Physical Layer Security Performance of Mobile Vehicular Networks

Xu, Lingwei; Yu, Xu; Wang, Han; Dong, Xinli; Liu, Yun; Lin, Wei; Wang, Xinjie; Wang, Jingjing

doi:10.1007/s11036-019-01224-8

Cited by 25 publications

(17 citation statements)

References 30 publications

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“…Alternating optimization (AO) and successive convex In vehicular ad hoc networks (VANET), PLS is a major concern, due to the broadcast nature of the wireless channels. Many papers, [26]- [28], have considered the analysis of PLS under such high mobility conditions and dynamic environments. In fact, RIS is proven to help compensating the multipath and Doppler effects in wireless propagation channels [6].…”

Section: -(A)mentioning

confidence: 99%

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Almohamad

Tahir

Al‐Kababji

et al. 2020

IEEE Open J. Commun. Soc.

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With the emergence of the internet of things (IoT) technology, wireless connectivity should be more ubiquitous than ever. In fact, the availability of wireless connection everywhere comes with security threats that, unfortunately, cannot be handled by conventional cryptographic solutions alone, especially in heterogeneous and decentralized future wireless networks. In general, physical layer security (PLS) helps in bridging this gap by taking advantage of the fading propagation channel. Moreover, the adoption of reconfigurable intelligent surfaces (RIS) in wireless networks makes the PLS techniques more efficient by involving the channel into the design loop. In this paper, we conduct a comprehensive literature review on the RIS-assisted PLS for future wireless communications. We start by introducing the basic concepts of RISs and their different applications in wireless communication networks and the most common PLS performance metrics. Then, we focus on the review and classification of RIS-assisted PLS applications, exhibiting multiple scenarios, system models, objectives, and methodologies. In fact, most of the works in this field formulate an optimization problem to maximize the secrecy rate (SR) or secrecy capacity (SC) at a legitimate user by jointly optimizing the beamformer at the transmitter and the RIS's coefficients, while the differences are in the adopted methodology to optimally/sub-optimally approach the solution. We finalize this survey by presenting some insightful recommendations and suggesting open problems for future research extensions.

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Section: -(A)mentioning

confidence: 99%

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Almohamad

Tahir

Al‐Kababji

et al. 2020

IEEE Open J. Commun. Soc.

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“…However, the aforementioned works in [18][19][20][21][22][23] have not considered the PHY-security aspects. Xu et al [24] evaluated the secrecy performance of a non-cooperative relaying networks with mobile nodes under N*Nakagami-m fading channels. However, the literature investigating the PHY-security aspects in cooperative networks with mobile nodes are limited [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Physical layer security in cooperative amplify‐and‐forward relay networks over mixed Nakagami‐ m and double Nakagami‐ m fading channels: performance evaluation and optimisation

Pandey

Yadav

2020

IET Communications

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“…However, due to the complex and variable environments,the mobile multiuser communication is more complicated. The N-Nakagami model is adopted in mobile communication [16]- [19]. The dynamic variation characteristics of N-Nakagami channels can cause the interruption of mobile communication.…”

Section: Introductionmentioning

confidence: 99%

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

Wang

Tao

et al. 2020

IEEE Access

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Due to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which utilizes transmit antenna selection (TAS). We derive novel closed-form expressions for the outage probability (OP) in terms of the Meijer's G-function. Then, a extreme learning machine (ELM)-based OP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test back-propagation (BP) neural network, locally weighted linear regression (LWLR), wavelet neural network, ELM, and support vector machine (SVM) methods. Compared with wavelet neural network, SVM, BP neural network, and LWLR methods, the Monte-Carlo results shows that the proposed prediction algorithm can consistently achieve higher OP performance prediction results. INDEX TERMS Extreme learning machine, multiuser diversity, outage probability, performance prediction.

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Physical Layer Security Performance of Mobile Vehicular Networks

Cited by 25 publications

References 30 publications

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey

Physical layer security in cooperative amplify‐and‐forward relay networks over mixed Nakagami‐ m and double Nakagami‐ m fading channels: performance evaluation and optimisation

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

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