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Yuanwei Liu
Researcher at Queen Mary University of London
Publications - 477
Citations - 18977
Yuanwei Liu is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 53, co-authored 359 publications receiving 11049 citations. Previous affiliations of Yuanwei Liu include Xidian University & University of Houston.
Papers
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Journal ArticleDOI
Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer
TL;DR: Simulation results demonstrate that the STAR-RIS has superiority over other conventional RISs in terms of its energy consumption, and both the proposed algorithms outperform the baseline DDPG algorithm, and the joint D DPG-DQN algorithm achieves a superior performance, albeit at an increased computational complexity.
Journal ArticleDOI
Effective Capacity Analysis of AmBC-NOMA Communication Systems
TL;DR: An ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) system framework is proposed and exact expressions for the effective capacity (EC) of two NOMA users and theBackscatter device (BD) are derived.
Posted Content
Resource Allocation in Intelligent Reflecting Surface Assisted NOMA Systems
TL;DR: This article investigates the downlink communications of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems and formulate a joint optimization problem over the channel assignment, decoding order of NOMA users, power allocation, and reflection coefficients to maximize the system throughput.
Posted Content
Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches.
TL;DR: In this article, a novel framework of reconfigurable intelligent surfaces (RISs)-enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enable mobility of the RIS and enhance the service quality for mobile users.
Book ChapterDOI
User Grouping and Power Allocation in NOMA Systems : A Reinforcement Learning-Based Solution
TL;DR: A pioneering solution to the problem of user grouping and power allocation in Non-Orthogonal Multiple Access (NOMA) systems by invoking the Object Migration Automata and one of its variants to resolve the user grouping problem for NOMA systems in stochastic environments.