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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.20659 |
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| _version_ | 1866916969383460864 |
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| author | Xiao, Zhiqiang Cao, Yuwen Bouazizi, Mondher Ohtsuki, Tomoaki Mumtaz, Shahid |
| author_facet | Xiao, Zhiqiang Cao, Yuwen Bouazizi, Mondher Ohtsuki, Tomoaki Mumtaz, Shahid |
| contents | Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target domain and the source domain, simple fine-tuning limits the model's performance in the target domain. To tackle this problem, we propose a transfer learning-based beam prediction method that combines fine-tuning with domain adaptation. We integrate a domain classifier into fine-tuning the pre-trained model. The model extracts domain-invariant features in adversarial training with domain classifier, which can enhance model performance in the target domain. Simulation results demonstrate that the proposed transfer learning-based beam prediction method achieves better achievable rate performance than the pure fine-tuning method in the target domain, and close to those when the training is done from scratch on the target domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20659 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications Xiao, Zhiqiang Cao, Yuwen Bouazizi, Mondher Ohtsuki, Tomoaki Mumtaz, Shahid Information Theory Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target domain and the source domain, simple fine-tuning limits the model's performance in the target domain. To tackle this problem, we propose a transfer learning-based beam prediction method that combines fine-tuning with domain adaptation. We integrate a domain classifier into fine-tuning the pre-trained model. The model extracts domain-invariant features in adversarial training with domain classifier, which can enhance model performance in the target domain. Simulation results demonstrate that the proposed transfer learning-based beam prediction method achieves better achievable rate performance than the pure fine-tuning method in the target domain, and close to those when the training is done from scratch on the target domain. |
| title | A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications |
| topic | Information Theory |
| url | https://arxiv.org/abs/2509.20659 |