Secrecy Outage Performance of Ground-to-Air Communications With Multiple Aerial Eavesdroppers and Its Deep Learning Evaluation

Bao, Tingnan; Zhu, Jun; Yang, Hong-Chuan; Hasna, Mazen O.

doi:10.1109/lwc.2020.2990337

Cited by 35 publications

(26 citation statements)

References 23 publications

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“…Let m G ν X and Ω G E[G r ]. From (23), we have E[G 2 r ] = Ω s Ω d , and thus, we attain the PDF of G r in (9).…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 97%

“…Recently, in [8], based on empirical data obtained in A2G trials, A2G channels were modeled using Nakagami-m multipath fading and inverse-Gamma (IG) shadowing. In [9], the authors used a deep neural network (DNN) to predict the secrecy performance of A2G communications. More recently, the concept of aerial RIS (aerial-RIS)-enabled communication systems was proposed in [10]; however, the authors modeled A2G links using the free-space path-loss model and neglected the fading and shadowing effects.…”

Section: Introductionmentioning

confidence: 99%

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Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems

Nhu¹,

Kaddoum²,

Nguyen

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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In this paper 1 , we propose and investigate an aerial reconfigurable intelligent surface (aerial-RIS)-aided wireless communication system. Specifically, considering practical composite fading channels, we characterize the air-to-ground (A2G) links by Namkagami-m small-scale fading and inverse-Gamma largescale shadowing. To investigate the delay-limited performance of the proposed system, we derive a tight approximate closedform expression for the end-to-end outage probability (OP). Next, considering a mobile environment, where performance analysis is intractable, we rely on machine learning-based performance prediction to evaluate the performance of the mobile aerial-RIS-aided system. Specifically, taking into account the threedimensional (3D) spatial movement of the aerial-RIS, we build a deep neural network (DNN) to accurately predict the OP. We show that: (i) fading and shadowing conditions have strong impact on the OP, (ii) as the number of reflecting elements increases, aerial-RIS achieves higher energy efficiency (EE), and (iii) the aerial-RIS-aided system outperforms conventional relaying systems.

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“…Let m G ν X and Ω G E[G r ]. From (23), we have E[G 2 r ] = Ω s Ω d , and thus, we attain the PDF of G r in (9).…”

Section: Appendix a Proof Of Lemmamentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems

Nhu¹,

Kaddoum²,

Nguyen

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

View full text Add to dashboard Cite

show abstract

“…These algorithms are often used for feature extraction and dimensionality reduction tasks which help to reduce the computational cost of processing the data with the least possible compromise in the performance of the data processing task. Neural networks have also proven outstandingly helpful in many different types of communication systems tasks, with auto-encoders being utilized in PHY [214]- [216] and a variety of other deep learning techniques used in remote sensing [217]- [219] and security [220] applications of airborne/satellite communications. For further readings and insights we would refer the interested readers to explore [207], [208].…”

Section: A Overview On MLmentioning

confidence: 99%

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Solanki¹,

Chatzinotas²,

Kodheli³

et al. 2021

Preprint

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Non-terrestrial networks (NTNs) traditionally had certain limited applications. However, the recent technological advancements opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting its relevance to 5G networks and essentially, how it will play a pivotal role for the development of 6G and beyond wireless networks. The survey discusses important features of NTNs integration into TNs by delving into the new range of services and use cases, various architectures, and new approaches being adopted to develop new wireless ecosystem. Our survey includes the major progresses and outcomes from academic research as well as industrial efforts. We first start with introducing the relevant 5G use cases and general integration challenges such as handover and deployment difficulties. Then, we review the NTNs operations in mmWave and their potential for internet of things (IoT). Further, we discuss the significance of mobile edge computing (MEC) and machine learning (ML) in NTNs by reviewing the relevant research works. Furthermore, we also discuss the corresponding higher layer advancements and relevant field trials/prototyping at both academic and industrial levels. Finally, we identify and review 6G and beyond application scenarios, novel architectures, technological enablers, and higher layer aspects pertinent to NTNs integration.

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“…Nevertheless, using machine learning approaches may raise new secrecy problem, in e.g., data gathering or parameter updating. Recently, a number of works have emerged in the area of machine learning-aided secure UAV communications [111], [199]- [206]. However, there still exist much more scenarios that machine learning may find its application, and a systematic design framework is still needed for the learningaided secure UAV communications.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Physical Layer Security for UAV Communications in 5G and Beyond Networks

Wang,

Gao

et al. 2021

Preprint

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Due to its high mobility and flexible deployment, unmanned aerial vehicle (UAV) is drawing unprecedented interest in both military and civil applications to enable agile wireless communications and provide ubiquitous connectivity. Mainly operating in an open environment, UAV communications can benefit from dominant line-of-sight links; however, it on the other hand renders the UAVs more vulnerable to malicious eavesdropping or jamming attacks. Recently, physical layer security (PLS), which exploits the inherent randomness of the wireless channels for secure communications, has been introduced to UAV systems as an important complement to the conventional cryptographybased approaches. In this paper, a comprehensive survey on the current achievements of the UAV-aided wireless communications is conducted from the PLS perspective. We first introduce the basic concepts of UAV communications including the typical static/mobile deployment scenarios, the unique characteristics of air-to-ground channels, as well as various roles that a UAV may act when PLS is concerned. Then, we introduce the widely used secrecy performance metrics and start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems, and extend the discussion to a more general scenario where the UAVs' mobility is further exploited. For both cases, respectively, we summarize the commonly adopted methodologies in the corresponding analysis and design, then describe important works in the literature in detail. Finally, potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS in 5G and beyond networks.

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Secrecy Outage Performance of Ground-to-Air Communications With Multiple Aerial Eavesdroppers and Its Deep Learning Evaluation

Cited by 35 publications

References 23 publications

Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems

Aerial Reconfigurable Intelligent Surface-Aided Wireless Communication Systems

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Physical Layer Security for UAV Communications in 5G and Beyond Networks

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