This paper presents a geometry-based stochastic channel model for a 3GPP millimeter-wave (mmWave) high-speed train (HST) scenario. An important demand of the mmWave HST channel model is the spatial consistency during movement. In order to meet this requirement, the environment pattern containing the most effective geometry information is proposed, which is useful for generating integral and accurate channel geometry. Since the measurement of a mmWave channel is deficient for high mobility, the proposed channel model is developed using a ray-tracing (RT) simulator that is validated with the channel measurements performed in the HST scenario at 93.2 GHz. The paper gives a full parameterization for the proposed model and its implementation for channel simulation. The verification is made through a comparison of the proposed model and the measurement-validated RT simulations in terms of the power delay profile (PDP), the Ricean K-factor and the second-order statistics in the delay and angle domains. Finally, for the design of mmWave HST communication systems the time-varying channel characteristics are evaluated in the delay and Doppler domains.

In the future railway services, wireless communication is the fundamental part and millimeter wave (mmWave) is foreseen to be a key enabler towards the smart railway. An accurate understanding of the propagation environment can assist designing both systems and railway infrastructures for better communication services. In this paper, the influence of typical objects to the mmWave propagation channel are analyzed for “Train-to-infrastructure” and “Intra-wagon” railway scenarios with various configurations. Propagation measurements are conducted in the mmWave band for the 12 most common railway materials. The corresponding electromagnetic parameters are obtained and a 3D ray tracing (RT) simulator is calibrated. The mean absolute error of the simulated S21 parameter is -53.5 dB, indicating that the calibrated RT can be used to generate the close-to-real mmWave channel for railway scenarios. Statistically consistent scenarios and deployments are generated, which enables drawing unbiased numerical results based on intensive RT simulations. The influence of typical objects and corresponding material compositions are then compared and significant objects are determined for each scenario. The results of this work not only imply how the propagation environment impacts on the propagation channel, but also makes suggestions to efficiently reconstruct railway environment models for an accurate RT based channel model. Moreover, the understanding of the influence of the environment at object and material levels will in turn guide the construction of railway infrastructure for better railway services.

Channel modeling is considered as a fundamental step in the design, deployment, and optimization of vehicular wireless communication systems. For typical vehicular communication scenarios in urban areas, dense multipath may exist in the wireless channels. The propagation graph (PG) method is an efficient approach to simulate multipath radio propagation. In this paper, we extend the PG method into a Graph Neural Network (GNN) enabled data-driven method for calculating channel transfer function (CTF) and channel impulse response (CIR) in a given space. ChebNet, a classical GNN, is utilized for estimating the scattering coefficients of the edge gains in the PG method. The proposed GNN-enabled method performs better than baseline algorithms, such as multilayer perceptron (MLP), simulated annealing (SA) algorithm, and genetic algorithm (GA) in effectively estimating a large number of scattering coefficients in PG. Mean absolute errors of the proposed method are provided and evaluated in this paper. Additionally, the potential future research directions of the GNN-enabled PG method for channel modeling are discussed.

Millimeter-wave automotive radar is essential for realizing autonomous driving. Realistic channel model and simulation are important for system and sensing algorithm design. This work introduces channel measurements and ray-tracing (RT) channel simulations for frequency modulated continuous wave (FMCW) automotive mmWave radar. The 77 GHz channel measurements in an urban crossroads environment are presented. The dominant echoes (multi-path components) of the measurement are detected and matched with corresponding objects for each frame. A measurement-based electromagnetic (EM) parameter estimation method is proposed to find the optimal parameter set that minimizes the error of simulated radar cross section (RCS). As a result, the issue of lacking reliable EM material parameters for RT simulation at 77 GHz frequency band is tackled. Simulation results are validated in power, range, velocity, and angle domains with the provided EM parameter set. Extending validated RT simulations in similar environments with various configurations allows more reliable channels for rigorous testing without the limitation of channel measurement.