Wireless Key Generation from Imperfect Channel State Information: Performance Analysis and Improvements

Abstract

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).

Learning-Based Secret Key Generation in Relay Channels Under Adversarial Attacks

“…Remarkably, none of the aforementioned works have leveraged the potential capabilities of ML/DL techniques to come up with an intelligent security solution for WSKG. In fact, to the best of our knowledge, there are only a few papers addressing learning-aided schemes for wireless key agreement protocols [21]- [24]. For instance, [21] and [22] utilized a fully-connected (FC) neural network for a simple P2P communication in the presence of a simple passive eavesdropper.…”

“…In fact, to the best of our knowledge, there are only a few papers addressing learning-aided schemes for wireless key agreement protocols [21]- [24]. For instance, [21] and [22] utilized a fully-connected (FC) neural network for a simple P2P communication in the presence of a simple passive eavesdropper. Considering a similar topology, convolutional neural networks were proposed in [23].…”

“…Considering a similar topology, convolutional neural networks were proposed in [23]. However, the sequence-type nature of observations during WSKG scheme was not addressed in [21]- [23]. In other words, they did not take into account the potential capabilities of state-aware neural networks, which can capture the relevant information within the chain of PHY data sequences.…”

“…To have an accurate understanding of practical wireless systems, we also consider a realistic scenario, in which the transceivers of legitimate endpoints, as well as the intermediate relay, suffer from HIs, which was not addressed in [10], [11], [16]- [19], [21]. Moreover, different from [7], [10], [16], [17], [19], we assume that the legitimate endpoints are faced with both channel estimation errors and imperfect reciprocity.…”

A Physical Layer Key Generation Scheme Based on Deep Learning Compensation and Balanced Vector Quantization

“…Letafati et al [31] utilize echo-state networks to compensate for the observation mismatch between legitimate communicating parties caused by unbalanced hardware impairments. Guan et al [32] propose a feed-forward neural network-based prediction (NNBP) algorithm to enhance key consistency.…”