This paper presents an innovative method that can be used to produce deterministic channel models for 5G industrial internet-of-things (IIoT) scenarios. Ray-tracing (RT) channel emulation can capture many of the specific properties of a propagation scenario, which is incredibly beneficial when facing various industrial environments and deployment setups. But the environment's complexity, composed of many metallic objects of different sizes and shapes, pushes the RT tool to its limits. In particular, the scattering or diffusion phenomena can bring significant components. Thus, in this article, the Volcano RT channel simulation is tuned and benchmarked against field measurements found in the literature at two frequencies relevant to 5G industrial networks: 3.7 GHz (mid-band) and 28 GHz (millimeter-wave (mmWave) band), to produce calibrated ray-based channel model. Both specular and diffuse scattering contributions are calculated. Finally, the tuned RT data is compared to measured large-scale parameters, such as the power delay profile (PDP), the cumulative distribution function (CDF) of delay spreads (DSs), both in line-of-sight (LoS) and non-LoS (NLoS) situations and relevant IIoT channel properties are further explored.

翻译：本文提出一种创新方法，可用于生成5G工业物联网（IIoT）场景下的确定性信道模型。射线追踪（RT）信道仿真能够捕捉传播场景的诸多特定属性，在应对多样化的工业环境与部署配置时极具优势。然而，由大量不同尺寸与形状的金属物体构成的复杂环境，使RT工具面临极限挑战——特别是散射或漫反射现象可能产生显著分量。为此，本文对Volcano RT信道仿真进行调谐，并以文献中5G工业网络相关频段（中频段3.7 GHz与毫米波频段28 GHz）的现场测量数据为基准，生成经校准的基于射线信道模型。我们同时计算镜面反射与漫散射贡献。最终，将调谐后的RT数据与实测大尺度参数（如功率延迟分布PDP、视距LoS与非视距NLoS场景下的延迟扩展DS累积分布函数CDF）进行对比，并进一步探索相关IIoT信道特性。