Tuning of Ray-Based Channel Model for 5G Indoor Industrial Scenarios

TL;DR

This paper develops a calibrated ray-tracing model for 5G indoor industrial environments, accurately capturing scattering effects at 3.7 GHz and 28 GHz by tuning against real measurements.

Contribution

It introduces a method to tune and benchmark ray-tracing simulations for complex industrial scenarios, including diffuse scattering, to produce realistic 5G channel models.

Findings

The tuned model matches measured large-scale parameters.

Both specular and diffuse scattering are effectively incorporated.

Model validation covers LoS and NLoS conditions.

Abstract

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.