Physical layer-based key generation encounters the reciprocity bottleneck when applied in frequency-division duplexing (FDD) systems. This letter constructs the reciprocal channel characteristics of the uplink and downlink transmissions for key generation in FDD systems. The frequency dependence of propagation characteristics in both large-scale fading and small-scale fading is investigated. A general channel model is then established by considering frequency impact. A reciprocal channel construction framework is finally created. Numerical results demonstrate that our algorithm can construct bidirectional channel coefficients with high correlation and thus enable key generation technology to be used in FDD systems.
Physical layer-based key generation encounters the reciprocity bottleneck when applied in frequency-division duplexing (FDD) systems. This letter constructs the reciprocal channel characteristics of the uplink and downlink transmissions for key generation in FDD systems. The frequency dependence of propagation characteristics in both large-scale fading and small-scale fading is investigated. A general channel model is then established by considering frequency impact. A reciprocal channel construction framework is finally created. Numerical results demonstrate that our algorithm can construct bidirectional channel coefficients with high correlation and thus enable key generation technology to be used in FDD systems.
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.
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 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 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.
Most of current research on channel-based key generation focused on a time division duplex (TDD) system as the channel reciprocity factor applied directly to the generation of a secret key.However, most of commercial cellular systems rely on a frequency division duplex (FDD) mode.In this paper, we study the utilization of the uplink and downlink of FDD systems for the generation of common secret key between two users in the presents of inactive eavesdropper.The clue of our work is to explore the fading gain of the wireless channel between two users to generate a symmetric key.We develop the upper bound of the generation rate for shared key of the suggested mechanism and give numerical examples to show the performance of our suggested approach.
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.