Cooperative Multi-Agent Deep Reinforcement Learning for Resource Management in Full Flexible VHTS Systems
نویسندگان
چکیده
Very high throughput satellite (VHTS) systems are expected to have a huge increase in traffic demand the near future. Nevertheless, this will not be uniform over entire service area due non-uniform distribution of users and changes during day. This problem is addressed by using flexible payload architectures, which allow allocation resources flexibly meet each beam, leading dynamic resource management (DRM) approaches. However, DRM adds significant complexity VHTS systems, so paper we discuss use one reinforcement learning (RL) algorithm two deep (DRL) algorithms manage available architectures for DRM. These Q-Learning (QL), Deep (DQL) Double (DDQL) compared based on their performance, added latency. On other hand, work demonstrates superiority cooperative multiagent (CMA) decentralized has single agent (SA).
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ژورنال
عنوان ژورنال: IEEE Transactions on Cognitive Communications and Networking
سال: 2022
ISSN: ['2332-7731', '2372-2045']
DOI: https://doi.org/10.1109/tccn.2021.3087586