LSTM Guided Modulation Classification and Experimental Validation for Sub-Nyquist Rate Wideband Spectrum Sensing-Reference-Cited by-同舟云学术

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LSTM Guided Modulation Classification and Experimental Validation for Sub-Nyquist Rate Wideband Spectrum Sensing

Published:2019-01 Issue: Volume: Page:
ISSN:
Container-title:2019 11th International Conference on Communication Systems & Networks (COMSNETS)
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Author:

Chandhok Shivam,Joshi Himani,Darak S. J.,Subramanyam A V

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8703514/8711008/08711345.pdf?arnumber=8711345

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. DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization;Engineering Applications of Artificial Intelligence;2023-06

2. Generative Adversarial Network-Based Signal Inpainting for Automatic Modulation Classification;IEEE Access;2023

3. An End-to-End Deep Learning Framework for Wideband Signal Recognition;IEEE Access;2023

4. A Deep Learning-based Signal Classification Approach for Spectrum Sensing using Long Short-Term Memory (LSTM) Networks;2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE);2022-12-13

5. Deep Learning for Spectral Filling in Radio Frequency Applications;2022 Sensor Signal Processing for Defence Conference (SSPD);2022-09

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