Deep Learning-Based Detection for Moderate-Density Code Multiple Access in IoT Networks-Reference-Cited by-同舟云学术

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Deep Learning-Based Detection for Moderate-Density Code Multiple Access in IoT Networks

Published:2020-01 Issue:1 Volume:24 Page:122-125
ISSN:1089-7798
Container-title:IEEE Communications Letters
language:
Short-container-title:IEEE Commun. Lett.

Author:

Han Yu^ORCID,Wang Zhenyong^ORCID,Guo Qing^ORCID,Xiang Wei^ORCID

Funder

National Natural Science Foundation of China

Fundamental Research Funds of Shenzhen Innovation of Science and Technology Committee

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/4234/8952820/08894490.pdf?arnumber=8894490

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Residual Learning-Aided Convolutional Autoencoder for SCMA;IEEE Communications Letters;2023-05

2. Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs;Transactions on Emerging Telecommunications Technologies;2023-04-19

3. Sparse Code Multiple Access Scheme Based on Variational Learning;IEEE Transactions on Communications;2022-12

4. Design of Nonorthogonal Multiple Access Systems: An Information Bottleneck Perspective;IEEE Communications Letters;2022-08

5. Digital Teaching Management System Based on Deep Learning of Internet of Things;Mobile Information Systems;2022-06-01

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