environment recorded during a prior mission.

Private 5G and future 6G networks offer significant potential for automation in vertical domains but must face the challenge of dynamic and rapid adaptation to new operational requirements. In this context, non-stationary, ad-hoc network operation is essential for continuously adapting reliable network solutions to rapidly changing environments. In this paper, we present IndoorDRaGon as a novel signal-strength prediction method for network planning of dynamic environments that combines expert knowledge from the mobile communications domain with lightweight machine learning methods based on random forests to achieve accurate and computationally efficient spatiotemporal quality of service predictions. In a comprehensive performance evaluation, the performance of IndoorDRaGon is compared to real-world measurements, ray tracing analysis and a vast range of state-of-the-art channel models. It is found that IndoorDRaGon achieves significantly better accuracy for unseen environments than the latter, even when only a tiny portion of measurements is considered in the training data.