Study On Optical Communications, Volume. 49, Issue 5, 16(2023)

Joint PAPR Reduction Technology Enabled by Neural Network-based Coding and Companding

Jun-yuan Nie1, Da-wei Zhang1、*, and Jing Zhang2
Author Affiliations
  • 1School of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2Key Lab.of Optical Fiber Sensing and Communications, University of Electronic Science and Technology, Chengdu 611731, China
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    Multi-carrier modulation technology is an advanced modulation method commonly used in broadband communication systems. However, multi-carrier signals will produce a very large Peak to Average Power Ratio (PAPR) in the time domain, which will lead to non-linear damage and seriously affects the performance of system. In this paper, a joint PAPR reduction technology enabled by neural network-based coding and companding is proposed. The frequency-domain coding is implemented by a fully connected layer, which greatly reduces the complexity and difficulty of network training. A small proportion of spread spectrum is introduced to provide coding gain, and the time-domain companding network uses a nonlinear convolutional neural network to reduce PAPR. The effect of the scheme is shown through simulation under various parameter conditions, and compared with various schemes. The simulation results show that the scheme can reduce PAPR by 5 dB while introducing rare distortion. Finally, the scheme is experimentally verified. The experimental results show that the Bit Error Ratio (BER) is reduced by 75% compared with the clipping scheme when the PAPR is reduced by 5 dB, which verifies the feasibility of the scheme.

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    Jun-yuan Nie, Da-wei Zhang, Jing Zhang. Joint PAPR Reduction Technology Enabled by Neural Network-based Coding and Companding[J]. Study On Optical Communications, 2023, 49(5): 16

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    Paper Information

    Category: Research Articles

    Received: May. 1, 2022

    Accepted: --

    Published Online: Nov. 22, 2023

    The Author Email: Zhang Da-wei (dwzhang@hust.edu.cn)

    DOI:10.13756/j.gtxyj.2023.05.003

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