Simulating UAV's Movement for Servicing User Groups with a Reference Point in Wireless Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU137105" target="_blank" >RIV/00216305:26220/20:PU137105 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-65729-1_37" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-65729-1_37</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-65729-1_37" target="_blank" >10.1007/978-3-030-65729-1_37</a>
Alternative languages
Result language
angličtina
Original language name
Simulating UAV's Movement for Servicing User Groups with a Reference Point in Wireless Networks
Original language description
Current cellular networks face outbreaks of an extremely high demand for communication capacity and coverage during the mass events. This article discusses a scenario with events in remote areas. It is expected that the unmanned aerial vehicles (UAVs) equipped with the directional antennas will become one of the key components of these networks and provide the solution. It attracts considerable attention in basic and applied research and commerce for its rapid deployment and exible extension of the users coverage, mobility of UAV access points (APs) and a higher probability of line-of-sight channels. However, it also creates new issues to be addressed. The critical task is to maximize coverage area with the required quality of service to provide the connection for the maximum number of users. At the same time, analysis of the performance indicators of such networks, taking into account the mobility of both access points and users, is challenging. One of the key quality indicators is the probability of coverage. The aim of this work is to consider two drones' mobility models to cover users with small cells, and to solve the problem of maximizing coverage probability using the simulation. With a given threshold signal-to-noise ratio, it is shown that using the particle swarm method as an adaptive navigation algorithm allows achieving higher coverage probability values as opposed to k-means algorithm. A comparative analysis of adaptive navigation is presented.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20202 - Communication engineering and systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Internet of Things, Smart Spaces, and Next Generation Networks and Systems
ISBN
978-3-030-65729-1
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
415-425
Publisher name
Springer, Cham
Place of publication
Neuveden
Event location
St. Petersburg
Event date
Aug 26, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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