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Wireless Personal Communications

A Highly Consistent and High-speed Physical Layer Key Generation Scheme

Jiayu Zhao, Jingmei Liu, Ruibin Dai

Published Nov 1, 2023• 1 citation
Publisher
DOI
10.1007/s11277-023-10806-5

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Physical layer secure key distribution (PLSKD) in fiber networks provides high-level security originating from the channel uniqueness, while the channel reciprocity is shared between the legal users via bidirectional transmission. To ensure the key consistency, perfect wideband bidirectional optical devices are required for high-speed PLSKD. Here, we propose and demonstrate a unidirectional PLSKD scheme, where the channel reciprocity is reproduced by neural networks (NNs) so that highly correlated key waveforms are generated and shared between the legal users. Experimental results show that an error-free key generation rate (KGR) of 3.8 Gb/s with excellent randomness is achieved for an 11-km standard single-mode fiber (SSMF) channel. The proposed scheme alleviates the stringent requirement of bidirectional channel transmission in high-speed PLSKD and enables the integration of PLSKD and data transmission in the same fiber channel.
The fifth-generation mobile communication technology (5G) supports wireless data transmission across civil, commercial, industrial, and even military networks, where vast amounts of privacy data are constantly transmitted. However, the open nature of the air interface in 5G systems makes them vulnerable to various attacks. Physical-layer key generation (PKG) has been recognized as a highly promising technology for ensuring data security in 5G systems, while existing PKG schemes achieve low key entropy (i.e., low key randomness) due to poor channel probing and quantization. In this paper, we propose a PKG scheme with high key entropy, tailored to the unique characteristics of 5G systems. First, we design a channel probing method based on the demodulation reference signal in accordance with 5G standards, enhancing the similarity between channel measurements. Next, we introduce a quantization method based on local increment and monotonicity, which effectively leverages channel characteristics to achieve high-speed key generation and improve key entropy. Finally, we use both MATLAB simulation and real-world channel measurements to achieve comprehensive verification of the proposed scheme. The simulation results showed that the proposed scheme increases the key entropy by at least 25% with nearly the same key generation rate compared with existing PKG schemes for 5G systems. The experiment using real-world channel measurements also confirmed that the proposed scheme has higher key entropy.
Using the physical layer channel information of wireless devices to establish the highly consistent secret keys is a promising technology for improving the security of wireless networks. Nevertheless, in the time division duplex system, the reciprocity of the wireless channel that is the basic principle of key generation is impaired by nonsimultaneous sampling and noise factors. Existing physical layer key generation approaches rely on hand-crafted feature extraction algorithms, which have high overhead or security issues and are impractical in real-world situations. This paper presents a novel physical layer key generation method to extract highly consistent keys from imperfect channel responses, which exploits channel reciprocity through deep learning. Specifically, we first design the Channel Reciprocity Learning Net (CRLNet), a neural network for efficiently learning channel reciprocity features from the wireless channel in TDD OFDM systems. Later, a new key generation scheme based on CRLNet is developed that can achieve a high key agreement rate. Experiments indicate that the CRLNet-based key generation scheme performs excellently in terms of key generation rate, key error rate, and randomness, confirming that our method has better performance and lower overhead than existing methods.