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[Paper Review] Network-connected UAV communications: Potentials and challenges

Haichao Wang, Jinlong Wang|arXiv (Cornell University)|Dec 28, 2018
UAV Applications and Optimization7 references44 citations
TL;DR

This paper proposes a flexible, network-connected UAV communication architecture to enable high-rate data transmission and ultra-reliable remote control. It analyzes signal propagation and interference, and presents optimized solutions in antenna design, nonorthogonal multiple access, and network association, demonstrating feasibility through case studies.

ABSTRACT

This article explores the use of network-connected unmanned aerial vehicle (UAV) communications as a compelling solution to achieve high-rate information transmission and support ultra-reliable UAV remote command and control. We first discuss the use cases of UAVs and the resulting communication requirements, accompanied with a flexible architecture for network-connected UAV communications. Then, the signal transmission and interference characteristics are theoretically analyzed, and subsequently we highlight the design and optimization considerations, including antenna design, nonorthogonal multiple access communications, as well as network selection and association optimization. Finally, case studies are provided to show the feasibility of network-connected UAV communications.

Motivation & Objective

  • To address the growing demand for high-rate, reliable communication in UAV applications such as surveillance, delivery, and remote sensing.
  • To identify and define the communication requirements driven by diverse UAV use cases.
  • To propose a flexible network-connected UAV communication architecture supporting scalable and reliable connectivity.
  • To analyze signal transmission and interference characteristics in UAV networks for improved system design.
  • To optimize key system components including antenna design, multiple access schemes, and network association strategies.

Proposed method

  • Proposes a flexible network-connected UAV communication architecture integrating ground networks and UAVs for scalable deployment.
  • Analyzes signal propagation and interference using theoretical models to understand channel behavior in UAV scenarios.
  • Introduces nonorthogonal multiple access (NOMA) to enhance spectral efficiency and support multiple UAVs simultaneously.
  • Optimizes network selection and association by formulating joint resource allocation and user association strategies.
  • Designs UAV antennas with beamforming and directivity to improve link reliability and reduce interference.
  • Validates the proposed framework through case studies demonstrating performance gains in data rate and reliability.

Experimental results

Research questions

  • RQ1How can UAV communications be architected to support high data rates and ultra-reliable command and control?
  • RQ2What are the key signal transmission and interference characteristics in UAV networks?
  • RQ3How can nonorthogonal multiple access improve spectral efficiency in UAV networks?
  • RQ4What optimization strategies enhance network selection and user association in UAV communications?
  • RQ5What are the practical performance gains of the proposed network-connected UAV communication framework?

Key findings

  • The proposed network-connected UAV communication architecture enables scalable and reliable high-rate data transmission.
  • Theoretical analysis confirms that UAVs experience unique line-of-sight and shadowing effects due to altitude and mobility.
  • Nonorthogonal multiple access (NOMA) significantly improves spectral efficiency compared to orthogonal schemes in dense UAV deployments.
  • Optimized network selection and association reduce latency and improve throughput in multi-UAV scenarios.
  • Case studies demonstrate that the proposed system achieves high data rates and reliable command links under realistic deployment conditions.
  • Antenna design with beamforming enhances link reliability and reduces interference in dynamic UAV environments.

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This review was created by AI and reviewed by human editors.