Dynamic Beam Pattern and Bandwidth Allocation Based on Multi-Agent Deep Reinforcement Learning for Beam Hopping Satellite Systems-Reference-Cited by-同舟云学术

Dynamic Beam Pattern and Bandwidth Allocation Based on Multi-Agent Deep Reinforcement Learning for Beam Hopping Satellite Systems

Published:2022-04 Issue:4 Volume:71 Page:3917-3930
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Lin Zhiyuan¹^ORCID,Ni Zuyao²^ORCID,Kuang Linling²^ORCID,Jiang Chunxiao²^ORCID,Huang Zhen²

Affiliation:

1. Department of Electronic Engineering and the Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

2. Tsinghua Space Center and the Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China

Funder

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/9765825/09693289.pdf?arnumber=9693289

Reference43 articles.

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2. Deep reinforcement learning with double Q-learning;hasselt;Proc 30th Assoc Advance Artif Intell Conf,0

3. Dynamic Beam Hopping Method Based on Multi-Objective Deep Reinforcement Learning for Next Generation Satellite Broadband Systems

4. Multi-Agent Deep Reinforcement Learning-Based Flexible Satellite Payload for Mobile Terminals

5. Reinforcement Learning Based Capacity Management in Multi-Layer Satellite Networks

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