Weighted fair energy transfer in a UAV network: A multi-agent deep reinforcement learning approach-Reference-Cited by-同舟云学术

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Weighted fair energy transfer in a UAV network: A multi-agent deep reinforcement learning approach

Published:2024-04 Issue: Volume:292 Page:130527
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Murshed Shabab^ORCID,Nibir Abu Shaikh,Razzaque Md. Abdur,Roy Palash^ORCID,Elhendi Ahmed Zohier,Hassan Md. Rafiul,Hassan Mohammad Mehedi

Funder

King Saud University

Publisher

Elsevier BV

Reference41 articles.

1. Mission adaptable autonomous vehicles;Schiller,1991

2. Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends;Mohsan;Intell Service Robot,2023

3. Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles;Yao;Energy,2023

4. Performance evaluation of multi-UAV system in post-disaster application: Validated by HITL simulator;Aljehani;IEEE Access,2019

5. UAV network for surveillance of inaccessible regions with zero blind spots;Kumar,2020

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1. Evaluating energy harvesting UAV-NOMA network with random user pairing in the finite blocklength regime;Vehicular Communications;2024-10

2. Optimizing UAV-UGV coalition operations: A hybrid clustering and multi-agent reinforcement learning approach for path planning in obstructed environment;Ad Hoc Networks;2024-07

3. Attention Model-Driven Maddpg Algorithm for Delay and Cost Aware Placement of Service Function Chains in 5g;2024

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