Physical Layer Secret Key Generation Based on Autoencoder for Weakly Correlated Channels

Han, Jingyuan; Zeng, Xin; Xue, Xiaoping; Ma, Jingxiao

doi:10.1109/iccc49849.2020.9238931

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…(4) KGNet [28]: a key generation neural network (KGNet) is proposed to generate reciprocal channel features in FDD communication systems based on band feature maps. ( 5) AE [30]: the inverse features are extracted from the weakly correlated channel estimates with a trained autoencoder to generate features for quantization in various channel environments.…”

Section: Crcnet Performance Evaluation Te Function Ofmentioning

confidence: 99%

“…Letafati et al [29] utilized RNNs to compensate for discrepancies in observations from both sides caused by injected signals from manin-the-middle attacks and faws in legitimate transceivers. Han et al [30] model communication as an end-to-end autoencoder to improve channel reciprocity and perform better than the original scheme. Letafati et al [31] utilize echo-state networks to compensate for the observation mismatch between legitimate communicating parties caused by unbalanced hardware impairments.…”

Section: Introductionmentioning

confidence: 99%

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A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

Chen

et al. 2023

Security and Communication Networks

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Channel reciprocity is the foundation for physical layer key generation, which is influenced by noise, hardware impairments, and synchronization offsets. Weak channel reciprocity will result in a high key disagreement rate (KDR). The existing solutions for improving channel reciprocity cannot achieve satisfactory performance improvements. Furthermore, the existing quantization algorithms generally use one-dimensional channel features to quantize and generate secret keys, which cannot fully utilize channel information. The multidimensional vector quantization technique also needs to improve in terms of randomness and time complexity. This paper proposes a physical layer key generation scheme based on deep learning and balanced vector quantization. Specifically, we build a channel reciprocity compensation network (CRCNet) to learn the mapping relationship between Alice and Bob’s channel measurements. Alice compensates for channel measurements via a trained CRCNet to reduce channel measurement errors between legitimate users and enhance channel reciprocity. We also propose a balanced vector quantization algorithm based on integer linear programming (ILP-BVQ). ILP-BVQ reduces the time complexity of quantization on the basis of ensuring key randomness and a low KDR. Simulation results showed that the proposed CRCNet performs better in terms of channel reciprocity and KDR, while the proposed ILP-BVQ algorithm improves time consumption and key randomness.

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Section: Crcnet Performance Evaluation Te Function Ofmentioning

confidence: 99%