Channel Knowledge Map Construction: Recent Advances and Open Challenges
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912693138489344 |
|---|---|
| author | Ren, Zixiang Zhou, Juncong Xu, Jie Qiu, Ling Zeng, Yong Hu, Han Zhang, Juyong Zhang, Rui |
| author_facet | Ren, Zixiang Zhou, Juncong Xu, Jie Qiu, Ling Zeng, Yong Hu, Han Zhang, Juyong Zhang, Rui |
| contents | Channel knowledge map (CKM) has emerged as a pivotal technology for environment-aware wireless communications and sensing, which provides a priori location-specific channel knowledge to facilitate network optimization. Efficient CKM construction is an important technical problem for its effective implementation. This article provides a comprehensive overview of recent advances in CKM construction. First, we examine classical interpolation-based CKM construction methods, highlighting their limitations in practical deployments. Next, we explore image processing and generative artificial intelligence (AI) techniques, which leverage feature extraction to construct CKMs based on environmental knowledge. Furthermore, we present emerging wireless radiance field (WRF) frameworks that exploit neural radiance fields or Gaussian splatting to construct high-fidelity CKMs from sparse measurement data. Finally, we outline various future research directions in real-time and cross-domain CKM construction, as well as cost-efficient deployment of CKMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04944 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel Knowledge Map Construction: Recent Advances and Open Challenges Ren, Zixiang Zhou, Juncong Xu, Jie Qiu, Ling Zeng, Yong Hu, Han Zhang, Juyong Zhang, Rui Signal Processing Channel knowledge map (CKM) has emerged as a pivotal technology for environment-aware wireless communications and sensing, which provides a priori location-specific channel knowledge to facilitate network optimization. Efficient CKM construction is an important technical problem for its effective implementation. This article provides a comprehensive overview of recent advances in CKM construction. First, we examine classical interpolation-based CKM construction methods, highlighting their limitations in practical deployments. Next, we explore image processing and generative artificial intelligence (AI) techniques, which leverage feature extraction to construct CKMs based on environmental knowledge. Furthermore, we present emerging wireless radiance field (WRF) frameworks that exploit neural radiance fields or Gaussian splatting to construct high-fidelity CKMs from sparse measurement data. Finally, we outline various future research directions in real-time and cross-domain CKM construction, as well as cost-efficient deployment of CKMs. |
| title | Channel Knowledge Map Construction: Recent Advances and Open Challenges |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.04944 |