Y
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.
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Competitive MA-DRL for Transmit Power Pool Design in Semi-Grant-Free NOMA Systems.
TL;DR: In this paper, the authors exploit the capability of multi-agent deep reinforcement learning (MA-DRL) technique to generate a transmit power pool (PP) for Internet of things (IoT) networks with semi-grant-free NOMA.
Proceedings ArticleDOI
Performance analysis of non-regenerative relay assisted NOMA system
TL;DR: It is demonstrated that the system capacity performance is enhanced by NOMA compared to the OMA encoding scheme in the non-regenerative relay assisted system.
Journal ArticleDOI
Physical Layer Security in Near-Field Communications: What Will Be Changed?
TL;DR: In this paper , a two-stage algorithm is proposed to maximize the near-field secrecy capacity, based on the fully-digital beamformers obtained in the first stage, the optimal analog beamforming and baseband digital beamforming can be alternatingly derived in the closed-form expressions in the second stage.
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Intelligent Reflecting Surface Enhanced Multi-UAV NOMA Networks
TL;DR: In this article, a new transmission framework is proposed, where multiple UAV-mounted base stations employ NOMA to serve multiple groups of ground users with the aid of an RIS.
Journal ArticleDOI
Cluster-Free NOMA Communications Toward Next Generation Multiple Access
TL;DR: A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancellation (SIC), and a Matching-SCA algorithm is proposed to reduce the computational complexity and alleviate the parameter initialization sensitivity of ADMM- SCA.