Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning

Published:2023-06-26 Issue: Volume: Page:
ISSN:
Container-title:2023 21st IEEE Interregional NEWCAS Conference (NEWCAS)
language:
Short-container-title:

Author:

Chen Di¹,Gao Hui¹,Chen Na²,Cao Ruohan¹

Affiliation:

1. Beijing University of Posts and Telecommunications,Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education School of Information and Communication Engineering,Beijing,China,100876

2. Nara Institute of Science and Technology,Graduate School of Science and Technology,Ikoma,Japan,630-0192

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10198027/10198028/10198052.pdf?arnumber=10198052

Reference19 articles.

1. Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design

2. Deep Reinforcement Learning based Joint Active and Passive Beamforming Design for RIS-Assisted MISO Systems

3. Spatially sparse precoding in millimeter wave MIMO systems;ayach;IEEE Trans Wireless Commun,2014

4. Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

5. Deep Reinforcement Learning for Practical Phase Shift Optimization in RIS-assisted Networks over Short Packet Communications

Cited by 1 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. Energy-Efficient Beamforming for RISs-Aided Communications: Gradient Based Meta Learning;ICC 2024 - IEEE International Conference on Communications;2024-06-09