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Chuan Zhang
Researcher at Southeast University
Publications - 256
Citations - 4041
Chuan Zhang is an academic researcher from Southeast University. The author has contributed to research in topics: Decoding methods & MIMO. The author has an hindex of 23, co-authored 228 publications receiving 2412 citations. Previous affiliations of Chuan Zhang include University of Minnesota & Nanjing University.
Papers
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Journal ArticleDOI
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
Xiaohu You,Cheng-Xiang Wang,Jie Huang,Xiqi Gao,Zaichen Zhang,Michael Mao Wang,Yongming Huang,Chuan Zhang,Yanxiang Jiang,Jiaheng Wang,Min Zhu,Bin Sheng,Dongming Wang,Zhiwen Pan,Pengcheng Zhu,Yang Yang,Zening Liu,Ping Zhang,Xiaofeng Tao,Shaoqian Li,Zhi Chen,Xinying Ma,Chih-Lin I,Shuangfeng Han,Ke Li,Pan Chengkang,Zhiming Zheng,Lajos Hanzo,Xuemin Shen,Yingjie Jay Guo,Zhiguo Ding,Harald Haas,Wen Tong,Peiying Zhu,Guanghua Yang,Jun Wang,Eric G. Larsson,Hien Quoc Ngo,Wei Hong,Haiming Wang,Debin Hou,Jixin Chen,Zhe Chen,Zhang-Cheng Hao,Geoffrey Ye Li,Rahim Tafazolli,Yue Gao,H. Vincent Poor,Gerhard P. Fettweis,Ying-Chang Liang +49 more
TL;DR: 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
Journal ArticleDOI
Low-Latency Sequential and Overlapped Architectures for Successive Cancellation Polar Decoder
Chuan Zhang,Keshab K. Parhi +1 more
TL;DR: A complete hardware architecture is first derived for the conventional tree SC decoder and the feedback part is presented next, and a systematic approach to construct different overlapped SC polar decoder architectures is presented.
Proceedings ArticleDOI
Improved polar decoder based on deep learning
TL;DR: This paper presents the multiple scaled belief propagation (BP) algorithm, aiming at obtaining faster convergence and better performance, and deep neural network decoder (NND) with low complexity and latency, is proposed for any code length.
Journal ArticleDOI
AI for 5G: research directions and paradigms
TL;DR: In this paper, the authors provide an overview that first combs through several promising research directions in AI for 5G technologies based on an understanding of the key technologies in 5G and provides design paradigms including 5G network optimization, optimal resource allocation, 5G physical layer unified acceleration, end-to-end physical layer joint optimization, and so on.
Posted Content
AI for 5G: Research Directions and Paradigms
TL;DR: This study focuses on providing design paradigms including 5G network optimization, optimal resource allocation, 5G physical layer unified acceleration, end-to-end physical layer joint optimization, and so on based on an understanding of the key technologies in 5G.