A deep reinforcement learning-based UAV-smallcell system for mobile terminals geolocalization in disaster scenarios

Authors:

RARoberta Avanzato
Roberta Avanzato
FBFrancesco Beritelli
Francesco Beritelli
RRRaoul Raftopoulos
Raoul Raftopoulos
GSGiovanni Schembra
Giovanni Schembra

Open Access

Publisher website

6 February 2025

journal article
Published by Elsevier in Computer Communications

Vol. 234
DOI:10.1016/j.comcom.2025.108088

Abstract

No abstract available

This publication has 20 references indexed in Scilit:

Cellular Network-Supported Machine Learning Techniques for Autonomous UAV Trajectory Planning
IEEE Access, 2022
Energy-Optimized Trajectory Planning for Solar-Powered Aircraft in a Wind Field Using Reinforcement Learning
IEEE Access, 2022
Collaborative Decision-Making Method for Multi-UAV Based on Multiagent Reinforcement Learning
IEEE Access, 2022
Autonomous 3-D UAV Localization Using Cellular Networks: Deep Supervised Learning Versus Reinforcement Learning Approaches
IEEE Access, 2021
Unmanned Aerial Vehicle Path Planning Algorithm Based on Deep Reinforcement Learning in Large-Scale and Dynamic Environments
IEEE Access, 2021
Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges
IEEE Access, 2021
A Smart UAV-Femtocell Data Sensing System for Post-Earthquake Localization of People
IEEE Access, 2020
Path Planning for UAV Ground Target Tracking via Deep Reinforcement Learning
IEEE Access, 2020
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems
IEEE Communications Surveys & Tutorials, 2019
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
1992