Beamforming Optimization for IRS-Assisted mmWave V2I Communication Systems via Reinforcement Learning-Reference-Cited by-同舟云学术

Beamforming Optimization for IRS-Assisted mmWave V2I Communication Systems via Reinforcement Learning

Published:2022 Issue: Volume:10 Page:60521-60533
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lee Yeongrok¹^ORCID,Lee Ju-Hyung²^ORCID,Ko Young-Chai¹^ORCID

Affiliation:

1. School of Electrical Engineering, Korea University, Seoul, South Korea

2. Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

Funder

ICT R&D program of MSIT/IITP

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09791227.pdf?arnumber=9791227

Reference57 articles.

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2. Channel characterization for mmWave vehicle-to-infrastructure communications in urban street environment;he;Proc Eur Conf Antennas Propag (EuCap),2019

3. Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications

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

5. Hybrid Beamforming for Reconfigurable Intelligent Surface based Multi-User Communications: Achievable Rates With Limited Discrete Phase Shifts

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