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.

The development of communication systems for in-telligent transportation systems (ITS) relies on their performance in high-mobility scenarios. Such scenarios introduce rapid fluctuations in wireless channel properties. As a promising solution for vehicle-to-everything (V2X) communication, the orthogonal time frequency space (OTFS) approach has emerged. Nevertheless, the performance of OTFS systems is closely tied to time- and frequency diversity of the wireless propagation channel. However, there is a lack of understanding of the stationarity of the wireless channels, especially in the millimeter wave (mmWave) frequency bands. In this paper, we address this research gap by conducting a comprehensive stationarity analysis of measured sub-6 GHz and mmWave high-speed wireless channels. We evaluate the spatial stationarity of a scenario, where the transmitter is moving at high velocity. Furthermore, we investigate the influence of the transmit antenna orientation on the channel spatial stationarity. We could show that the spatial stationarity is proportional to the wavelength.

The role of wireless communications in various domains of intelligent transportation systems is significant; it is evident that dependable message exchange between nodes (cars, bikes, pedestrians, infrastructure, etc.) has to be guaranteed to fulfill the stringent requirements for future transportation systems. A precise site-specific digital twin is seen as a key enabler for the cost-effective development and validation of future vehicular communication systems. Furthermore, achieving a realistic digital twin for dependable wireless communications requires accurate measurement, modeling, and emulation of wireless communication channels. However, contemporary approaches in these domains are not efficient enough to satisfy the foreseen needs. In this position paper, we overview the current solutions, indicate their limitations, and discuss the most prospective paths for future investigation.

Future wireless multiple-input multiple-output (MIMO) communication systems will employ sub-6 GHz and millimeter wave (mmWave) frequency bands working cooperatively. Establishing a MIMO communication link usually relies on estimating channel state information (CSI) which is difficult to acquire at mmWave frequencies due to a low signal-to-noise ratio (SNR). In this paper, we propose three novel methods to estimate mmWave MIMO channels using out-of-band information obtained from the sub-6 GHz band. We compare the proposed channel estimation methods with a conventional one utilizing only in-band information. Simulation results show that the proposed methods outperform the conventional mmWave channel estimation method in terms of achievable spectral efficiency, especially at low SNR and high K-factor.

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.

Next-generation mobile communication systems are planned to support millimeter Wave (mmWave) transmission in scenarios with high-mobility, such as in private industrial networks. To cope with propagation environments with unprecedented challenges, data-driven methodologies such as Machine Learning (ML) are expected to act as a fundamental tool for decision support in future mobile systems. However, high-quality measurement datasets need to be made available to the research community in order to develop and benchmark ML-based methodologies for next-generation wireless networks. We present a reliable testbed for collecting channel measurements at sub-6 GHz and mmWave frequencies. Further, we describe a rich dataset collected using the presented testbed. Our public dataset enables the development and testing of innovative ML-based channel simulators for both sub-6GHz and mmWave bands on real-world data. We conclude this paper by discussing promising experimental results on two illustrative ML tasks leveraging on our dataset, namely, channel impulse response forecasting and synthetic channel transfer function generation, upon which we propose future exploratory research directions. The original dataset employed in this work is available on IEEE DataPort (https://dx.doi.org/10.21227/3tpp-j394), and the code utilized in our numerical experiments is publicly accessible via CodeOcean (https://codeocean.com/capsule/9619772/tree).

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.

Analysis and modeling of wireless communication systems are dependent on the validity of the wide-sense stationarity uncorrelated scattering (WSSUS) assumption. However, in high-mobility scenarios, the WSSUS assumption is approximately fulfilled just over a short time period. This paper focuses on the stationarity evaluation of high-mobility multi-band channels. We evaluate the stationarity time, the time over which WSSUS is fulfilled approximately. The investigation is performed over real, measured high-mobility channels for two frequency bands, 2.55 and 25.5 GHz. Furthermore, we demonstrate the influence of the user velocity on the stationarity time. We show that the stationarity time decreases with increased relative velocity between the transmitter and the receiver. Furthermore, we show the similarity of the stationarity regions between sub-6 GHz and mmWave channels. Finally, we demonstrate that the sub-6 GHz channels are characterized by longer stationarity time.

Growing intelligent transportation systems demand a vehicular communication technology that can satisfy high requirements in terms of data rates, latency, reliability and number of connected devices. To evaluate the performance of such communication technology, real-world measurements are required for various channel conditions. Since vehicular measurement campaigns are expensive and time-consuming, a high-mobility environment poses enormous challenges for performance measurements. Using the existing technique of time-stretching the transmit signals, such experiments can be emulated through measurements at a single lower velocity by inducing effects caused by higher velocities. The existing time-stretching technique poses the problem of different channel estimation quality between the time-stretched and the original system. To ensure that the technique gives accurate results in practical systems, we adapt the pilot-based channel estimation scheme within the existing time-stretching technique. Furthermore, we evaluate the proposed channel estimation scheme through simulations and a high-speed vehicular channel measurement campaign at the center frequency of 2.55 GHz.

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.