Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory

This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on communications Ročník 68; číslo 11; s. 6840 - 6857
Hlavní autori: Shiri, Hamid, Park, Jihong, Bennis, Mehdi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Autonomous UAV
communication-efficient online path control
Computer & video games
Control methods
Control theory
Federated learning
Game theory
Mathematical model
mean-field game
Neural networks
Real time
Real-time systems
Sociology
Stability analysis
Statistics
Training
Unmanned aerial vehicles
ISSN:0090-6778, 1558-0857
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-time applications such as massive UAV control. Thus, a function approximator based on neural networks (NN) is utilized to approximate the solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Nevertheless, using an approximate solution can violate the conditions for convergence of the MFG framework. Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions. The stability analysis of the NN based MFG approach is presented and the performance of the proposed FL-MFG is elaborated by the simulations.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2020.3017281