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IEEE Communications Letters

Secret Key Generation Scheme Based on the Channel Covariance Matrix Eigenvalues in FDD Systems

Boqian Liu, Aiqun Hu, Guyue Li

Published Jun 25, 2019• 23 citations
Publisher

Abstract

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.

DOI
10.1109/lcomm.2019.2924420

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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.
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.
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).
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.
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.
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. Since the reciprocity holds for the same frequency ranges, if we consider the FDD bands very close to each other, we can anticipate continuity between uplink and downlink bands which can guarantee the required reciprocity. In this paper, we use phase differences between neighboring antennas in an antenna array to construct reciprocal channel features. Moreover, we increase the reciprocity by applying polynomial curve-fitting on the measurements so that two users can generate highly similar keys in FDD systems. Exploiting an effective pre-processing procedure, our proposed scheme achieves competitive performance in terms of efficiency and key disagreement rate (KDR). In addition, we present a detailed statistical analysis to determine the error probability for generated keys from both sides. Numerical simulation results are presented to verify the feasibility and effectiveness of the proposed scheme.
Physical-layer secret key generation (PSKG) stands as a promising privacy protection technique, establishing shared encryption keys through the analysis of highly correlated wireless channel measurements. This approach relies on exploiting reciprocal channel characteristics between uplink and downlink transmissions. Nonetheless, the distinct carrier frequencies employed for uplink and downlink in frequency-division duplexing (FDD) systems pose a challenge in identifying common features. This paper presents a novel approach that exploits the inherent reciprocity between scattering parameters of passive two-port networks within same frequency ranges to overcome this obstacle. By capitalizing this reciprocity and considering closely situated FDD bands, a seamless continuity is anticipated in phase differences extracted form the corresponding S-parameters, between neighboring antennas of an antenna array from both uplink and downlink directions. This continuity, thereby ensures consistency in the generated keys from both transmission ends. Furthermore, a two-stage pre-processing method is proposed to enhance performance effectively. Additionally, the paper suggests the utilization of polynomial curve-fitting through measurement data to improve reciprocity and proposes a non-linear framework for quantizing the merging points of the two FDD bands. A statistical analysis employing multiple linear regression is provided to determine the error probability associated with the generated keys. Empirical results validate the feasibility and effectiveness of the proposed key generation scheme, affirming its attributes in terms of randomness, efficiency, key distribution uniformity, and key disagreement ratio (KDR).