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

Abstract

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.

Intelligent Reflecting Surface-Assisted Physical Layer Key Generation with Deep Learning in MIMO Systems

“…Zhang et al [ 3 ] applied deep learning for PLKG in FDD for the first time, they first proved the existence of the band feature mapping function and proposed a key generation neural network (KGNet), the results turned out to be good. He et al [ 30 ] designed the Channel Reciprocity Learning Net (CRLNet) to learn the channel reciprocity features of the wireless channels, and the CRLNet-based key generation scheme showed good performance. Zhou et al [ 31 ] proposed a PLKG scheme combining the autoencoder and the multi-task learning, and this scheme can extract the reciprocal features from the weakly correlated channel.…”

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

“…According to the acceleration, rotational speed, vibration, and other signals collected during the working process of the rolling bearing, the method of deep learning [21][22][23][24][25][26] is used for fault diagnosis, and the online fault diagnosis system for the rolling bearing is designed. On this basis, mechanical equipment's health management and failure prediction are realized.…”