Deep Reinforcement Learning Based Data Offloading in Multi-Layer Ka/Q Band LEO Satellite-Terrestrial Networks
Author:
Affiliation:
1. Beijing University of Posts and Telecom.,Beijing,P.R. China
2. Beijing University of Posts and Telecom.,Purple Mountain Laboratories,Beijing,Nanjing,P. R. China
Funder
National Key Research and Development Program of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9657830/9657831/09658029.pdf?arnumber=9658029
Reference17 articles.
1. Prediction Model of Fade Duration Statistics for Satellite Communications at Ka- and $Q$ -Bands
2. A unified statistical rain-attenuation model for communication link fade predictions and optimal stochastic fade control design using a location-dependent rain-statistics database
3. Joint power allocation and transparent repeater gain setting algorithm in satellite communication systems
4. Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks
5. Channel-Aware Mission Scheduling in Broadband Data Relay Satellite Networks
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Deep Reinforcement Learning for QoE-Aware Offloading in Space-Terrestrial Integrated Networks;2023 19th International Conference on Mobility, Sensing and Networking (MSN);2023-12-14
2. Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing;Drones;2023-06-07
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