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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03813 |
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| _version_ | 1866908518934642688 |
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| author | Shiroya, Parth Ashokbhai Shashidhar, Swarnagowri Ashtekar, Amod Kar, Krishna Aindrila Lomboy, Rafaela Davis, Dalton Eltayeb, Mohammed E. |
| author_facet | Shiroya, Parth Ashokbhai Shashidhar, Swarnagowri Ashtekar, Amod Kar, Krishna Aindrila Lomboy, Rafaela Davis, Dalton Eltayeb, Mohammed E. |
| contents | Reliable connectivity in millimeter-wave (mmWave) and sub-terahertz (sub-THz) networks depends on reflections from surrounding surfaces, as high-frequency signals are highly vulnerable to blockage. The scattering behavior of a surface is determined not only by material permittivity but also by roughness, which governs whether energy remains in the specular direction or is diffusely scattered. This paper presents a LiDAR-driven machine learning framework for classifying indoor surfaces into semi-specular and low-specular categories, using optical reflectivity as a proxy for electromagnetic scattering behavior. A dataset of over 78,000 points from 15 representative indoor materials was collected and partitioned into 3 cm x 3 cm patches to enable classification from partial views. Patch-level features capturing geometry and intensity, including elevation angle, natural-log-scaled intensity, and max-to-mean ratio, were extracted and used to train Random Forest, XGBoost, and neural network classifiers. Results show that ensemble tree-based models consistently provide the best trade-off between accuracy and robustness, confirming that LiDAR-derived features capture roughness-induced scattering effects. The proposed framework enables the generation of scatter aware environment maps and digital twins, supporting adaptive beam management, blockage recovery, and environment-aware connectivity in next-generation networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environments Shiroya, Parth Ashokbhai Shashidhar, Swarnagowri Ashtekar, Amod Kar, Krishna Aindrila Lomboy, Rafaela Davis, Dalton Eltayeb, Mohammed E. Machine Learning Reliable connectivity in millimeter-wave (mmWave) and sub-terahertz (sub-THz) networks depends on reflections from surrounding surfaces, as high-frequency signals are highly vulnerable to blockage. The scattering behavior of a surface is determined not only by material permittivity but also by roughness, which governs whether energy remains in the specular direction or is diffusely scattered. This paper presents a LiDAR-driven machine learning framework for classifying indoor surfaces into semi-specular and low-specular categories, using optical reflectivity as a proxy for electromagnetic scattering behavior. A dataset of over 78,000 points from 15 representative indoor materials was collected and partitioned into 3 cm x 3 cm patches to enable classification from partial views. Patch-level features capturing geometry and intensity, including elevation angle, natural-log-scaled intensity, and max-to-mean ratio, were extracted and used to train Random Forest, XGBoost, and neural network classifiers. Results show that ensemble tree-based models consistently provide the best trade-off between accuracy and robustness, confirming that LiDAR-derived features capture roughness-induced scattering effects. The proposed framework enables the generation of scatter aware environment maps and digital twins, supporting adaptive beam management, blockage recovery, and environment-aware connectivity in next-generation networks. |
| title | Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environments |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.03813 |