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

TL;DR

This paper evaluates and refines 3GPP and ray-tracing channel models for 5G industrial environments, enhancing deployment accuracy through calibration with real measurements and comparing their effectiveness in network planning.

Contribution

It introduces a calibration method for ray-tracing models using real measurements and compares its performance with 3GPP models for industrial 5G network planning.

Findings

Calibrated RT model achieves RMSE < 7 dB.

RT model provides accurate coverage maps.

Deployment simulations show RT model's effectiveness.

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.