A Physical Layer Security Communication Mechanism Based on End-to-End Learning

Due to the randomness of wireless channels, it is possible to achieve secure communications by designing communication systems according to the characteristics of wireless channels. In this work, a physical layer security communication mechanism based on end-to-end learning is introduced. The proposed mechanism achieves secure communications by designing the loss function of end-to-end learning. We evaluate secrecy performances of the proposed mechanism through block error rate (BLER) under three classic circumstances which different decoding models are applied by the eavesdropper. Self-training decoding model, the legitimate decoding model and unsupervised learning (K-means clustering) model are alternative for the eavesdropper. The experiments results show that K-means clustering model can get the best BLER performance for the eavesdropper with some prior information, but its BLER is still higher than 0.5. Even if the eavesdropper has a much higher than the legitimate users, there is no improvement in its BLER traditional physical layer security E
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. Compared to methods, the proposed mechanism can achieve secure communications without extra key extraction.