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220822 ||| eng |
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|a 9783036546643
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|a books978-3-0365-4664-3
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|a 9783036546636
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|a Deruyck, Margot
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| 245 |
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|a Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (264 p.)
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| 653 |
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|a drones
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|a stratospheric communication platform
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|a machine learning
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|a FANET
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| 653 |
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|a quality of service
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| 653 |
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|a resource allocation
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| 653 |
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|a unmanned aerial vehicles
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|a UAV positioning
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| 653 |
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|a data delivery
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| 653 |
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|a UAV-assisted network
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| 653 |
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|a friendly jamming
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| 653 |
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|a unmanned aerial vehicle
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| 653 |
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|a n/a
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|a detection techniques
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| 653 |
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|a DTN
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| 653 |
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|a privacy
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| 653 |
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|a IoT
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| 653 |
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|a RF radio communication
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| 653 |
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|a power control
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| 653 |
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|a aerial communication
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| 653 |
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|a clustered two-stage-fusion cooperative spectrum sensing
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a GPS spoofing attacks
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| 653 |
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|a network
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|a UAV base station
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| 653 |
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|a cellular communications
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|a multi-armed bandit
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| 653 |
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|a communication
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| 653 |
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|a MIMO
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a not-spots
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| 653 |
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|a dynamic selection
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| 653 |
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|a High Altitude Platform Station (HAPS)
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| 653 |
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|a non-orthogonal multiple access
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|a wireless communications
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| 653 |
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|a transmit time allocation
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|a reinforcement learning
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|a uplink transmission
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| 653 |
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|a Deep Q-learning (DQL)
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| 653 |
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|a Double Deep Q-learning (DDQL)
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| 653 |
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|a security
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| 653 |
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|a continuous hidden Markov model
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| 653 |
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|a UAV
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| 653 |
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|a mobility
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| 653 |
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|a SNR estimation
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| 653 |
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|a dynamic spectrum access
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|a blind beamforming
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|a mobility schedule
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|a ultra reliable low latency communication
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|a global positioning system
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|a 5G
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|a internet of things
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|a UAV relay networks
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| 653 |
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|a signal recovery
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| 653 |
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|a Internet of drones
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| 653 |
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|a D2D
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| 653 |
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|a dynamic spectrum sharing
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| 653 |
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|a cognitive UAV networks
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| 653 |
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|a resource management
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| 653 |
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|a interference management
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| 653 |
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|a hyperparameter tuning
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| 653 |
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|a routing algorithms
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| 653 |
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|a drone-based mobile secure zone
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| 653 |
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|a Wi-Fi direct
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| 653 |
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|a millimeter-wave band
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| 700 |
1 |
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|a Deruyck, Margot
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-4664-3
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/5760
|7 0
|x Verlag
|3 Volltext
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/91127
|z DOAB: description of the publication
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|a 900
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|a 380
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|a 600
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|a 620
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|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.
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