Deep Reinforcement Learning for Practical Phase Shift Optimization in RIS-assisted Networks over Short Packet Communications
Author:
Affiliation:
1. University of Oulu,Centre for Wireless Communications (CWC),Oulu,Finland
Funder
Academy of Finland
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9815454/9815565/09815804.pdf?arnumber=9815804
Reference20 articles.
1. Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems
2. Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design
3. Learning-Based Robust and Secure Transmission for Reconfigurable Intelligent Surface Aided Millimeter Wave UAV Communications
4. Joint Beamforming and Phase Shift Optimization for Multicell IRS-aided OFDMA-URLLC Systems
5. URLLC Facilitated by Mobile UAV Relay and RIS: A Joint Design of Passive Beamforming, Blocklength, and UAV Positioning
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1. Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning;2023 21st IEEE Interregional NEWCAS Conference (NEWCAS);2023-06-26
2. Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems;IEEE Internet of Things Journal;2023-05-15
3. A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications;Sensors;2023-02-24
4. RIS-Empowered MEC for URLLC Systems with Digital-Twin-Driven Architecture;IEEE Transactions on Communications;2023
5. DNN-DANM: A High-Accuracy Two-Dimensional DOA Estimation Method Using Practical RIS;IEEE Transactions on Vehicular Technology;2023
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