Joint Relay Selection and Power Allocation for Time-Varying Energy Harvesting-Driven UASNs: A Stratified Reinforcement Learning Approach
Author:
Affiliation:
1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, China
2. Department of Technology, National Deep Sea Center, Qingdao, China
Funder
National Natural Science Foundation of China
S&T Program of Hebei
Science and Technology Research Project of Universities in Hebei
Taishan Scholar Project Funding
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/9920237/09877737.pdf?arnumber=9877737
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1. Joint Resource Allocation for Time-Varying Underwater Acoustic Communication System: A Self-Reflection Adversarial Bandit Approach;IEEE Transactions on Systems, Man, and Cybernetics: Systems;2024-04
2. Relay selection in Underwater Acoustic Sensor Networks for QoS-based cooperative communication using game theory;Computer Communications;2024-04
3. Multi-hop relay selection for underwater acoustic sensor networks: A dynamic combinatorial multi-armed bandit learning approach;Computer Networks;2024-04
4. Power Allocation Based on Multi-Agent Deep Deterministic Policy Gradient for Underwater Acoustic Communication Networks;Electronics;2024-01-09
5. A Heterogeneous Reinforcement Learning Approach for Joint Relay Selection and Power Allocation in Time-Varying Uasns with Energy Harvesting;2024
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