The reciprocity nature of a wireless channel has been leveraged to generate and establish secret keys between two devices communicating over the channel. This is typically done by having the two devices exchange probing signals to measure their channel state information (CSI), which each then uses to derive secret keys. While extensive research on secret key generation primarily focuses on theoretical and simulation analyses, the practical implementation and adoption of key generation encounter obstacles arising from hardware constraints and varying channel conditions. To address this research gap, we experimentally investigate the channel measurements by collecting CSI data using resource-constrained devices. Our experiments reveal a significant degradation in the correlation between the collected CSI from two low-cost devices limiting the key generation performance. Through our experimental investigations, we first demonstrate that wavelet coherence provides insights on the reciprocity of the channel measurements on both time and frequency. We then propose a new wavelet coherence-based CSI data reconstruction technique that uses wavelet coherence and time-lagged cross-correlation to reconstruct CSI data that is consistent between the two participating devices, resulting in significant improvement in the channel measurements. Additionally, we propose a secret-key generation scheme that exploits the proposed wavelet coherence-based CSI data reconstruction, yielding a significant reduction in the bit error rates and an increase in the key generation rates.
The reciprocity nature of a wireless channel has been leveraged to generate and establish secret keys between two devices communicating over the channel. This is typically done by having the two devices exchange probing signals to measure their channel state information (CSI), which each then uses to derive secret keys. While extensive research on secret key generation primarily focuses on theoretical and simulation analyses, the practical implementation and adoption of key generation encounter obstacles arising from hardware constraints and varying channel conditions. To address this research gap, we experimentally investigate the channel measurements by collecting CSI data using resource-constrained devices. Our experiments reveal a significant degradation in the correlation between the collected CSI from two low-cost devices limiting the key generation performance. Through our experimental investigations, we first demonstrate that wavelet coherence provides insights on the reciprocity of the channel measurements on both time and frequency. We then propose a new wavelet coherence-based CSI data reconstruction technique that uses wavelet coherence and time-lagged cross-correlation to reconstruct CSI data that is consistent between the two participating devices, resulting in significant improvement in the channel measurements. Additionally, we propose a secret-key generation scheme that exploits the proposed wavelet coherence-based CSI data reconstruction, yielding a significant reduction in the bit error rates and an increase in the key generation rates.
Secret-key generation and agreement based on wireless channel reciprocity offers a promising avenue for securing IoT networks. However, existing approaches predominantly rely on the similarity of instantaneous channel measurement samples between communicating devices. This narrow view of reciprocity is often impractical, as it is highly susceptible to noise, asynchronous sampling, channel fading, and other system-level imperfections-all of which significantly impair key generation performance. Furthermore, the quantization step common in traditional schemes introduces irreversible errors, further lim-iting efficiency. In this work, we propose a novel approach for secret-key generation by using wavelet scattering networks to extract robust and reciprocal CSI features. Dimensionality reduction is applied to uncover hidden cluster structures, which are then used to build hidden Markov models for efficient key agreement. Our approach eliminates the need for quantization and effectively captures channel randomness. It achieves a 5x improvement in key generation rate compared to traditional benchmarks, providing a secure and efficient solution for key generation in resource-constrained IoT environments.
Secret key generation from wireless channel is an emerging technique to provide secure keys for message encryption. However, the measured channel state information (CSI) requires high reciprocity in order to obtain symmetric keys. In this paper, we propose a novel loop-back transmission scheme for time division duplex (TDD) systems, which is named as LB-TDD. The proposed LB-TDD scheme can effectively reduce the CSI non-reciprocity due to the hardware fingerprint interference and system synchronization offset. We initially discuss the benefits and optimizations of using LB-TDD for CSI reciprocity enhancement. After that, a secret key generation method based on orthogonal frequency division multiplexing (OFDM) probing signals is introduced. Finally, a channel model and a practical universal software radio peripheral (USRP) system is setup for both simulation and experimental verification. Evaluation results show that our proposed LB-TDD scheme can effectively eliminate CSI non-reciprocity for secret key generation.
The basis of generating secret key from the common wireless channel at two communication parties is reciprocity. However, due to non-simultaneous measurements and unavoidable estimation error, bias always exists and key disagreement occurs. In this paper, performance analysis and improvements for key generation exploiting imperfect channel statement information (CSI) is studied. Firstly, the closed-form expression for key disagreement rate (KDR) is derived to evaluate the key mismatch caused by time delay and estimation error. Secondly, the energy efficiency involving power allocation between key generation and private data transmission is presented. After that, a neural network based prediction (NNBP) algorithm is proposed to improve the key consistency. Simulation results verify the theoretical analysis and show that NNBP achieves significant improvements. And it's interesting to find that NNBP algorithm achieves a higher performance gain in low signal-to-noise ration (SNR) and high delay condition, which is different from other methods, like guard band based quantizer (GBBQ).
As an alternative and appealing approach to providing information security in wireless communication systems, secret key generation at physical layer has demonstrated its potential in terms of efficiency and reliability over traditional cryptographic methods. Without the necessity of a management centre for key distribution or reliance on computational complexity, physical layer key generation protocols enable two wireless entities to extract identical and dynamic keys from the randomness of the wireless channels associated with them.\nIn this thesis, the reliability of secret key generation at the physical layer is examined in practical wireless channels with imperfect channel state information (CSI). Theoretical analyses are provided to relate key match rate with channel's signal-to-noise ratio (SNR), degrees of channel reciprocity, and iterations of information reconciliation.\nIn order to increase key match rate of physical layer secret key generation, improved schemes in the steps of channel estimation and sample quantization are proposed respectively. In the channel estimation step, multiple observations of the wireless channels are integrated with a linear processor to provide a synthesized and more accurate estimation of the wireless channel. In the sample quantization step, a magnitude based quantization method with two thresholds is proposed to quantize partial samples, where specific quantization areas are selected to reduce cross-over errors. Significant improvements in key match rate are proven for both schemes in theoretical analysis and numerical simulations. Key match rate can even achieve 100% in both schemes with the assistance of information reconciliation process.\nIn the end, a practical application of physical layer secret key generation is presented, where dynamic keys extracted from the wireless channels are utilized for securing secret data transmission and providing efficient access control.
The basis of generating secret key from the common wireless channel at two communication parties is reciprocity. However, due to non-simultaneous measurements and unavoidable estimation error, bias always exists and key disagreement occurs. In this paper, performance analysis and improvements for key generation exploiting imperfect channel statement information (CSI) is studied. Firstly, the closed-form expression for key disagreement rate (KDR) is derived to evaluate the key mismatch caused by time delay and estimation error. Secondly, the energy efficiency involving power allocation between key generation and private data transmission is presented. After that, a neural network based prediction (NNBP) algorithm is proposed to improve the key consistency. Simulation results verify the theoretical analysis and show that NNBP achieves significant improvements. And it's interesting to find that NNBP algorithm achieves a higher performance gain in low signal-to-noise ration (SNR) and high delay condition, which is different from other methods, like guard band based quantizer (GBBQ).
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.