Deep Reinforcement Learning Based Beamforming Codebook Design for RIS-aided mmWave Systems-Reference-Cited by-同舟云学术

Deep Reinforcement Learning Based Beamforming Codebook Design for RIS-aided mmWave Systems

Published:2023-01-08 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)
language:
Short-container-title:

Author:

Abdallah Asmaa¹,Celik Abdulkadir¹,Mansour Mohammad M.²,Eltawil Ahmed M.¹

Affiliation:

1. King Abdullah University of Science and Technology (KAUST),Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division,Thuwal,KSA,23955-6900

2. American University of Beirut,Department of Elect. and Computer Engineering,Beirut,Lebanon,1107 2020

Funder

KAUST

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10059462/10059600/10060056.pdf?arnumber=10060056

Reference21 articles.

1. Learning reflection beam-forming codebooks for arbitrary RIS and non-stationary channels;zhang;ArXiv Preprint,2021

2. Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning

3. Multiagent deep reinforcement learning: Challenges and directions towards human-like approaches;wong;ArXiv Preprint,2021

4. Reinforcement learning of beam code-books in millimeter wave and terahertz MIMO systems;zhang;IEEE Trans Commun,2022

5. An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems

Cited by 8 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. 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. Multi-Agent Deep Reinforcement Learning for Beam Codebook Design in RIS-Aided Systems;IEEE Transactions on Wireless Communications;2024-07

3. Codebook Configuration for RIS-Aided Systems via Implicit Neural Representations;ICC 2024 - IEEE International Conference on Communications;2024-06-09

4. A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks;IEEE Open Journal of the Communications Society;2024

5. Achieving Optimum Received Power for Discrete-Phase RISs With Elementwise Updates in the Least Number of Steps;IEEE Open Journal of the Communications Society;2024