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Lecture notes in computer science

A Location Prediction-based Physical Layer Security Scheme for Suspicious Eavesdroppers

Yuqi Tian, Yan Huo, Chunqiang Hu, Qinghe Gao, Tao Jing

Published Jan 1, 2017• 2 citations
Publisher
DOI
10.1007/978-3-319-60033-8_72

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arXiv:1412.6882•IEEE Transactions on Wireless Communications•Dec 22, 2015•74 citations
This paper aims to improve security performance of data transmission with a mobile eavesdropper in a wireless network. The instantaneous channel state information (CSI) of the mobile eavesdropper is unknown to legitimate users during the communication process. Different from existing work, we intend to reduce power consumption of friendly jamming signals. Motivated by the goal, this work presents a location-based prediction scheme to predict where the eavesdropper will be later and to decide whether a friendly jamming measure should be selected against the eavesdropper. The legitimate users only take the measure when the prediction result shows that there will be a risk during data transmission. According to the proposed method, system power can be saved to a large degree. Particularly, we first derive the expression of the secrecy outage probability and set a secrecy performance target. After providing a Markov mobile model of an eavesdropper, we design a prediction scheme to predict its location, so as to decide whether to employ cooperative jamming or not, and then design a power allocation scheme and a fast suboptimal helper selection method to achieve targeted and efficient cooperative jamming. Finally, numerical simulation results demonstrate the effectiveness of the proposed schemes.
Location information has been shown to be useful for a wide variety of applications in wireless networks, while its role in physical layer security has so far drawn little attention. In this work, we propose a new location-based secure transmission scheme for wiretap channels, where the accurate locations of the sources, destinations and any other authorized transceivers are known, but only an estimate of the eavesdropper's location is available. We outline how such an estimate of the eavesdropper's location can still allow for quantitative assessment of key security metrics. To provide focus, we describe how optimization of the effective secrecy throughput of a relay wiretap channel is obtained in our scheme, and investigate in detail the impact of the location uncertainty on the system performance. The work reported here provides insights into the design of new location-based physical layer security schemes in which the only information available on an eavesdropper is a noisy estimate of her location.
We propose a new optimal location-based beamforming (LBB) scheme for the wiretap channel, where both the main channel and the eavesdropper's channel are subject to Rician fading. In our LBB scheme, the two key inputs are the location of the legitimate receiver and the location of the potential eavesdropper. Notably, our scheme does not require any channel state information of the main channel or the eavesdropper's channel being available at the transmitter. This makes our scheme easy to deploy in a host of application settings in which the location inputs are known. Our beamforming solution assumes a multiple-antenna transmitter and a multiple-antenna eavesdropper, and its aim is to maximize the physical layer security of the channel. To obtain our solution, we first derive the secrecy outage probability of the LBB scheme in an easy-to-evaluate expression that is valid for arbitrary real values of the Rician K-factors of the main channel and the eavesdropper's channel. Using this expression, we then determine the location-based beamformer solution that minimizes the secrecy outage probability. To assess the usefulness of our new scheme, and to quantify the value of the location information to physical layer security, we compare our scheme to other schemes, some of which do not utilize any location information. Our new beamformer solution provides optimal physical layer security for a wide range of location-based applications.