Scholarlink

The deployment of 5G networks in complex industrial environments poses significant challenges, including extensive coverage requirements, intricate physical layouts, and costly infrastructure deployment. Accurate and cost-effective planning is crucial, prompting the use of high-fidelity ray-tracing-based wireless simulators, such as NVIDIA’s Sionna RT, to generate predictive coverage maps before physical installation. However, validating these simulations remains critical for practical use. This study presents a comprehensive framework employing autonomous mobile robots integrated with Simultaneous Localization and Mapping (SLAM) capabilities for validating simulated 5G coverage maps. Two distinct scenarios–an indoor research lab and an outdoor urban environment–were modeled in 3D and simulated using Sionna RT, producing Received Signal Strength Indicator (RSSI)-based coverage maps. Empirical validation was conducted with an autonomous robot equipped with a Teltonika RUTX50 5G router, capturing real-world RSSI, Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) data, alongside accurate spatial referencing from SLAM. Preliminary results demonstrate alignment between simulated and measured coverage maps, underscoring the viability of robot-based approaches for systematic Radio Frequency (RF) mapping and simulator validation.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Toward Robot-Based Validation of Simulated 5G Coverage Maps in an Industrial Context

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Fatma Ltaief,
  • Guillaume Lozenguez,
  • Anne Savard,
  • Luc Fabresse

摘要

The deployment of 5G networks in complex industrial environments poses significant challenges, including extensive coverage requirements, intricate physical layouts, and costly infrastructure deployment. Accurate and cost-effective planning is crucial, prompting the use of high-fidelity ray-tracing-based wireless simulators, such as NVIDIA’s Sionna RT, to generate predictive coverage maps before physical installation. However, validating these simulations remains critical for practical use. This study presents a comprehensive framework employing autonomous mobile robots integrated with Simultaneous Localization and Mapping (SLAM) capabilities for validating simulated 5G coverage maps. Two distinct scenarios–an indoor research lab and an outdoor urban environment–were modeled in 3D and simulated using Sionna RT, producing Received Signal Strength Indicator (RSSI)-based coverage maps. Empirical validation was conducted with an autonomous robot equipped with a Teltonika RUTX50 5G router, capturing real-world RSSI, Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR) data, alongside accurate spatial referencing from SLAM. Preliminary results demonstrate alignment between simulated and measured coverage maps, underscoring the viability of robot-based approaches for systematic Radio Frequency (RF) mapping and simulator validation.