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.
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.
Physical layer characteristics of a wireless channel, which can be measured independently by the two communicating parties and yield near-identical results due to the channel reciprocity property, have been shown to be an important source of shared secrecy generation. Various types of channel state information (CSI), such as the received signal strength (RSS) and the channel impulse response (CIR) can be utilized for this purpose. Through periodic probing of the CSI, a continuous cycle of secret bit generation and key renewal can be maintained. However, many forms of wireless CSI inherently have substantial temporal correlation, which may hinder the secrecy generation process. Accordingly, various autocorrelation reduction (decorrelation) methods have been proposed, using either sub-sampling approaches that discard potentially valuable CSI data, or, computationally expensive transform domain approaches such as the Discrete Cosine Transform (DCT), Karhunen–Loeve Transform (KLT), and principal component analysis (PCA) that may not be feasible or desirable for resource and energy constrained devices. This paper proposes a low-complexity method for reducing the autocorrelation of the CSI measurements through a reordering of the data based on integer sequences such as the Fibonacci sequences, and applies it to various types of CSI data that represent both the individual path level CIR and the aggregate level multipath gain or RSS. We evaluate the performance of the method in three standard multipath ITU channel models. Fibonacci sequences are observed to be an effective means of decorrelating the channel measurement data, thereby eliminating the need for computationally intensive methods.
This paper proposes a novel multi-party key generation method that jointly utilizes channel state information (CSI) and blockchain technology to enhance security in distributed systems. The proposed method starts by extracting CSI from wireless channels, leveraging the channels’ inherent randomness and reciprocity to generate secure key fragments shared among legitimate parties. Then, the key generation process involves several stages, including quantization, reconciliation, and privacy amplification, ensuring that the resulting keys are secure and synchronized across participants. Blockchain technology is then leveraged to securely commit these keys, ensuring that the key agreements are recorded in a decentralized, tamper-resistant ledger. The proposed method effectively combines the physical-layer properties of CSI with the decentralized nature of blockchain, providing robust protection against eavesdropping and tampering attacks. Theoretical analyses and simulation results demonstrate the effectiveness of the proposed method in terms of key mismatch probability and secrecy capacity. Additionally, the randomness of the generated keys by the proposed method is validated using the National Institute of Standards and Technology randomness tests.
Device-to-device communication is increasingly important for emerging wireless applications, including the Internet of Things and industry mobile networks. To establish a secure communication channel between multiple legitimate devices, key agreement is an essential first step. Physical layer channel information is a promising technology for achieving communication security by generating a shared channel key. In this paper, we propose an efficient and random method for generating channel keys based on channel state information (CSI). Firstly, we preprocess the available subcarriers to reduce the correlation of adjacent subcarriers. Secondly, we propose a novel quantization process to generate random and secure channel keys. The part consists of two phases: the quantization phase and the inconsistency removal phase. In the quantization stage, CSI sub carriers are grouped into small blocks, and appropriate thresholds are generated. Then, wireless users quantize the CSI subcarriers into initial bit streams of 0 and 1 with different thresholds. In the inconsistency removal phase, we design an error correction mechanism where wireless users exchange the discarded index sequences in the initial bit streams to achieve key sequence consistency. We implement the proposed scheme using off-the-shelf wireless devices and evaluate its performance. The experimental results show that each CSI packet can produce 30 bits, which have passed the NIST randomness tests.
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.
Physical layer encryption is an emerging security paradigm which supplements the higher-layer encryption solutions. To realize physical layer encryption, channel measurement is typically utilized as a randomness source to generate secret keys. Due to non-perfect reciprocity of the channels and the inevitable channel estimation errors resulting from noises and interferences, the channel measurements at the legitimate transceivers are not the same, yielding a mismatch in the generated keys. To address this issue, a distributed physical layer key generation scheme based on deep learning is proposed in this paper. A channel state information (CSI) learning neural network (CLNet) based on the autoencoder is deployed at both the transmitter and the receiver side to refine the initial channel estimation results. The CLNet takes the least square (LS) channel estimations as input and outputs the channel estimations more similar to the real channel. To train this CLNet, a channel equalization module together with a pre-trained maximum-likelihood (ML) detection network is deployed, which is used to make a decision on the transmitted pilot signal. Since the pilot is known as a priori, the cross entropy loss between the known pilot and the recovered one can be calculated, which implicitly indicates the accuracy of the refined CSI. Further, the weighted sum of the aforementioned cross entropy loss and the mean square loss between the original and the refined CSI estimates is utilized for training, which not only improves the CSI estimation quality but also avoids the over-fitting effect. Extensive simulation results show that compared with benchmark schemes, the proposed scheme improves the key agreement rate and the key generation rate at the legitimate users. Moreover, our method ensures a higher key disagreement rate between the legitimate user and the eavesdropper as well, especially in the low signal-to-noise ratio (SNR) regime.