基于神经网络的编码压扩联合峰均比降低技术

聂俊缘; 张大卫; 张静

doi:10.13756/j.gtxyj.2023.05.003

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基于神经网络的编码压扩联合峰均比降低技术

光通信系统与网络技术 | 更新时间：2023-10-07

- 基于神经网络的编码压扩联合峰均比降低技术
- Joint PAPR Reduction Technology Enabled by Neural Network-based Coding and Companding
- 光通信研究 2023年第5期页码：16-22
- 作者机构：
  
  1.华中科技大学电子　信息与通信学院，武汉　430074
  2.电子科技大学　光纤传感与通信重点实验室，成都　611731
- 作者简介：
  
  聂俊缘（1995-），男，湖北黄冈人。硕士，主要研究方向为深度学习在光通信中的应用。
  张大卫，讲师。dwzhang@hust.edu.cn
- 基金信息：
- DOI：10.13756/j.gtxyj.2023.05.003
  中图分类号： TN929
- 纸质出版日期：2023-10-10，
  
  收稿日期：2022-05-01，
  
  修回日期：2022-09-01，
扫描看全文
聂俊缘,张大卫,张静.基于神经网络的编码压扩联合峰均比降低技术[J].光通信研究,2023(5):16-22.

Nie J Y, Zhang D W, Zhang J.Joint PAPR Reduction Technology Enabled by Neural Network-based Coding and Companding[J].Study on Optical Communications, 2023(5):16-22.
聂俊缘,张大卫,张静.基于神经网络的编码压扩联合峰均比降低技术[J].光通信研究,2023(5):16-22. DOI： 10.13756/j.gtxyj.2023.05.003.

Nie J Y, Zhang D W, Zhang J.Joint PAPR Reduction Technology Enabled by Neural Network-based Coding and Companding[J].Study on Optical Communications, 2023(5):16-22. DOI： 10.13756/j.gtxyj.2023.05.003.

摘要

多载波调制技术是当下宽带通信系统中常用的一种先进的调制方式，然而，多载波信号在时域上会产生非常大的峰均功率比（PAPR），这将导致信号非线性损伤，严重影响信号传输性能与通信质量。文章提出了基于神经网络的频域编码和时域压扩联合PAPR降低方案，频域编码采用一层全连接层来实现，极大降低了复杂度和网络训练难度，同时引入小比例的扩频用以提供编码增益，时域压扩网络采用了非线性的卷积神经网络来降低PAPR。通过在各种参数条件下的仿真展示了该方案的效果，并与多种方案对比。仿真结果表明，该方案在引入极小失真下，PAPR可降低5 dB。最后，对该方案进行了实验验证，结果表明，在降低PAPR 5 dB情况下，该方案的误码率（BER）性能相比剪切方案降低了75%，验证了方案的可行性。

Abstract

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.

关键词

神经网络频域编码时域压扩峰均功率比多载波系统

Keywords

neural networkfrequency space codingtime space compandingPAPRmulti-carrier system

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