Deep Learning-Based Channel Reciprocity Learning for Physical Layer Secret Key Generation

• Published in: Security and communication networks Vol. 2022; pp. 1 - 11
• Main Authors: He, Haoyu, Chen, Yanru, Huang, Xinmao, Xing, Minghai, Li, Yang, Xing, Bin, Chen, Liangyin
• Format: Journal Article
• Language: English
• Published: London Hindawi 19-03-2022; Hindawi Limited
• Subjects: Algorithms; Deep learning; Design; Feature extraction; Machine learning; Methods; Neural networks; Probability distribution; Reciprocity; Reconciliation; Time division; Unmanned aerial vehicles; Wavelet transforms; Wireless communications; Wireless networks
• ISSN: 1939-0114; 1939-0122
• DOI: 10.1155/2022/1844345

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.