Semantic-driven Wireless Environment Knowledge Representation for Efficiency-Accuracy Balanced Beam Prediction in Vehicular Networks
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915632138682368 |
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| author | Wang, Jialin Zhang, Jianhua Li, Yu Sun, Yutong Zhang, Yuxiang |
| author_facet | Wang, Jialin Zhang, Jianhua Li, Yu Sun, Yutong Zhang, Yuxiang |
| contents | The rapid evolution of the internet of vehicles demands ultra-reliable low-latency communication in high-mobility environments, where conventional beam prediction methods suffer from high-dimensional inputs, prolonged training times, and limited interpretability. To address these challenges, the propagation environment semantics-aware wireless environment knowledge beam prediction (PES-WEKBP) framework is proposed. PES-WEKBP pioneers a novel electromagnetic (EM)-grounded knowledge distillation method, transforming raw visual data into an ultra-lean, interpretable material and location-related wireless environment knowledge matrix. This matrix explicitly encodes critical propagation environment semantics, which is material EM properties and spatial relationships through a physics-informed parameterization process, distilling the environment and channel interplay into a minimal yet information-dense representation. A lightweight decision network then leverages this highly compressed knowledge for low-complexity beam prediction. To holistically evaluate the performance of PES-WEKBP, we first design the prediction consistency-efficiency index (PCEI), which combines prediction accuracy with a stability-penalized logarithmic training time to ensure a balanced optimization of reliability and computational efficiency. Experiments validate that PES-WEKBP achieves a 99.75% to 99.96% dimension reduction and improves accuracy by 5.52% to 8.19%, which outperforms state-of-the-art methods in PCEI scores across diverse vehicular scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-driven Wireless Environment Knowledge Representation for Efficiency-Accuracy Balanced Beam Prediction in Vehicular Networks Wang, Jialin Zhang, Jianhua Li, Yu Sun, Yutong Zhang, Yuxiang Signal Processing Image and Video Processing The rapid evolution of the internet of vehicles demands ultra-reliable low-latency communication in high-mobility environments, where conventional beam prediction methods suffer from high-dimensional inputs, prolonged training times, and limited interpretability. To address these challenges, the propagation environment semantics-aware wireless environment knowledge beam prediction (PES-WEKBP) framework is proposed. PES-WEKBP pioneers a novel electromagnetic (EM)-grounded knowledge distillation method, transforming raw visual data into an ultra-lean, interpretable material and location-related wireless environment knowledge matrix. This matrix explicitly encodes critical propagation environment semantics, which is material EM properties and spatial relationships through a physics-informed parameterization process, distilling the environment and channel interplay into a minimal yet information-dense representation. A lightweight decision network then leverages this highly compressed knowledge for low-complexity beam prediction. To holistically evaluate the performance of PES-WEKBP, we first design the prediction consistency-efficiency index (PCEI), which combines prediction accuracy with a stability-penalized logarithmic training time to ensure a balanced optimization of reliability and computational efficiency. Experiments validate that PES-WEKBP achieves a 99.75% to 99.96% dimension reduction and improves accuracy by 5.52% to 8.19%, which outperforms state-of-the-art methods in PCEI scores across diverse vehicular scenarios. |
| title | Semantic-driven Wireless Environment Knowledge Representation for Efficiency-Accuracy Balanced Beam Prediction in Vehicular Networks |
| topic | Signal Processing Image and Video Processing |
| url | https://arxiv.org/abs/2511.17552 |