水下可见光通信星座几何整形和人工智能技术

林显浩; 迟楠

doi:10.13756/j.gtxyj.2023.04.005

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水下可见光通信星座几何整形和人工智能技术

专题：水下光通信技术 | 更新时间：2023-08-14

- 水下可见光通信星座几何整形和人工智能技术
- Constellation Geometrically-shaping and Artificial Intelligence Technology in Underwater Visible Light Communication
- 光通信研究 2023年第4期页码：21-27
- 作者机构：
  
  1.复旦大学　通信科学与工程系　电磁波信息科学教育部重点实验室，上海　200433
  2.鹏城实验室，广东　深圳　518055
  3.上海低轨卫星通信与应用工程技术研究中心，上海　200433
  4.上海市低轨卫星通信技术协同创新中心，上海　200433
- 作者简介：
  
  林显浩（2000-），男，广东湛江人。博士，主要研究方向为可见光通信。
  迟楠，教授。E-mail：nanchi@fudan.edu.cn
- 基金信息：
  
  国家重点研发计划资助项目(2022YFB2802803);国家自然科学基金资助项目(61925104;62031011;62201157);鹏城实验室资助项目
- DOI：10.13756/j.gtxyj.2023.04.005
  中图分类号： TN929
- 纸质出版日期：2023-08-10，
  
  收稿日期：2023-03-06，
  
  修回日期：2023-03-10，
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林显浩，迟楠.水下可见光通信星座几何整形和人工智能技术[J].光通信研究，2023(4):21-27.

Lin X H, Chi N. Constellation Geometrically shaping and Artificial Intelligence Technology in Underwater Visible Light Communication [J].Study on Optical Communications, 2023(4):21-27.
林显浩，迟楠.水下可见光通信星座几何整形和人工智能技术[J].光通信研究，2023(4):21-27. DOI： 10.13756/j.gtxyj.2023.04.005.

Lin X H, Chi N. Constellation Geometrically shaping and Artificial Intelligence Technology in Underwater Visible Light Communication [J].Study on Optical Communications, 2023(4):21-27. DOI： 10.13756/j.gtxyj.2023.04.005.

摘要

水下可见光通信（UVLC）具有高传输速率、大容量、低延时和低成本等优势，已成为水下通信领域可行且很有吸引力的替代方案，应用前景广阔。然而，UVLC性能受到带宽、各种线性或非线性效应等瓶颈问题的局限。为了缓解这些问题，文章研究了基于几何整形的振幅移相键控（APSK）调制和编码映射，提出了基于双向循环神经网络（BRNN）的波形级后均衡器。此外，文章还提出了用基于深度神经网络（DNN）的波形到符号接收机来代替传统的匹配滤波、下采样和后均衡等操作。实验表明，与传统接收机相比，基于BRNN后均衡电压动态范围提升了170 mV（69%），基于DNN的波形到符号接收机电压动态范围提升了245 mV（100%）。实验结果证明，基于BRNN后均衡和基于DNN的波形到符号接收机能有效提升系统性能，是未来UVLC中很有前景的技术方案。

Abstract

Underwater Visible Light Communication (UVLC) has great advantages of high transmission rate

large capacity

low latency

and low cost

which has become a feasible and attractive alternative in the field of underwater communication

with broad application prospects. However

UVLC performance is limited by bottleneck issues such as bandwidth limitations and various linear or nonlinear effects. In order to alleviate these problems

we studied geometrically-shaping based Amplitude Phase Shift Keying (APSK) modulation and coding mapping. A waveform-stage post equalizer based on Bidirectional Recurrent Neural Network (BRNN) is also proposed. In addition

a waveform-to-symbol receiver based on Deep Neural Network (DNN) is proposed to replace the traditional matching filtering

down-sampling

post-equalization and other operations. Compared with traditional receiver

the dynamic range of voltage using BRNN based post equalizer is improved by 170 mV(69%) and it is improved by 245 mV(100%) using waveform-to-symbol receiver based on DNN. In the paper

a waveform-stage post equalizer based on BRNN and a waveform-to-symbol receiver based on DNN are experimentally verified to be promising schemes in future UVLC.

关键词

水下可见光通信振幅移相键控双向循环神经网络后均衡深度神经网络

Keywords

UVLCAPSKBRNNpost-equalizationDNN

references

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