8 Papers
24 Citations
Jing Xing is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: MIMO & Communications system. The author has an hindex of 3, co-authored 7 publications.
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Papers
Autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems
TL;DR: An intelligent HB design method based on the autoencoder (AE) neural network is proposed, which exhibits superior performance in terms of bit error rate (BER) and the chosen of hyper-parameter is discussed.
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Deep Neural Hybrid Beamforming for Multi-User mmWave Massive MIMO System
Jiyun Tao,Jing Xing,Jienan Chen,Chuan Zhang,Shengli Fu +4 more
- 01 Nov 2019
TL;DR: This work proposes a deep neural network based HB for the multi-User mmWave massive MIMO system, referred as DNHB, formulated as an autoencoder neural network, which is trained in a style of end-to-end self-supervised learning.
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Multiple Nodes Access of Wireless Beam Modulation for 6G-Enabled Internet of Things
TL;DR: This work proposes multiple IoT nodes access-based wireless beam modulation (WBM) technology for the mmWave transmission, where the WBM employs the over-the-air modulation to achieve the low-cost mmWave hardware design with hundreds of megabit per second transmission.
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•Posted Content
Hybrid Beamforming/Combining for Millimeter Wave MIMO: A Machine Learning Approach
TL;DR: In this article, a deep neural network based hybrid beamforming for multi-user mmWave massive MIMO system, referred as DNHB, has been proposed, which is formulated as an autoencoder neural network, trained in a style of end-to-end self-supervised learning.
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Patent
Millimeter wave MIMO hybrid beam forming optimization method based on deep learning
Chen Jienan,Jing Xing,Tao Jiyun,Liu Junkai +3 more
- 03 Dec 2019
TL;DR: In this paper, a millimeter wave MIMO hybrid beam forming optimization method based on deep learning was proposed, where the constraint conditions in a traditional millimeter-wave large-scale hybrid beam formation optimization problem can be mapped into a neural network, and a multi-user hybrid beam formingsystem is completely converted into an equivalent neural network.
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