A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direc...A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.展开更多
车载通信是高速串行器与解串器(serializer and deserializer,SerDes)技术应用的一个重要领域。针对复杂车载环境中链路高频衰减导致的符号间干扰在高阶调制方式下更为严重的问题,引入深度学习方法,设计一种基于Transformer编码器结构...车载通信是高速串行器与解串器(serializer and deserializer,SerDes)技术应用的一个重要领域。针对复杂车载环境中链路高频衰减导致的符号间干扰在高阶调制方式下更为严重的问题,引入深度学习方法,设计一种基于Transformer编码器结构的低复杂度信道均衡方案,以提高接收信号质量。该方案将输入序列转换为抽象的表示向量,然后利用编码器层提取表示向量的特征信息,最后全连接层根据特征信息对信号进行分类,从而实现高速SerDes信道均衡。实验结果表明:与传统自适应算法和全连接神经网络模型相比,所提方案能够有效降低高频衰减导致的信号失真,在计算复杂度降低19%和24%的情况下接收信噪比增益分别为1.8 dB和0.9 dB。通过在高速SerDes系统中应用所提信道均衡方案,可以提高信号传输质量以及增强系统的鲁棒性。展开更多
基金supported by the National Key Research and Development Program of China (Grant No.2019YFB1803700)the Key Technologies Research and Development Program of Tianjin (Grant No.20YFZCGX00440).
文摘A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.
文摘采用基于软判决和硬判决的方法,对跳时脉冲位置调制(time hopping-pulse position modulation,TH-PPM)和跳时脉冲幅度调制(time hopping-pulse amplitude modulation,TH-PAM)超宽带系统的误比特率性能进行了分析和比较.在加性高斯白噪声(additive white Gausses noise,AWGN)信道下,研究了TH-PPM和TH-PAM超宽带单用户系统接收端信号进行软判决和硬判决时的性能,同时分析比较系统在两种调制方式下采用不同脉冲重复次数时的性能差异.仿真结果表明,在AWGN信道下,TH-PPM和TH-PAM的系统性能均随脉冲重复次数的增加而明显改善,并且后者优于前者.此外,采用软判决时的系统性能优于采用硬判决时的系统性能.
文摘车载通信是高速串行器与解串器(serializer and deserializer,SerDes)技术应用的一个重要领域。针对复杂车载环境中链路高频衰减导致的符号间干扰在高阶调制方式下更为严重的问题,引入深度学习方法,设计一种基于Transformer编码器结构的低复杂度信道均衡方案,以提高接收信号质量。该方案将输入序列转换为抽象的表示向量,然后利用编码器层提取表示向量的特征信息,最后全连接层根据特征信息对信号进行分类,从而实现高速SerDes信道均衡。实验结果表明:与传统自适应算法和全连接神经网络模型相比,所提方案能够有效降低高频衰减导致的信号失真,在计算复杂度降低19%和24%的情况下接收信噪比增益分别为1.8 dB和0.9 dB。通过在高速SerDes系统中应用所提信道均衡方案,可以提高信号传输质量以及增强系统的鲁棒性。
