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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/2508.06054 |
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| _version_ | 1866915434478960640 |
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| author | Wang, Yiheng Zhang, Shutao Xue, Ye Chang, Tsung-Hui |
| author_facet | Wang, Yiheng Zhang, Shutao Xue, Ye Chang, Tsung-Hui |
| contents | This paper presents MM-LSCM, a self-supervised multi-modal neural radio radiance field framework for localized statistical channel modeling (LSCM) for next-generation network optimization. Traditional LSCM methods rely solely on RSRP data, limiting their ability to model environmental structures that affect signal propagation. To address this, we propose a dual-branch neural architecture that integrates RSRP data and LiDAR point cloud information, enhancing spatial awareness and predictive accuracy. MM-LSCM leverages volume-rendering-based multi-modal synthesis to align radio propagation with environmental obstacles and employs a self-supervised training approach, eliminating the need for costly labeled data. Experimental results demonstrate that MM-LSCM significantly outperforms conventional methods in channel reconstruction accuracy and robustness to noise, making it a promising solution for real-world wireless network optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Modal Neural Radio Radiance Field for Localized Statistical Channel Modelling Wang, Yiheng Zhang, Shutao Xue, Ye Chang, Tsung-Hui Signal Processing This paper presents MM-LSCM, a self-supervised multi-modal neural radio radiance field framework for localized statistical channel modeling (LSCM) for next-generation network optimization. Traditional LSCM methods rely solely on RSRP data, limiting their ability to model environmental structures that affect signal propagation. To address this, we propose a dual-branch neural architecture that integrates RSRP data and LiDAR point cloud information, enhancing spatial awareness and predictive accuracy. MM-LSCM leverages volume-rendering-based multi-modal synthesis to align radio propagation with environmental obstacles and employs a self-supervised training approach, eliminating the need for costly labeled data. Experimental results demonstrate that MM-LSCM significantly outperforms conventional methods in channel reconstruction accuracy and robustness to noise, making it a promising solution for real-world wireless network optimization. |
| title | Multi-Modal Neural Radio Radiance Field for Localized Statistical Channel Modelling |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.06054 |