We introduce a lightweight, multi-stage framework called Correlation-Enhanced CSI Sampling (CESS) to address this challenge. CESS combines full-slot channel interpolation, inter-slot CSI processing to close the temporal measurement gap, and intra-slot subcarrier processing to mitigate frequency-domain correlation issues. Through simulations, we compare CESS to a conventional PLKG baseline, showing that CESS substantially lowers the Key Disagreement Rate (KDR) and boosts the effective Key Generation Rate (KGR), with a minimum KGR of 3.5 kbps. These results underscore CESS’s potential for secure and reliable PLKG in real-world 5G TDD systems.
We introduce a lightweight, multi-stage framework called Correlation-Enhanced CSI Sampling (CESS) to address this challenge. CESS combines full-slot channel interpolation, inter-slot CSI processing to close the temporal measurement gap, and intra-slot subcarrier processing to mitigate frequency-domain correlation issues. Through simulations, we compare CESS to a conventional PLKG baseline, showing that CESS substantially lowers the Key Disagreement Rate (KDR) and boosts the effective Key Generation Rate (KGR), with a minimum KGR of 3.5 kbps. These results underscore CESS’s potential for secure and reliable PLKG in real-world 5G TDD systems.
Channel State Information (CSI) is widely used as a randomness source for Physical Layer Key Generation (PLKG). However, existing CSI-based PLKG schemes are often limited by insufficient CSI decorrelation, increasing eavesdropping risk and overestimating key generation rates. To address this issue, this letter reveals the intrinsic causes of CSI correlation and proposes an effective CSI decorrelation method consisting of inter-frame logarithmic differencing, Inverse Fast Fourier Transform (IFFT), and dominant peak extraction. Compared with a conventional approach, the proposed method reduces the normalized mutual information between an eavesdropper and a legitimate user by 5.29% on average, while improving the secrecy advantage.
Physical layer encryption is a promising solution to provide transmission security for Internet-of-Things (IoT). To realize physical layer encryption, channel state information (CSI) as a random source to generate secret keys should be exploited. The reciprocity of wireless channel between legitimate communication nodes which is typically assumed for time division duplex (TDD) systems, is of critical importance to ensure the consistency in generated keys at two nodes. However, in practical TDD systems, due to the asynchronous measurement of CSI, the receiver noise and the the difference in hardware, etc., the reciprocity of channel measurements between legitimate parties is hard to be guaranteed, which makes physical layer key generation approach infeasible. To address this issue, we propose a wireless key generation scheme based on deep learning techniques. Firstly, a feature extraction network, which consists of two auto-encoders trained jointly, is developed to extract the common feature from the different yet correlated channel observations at two legitimate nodes, which will be used as the source to generate keys. Then a quantizer with an adaptive guard band width is devised to output a binary secret-key sequence from the feature vector. Numerical results show that the proposed scheme achieves remarkable improvement in terms of key disagreement rate (KDR) and key generation rate (KGR) compared existing counterparts in the literature.
The rapid advancement and deployment of 5G NR technology in diverse applications have introduced new challenges to network security. While 5G NR’s architecture allows flexible customization of subcarrier spacing and frame structures, enabling devices to optimize configurations based on mobility, service characteristics, and coverage, research has largely neglected the potential of fine-tuning system parameters to enhance key generation efficiency and security. This paper addresses this gap by analyzing time-frequency domain correlation and key generation rate as key metrics. We explore the impact of parameter selection on the key generation process in 5G NR systems. Using the Pearson correlation coefficient of channel estimates in the time-frequency domain, we propose a parameter selection strategy to improve physical layer security, validated through simulations. Results show that in fast-fading environments, smaller subcarrier spacing yields better results, while in frequency-selective fading channels, larger subcarrier spacing significantly enhances key generation performance.
Physical-layer secret key generation (PSKG) is a well-known and effective method for boosting wireless security in the Internet of Things (IoT). This technique creates cryptographic keys from correlated wireless channel measurements by taking advantage of the reciprocal nature of uplink and downlink channels. However, in time-division duplex (TDD) systems, issues such as hardware imperfection and noise can disrupt channel reciprocity, making the key generation process more difficult. This paper presents a new PSKG approach that tackles these issues by using deep learning to enhance channel reciprocity, even with imperfect channel state information (CSI). Specifically, a denoising sparse autoencoder (DSAE) is introduced to efficiently learn and extract channel reciprocity features in TDD OFDM systems. Furthermore, this study presents a low-complexity DSAE-based PSKG scheme that utilizes the phase of channel frequency responses (CFR) for key generation. The reported results indicate that the proposed DSAE-based PSKG method notably decreases the key disagreement ratio (KDR) while improving randomness, offering a robust and practical solution for securing wireless communications in IoT environments.
Physical layer key generation (PLKG) offers a lightweight and low-complexity key management solution for future emerging wireless communication networks. However, current PLKG schemes face challenges in effective integration with practical communication systems, hindering their practical deployment. To solve this problem, this paper proposes a key generation scheme integrated with the 5 G physical layer protocol, leveraging the Demodulation Reference Signal (DM-RS) to extract channel estimation for PLKG, enabling efficient key generation. Furthermore, a soft-decision quantization method based on Multibit Adaptive Quantization is proposed which makes key reconciliation input compatible with the soft-decision input requirements of the Low-Density Parity-Check (LDPC) decoder in 5G channel coding module. Simulation results demonstrate that the proposed scheme successfully integrates into the 5G standard communication framework while still maintaining favorable key disagreement rate (KDR) performance, the key reconciliation using keys derived by soft-decision quantization method can achieve better key agreement performance than using keys derived by binary quantization.
Wireless channel key generation technology is an important mechanism to guarantee the security of wireless network, but influenced by the key length and the actual electromagnetic environment, wireless channel key generation technology is faced with the challenge of high-key generation rate (KGR) and low-key disagreement rate (KDR). The existing key generation methods also lack the full use of the channel state information (CSI). We propose a key generation method based on multisubcarrier phase difference to expand the randomness source dimension, eliminate the phase bias, offset part of the noise influence, and set the threshold screening data to reduce the influence of measurement error. We further propose a key generation method based on resampling of kernel density estimation (KDE), which yields highly reciprocal randomness sources by resampling the results of KDE of phase difference values. To fill the metric gap of whether a method keeps low KDR while increasing the KGR, the evaluation metric of effective improvement ratio (EIR) is proposed. The two methods we proposed have a higher EIR than the method of using multiple-input and multiple-output (MIMO) and increasing the quantization level, achieving the goal of increasing the KGR while maintaining the low KDR. The KGR can reach about 12146 bits/s, and the KDR is 1.83%. The keys obtained by both methods can effectively prevent passive eavesdropping and meet the randomness requirements.