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).
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).
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.
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.
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).
Common randomness of channels offers the possibility to create cryptographic keys without the need for a key exchange procedure. Channel reciprocity for TDD (time-division duplexing) systems has been used for this purpose many times. FDD (frequency-division duplexing) systems, however, were long considered to not provide any usable symmetry. However, since the scattering transmission parameters S12and S21would ideally be the same due to reciprocity, when using neighboring frequency ranges for both directions, they would just follow a continuous curve when putting them next to each other. To not rely on absolute phase, we use phase differences between antennas and apply a polynomial curve fitting, thereafter, quantize the midpoint between the two frequency ranges with the two measurement directions. This is shown to work even with some spacing between the two bands. For key reconciliation, we force the measurement point from one direction to be in the midpoint of the quantization interval by a grid shift (or likewise measurement data shift). Since the histogram over the quantization intervals does not follow a uniform distribution, some source coding / hashing will be necessary. The key disagreement rate toward an eavesdropper was found to be close to 0.5. Additionally, when using an antenna array, a random permutation of antenna measurements can even further improve the protection against eavesdropping.