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20152023
most citedChannel Customization for Limited Feedback in RIS-assisted FDD Systems

27 citations · 84 across the 11 of their papers we have counts for

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5 papers · 1 filter

eess.SP2023

Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning

Weijie Jin, Jing Zhang, Chao-Kai Wen +3

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly wi…

eess.SP202122 cited

Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning

Qisheng Wang, Xiao Li, Shi Jin +1

In this letter, we investigate the hybrid beamforming based on deep reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU) multiple-input-single-output (MISO) sy…

eess.SP20203 cited

Integrated Communication and Localization in mmWave Systems

Jie Yang, Jing Xu, Xiao Li +2

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have be…

eess.SP2019

MIMO Transmission through Reconfigurable Intelligent Surface: System Design, Analysis, and Implementation

Wankai Tang, Jun Yan Dai, Ming Zheng Chen +6

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by…

eess.SP2019

Physical Layer Security Enhancement Exploiting Intelligent Reflecting Surface

Keming Feng, Xiao Li, Yu Han +2

In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated. Assuming a single-antenna…