Analysis of 3GPP and Ray-Tracing Based Channel Model for 5G Industrial Network Planning

TL;DR

This work investigates the suitability of 3GPP's Indoor Factory channel model for industrial environments and proposes a calibrated ray-tracing (RT) approach using CW RxP measurements at 3.7 GHz in a real factory. The authors integrate a detailed 3D CAD model, perform iterative calibration (adjusting material properties and RT parameters), and compare PL and coverage predictions against measurements. The calibrated RT model achieves closer alignment to measurements (lower RMSE, std dev, and better correlation) than the 3GPP InF-SH model and yields more reliable coverage maps for radio planning. The results underscore the value of site-specific RT-based modeling and a digital-twin framework to optimize 5G industrial network deployments, especially under dynamic factory conditions.

Abstract

Appropriate channel models tailored to the specific needs of industrial environments are crucial for the 5G private industrial network design and guiding deployment strategies. This paper scrutinizes the applicability of 3GPP's channel model for industrial scenarios. The challenges in accurately modeling industrial channels are addressed, and a refinement strategy is proposed employing a ray-tracing (RT) based channel model calibrated with continuous-wave received power measurements collected in a manufacturing facility in Sweden. The calibration helps the RT model achieve a root mean square error (RMSE) and standard deviation of less than 7 dB. The 3GPP and the calibrated RT model are statistically compared with the measurements, and the coverage maps of both models are also analyzed. The calibrated RT model is used to simulate the network deployment in the factory to satisfy the reference signal received power (RSRP) requirement. The deployment performance is compared with the prediction from the 3GPP model in terms of the RSRP coverage map and coverage rate. Evaluation of deployment performance provides crucial insights into the efficacy of various channel modeling techniques for optimizing 5G industrial network planning.

Figures (8)

• Figure 1: Measurement scenario and measurement setup.
• Figure 2: 3D digital model of the measurement area with the Tx and Rx points.
• Figure 3: PL comparison among different models and measurements at 3.7 GHz.
• Figure 4: Channel gain (dB) heatmap for different models at 3.7 GHz.
• Figure 5: RxP (dBm) coverage maps for two different occupancies of storage racks at 3.7 GHz.