OctCGS: Octree-Contextual Gaussian Splatting with Explicit Multi-Order Propagation Modeling for Channel Knowledge Map Construction
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913154175336448 |
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| author | Zhang, Jinghan Gong, Xitao Wang, Qi Stirling-Gallacher, Richard A. Caire, Giuseppe |
| author_facet | Zhang, Jinghan Gong, Xitao Wang, Qi Stirling-Gallacher, Richard A. Caire, Giuseppe |
| contents | Channel knowledge maps (CKMs) learn the relation between transmitter (Tx) and receiver (Rx) positions and channel knowledge to support environment-aware wireless communications. Implicit neural methods can model continuous channel variation but often incur high training and inference cost, while existing Gaussian-splatting-based CKM methods improve efficiency yet still compress wireless multipath interactions into aggregated scattering representations. Consequently, explicit modeling of multi-bounce wireless propagation remains absent from CKM construction. We propose OctCGS, an octree-contextual Gaussian splatting framework that explicitly models the order of bounce jointly over Tx/Rx positions and carrier frequencies. OctCGS partitions the environment into a multi-resolution octree and anchors one Gaussian primitive to each leaf node. Rather than having each Gaussian independently encode all multi-path propagations, it models complex electromagnetic interactions among scatterers through tree attention over the octree hierarchy with controlled complexity. Experiments on simulated benchmarks show that OctCGS achieves a 2.99 dB channel-gain mean absolute error (MAE) and 0.065 channel gain normalized mean absolute error (NMAE), outperforming the strongest baseline by 0.88 dB MAE and 0.021 NMAE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22961 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OctCGS: Octree-Contextual Gaussian Splatting with Explicit Multi-Order Propagation Modeling for Channel Knowledge Map Construction Zhang, Jinghan Gong, Xitao Wang, Qi Stirling-Gallacher, Richard A. Caire, Giuseppe Signal Processing Channel knowledge maps (CKMs) learn the relation between transmitter (Tx) and receiver (Rx) positions and channel knowledge to support environment-aware wireless communications. Implicit neural methods can model continuous channel variation but often incur high training and inference cost, while existing Gaussian-splatting-based CKM methods improve efficiency yet still compress wireless multipath interactions into aggregated scattering representations. Consequently, explicit modeling of multi-bounce wireless propagation remains absent from CKM construction. We propose OctCGS, an octree-contextual Gaussian splatting framework that explicitly models the order of bounce jointly over Tx/Rx positions and carrier frequencies. OctCGS partitions the environment into a multi-resolution octree and anchors one Gaussian primitive to each leaf node. Rather than having each Gaussian independently encode all multi-path propagations, it models complex electromagnetic interactions among scatterers through tree attention over the octree hierarchy with controlled complexity. Experiments on simulated benchmarks show that OctCGS achieves a 2.99 dB channel-gain mean absolute error (MAE) and 0.065 channel gain normalized mean absolute error (NMAE), outperforming the strongest baseline by 0.88 dB MAE and 0.021 NMAE. |
| title | OctCGS: Octree-Contextual Gaussian Splatting with Explicit Multi-Order Propagation Modeling for Channel Knowledge Map Construction |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.22961 |