PAPR Reduction Scheme for Deep Learning-Based Communication Systems Using Autoencoders-Reference-Cited by-同舟云学术

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PAPR Reduction Scheme for Deep Learning-Based Communication Systems Using Autoencoders

Published:2020-05 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)
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Author:

Vahdat Melika,Roshandeh Koosha Pourtahmasi,Ardakani Masoud,Jiang Hai

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9121635/9128361/09128513.pdf?arnumber=9128513

Cited by 5 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. Generative AI for Physical Layer Communications: A Survey;IEEE Transactions on Cognitive Communications and Networking;2024-06

2. A conditionally applied neural network algorithm for PAPR reduction without the use of a recovery process;ETRI Journal;2023-10-16

3. Clustering and Fitting to Reduce PAPR in Multi-User OFDM Systems;2023 25th International Conference on Digital Signal Processing and its Applications (DSPA);2023-03-29

4. Mitigating Clipping Distortion in Multicarrier Transmissions Using Tensor-Train Deep Neural Networks;IEEE Transactions on Wireless Communications;2023-03

5. PESTnet - Pre-IFFT PAPR Estimation using Neural Networks for Improved OFDM Systems;2023 IEEE Wireless Communications and Networking Conference (WCNC);2023-03

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