Optimizing Drone Deployment for Maximized User Connectivity in Areas of Interest Via Deep Reinforcement Learning

In areas with limited communication capabilities, such as disaster zones, network service providers can deploy groups of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, serving as Drone Base Stations (DBSs) to temporarily supplement traditional communication infrastructure. In these...

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Vydané v:	Journal of network and systems management Ročník 33; číslo 3; s. 49
Hlavní autori:	Rajashekar, Kolichala, Garg, Ashutosh, M. Baswade, Anand, Sidhanta, Subhajit
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2025 Springer Nature B.V
Predmet:	Bandwidths Communication Communications Engineering Computer Communication Networks Computer Science Computer Systems Organization and Communication Networks Connectivity Deep learning Deployment Disasters Drone aircraft Drones Information Systems and Communication Service Infrastructure Learning Markov processes Networks Normalization Objectives Operations Research/Decision Theory Optimization Reinforcement Robotics Robots Unmanned aerial vehicles Drone base station Deep reinforcement learning DBS deployment Soft actor-critic (SAC) algorithm Normalized continuous action space for reinforcement learning Drone disaster emergency communications
ISSN:	1064-7570, 1573-7705
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