支持多环境下基于深度学习的FDD-OFDM系统物理层密钥生成
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments
AI总结：
本文针对FDD-OFDM系统在多环境下的物理层密钥生成问题，提出深度迁移学习和元学习的信道特征映射算法，通过预训练与快速适应机制提升新环境下密钥生成性能并降低成本。
摘要：
基于深度学习的物理层密钥生成（PKG）已被用于克服频分双工（FDD）正交频分复用（OFDM）系统中不完美的上下行信道互易性。然而，现有的研究主要集中在特定环境下为用户生成密钥，其中训练样本和测试样本遵循相同的分布，这对于实际应用是不现实的。本文将多环境下的PKG问题表述为一个基于学习的问题，通过从已知环境中学习数据和模型等知识，在多个新环境中快速高效地生成密钥。具体而言，我们提出了深度迁移学习（DTL）和基于元学习的信道特征映射算法用于密钥生成。这两种算法使用不同的训练方法在已知环境中预训练模型，然后快速适应并将模型部署到新环境中。仿真和实验结果表明，与无适应的方法相比，DTL和元学习算法均能提高生成密钥的性能。此外，复杂度分析表明，元学习算法能以更低的成本实现比DTL算法更好的性能。
英文摘要：
Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples follow the same distribution, which is unrealistic for real-world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation and experimental results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
