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| Autore: |
Deruyck Margot
|
| Titolo: |
Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking
|
| Pubblicazione: | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica: | 1 online resource (264 p.) |
| Soggetto topico: | History of engineering and technology |
| Technology: general issues | |
| Soggetto non controllato: | 5G |
| aerial communication | |
| blind beamforming | |
| cellular communications | |
| clustered two-stage-fusion cooperative spectrum sensing | |
| cognitive UAV networks | |
| communication | |
| continuous hidden Markov model | |
| D2D | |
| data delivery | |
| Deep Q-learning (DQL) | |
| detection techniques | |
| Double Deep Q-learning (DDQL) | |
| drone-based mobile secure zone | |
| drones | |
| DTN | |
| dynamic selection | |
| dynamic spectrum access | |
| dynamic spectrum sharing | |
| FANET | |
| friendly jamming | |
| global positioning system | |
| GPS spoofing attacks | |
| High Altitude Platform Station (HAPS) | |
| hyperparameter tuning | |
| interference management | |
| Internet of drones | |
| internet of things | |
| IoT | |
| machine learning | |
| millimeter-wave band | |
| MIMO | |
| mobility | |
| mobility schedule | |
| multi-armed bandit | |
| n/a | |
| network | |
| non-orthogonal multiple access | |
| not-spots | |
| power control | |
| privacy | |
| quality of service | |
| reinforcement learning | |
| resource allocation | |
| resource management | |
| RF radio communication | |
| routing algorithms | |
| security | |
| signal recovery | |
| SNR estimation | |
| stratospheric communication platform | |
| transmit time allocation | |
| UAV | |
| UAV base station | |
| UAV positioning | |
| UAV relay networks | |
| UAV-assisted network | |
| ultra reliable low latency communication | |
| unmanned aerial vehicle | |
| unmanned aerial vehicles | |
| uplink transmission | |
| Wi-Fi direct | |
| wireless communications | |
| Persona (resp. second.): | DeruyckMargot |
| Sommario/riassunto: | 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. |
| Altri titoli varianti: | Unmanned Aerial Vehicle |
| Titolo autorizzato: | Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking ![]() |
| Formato: | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910585935603321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |