J
Jie Yang
Researcher at Nanjing University of Posts and Telecommunications
Publications - 46
Citations - 3068
Jie Yang is an academic researcher from Nanjing University of Posts and Telecommunications. The author has contributed to research in topics: Deep learning & MIMO. The author has an hindex of 15, co-authored 42 publications receiving 1853 citations.
Papers
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Journal ArticleDOI
Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System
TL;DR: Simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness.
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Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios
TL;DR: A deep learning-based method, combined with two convolutional neural networks trained on different datasets, to achieve higher accuracy AMR, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio.
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Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding
TL;DR: In this paper, a deep learning-enabled mmWave massive MIMO framework for effective hybrid precoding is proposed, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN).
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Fast Beamforming Design via Deep Learning
TL;DR: This work proposes a deep learning based fast beamforming design method which separates the problem into power allocation and virtual uplink beamforming (VUB) design and designs a heuristic solution structure of the downlink beamforming through the virtual equivalent uplink channel based on optimum MMSE receiver.
Journal ArticleDOI
Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding
TL;DR: In this paper, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding is proposed, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN).