{"ID":2833916,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.02563","arxiv_id":"2512.02563","title":"Predictive Beamforming in Low-Altitude Wireless Networks: A Cross-Attention Approach","abstract":"Accurate beam prediction is essential for maintaining reliable links and high spectral efficiency in dynamic low-altitude wireless networks. However, existing approaches often fail to capture the deep correlations across heterogeneous sensing modalities, limiting their adaptability in complex three-dimensional environments. To overcome these challenges, we propose a multi-modal predictive beamforming method based on a cross-attention fusion mechanism that jointly leverages visual and structured sensor data. The proposed model utilizes a Convolutional Neural Network (CNN) to learn multi-scale spatial feature hierarchies from visual images and a Transformer encoder to capture cross-dimensional dependencies within sensor data. Then, a cross-attention fusion module is introduced to integrate complementary information between the two modalities, generating a unified and discriminative representation for accurate beam prediction. Through experimental evaluations conducted on a real-world dataset, our method reaches 79.7% Top-1 accuracy and 99.3% Top-3 accuracy, surpassing the 3D ResNet-Transformer baseline by 4.4%-23.2% across Top-1 to Top-5 metrics. These results verify that multi-modal cross-attention fusion is effective for intelligent beam selection in dynamic low-altitude wireless networks.","short_abstract":"Accurate beam prediction is essential for maintaining reliable links and high spectral efficiency in dynamic low-altitude wireless networks. However, existing approaches often fail to capture the deep correlations across heterogeneous sensing modalities, limiting their adaptability in complex three-dimensional environm...","url_abs":"https://arxiv.org/abs/2512.02563","url_pdf":"https://arxiv.org/pdf/2512.02563v1","authors":"[\"Xiaotong Zhao\",\"Yuanhao Cui\",\"Weijie Yuan\",\"Ziye Jia\",\"Heng Liu\",\"Chengwen Xing\"]","published":"2025-12-02T09:30:54Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Transformer\",\"Convolutional Neural Network\"]","has_code":false}