Next-generation wireless communication systems will incorporate millimeter wave (mmWave) as one of the key technologies to increase data rates. These forthcoming mmWave systems will integrate multiple-input multiple-output (MIMO) technology to ensure sufficient link margin and are expected to be deployed in conjunction with sub-6 GHz systems. Configuring a MIMO communication link usually relies on estimating channel state information (CSI), which is difficult to acquire at mmWave frequencies due to the low pre-beamforming signal-to-noise ratio (SNR). In this paper, we propose a novel approach to estimate mmWave MIMO channels by leveraging out-of-band information from a sub-6GHz band. Utilizing this approach, we develop three channel estimation methods and compare these methods with one using only in-band information. We investigate the influence of theK-factor, the SNR and the number of antennas on the performance of the proposed channel estimation methods through simulations. Additionally, we compare these methods in terms of their computational complexity. Finally, we validate the performance of the proposed methods through channel measurements conducted in an outdoor environment. The results demonstrate that the proposed methods outperform in-band mmWave channel estimation in terms of spectral efficiency, particularly in scenarios of low SNR and high K-factor.

Since the design of wireless MIMO systems requires knowledge of the double-directional (i.e., directionally resolved at both link ends) channel characteristics, and 5G/6G use higher frequency bands, there is the need for double-directional measurements in the mmWave spectrum, along with channel sounders that can accurately perform such measurements. This paper introduces a novel channel sounding approach based on a redirecting rotating mirror arrangement (ReRoMA). The method is low-cost and flexible as it requires only a single radio frequency chain at each link end and performs mechanical beamsteering. However, in contrast to existing rotating-horn systems, it physically separates the signal generation/transmission and the beam steering components, resulting in orders-of-magnitude faster measurements. The paper outlines the fundamental concept, describes details of the implementation, and demonstrates its application and accuracy using a 60GHz prototype for measurements in static reference scenarios, as well as dynamic measurements. We illustrate the detected propagation paths using dynamic angular and delay power spectra and correlate these findings with the surrounding environmental structure. Locations of environmental objects are detected within the Fourier resolution determined by bandwidth and horn width, with no noticeable degradation due to the faster measurements.

With the move towards 6G and associated technology deployment in higher frequency bands, measurements of directionally-resolved channels and sounders capable of performing such measurements are a necessity. In this paper, we present a new concept of channel sounding based on a Redirecting Rotating Mirror Arrangement (ReRoMA), capable of performing double-directional channel measurements at millimeter wave frequencies by mechanical beam steering orders of magnitude faster than existing rotating-horn arrangements. We present this new concept, describe a prototype operating at 60 GHz, and use it to perform, as proof-of-principle, a dynamic cart-to-cart channel measurements at a T-intersection scenario. We show that this sounding principle works and allows the directional evaluation of the channel. We visualize the different resolvable propagation paths in terms of dynamic angular and delay power spectrum, and relate them to the environmental geometry.

Cooperative connected automated mobility depends on sensing and wireless communication functions. With increasing carrier frequency both functions can be realized with the same hardware, however, the attenuation of radio signals increases quadratically with the carrier frequency. Hence, link setup becomes challenging in vehicular scenarios due to the required beam finding process. In this paper we investigate the multipath components of the vehicle-to-infrastructure (V2I) radio channel in three frequency bands with center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz using measurement data with 155.5 MHz bandwidth and a sounding repetition rate of $31.25 \mu \mathrm{~s}$. The channel impulse responses are collected simultaneously at all three carrier frequencies. Using the high temporal sampling rate we apply the CLEAN algorithm, enabling the estimation of the weight, delay and Doppler frequency of multipath components. By analyzing the collinearity of the Doppler normalized scattering function between the frequency bands we found that the collinearity between the 3.2 GHz and 34.3 GHz band as well as between the 3.2 GHz and 62.35 GHz is smaller in the non-line of sight (NLOS) region but increases for the line-of-sight (LOS).

Future vehicular communication systems will extend deployed frequency bands from sub-6 GHz to millimeter wave (mmWave). To investigate different propagation effects between sub-6 GHz and mmWave bands in high-mobility scenarios, we proposed a suitable testbed setup to compare these two bands in a fair manner. Experiments conducted using the proposed testbed provide realistic results, but they are only usable if they can be faithfully reproduced. To quantify the reproducibility of the proposed testbed, we perform channel measurements at center frequencies of 2.55 GHz and 25.5 GHz at a velocity of 50 km/h. We investigate the influence of antenna pattern, time between measurements, signal-to-interference-and-noise ratio (SINR) and signal bandwidth on the reproducibility in terms of the channel correlation.

One of the key research directions to increase the capacity of new radio (NR) vehicle-to-everything (V2X) communication systems is extension of employed frequency bands from sub-6 GHz to millimeter wave (mmWave) range. To investigate different propagation effects between sub-6 GHz and mmWave bands in high-mobility scenarios, one needs to conduct channel measurements in both frequency bands. Using a suitable testbed setup to compare these two bands in a fair manner, we perform channel measurements at center frequencies of 2.55 GHz and 25.5 GHz, velocities of 50 km/h and 100 km/h, and at 126 different spatial positions. Furthermore, we conduct a comparative study of the multi-band propagation based on measurement results. We estimate the power delay profile (PDP) and the Doppler power spectral density (DSD) from a large set of measurements collected in a measurement campaign. Finally, we compare measured wireless channels at the two employed frequency bands in terms of root-mean-square (RMS) delay spread and RMS Doppler spread.

Condition monitoring software is crucial for companies from all industrial branches that take care of the high availability of their automation systems. However, as the automation systems increase in complexity to support numerous business needs, the complexity behind the condition monitoring software development increases as well. It demands a deep understanding of various domain-specific requirements, state-of-the-art architectural concepts, and implementation technologies by software engineers. This paper copes with this complexity by proposing a model-driven approach to support software engineers in designing and implementing condition monitoring software. In that context, the paper contributes with a domain-specific language for condition monitoring software development (DSL4CMSD) along with a code generator that produces a set of python-based microservices. Furthermore, the paper discusses condition monitoring domain-specific requirements and presents a design process for their implementation using the DSL4CMSD. Finally, we evaluate the applicability of our modeling approach on the industrial heat exchanger monitoring case study.

Condition monitoring enables companies from all industrial branches to maintain high availability of their automation systems. Thus, condition monitoring is a fundamental technology to maximize operational productivity by predicting potential fault occurrences that lead to unavailability of the automation systems. Today, performing condition monitoring is successfully achieved using custom-developed software solutions. However, the development of these software solutions is a challenging task as it requires extensive knowledge from multiple engineering disciplines. In this context, there is a high need for a systematic way to formalize this knowledge. Ontologies have had significant success in fulfilling this need by providing a formal model of domain concepts and their relations.In this paper, we present our condition monitoring ontology for automation systems and contribute with a conceptual model. This ontology incorporates ISO standards for condition monitoring and key performance indicators (KPIs). KPIs aggregate numerous sensor values in a few understandable numbers and, therefore, provide more efficient insight in condition of automation systems. To evaluate our work, we developed a condition monitoring knowledge-based system for a centrifugal separator based on the proposed ontology in an industrial project and report about lessons learned.

The process of beer brewing is very complex as it has to fulfill strict demands on the product quality as well as on the availability and the performance of the plant. As a consequence, a condition monitoring of the beer brewing process and its visualization plays an important role such that all relevant deviations are detected as early as possible by the production manager. While a general process for condition monitoring already exists, there currently exists no approach to realize this process for the domain of beer brewing.Therefore, this paper presents a condition monitoring system for the beer brewing process developed in an industrial project. This condition monitoring system is based on Key Performance Indicators (KPIs) that support the production manager in evaluating the actual state of the production processes. A set of brewery-specific KPIs are determined and discussed in the paper. In addition, software architecture and visualization of the KPIs in a brewery-specific dashboard are presented. We evaluate our concept at various beer breweries and report about lessons that we have learned.