论文标题
使用基于水下无线光学通信的深度学习对星座图的识别和评估
Recognition and evaluation of constellation diagram using deep learning based on underwater wireless optical communication
论文作者
论文摘要
抽象的。在本文中，我们提出了一种基于水下无线光学通信（UWOC）的深度学习的星座图识别和评估方法。更具体地说，基于卷积神经网络（CNN）的UWOC系统的星座图分析仪设计用于调制格式识别（MFR），光信号噪声比（OSNR）和相误差估计。此外，无监督的学习用于从影响水下通道质量的各种因素中挖掘新的优化度量。拟议的新指标合成了几个原始索引，我们将其称为多噪声空间度量标准（MNSM）。提出的MNSM将星座的质量从高到低划分为多个级别，并反映了UWOC通道的质量。通过模拟，获得了四个广泛使用的M-QAM调制格式的星座图，以16个OSNR值（15dB〜30dB）获得，相位误差标准偏差范围为0°至45°。结果表明，MFR的准确性，OSNR和相位噪声的估计值分别为100％，95％和98.6％的精度。还进行了消融研究，以分析识别星座图的深度学习的表现。
Abstract. In this paper, we proposed a method of constellation diagram recognition and evaluation using deep learning based on underwater wireless optical communication (UWOC). More specifically, an constellation diagram analyzer for UWOC system based on convolutional neural network (CNN) is designed for modulation format recognition (MFR), optical signal noise ratio (OSNR) and phase error estimation. Besides, unsupervised learning is used to excavate a new optimization metric from various factors that affect the quality of underwater channel.The proposed new metric synthesizes several original indexes, which we termed it as multi noise spatial metric (MNSM). The proposed MNSM divides the quality of constellation from high to low into several levels and reflects the quality of UWOC channel. Through the simulation, the constellation diagrams of four widely used M-QAM modulation formats for 16 OSNR values (15dB~30dB) are obtained, with the phase error standard deviations ranging from 0° to 45°. The results show that the accuracy of MFR , the estimation of OSNR and phase noise are 100%, 95% and 98.6% accuracies are achieved respectively. The ablation studies are also carried out in order to analyze the performance of deep learning in the recognition of constellation diagrams.
