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Deep learning-based physical-layer secret key generation (PKG) has been usedto overcome the imperfect uplink/downlink channel reciprocity in frequencydivision duplexing (FDD) orthogonal frequency division multiplexing (OFDM)systems. However, existing efforts have focused on key generation for users ina specific environment where the training samples and test samples obey thesame distribution, which is unrealistic for real world applications. This paperformulates the PKG problem in multiple environments as a learning-based problemby learning the knowledge such as data and models from known environments togenerate keys quickly and efficiently in multiple new environments.Specifically, we propose deep transfer learning (DTL) and meta-learning-basedchannel feature mapping algorithms for key generation. The two algorithms usedifferent training methods to pre-train the model in the known environments,and then quickly adapt and deploy the model to new environments. Simulationresults show that compared with the methods without adaptation, the DTL andmeta-learning algorithms both can improve the performance of generated keys. Inaddition, the complexity analysis shows that the meta-learning algorithm canachieve better performance than the DTL algorithm with less time, lower CPU andGPU resources.

Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments