Reference : Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full F...
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems
Ortiz Gomez, Flor de Guadalupe mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Tarchi, Daniele mailto [University of Bologna > Department of Electrical, Electronic and Information Engineering]
Martinez, Ramon mailto [Universidad Politecnica de Madrid > Information Processing and Telecommunications Center]
Vanelli-Coralli, Alessandro mailto []
Salas, Miguel mailto []
Landeros, Salvador mailto [Agencia Espacial Mexicana]
IEEE Transactions on Cognitive Communications and Networking
Institute of Electrical and Electronics Engineers
United States
[en] dynamic resource management ; flexible payload ; deep reinforcement learning
[en] Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand in the near future. Nevertheless, this increase will not be uniform over the entire service area due to the non-uniform distribution of users and changes in traffic demand during the day. This problem is addressed by using flexible payload architectures, which allow the allocation of payload resources flexibly to meet the traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to VHTS systems, so in this paper we discuss the use of one reinforcement learning (RL) algorithm and two deep reinforcement learning (DRL) algorithms to manage the resources available in flexible payload architectures for DRM. These algorithms are Q-Learning (QL), Deep Q-Learning (DQL) and Double Deep Q-Learning (DDQL) which are compared based on their performance, complexity and added latency. On the other hand, this work demonstrates the superiority a cooperative multiagent (CMA) decentralized distribution has over a single agent (SA).
The original publication is available at

File(s) associated to this reference

Fulltext file(s):

Open access
Cooperative_Multi-Agent_Deep_Reinforcement_Learning_for_Resource_Management_in_Full_Flexible_VHTS_Systems.pdfAuthor postprint2.84 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.