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Autonomous Relay for Millimeter-Wave Wireless Communications
Shanghai Key Laboratory of Scalable Computing and Systems at Shanghai Jiao Tong University, China..
Shanghai Key Laboratory of Scalable Computing and Systems at Shanghai Jiao Tong University, China..
Shanghai Key Laboratory of Scalable Computing and Systems at Shanghai Jiao Tong University, China..
Shanghai Key Laboratory of Scalable Computing and Systems at Shanghai Jiao Tong University, China..
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2017 (English)In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008Article in journal (Refereed) Epub ahead of print
Abstract [en]

Millimeter-wave (mmWave) communication is therising technology for next-generation wireless transmission. Benefitedby its abundant bandwidth and short wavelength, mmWaveis advanced in multi-gigabit transmittability and beamforming.In contrast, the short wavelength also makes mmWave easilyblocked by obstacles. In order to bypass these obstacles, relaysare widely needed in mmWave communications. Unmannedautonomous vehicles (UAV), such as drones and self-drivingrobots, enable the mobile relays in real applications. Nevertheless,it is challenging for a UAV to find its optimal relay locationautomatically. On one hand, it is difficult to find the locationaccurately due to the complex and dynamic wireless environment;on the other hand, most applications require the relay to forwarddata immediately, so the autonomous process should be fast.To tackle this challenge, we propose a novel method AutoRelayspecialized for mmWave communications. In AutoRelay, the UAVsamples the link qualities of mmWave beams while moving. Basedon the real-time sampling, the UAV gradually adjusts its path toapproach the optimal location by leveraging compressive sensingtheory to estimate the link qualities in candidate space, whichincreases the accuracy and save the time. Performance resultsdemonstrate that AutoRelay outperforms existing methods inachieving an accurate and efficient relay strategy

Place, publisher, year, edition, pages
IEEE, 2017.
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-64750DOI: 10.1109/JSAC.2017.2720518OAI: oai:DiVA.org:ltu-64750DiVA: diva2:1119145
Available from: 2017-07-03 Created: 2017-07-03 Last updated: 2017-07-06

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Vasilakos, Athanasios
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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