UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Li, Bin; Fei, Zesong; Zhang, Yan

doi:10.1109/jiot.2018.2887086

Cited by 1,080 publications

(493 citation statements)

References 133 publications

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“…OES studies where to deploy and install wireless telecom equipment and servers. In recent years, research efforts on the management and automation of Unmanned Aerial Vehicles (UAVs) come into vogue [8] [9]. UAVs with a small server and an access point can be regarded as moving edge servers with strong maneuverability.…”

Section: B a Recapitulation Of Iecmentioning

confidence: 99%

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Deng

Zhao

Fang

et al. 2020

IEEE Internet Things J.

713

224

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Along with the deepening development in communication technologies and the surge of mobile devices, a brandnew computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the upgrade of hardware architectures. Billions of bytes of data, generated at the network edge, put great demands on data processing and structural optimization. Therefore, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this article, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing a more optimal solution to the key concerns in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on edge. This article focuses on giving insights into this new inter-disciplinary field from a broader vision and perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research programs in Edge Intelligence.

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Section: B a Recapitulation Of Iecmentioning

confidence: 99%

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Deng

Zhao

Fang

et al. 2020

IEEE Internet Things J.

713

224

View full text Add to dashboard Cite

show abstract

“…Another proximity-based computing technique is mobility assisted MEC. Therein, networks of connected vehicles, unmanned autonomous vehicle (UAV), and robots with high processing power can assist the computational tasks of the users [38], [39]. The high processing power of above devices that are dedicated to users provides low computational latencies.…”

Section: Low Latency Enabler 1 High Capacity Mmwave Linksmentioning

confidence: 99%

Wireless Edge Computing With Latency and Reliability Guarantees

et al. 2019

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Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and lowlatency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.

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“…Theorem 1. We derive the CP of the secure UAV-based relaying in (20), where δ t ∆ = 2 2Rt − 1, D and R are two positive integers controlling the accuracy of (20), Γ(·) represents the gamma function, ζ is given in (16), d, u, s, r are dummy variables, K ij , ij ∈ {au, ub} is given in (4), and I ν (·) is the modified Bessel function of the first kind with the order of ν.…”

Section: Iv-a Connection Probabilitymentioning

confidence: 99%

On the Performance of Low-Altitude UAV-Enabled Secure AF Relaying With Cooperative Jamming and SWIPT

Mamaghani

Hong

2019

IEEE Access

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This paper proposes a novel cooperative secure unmanned aerial vehicle (UAV) aided transmission protocol, where a source (Alice) sends confidential information to a destination (Bob) via an energy-constrained UAV-mounted amplifyand-forward (AF) relay in the presence of a ground eavesdropper (Eve). We adopt destination-assisted cooperative jamming (CJ) as well as simultaneous wireless information and power transfer (SWIPT) at the UAV-mounted relay to enhance physical-layer security (PLS) and transmission reliability. Assuming a low altitude UAV, we derive connection probability (CP), secrecy outage probability (SOP), instantaneous secrecy rate, and average secrecy rate (ASR) of the proposed protocol over Air-Ground (AG) channels, which are modeled as Rician fading with elevation-angel dependent parameters. By simulations, we verify our theoretical results and demonstrate significant performance improvement of our protocol, when compared to conventional transmission protocol with ground relaying and UAV-based transmission protocol without destination-assisted jamming. Finally, we evaluate the impacts of different system parameters and different UAV's locations on the proposed protocol in terms of ASR.Index Terms-UAV relaying, wireless information and power transfer, physical layer security, jamming.

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UAV Communications for 5G and Beyond: Recent Advances and Future Trends

Cited by 1,080 publications

References 133 publications

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Wireless Edge Computing With Latency and Reliability Guarantees

On the Performance of Low-Altitude UAV-Enabled Secure AF Relaying With Cooperative Jamming and SWIPT

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