{"ID":2837964,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18376","arxiv_id":"2511.18376","title":"BeamCKM: A Framework of Channel Knowledge Map Construction for Multi-Antenna Systems","abstract":"The channel knowledge map (CKM) enables efficient construction of high-fidelity mapping between spatial environments and channel parameters via electromagnetic information analysis. Nevertheless, existing studies are largely confined to single-antenna systems, failing to offer dedicated guidance for multi-antenna communication scenarios. To address the inherent conflict between traditional real-value pathloss map and multi-degree-of-freedom (DoF) coherent beamforming in B5G/6G systems, this paper proposes a novel concept of BeamCKM and CKMTransUNet architecture. The CKMTransUNet approach combines a UNet backbone for multi-scale feature extraction with a vision transformer (ViT) module to capture global dependencies among encoded linear vectors, utilizing a composite loss function to characterize the beam propagation characteristics. Furthermore, based on the CKMTransUNet backbone, this paper presents a methodology named M3ChanNet. It leverages the multi-modal learning technique and cross-attention mechanisms to extract intrinsic side information from environmental profiles and real-time multi-beam observations, thereby further improving the map construction accuracy. Simulation results demonstrate that the proposed method consistently outperforms state-of-the-art (SOTA) interpolation methods and deep learning (DL) approaches, delivering superior performance even when environmental contours are inaccurate. For reproducibility, the code is publicly accessible at https://github.com/github-whh/BeamCKM.","short_abstract":"The channel knowledge map (CKM) enables efficient construction of high-fidelity mapping between spatial environments and channel parameters via electromagnetic information analysis. Nevertheless, existing studies are largely confined to single-antenna systems, failing to offer dedicated guidance for multi-antenna commu...","url_abs":"https://arxiv.org/abs/2511.18376","url_pdf":"https://arxiv.org/pdf/2511.18376v2","authors":"[\"Haohan Wang\",\"Xu Shi\",\"Hengyu Zhang\",\"Yashuai Cao\",\"Sufang Yang\",\"Jintao Wang\",\"Kaibin Huang\"]","published":"2025-11-23T09:53:03Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Vision Transformer\",\"Transformer\"]","has_code":false,"code_links":[{"ID":606725,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2837964,"paper_url":"https://arxiv.org/abs/2511.18376","paper_title":"BeamCKM: A Framework of Channel Knowledge Map Construction for Multi-Antenna Systems","repo_url":"https://github.com/github-whh/BeamCKM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}