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Ad Hoc Networks

Lightweight and Fast Physical-Layer Key Generation in FDD Systems using Random Forest

Zhi Chai, Xinyong Peng, Xuetong Chen, Liuming Zhang, Xinran Huang, Xuelin Yang

Published Jul 29, 2023• 2 citations
Publisher
DOI
10.1016/j.adhoc.2023.103261

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While Physical Layer Key Generation (PKG) offers lightweight security for 6G IoT, channel non-reciprocity in FDD massive MIMO degrades key consistency. This paper presents a comprehensive review of Physical Layer Key Generation (PKG) techniques within Frequency Division Duplex (FDD) massive MIMO systems. While PKG leverages the inherent randomness of wireless channels to establish lightweight security for communication nodes, FDD systems pose a significant challenge. The separated uplink and downlink bands produce non-reciprocal fast-fading channel responses, which inherently degrade the consistency of keys generated from instantaneous channel state information (CSI). To address this, this study categorizes existing methodologies into three primary routes: stable propagation feature extraction, intelligent feature mapping, and system engineering optimization. It examines their mechanisms across the angle, beam, and statistical covariance domains, comparing representative schemes in terms of Key Disagreement Rate (KDR), overhead, and computational complexity. Ultimately, future research must integrate physical priors with data-driven models to ensure robust security in practical 6G networks.
Physical-layer key generation (PKG) establishes cryptographic keys from highly correlated measurements of wireless channels, which relies on reciprocal channel characteristics between uplink and downlink, is a promising wireless security technique for Internet of Things (IoT). However, it is challenging to extract common features in frequency-division duplexing (FDD) systems as uplink and downlink transmissions operate at different frequency bands whose channel frequency responses are not reciprocal anymore. Existing PKG methods for FDD systems have many limitations, i.e., high overhead and security problems. This article proposes a novel PKG scheme that uses the feature mapping function between different frequency bands obtained by deep learning to make two users generate highly similar channel features in FDD systems. In particular, this is the first time to apply deep learning for PKG in FDD systems. We first prove the existence of the band feature mapping function for a given environment and a feedforward network with a single hidden layer can approximate the mapping function. Then, a key generation neural network (KGNet) is proposed for reciprocal channel feature construction, and a key generation scheme based on the KGNet is also proposed. Numerical results verify the excellent performance of the KGNet-based key generation scheme in terms of randomness, key generation ratio, and key error rate. Besides, the overhead analysis shows that the method proposed in this article can be used for resource-constrained IoT devices in FDD systems.
The utilization of physical layer Secret Key Generation (SKG) is increasingly prevalent in securing wireless communication within Internet of Things systems, particularly in Narrow-band IoT. While most key-generation schemes are tailored for Time Division Duplex (TDD) systems, generating secret keys in Frequency Division Duplex (FDD) systems presents challenges due to the distinct frequency bands for uplink and downlink. In response to this, we propose an efficient FDD-based key generation technique that capitalizes on the reciprocity of scattering matrix parametersS12andS21within the same frequency range. Specifically, we harness the capabilities of MUSIC algorithm during the direction of arrival estimation phase. Numerical results demonstrate promising outcomes in addressing key challenges, including randomness, and Key Disagreement Ratio (KDR).