Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking
The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly c...
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Format: | Electronic Book Chapter |
Language: | English |
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Basel
MDPI - Multidisciplinary Digital Publishing Institute
2022
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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041 | 0 | |a eng | |
042 | |a dc | ||
072 | 7 | |a TB |2 bicssc | |
072 | 7 | |a TBX |2 bicssc | |
100 | 1 | |a Deruyck, Margot |4 edt | |
700 | 1 | |a Deruyck, Margot |4 oth | |
245 | 1 | 0 | |a Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking |
260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2022 | ||
300 | |a 1 electronic resource (264 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network's infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Technology: general issues |2 bicssc | |
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a unmanned aerial vehicle | ||
653 | |a UAV positioning | ||
653 | |a machine learning | ||
653 | |a wireless communications | ||
653 | |a drones | ||
653 | |a network | ||
653 | |a DTN | ||
653 | |a mobility schedule | ||
653 | |a routing algorithms | ||
653 | |a data delivery | ||
653 | |a Internet of drones | ||
653 | |a communication | ||
653 | |a security | ||
653 | |a privacy | ||
653 | |a UAV base station | ||
653 | |a MIMO | ||
653 | |a millimeter-wave band | ||
653 | |a blind beamforming | ||
653 | |a signal recovery | ||
653 | |a UAV relay networks | ||
653 | |a resource management | ||
653 | |a transmit time allocation | ||
653 | |a unmanned aerial vehicles | ||
653 | |a dynamic spectrum access | ||
653 | |a quality of service | ||
653 | |a reinforcement learning | ||
653 | |a multi-armed bandit | ||
653 | |a aerial communication | ||
653 | |a FANET | ||
653 | |a not-spots | ||
653 | |a stratospheric communication platform | ||
653 | |a UAV | ||
653 | |a UAV-assisted network | ||
653 | |a 5G | ||
653 | |a global positioning system | ||
653 | |a GPS spoofing attacks | ||
653 | |a detection techniques | ||
653 | |a dynamic selection | ||
653 | |a hyperparameter tuning | ||
653 | |a IoT | ||
653 | |a RF radio communication | ||
653 | |a Wi-Fi direct | ||
653 | |a D2D | ||
653 | |a drone-based mobile secure zone | ||
653 | |a friendly jamming | ||
653 | |a mobility | ||
653 | |a internet of things | ||
653 | |a non-orthogonal multiple access | ||
653 | |a resource allocation | ||
653 | |a ultra reliable low latency communication | ||
653 | |a uplink transmission | ||
653 | |a Deep Q-learning (DQL) | ||
653 | |a Double Deep Q-learning (DDQL) | ||
653 | |a dynamic spectrum sharing | ||
653 | |a High Altitude Platform Station (HAPS) | ||
653 | |a cellular communications | ||
653 | |a power control | ||
653 | |a interference management | ||
653 | |a cognitive UAV networks | ||
653 | |a clustered two-stage-fusion cooperative spectrum sensing | ||
653 | |a continuous hidden Markov model | ||
653 | |a SNR estimation | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/5760 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/91127 |7 0 |z DOAB: description of the publication |