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).
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 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.
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.
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).
This article presents a novel low-complexity deep learning model for physical-layer secret key generation (PSKG), specifically designed to enhance wireless security in the Internet of Things (IoT). PSKG typically generates cryptographic keys by exploiting the reciprocal nature of wireless channels; however, this reciprocity is frequently compromised in time-division duplex (TDD) systems due to hardware imperfections and noise, which significantly complicate the key generation process. To effectively address these challenges, the proposed deep neural network (DNN) is developed to efficiently learn and enhance reciprocity features, even in the presence of imperfect channel state information (CSI). Furthermore, the study introduces a DNN-based PSKG method that strategically leverages the phase of channel frequency responses for key extraction. The results conclusively demonstrate that the proposed model substantially reduces the key disagreement ratio (KDR) and enhances randomness, thus providing a robust and practical solution for securing wireless communications in IoT environments.
In physical-layer secret key generation, key generation performance is susceptible to hardware impairments (HIs), which can degrade channel reciprocity. Additionally, the security of loop-back mechanism based schemes in frequency division duplex (FDD) systems requires further enhancement. To address these challenges, this paper proposes a secure key generation scheme based on a loop-back mechanism for FDD systems. By transmitting signals across different frequency bands in a loop-back fashion, the proposed scheme mitigates the adverse effects of HI variations across frequency bands and enhances system security. Theoretical analyses are conducted on the normalized mean square error and the secret key rate of the loop-back mechanism in both FDD and time division duplex (TDD) systems, providing a clear security assessment of the proposed scheme. Simulation and experimental results demonstrate that by accounting for HI differences in the frequency domain, the proposed scheme improves channel reciprocity, enhances the secret key rate, achieves a higher key generation ratio (KGR), and reduces the key disagreement rate (KDR) compared to state-of-the-art methods.
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.