S
Shi Jin
Researcher at Southeast University
Publications - 116
Citations - 3707
Shi Jin is an academic researcher from Southeast University. The author has contributed to research in topics: MIMO & Communication channel. The author has an hindex of 27, co-authored 116 publications receiving 2449 citations. Previous affiliations of Shi Jin include National Sun Yat-sen University.
Papers
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Journal ArticleDOI
Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels
TL;DR: A real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), is developed by extending a novel deep learning (DL)-based CSI sensing and recovery network that outperforms existing compressive sensing-based and DL-based methods and is remarkably robust to CR reduction.
Proceedings ArticleDOI
A Model-Driven Deep Learning Network for MIMO Detection
TL;DR: Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.
Journal ArticleDOI
ComNet: Combination of Deep Learning and Expert Knowledge in OFDM Receivers
TL;DR: In this article, a model-driven deep learning (DL) approach that combines DL with the expert knowledge to replace the existing orthogonal frequency-division multiplexing receiver in wireless communications is proposed.
Posted Content
Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels
TL;DR: In this article, a real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), was developed by extending a novel deep learning (DL)-based CSI sensing and recovery network.
Journal ArticleDOI
Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
TL;DR: In this article, a multiple-rate compressive sensing neural network framework was proposed to compress and quantize the channel state information (CSI) in massive MIMO networks, which not only improves reconstruction accuracy but also decreases storage space at the UE.