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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2510.08140 |
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| _version_ | 1866908981492973568 |
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| author | Wang, Yancheng Huang, Chuan Zhang, Songyang Chen, Guanying Guo, Wei Lan, Shenglun Xu, Lexi Cheng, Xinzhou Tang, Xiongyan Cui, Shuguang |
| author_facet | Wang, Yancheng Huang, Chuan Zhang, Songyang Chen, Guanying Guo, Wei Lan, Shenglun Xu, Lexi Cheng, Xinzhou Tang, Xiongyan Cui, Shuguang |
| contents | The substantial communication resources consumed by conventional pilot-based channel sounding impose an unsustainable overhead, presenting a critical scalability challenge for the future 6G networks characterized by massive channel dimensions, ultra-wide bandwidth, and dense user deployments. As a generalization of radio map, channel knowledge map (CKM) offers a paradigm shift, enabling access to location-tagged channel information without exhaustive measurements. To fully utilize the power of CKM, this work highlights the necessity of leveraging three-dimensional (3D) environmental information, beyond conventional two-dimensional (2D) visual representations, to construct high-precision CKMs. Specifically, we present a novel framework that integrates 3D point clouds into CKM construction through a hybrid model- and data-driven approach, with extensive case studies in real-world scenarios. The experimental results demonstrate the potential for constructing precise CKMs based on 3D environments enhanced with semantic understanding, together with their applications in the next-generation wireless communications. We also release a real-world dataset of measured channel paired with high-resolution 3D environmental data to support future research and validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Precise Channel Knowledge Map: Exploiting Environmental Information from 2D Visuals to 3D Point Clouds Wang, Yancheng Huang, Chuan Zhang, Songyang Chen, Guanying Guo, Wei Lan, Shenglun Xu, Lexi Cheng, Xinzhou Tang, Xiongyan Cui, Shuguang Signal Processing The substantial communication resources consumed by conventional pilot-based channel sounding impose an unsustainable overhead, presenting a critical scalability challenge for the future 6G networks characterized by massive channel dimensions, ultra-wide bandwidth, and dense user deployments. As a generalization of radio map, channel knowledge map (CKM) offers a paradigm shift, enabling access to location-tagged channel information without exhaustive measurements. To fully utilize the power of CKM, this work highlights the necessity of leveraging three-dimensional (3D) environmental information, beyond conventional two-dimensional (2D) visual representations, to construct high-precision CKMs. Specifically, we present a novel framework that integrates 3D point clouds into CKM construction through a hybrid model- and data-driven approach, with extensive case studies in real-world scenarios. The experimental results demonstrate the potential for constructing precise CKMs based on 3D environments enhanced with semantic understanding, together with their applications in the next-generation wireless communications. We also release a real-world dataset of measured channel paired with high-resolution 3D environmental data to support future research and validation. |
| title | Towards Precise Channel Knowledge Map: Exploiting Environmental Information from 2D Visuals to 3D Point Clouds |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2510.08140 |