Academic Journal
Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems
| العنوان: | Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems |
|---|---|
| المؤلفون: | ORTIZ GOMEZ, Flor de Guadalupe, Tarchi, Daniele, Martinez, Ramon, Vanelli-Coralli, Alessandro, Salas, Miguel, Landeros, Salvador |
| المصدر: | IEEE Transactions on Cognitive Communications and Networking, 8 (1) (2022-03) |
| بيانات النشر: | Institute of Electrical and Electronics Engineers |
| سنة النشر: | 2022 |
| المجموعة: | University of Luxembourg: ORBilu - Open Repository and Bibliography |
| مصطلحات موضوعية: | dynamic resource management, flexible payload, deep reinforcement learning, Engineering, computing & technology, Electrical & electronics engineering, Ingénierie, informatique & technologie, Ingénierie électrique & électronique |
| الوصف: | peer reviewed ; 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). |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2332-7731 |
| Relation: | urn:issn:2332-7731; https://orbilu.uni.lu/handle/10993/50881; info:hdl:10993/50881; wos:000766629000027 |
| DOI: | 10.1109/TCCN.2021.3087586 |
| الاتاحة: | https://orbilu.uni.lu/handle/10993/50881 https://orbilu.uni.lu/bitstream/10993/50881/1/Cooperative_Multi-Agent_Deep_Reinforcement_Learning_for_Resource_Management_in_Full_Flexible_VHTS_Systems.pdf https://doi.org/10.1109/TCCN.2021.3087586 |
| Rights: | open access ; http://purl.org/coar/access_right/c_abf2 ; info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.286E0203 |
| قاعدة البيانات: | BASE |
| تدمد: | 23327731 |
|---|---|
| DOI: | 10.1109/TCCN.2021.3087586 |