In TDD-OFDM systems, key generation relies on channel reciprocity, which assumes that the channel characteristics of the transmit and receive channels are identical. However, practical conditions such as hardware asymmetry, environmental noise, and multipath effects often disrupt this reciprocity, negatively impacting the accuracy and consistency of key generation. To address these issues, this paper proposes a novel key generation scheme based on Self-Organizing Maps (SOM) and K-Means quantization algorithm for TDD-OFDM system under unstable channel. The proposed approach uses Channel Reciprocity Learning Network (CRLN) for feature mapping to enhance the similarity of channel characteristics between the base station (Alice) and the legitimate user (Bob), effectively reducing the key disagreement rate. The physical layer key is derived from two sets of similar channel features, and the initial key is generated using the SOM-KMeans clustering algorithm combined with Gray coding. Experimental results demonstrate that this proposed method significantly lowers the key disagreement rate in unstable channels and achieves a superior key generation rate at high Signal-to-Noise Ratios (SNR) compared to other quantization techniques. Furthermore, the initial keys produced successfully pass the NIST randomness tests.
In TDD-OFDM systems, key generation relies on channel reciprocity, which assumes that the channel characteristics of the transmit and receive channels are identical. However, practical conditions such as hardware asymmetry, environmental noise, and multipath effects often disrupt this reciprocity, negatively impacting the accuracy and consistency of key generation. To address these issues, this paper proposes a novel key generation scheme based on Self-Organizing Maps (SOM) and K-Means quantization algorithm for TDD-OFDM system under unstable channel. The proposed approach uses Channel Reciprocity Learning Network (CRLN) for feature mapping to enhance the similarity of channel characteristics between the base station (Alice) and the legitimate user (Bob), effectively reducing the key disagreement rate. The physical layer key is derived from two sets of similar channel features, and the initial key is generated using the SOM-KMeans clustering algorithm combined with Gray coding. Experimental results demonstrate that this proposed method significantly lowers the key disagreement rate in unstable channels and achieves a superior key generation rate at high Signal-to-Noise Ratios (SNR) compared to other quantization techniques. Furthermore, the initial keys produced successfully pass the NIST randomness tests.
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.
Using the physical layer channel information of wireless devices to establish the highly consistent secret keys is a promising technology for improving the security of wireless networks. Nevertheless, in the time division duplex system, the reciprocity of the wireless channel that is the basic principle of key generation is impaired by nonsimultaneous sampling and noise factors. Existing physical layer key generation approaches rely on hand-crafted feature extraction algorithms, which have high overhead or security issues and are impractical in real-world situations. This paper presents a novel physical layer key generation method to extract highly consistent keys from imperfect channel responses, which exploits channel reciprocity through deep learning. Specifically, we first design the Channel Reciprocity Learning Net (CRLNet), a neural network for efficiently learning channel reciprocity features from the wireless channel in TDD OFDM systems. Later, a new key generation scheme based on CRLNet is developed that can achieve a high key agreement rate. Experiments indicate that the CRLNet-based key generation scheme performs excellently in terms of key generation rate, key error rate, and randomness, confirming that our method has better performance and lower overhead than existing methods.
Channel reciprocity is the foundation for physical layer key generation, which is influenced by noise, hardware impairments, and synchronization offsets. Weak channel reciprocity will result in a high key disagreement rate (KDR). The existing solutions for improving channel reciprocity cannot achieve satisfactory performance improvements. Furthermore, the existing quantization algorithms generally use one-dimensional channel features to quantize and generate secret keys, which cannot fully utilize channel information. The multidimensional vector quantization technique also needs to improve in terms of randomness and time complexity. This paper proposes a physical layer key generation scheme based on deep learning and balanced vector quantization. Specifically, we build a channel reciprocity compensation network (CRCNet) to learn the mapping relationship between Alice and Bob’s channel measurements. Alice compensates for channel measurements via a trained CRCNet to reduce channel measurement errors between legitimate users and enhance channel reciprocity. We also propose a balanced vector quantization algorithm based on integer linear programming (ILP-BVQ). ILP-BVQ reduces the time complexity of quantization on the basis of ensuring key randomness and a low KDR. Simulation results showed that the proposed CRCNet performs better in terms of channel reciprocity and KDR, while the proposed ILP-BVQ algorithm improves time consumption and key randomness.
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.
In physical layer security systems there is a clear need to exploit the radio link characteristics to automatically generate an encryption key between two end points. The success of the key generation depends on the channel reciprocity, which is impacted by the non-simultaneous measurements and the white nature of the noise. In this paper, an OFDM subcarriers' channel responses based key generation system with enhanced channel reciprocity is proposed. By theoretically modelling the OFDM subcarriers' channel responses, the channel reciprocity is modelled and analyzed. A low pass filter is accordingly designed to improve the channel reciprocity by suppressing the noise. This feature is essential in low SNR environments in order to reduce the risk of the failure of the information reconciliation phase during key generation. The simulation results show that the low pass filter improves the channel reciprocity, decreases the key disagreement, and effectively increases the success of the key generation.
One of the fundamental problems in cryptography is the generation of a common secret key between two legitimate parties to prevent eavesdropping. In this paper, we propose an information-theoretic secret key generation (SKG) method for time division duplexing (TDD)-based orthogonal frequency-division multiplexing (OFDM) systems over multipath fading channels. By exploring physical layer properties of the wireless medium, i.e., the reciprocity, randomness, and privacy features of the radio channel, an SKG method is proposed to maximize the number of secret bits given a target secret key disagreement ratio (SKDR). In the proposed SKG method, the phase information of the estimated channel state information (CSI) is distilled for SKG, and a special guard band (GB) scheme is designed to achieve the target SKDR with a small phase information loss. The proposed GB consists of both the amplitude GB (AGB) and phase GB (PGB), where the AGB is determined by the average signal-to-interference plus noise ratio (SINR), whereas the PGB adapts itself to the instantaneous SINR and thus incurs a smaller phase information loss in the higher SINR region. Analyses show that this GB scheme trades off a small loss of channel phase information for a better SKDR performance, and achieves a much larger number of quantization levels for a given SKDR due to the fact that the PGB decreases quickly as the SINR increases. Based on the performance analysis on the SKDR, the average secret key length, the phase information loss percentage (PILP), and the optimal GB and quantization level of the adaptive quantizor are derived for a given target SKDR. Both analytical and simulation results are presented to demonstrate the superiority of the proposed scheme for TDD-OFDM systems over frequency-selective fading channels.