{"ID":5551665,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T13:37:00.247962456Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00936","arxiv_id":"2607.00936","title":"Lightweight Vision-Aided Beam Tracking for Cross-Environment mmWave Communications","abstract":"Sensing-aided beam tracking is a promising approach to reduce the overhead for millimeter-wave beam management. However, real-world application remains challenging due to rapid channel variations and substantial environmental differences across deployment scenarios. Developing low-complexity sensing assisted approaches that generalize to diverse environments can alleviate the problem. With this motivation, this paper proposes a lightweight vision-aided model for cross-environment beam tracking. The task is formulated as a sequence-to-sequence classification problem, where the model jointly predicts the current and future optimal beams from past visual observations. We develop a low-complexity model based on depthwise separable convolutions and introduce hierarchical data augmentation and beam power-based label smoothing to improve robustness and generalization. Experimental results on real-world images from two geometrically distinct DeepSense 6G scenarios show that the proposed strategies consistently improve cross-environment beam prediction accuracy up to 84% across the current and three future time slots, outperforming the state-of-the-art solution. Notably, this performance is achieved while reducing the number of model parameters and computational complexity by factors of approximately 52 and 79, respectively, compared with the high-capacity ResNet baseline.","short_abstract":"Sensing-aided beam tracking is a promising approach to reduce the overhead for millimeter-wave beam management. However, real-world application remains challenging due to rapid channel variations and substantial environmental differences across deployment scenarios. Developing low-complexity sensing assisted approaches...","url_abs":"https://arxiv.org/abs/2607.00936","url_pdf":"https://arxiv.org/pdf/2607.00936v1","authors":"[\"Mengyuan Ma\",\"Ahmed Alkhateeb\",\"Nhan Thanh Nguyen\",\"A. Lee Swindlehurst\",\"Markku Juntti\"]","published":"2026-07-01T13:40:24Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}