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Lecture notes in computer science

Wireless Key Exchange Using Frequency Impairments

Jörn Müller‐Quade, Antonio Sobreira de Almeida

Published Jan 1, 2015• 0 citations
Publisher
DOI
10.1007/978-3-319-15087-1_22

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Secrecy Analysis of I/Q Imbalance with Hardware Impairments over Fox’s H-Fading channel

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Impairments baseband model in-phase and quadrature-phase Imbalance (IQI) and Residual hardware impairments (RHI) are two key factors degrading the performance of wireless communication systems (WCS). In this paper, the joint effect of both IQI and RHI on the secrecy outage probability (SOP) of a WCS subject to Fox’s H-fading channel is investigated. Such a fading model generalizes most, if not all, of well-known fading and turbulence models. To this end, novel generalized closed-form expression is derived for the secrecy outage probability for all possible scenarios namely (i) Transmitter (Tx) only impaired, (ii) Receiver (Rx) only impaired, and (iii) both Tx and Rx are impaired. α−µ has been considered as a particular case of Fox’s H-fading distribution. The analytical derivations, revealed by simulation results, demonstrate that the RF impairments’ effects should be seriously taken into account in the design of secure next-generation wireless technologies. On the other hand, the numerical results show that the parameters of fading channels can be used as a key to mitigating the effects of RF impairments on the secrecy performance.

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Deep Learning for Hardware-Impaired Wireless Secret Key Generation with Man-in-the-Middle Attacks

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Wireless secret key generation (WSKG) allows efficient key agreement protocols for securing the sixth generation (6G) wireless networks. Nevertheless, due to external adversaries or internal impairments, WSKG schemes might become vulner-able during the randomness distillation, where the legitimate nodes try to observe their source of common randomness. In this paper, we investigate the WSKG scheme with legitimate parties suffering from hardware impairments (HIs), while an active adversary acts as a man-in-the-middle (MiM) via injecting fake pilot signals. We first utilize randomized pilots to overcome the MiM. We also leverage the concept of recurrent neural networks (RNNs) to further enhance the randomness distillation. More specifically, the long short-term memory networks (LSTMs)-as a well-established type of RNNs-are implemented to learn the long-term dependencies between the observations of legitimate parties. The achievable secret key rate (SKR) and the impact of MiM on system's performance are analyzed. Our numerical results verify the performance gain of our proposed learning-based approach compared with the state-of-the-art methods and provide useful insights on system design. We show that our RNN-based approach achieves 30% and 15% improvement in terms of observation mismatches compared with the naïve scheme and the fully-connected benchmarks, respectively.

Globecom•Dec 1, 2021•29 citations
Impairments baseband model in-phase and quadrature-phase Imbalance (IQI) and Residual hardware impairments (RHI) are two key factors degrading the performance of wireless communication systems (WCS). In this paper, the joint effect of both IQI and RHI on the secrecy outage probability (SOP) of a WCS subject to Fox’s H-fading channel is investigated. Such a fading model generalizes most, if not all, of well-known fading and turbulence models. To this end, novel generalized closed-form expression is derived for the secrecy outage probability for all possible scenarios namely (i) Transmitter (Tx) only impaired, (ii) Receiver (Rx) only impaired, and (iii) both Tx and Rx are impaired. α−µ has been considered as a particular case of Fox’s H-fading distribution. The analytical derivations, revealed by simulation results, demonstrate that the RF impairments’ effects should be seriously taken into account in the design of secure next-generation wireless technologies. On the other hand, the numerical results show that the parameters of fading channels can be used as a key to mitigating the effects of RF impairments on the secrecy performance.
Wireless secret key generation (WSKG) allows efficient key agreement protocols for securing the sixth generation (6G) wireless networks. Nevertheless, due to external adversaries or internal impairments, WSKG schemes might become vulner-able during the randomness distillation, where the legitimate nodes try to observe their source of common randomness. In this paper, we investigate the WSKG scheme with legitimate parties suffering from hardware impairments (HIs), while an active adversary acts as a man-in-the-middle (MiM) via injecting fake pilot signals. We first utilize randomized pilots to overcome the MiM. We also leverage the concept of recurrent neural networks (RNNs) to further enhance the randomness distillation. More specifically, the long short-term memory networks (LSTMs)-as a well-established type of RNNs-are implemented to learn the long-term dependencies between the observations of legitimate parties. The achievable secret key rate (SKR) and the impact of MiM on system's performance are analyzed. Our numerical results verify the performance gain of our proposed learning-based approach compared with the state-of-the-art methods and provide useful insights on system design. We show that our RNN-based approach achieves 30% and 15% improvement in terms of observation mismatches compared with the naïve scheme and the fully-connected benchmarks, respectively.