Z
Zhijin Qin
Researcher at Queen Mary University of London
Publications - 163
Citations - 7568
Zhijin Qin 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 27, co-authored 129 publications receiving 4259 citations. Previous affiliations of Zhijin Qin include Intel & Lancaster University.
Papers
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Nonorthogonal Multiple Access for 5G and Beyond
TL;DR: This work provides a comprehensive overview of the state of the art in power-domain multiplexing-aided NOMA, with a focus on the theoretical N OMA principles, multiple-antenna- aided NomA design, and on the interplay between NOMa and cooperative transmission.
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Enhancing the Physical Layer Security of Non-Orthogonal Multiple Access in Large-Scale Networks
TL;DR: In this paper, the authors investigated the physical layer security of NOMA in large-scale networks with invoking stochastic geometry and derived new exact expressions of the security outage probability for both single-antenna and multipleantenna aided transmission scenarios.
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Deep Learning Enabled Semantic Communication Systems
TL;DR: In this paper, a deep learning based semantic communication system, named DeepSC, for text transmission based on the Transformer, aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications.
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Modulation and Multiple Access for 5G Networks
TL;DR: In this paper, a comprehensive overview of the most promising modulation and multiple access (MA) schemes for 5G networks is presented, including modulation techniques in orthogonal MA (OMA) and various types of non-OMA (NOMA).
Posted Content
Reconfigurable Intelligent Surfaces: Principles and Opportunities
Yuanwei Liu,Xiao Liu,Xidong Mu,Tianwei Hou,Jiaqi Xu,Zhijin Qin,Marco Di Renzo,Naofal Al-Dhahir +7 more
TL;DR: A comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies is provided in this article.