Title: A Deep Transfer Learning-Based Low-overhead Beam Prediction in Vehicle Communications Show Chinese title

Title: 基于深度迁移学习的车辆通信中低开销波束预测

Abstract: 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. Show Chinese abstract

Abstract: 基于迁移学习的波束预测方法主要依赖于简单的微调。 当目标域和源域的数据分布存在显著差异时，简单的微调会限制模型在目标域中的性能。 为了解决这个问题，我们提出了一种结合微调与领域自适应的基于迁移学习的波束预测方法。 我们在微调预训练模型时集成了一个领域分类器。 模型通过领域分类器进行对抗训练以提取领域不变特征，这可以增强模型在目标域中的性能。 仿真结果表明，所提出的基于迁移学习的波束预测方法在目标域中比纯微调方法实现了更好的可实现速率性能，并且接近于在目标域从头开始训练的性能。