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IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Secret Key Generation Scheme Based on Generative Adversarial Networks in FDD Systems

Zongyue Hou, Xinwei Zhang

Published May 10, 2021• 9 citations
Publisher

Abstract

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.

DOI
10.1109/infocomwkshps51825.2021.9484457

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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.
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.
Physical Layer Secret Key Generation (PLSKG), which utilizes wireless channel reciprocity, is increasingly adopted to secure wireless communications. In Time Division Duplexing (TDD) systems, the inherent channel reciprocity facilitates the generation of cryptographic keys without requiring a key exchange process. Conversely, Frequency Division Duplexing (FDD) systems present greater challenges for key generation due to the use of distinct frequency bands for uplink and downlink, resulting in differing frequency responses. In this paper, we address this challenge by modeling the channel along with connecting cables as a two-port network and utilizing the reciprocity principles applicable to such networks within the same frequency range. For key generation, we construct the channel profile for each FDD band by utilizing the amplitude of the bidirectional scattering parameters S12and S21in two closely spaced FDD bands, thereby ensuring the required reciprocity in FDD systems. To assess the practicality of the proposed scheme, we analyze its performance across various indoor scenarios and compare it with other key generation schemes using multiple performance indicators. The comparative results confirm the feasibility and effectiveness of our scheme in terms of randomness and key disagreement ratio.
Secret key generation based on wireless channel reciprocity has received widespread attention. However, in frequency division duplexing (FDD) systems, since the carrier frequencies of the uplink and downlink are different and the channel coefficients are no longer reciprocal, key generation for FDD systems is challenging. In this paper, a Complex-Valued neural Network (CVNet) is proposed to predict the downlink channel and generate reciprocal channel characteristics. Then, based on the trained CVNet, we propose a key generation protocol for FDD systems. Numerical results show that the CVNet achieves better performance in terms of prediction accuracy, bit disagreement rate, and bit generation ratio than a traditional Real-Valued Network (RVNet) under high signal-to-noise ratios. Furthermore, the training parameters required by the CVNet account for only half of that required by the RVNet.
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.
Secret key generation based on wireless channel reciprocity has received quite some attention, recently. Channel reciprocity as an inherent feature of time division duplexing (TDD) systems leads to creating cryptographic keys without the need for a key exchange procedure. Using different carrier frequencies for the uplink and the downlink, however, means that there is no such symmetry in the frequency response of frequency division duplex (FDD) systems and it makes the key generation for these systems challenging. In this paper, we use the phase differences between neighboring antennas as a frequency-independent channel parameter that provides the required reciprocity in FDD systems. For our studies, we consider neighboring frequency ranges for both directions, which should result in a continuous curve through the two frequency ranges in the two directions. For denoising we use a polynomial curve fitting procedure on the phase differences between antennas. Moreover, we propose a non-linear scheme to quantize the midpoint between the two frequency ranges with the two measurement directions. As a consequence, in contrast to using a linear scheme, the proposed framework would be highly desirable for secret key applications as it gives rise to a more uniform distribution for the resulting histogram over the quantization intervals. Numerical simulation results are shown to verify the effectiveness of the proposed method.