This demo presents a hardware-in-the-loop (HiL) framework for validating and verifying wireless communication hardware under controlled and repeatable laboratory conditions. The setup integrates modems as transmitters and receivers with a real-time channel emulator. To simplify the hardware interfaces we exchange multi-path component parameters between the channel model and the radio channel emulator, following the proposed structure in the ongoing IEEE P1944 standardization for site-specific radio channel representations. The demo features an urban vehicular communication scenario, demonstrating the capability to replicate realistic propagation conditions with non-stationary propagation conditions. Our HiL test system allows dynamic motion changes of the vehicles based on the received data during the emulation process. Site-specific channel emulation enables the lab-based validation of realistic vehicular applications, 5G and 6G physical layer technologies, and the training and testing of AI/ML based receiver architectures.

Effective communication between vehicles and road users is essential for reducing accidents and congestion. Reli-able wireless communication is crucial for decision-making in advanced driver assistance systems and autonomous vehicles. In this work, we propose a convolutional neural network to predict the frame error rate in vehicle-to-infrastructure scenarios. Using a geometry-based stochastic channel model and hardware-in-the-loop emulation, we generate a dataset on which our model achieves 90 % validation accuracy. To adapt the model to new data, such as vehicle-to-vehicle scenarios, and to reduce computational costs for retraining the entire model from scratch, we explore methods like fine-tuning, transfer learning, and learning without forgetting (LwF). While these methods improve performance on new data, they reduce accuracy on the original data. To address this, we modify LwF by including some original data, achieving a balanced accuracy of 81.96%.

Wireless vehicular communication will increase the safety of road users. The reliability of vehicular communication links is of high importance as links with low reliability may diminish the advantage of having situational traffic information. The goal of our investigation is to obtain a reliable coverage area for non-stationary vehicular scenarios. Therefore we propose a deep neural network (DNN) for predicting the expected frame error rate (FER). The DNN is trained in a supervised fashion, where a time-limited sequence of channel frequency responses has been labeled with its corresponding FER values assuming an underlying wireless communication system, i.e. IEEE 802.11p. For generating the training dataset we use a geometry-based stochastic channel model (GSCM). We obtain the ground truth FER by emulating the time-varying frequency responses using a hardware-in-the-loop setup. Our GSCM provides the propagation path parameters which we use to fix the statistics of the fading process at one point in space for an arbitrary amount of time, enabling accurate FER estimation. Using this dataset we achieve an accuracy of 85% of the DNN. We use the trained model to predict the FER for measured time-varying channel transfer functions obtained during a measurement campaign. We compare the predicted output of the DNN to the measured FER on the road and obtain a prediction accuracy of 78%.

In this paper we present a hardware-in-the-loop (HiL) framework for testing wireless vehicle-to-everything (V2X) communication hardware, i.e., modems under realistic channel conditions. The framework includes a wireless channel emulator, which is capable of emulating non-stationary wireless channels in real-time. We validate the HiL framework by comparing the frame error rate (FER) obtained via emulation with data obtained during a V2X measurement campaign using the same IEEE 802.11p based modems. To do this we acquire measured time-variant channel transfer function and FER measurements simultaneously. The results show that our HiL approach is feasible and that we can obtain FER measurements in the laboratory that closely match the measurement results obtained on the road, giving the maximal distance of 0.099 between their cumulative distribution functions.

This paper discusses a freely available and open dataset containing vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-pedestrian (V2P) OFDM-based wireless channel measurement data including synchronised sensor data such as radar, LiDAR and high precision GPS. The wireless channel measurement is conducted at the carrier frequencies of 3.2 GHz and 5.81 GHz which are the most promising frequency bands in which future V2X communication systems will operate. The dataset contains the wireless channel measurement data of various V2X scenarios along with synchronized sensor information from a vehicle. In addition to the wireless channel measurement data, the dataset also includes frame error rate measurements from a IEEE 802.11p based communication system, synchronized to the other measurement data.

In this paper we present and discuss results of a wireless vehicle-to-vehicle (V2V) dualband channel measurement campaign at center frequencies of 3.2 GHz and 34.3 GHz in an urban street scenario. The measurement is conducted using a bandwidth of 155.5 MHz and a sounding repetition rate of 62.5 $\mu \mathrm{s}$ for both bands. At the transmitter side we use omni-directional antennas and at the receiver side directional antennas with 17° opening angles. With this setup, we present the first comparison of simultaneous and dynamic multiband V2V measurements using the time-variant power delay profile (PDP) and the Doppler spectral density (DSD). We find close similarities for the line-of-sight, specular as well as diffuse reflections in both frequency bands, enabling future work for out-of-band beam finding in vehicular mmWave systems.

Repeatable system-level test methods for vehicle-to-everything communication (V2X) are crucial in improving road safety and supporting connected autonomous driving. They rely on geometrical channel models that provide realistic signal propagation delays, Doppler shifts and path loss. In this work we focus on the validation of an OpenStreetMap (OSM) geometry based stochastic channel model (GSCM) that has a high degree of automation and low computational complexity. In an urban intersection scenario we compare the time-variant power delay profile (PDP) and Doppler spectral density (DSD) obtained from a measurement campaign to the ones obtained by the OSM-GSCM. Furthermore, we measure packet error rates (PER) of commercial modem hardware by connecting a transmitter and a receiver to the AIT channel emulator, i.e. using a hardware-in-the-loop (HiL) setup. We compare the PER obtained when the channel emulator uses the OSM-GSCM as input and when it uses the measured impulse responses. The OSM-GSCM path loss shows minor differences to the measurements due to the 2D structure of the OSM-GSCM, while the PER, PDP and DSD exhibit a good match between OSM-GSCM and empirical measurement data.

The advantages of measuring multiple wireless links simultaneously has been gaining attention due to the growing complexity of wireless communication systems. Analyzing vehicular communication systems presents a particular challenge due to their rapid time-varying nature. Therefore multi-node channel sounding is crucial for such endeavors. In this paper, we present the architecture and practical implementation of a scalable mobile multi-node channel sounder, optimized for use in vehicular scenarios. We perform a measurement campaign with three moving nodes, which includes a line of sight (LoS) connection on two links and non LoS(NLoS) conditions on the third link. We present the results on the obtained channel delay and Doppler characteristics, followed by the assessment of the degree of correlation of the analyzed channels and time-variant channel rates, hence investigating the suitability of the channel's physical attributes for relaying. The results show low cross-correlation between the transfer functions of the direct and the relaying link, while a higher rate is calculated for the relaying link.