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Electronics Letters

Channel covariance matrix based secret key generation for low‐power terminals in frequency division duplex systems

Zheng Wan, Kaizhi Huang, Yangming Lou, Yajun Chen

Published Feb 24, 2021• 3 citations
Publisher

Abstract

Abstract The existing secret key generation (SKG) techniques are not applicable for frequency division duplex (FDD) Internet of Things networks due to the low power constraints and limited computing resources. This letter proposes a SKG scheme based on the channel covariance matrix in FDD systems. First of all, a novel algorithm is proposed for inferring the downlink channel covariance matrix from the observed uplink channel covariance matrix with the aid of the transformation matrix. Then, a complete SKG scheme for low‐power terminals in FDD systems is designed to generate keys from the downlink channel covariance matrix. Finally, simulation results show that this scheme can generate high‐agreement secret keys in FDD systems. Compared with the loopback mechanism‐based SKG scheme, this scheme is suitable for low‐power terminals and can achieve significant performance improvement in terms of the key disagreement ratio.

DOI
10.1049/ell2.12123

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Abstract The existing secret key generation (SKG) techniques are not applicable for frequency division duplex (FDD) Internet of Things networks due to the low power constraints and limited computing resources. This letter proposes a SKG scheme based on the channel covariance matrix in FDD systems. First of all, a novel algorithm is proposed for inferring the downlink channel covariance matrix from the observed uplink channel covariance matrix with the aid of the transformation matrix. Then, a complete SKG scheme for low‐power terminals in FDD systems is designed to generate keys from the downlink channel covariance matrix. Finally, simulation results show that this scheme can generate high‐agreement secret keys in FDD systems. Compared with the loopback mechanism‐based SKG scheme, this scheme is suitable for low‐power terminals and can achieve significant performance improvement in terms of the key disagreement ratio.
In this letter, an efficient secret key generation (SKG) scheme exploiting the eigenvalue reciprocity of uplink (UL) and downlink (DL) channel covariance matrices in frequency division duplex (FDD) systems is developed. Our proposed method achieves a better performance in terms of SKG rate and secret key disagreement (SKD) ratio compared with the loopback method in the previous literature, meanwhile free from the security issue of loopback strategies. Numerical simulation results are presented to verify the feasibility and effectiveness of the proposed scheme.
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).
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.
Channel reciprocity is an inherent feature of time division duplex (TDD) system while in frequency division duplex (FDD) system it is limited due to different frequencies being used for the uplink and the downlink. As a consequence, channel reciprocity-based secret generating methods used in TDD systems cannot be applied directly to FDD systems. In this paper, we present a novel secret key generation method for FDD system, for which the communication pair estimate the channel state information (CSI) of the same time in the uplink by a specially designed forwarding strategy and generate the secret key from the CSI estimates. Numerical simulations are implemented to verify the effectiveness of the proposed method.
Summary Most recent research on channel‐based key generation oriented to time division duplex system because the channel reciprocity feature is applied directly for secret key generation. Most of commercial cellular systems depend on frequency division duplex (FDD) systems. In this paper, we investigate the impact of uplink and downlink of FDD systems for the generation of shared secret keys between two parties in the presents of passive eavesdropper. In addition, we are considering improving the rate of the secret key for wireless communication in FDD mode. The main idea is to use the fading coefficient of the channels between the relay and other parties as an additional random common source for the secret key generation. Also, explore the using of channel estimation techniques to reduce the channel training sequence and study its effect on the generation of shared key for wireless communications in FDD mode. We derive the upper bound of the generated shared key rate for four scenarios and give numerical examples to reveal the performance of our suggested improvement approaches.
In frequency division duplexing (FDD) systems, the uplink and downlink transmit information in different frequency bands, so it is difficult to use channel reciprocity to generate secret keys. Existing key generation methods for FDD systems have excessive overhead and security problems. This paper uses deep learning to predict the downlink channel state information (CSI) from the uplink CSI, so that two users can generate highly similar downlink CSI in FDD systems. We propose a key generation scheme based on boundary equilibrium generative adversarial network (BEGAN), including channel estimation, reciprocal channel feature construction, quantization, information reconciliation and privacy amplification. Numerical simulation results are presented to verify the feasibility and effectiveness of the proposed scheme.