Distributed RIS-Assisted FD Systems with Discrete Phase Shifts: A Reinforcement Learning Approach
Author:
Affiliation:
1. Memorial University,Faculty of Engineering and Applied Science,St. John's,NL,Canada
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001723.pdf?arnumber=10001723
Reference18 articles.
1. Beamforming Optimization for Wireless Network Aided by Intelligent Reflecting Surface With Discrete Phase Shifts
2. Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?
3. Beamforming Optimization for Intelligent Reflecting Surface with Discrete Phase Shifts
4. Performance Analysis of RIS-Aided Systems With Practical Phase Shift and Amplitude Response
5. Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems
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1. Performance Evaluation of Deep Q Networks for Hybrid Reconfigurable Intelligent Surface in 6G Networks;2024 International Conference on Computer, Information and Telecommunication Systems (CITS);2024-07-17
2. Optimizing Intelligent Reflecting Surfaces with Discrete Phase Shifts and Pilot Overhead Reduction Using Deep Learning;2024 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom);2024-06-24
3. Fairness-Aware Computation Offloading With Trajectory Optimization and Phase-Shift Design in RIS-Assisted Multi-UAV MEC Network;IEEE Internet of Things Journal;2024-06-01
4. Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions;IEEE Open Journal of Vehicular Technology;2024
5. On Discrete Phase Shifts Optimization of RIS-Aided FD Systems: Are All RIS Elements Needed?;ICC 2023 - IEEE International Conference on Communications;2023-05-28
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