CN114598581A - Two-stage detection model training method, identification method and device for probabilistic shaped signal - Google Patents
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Abstract
本发明提供一种概率整形信号的双阶段检测模型训练方法、识别方法及装置,在信号接收端通过DSP模块处理得到的电域信号,基于盲均衡算法处理后的幅度柱状图,通过机器学习的方式构建幅度柱状图至光信噪比和调制格式的映射,以实现对概率整形信号的光信噪比和调制格式的识别。同时,由于信号激光器和本振激光器在洛伦兹线型下的相位噪声是微纳过程,且相位噪声增量的方差与激光器线宽是线性关系的特性,通过机器学习的方式构建不同符号间隔下相位噪声增量的均方根植和平均值映射至激光器线宽,以实现对激光器线宽的识别,并有效提高识别的泛化能力。
The invention provides a two-stage detection model training method, identification method and device for probabilistic shaping signals. The electrical domain signal obtained by processing the DSP module at the signal receiving end is based on the amplitude histogram processed by the blind equalization algorithm. The mapping of the amplitude histogram to the optical signal-to-noise ratio and the modulation format is constructed in a manner to realize the identification of the optical signal-to-noise ratio and the modulation format of the probability shaped signal. At the same time, since the phase noise of the signal laser and the local oscillator laser under the Lorentzian line type is a micro-nano process, and the variance of the phase noise increment is linearly related to the laser linewidth, different symbol intervals are constructed by machine learning. The root mean square root and average value of the lower phase noise increments are mapped to the laser linewidth to realize the identification of the laser linewidth and effectively improve the generalization ability of the identification.
Description
技术领域technical field
本发明涉及光通信技术领域,尤其涉及一种概率整形信号的双阶段检测模型训练方法、识别方法及装置。The invention relates to the technical field of optical communication, in particular to a training method, identification method and device for a two-stage detection model of a probability shaping signal.
背景技术Background technique
近年来,云设备、智能设备等新技术发展迅速,数据流量持续增长。随着光通信的发展,单模光纤的容量已经逐渐逼近香农极限,为了应对急剧增长的数据流量需求,物理层的资源需要被更有效利用。弹性光网络能够根据信道条件和容量需求自适应地调整调制格式和数据速率,可以更好的分配物理资源。为了使弹性光网络能够灵活地控制光传输系统,光性能监测和调制格式识别被广泛研究。In recent years, new technologies such as cloud devices and smart devices have developed rapidly, and data traffic has continued to grow. With the development of optical communication, the capacity of single-mode fiber has gradually approached the Shannon limit. In order to cope with the rapidly increasing data traffic demand, the resources of the physical layer need to be used more efficiently. The elastic optical network can adaptively adjust the modulation format and data rate according to channel conditions and capacity requirements, and can better allocate physical resources. To enable elastic optical networks to flexibly control optical transmission systems, optical performance monitoring and modulation format identification have been extensively studied.
光信噪比(OSNR)和激光器线宽的参数对于弹性光网络中传输系统的设计极其重要,激光器线宽的监测还有助于诊断激光器中突发的异常。此外,调制格式识别(MFI)对弹性光网络中接收机算法的选取尤为重要。随着数字信号处理(DSP)技术的发展,光性能监测由光域转向电域,这使监测成本进一步下降。为了实现高精度的性能监测,机器学习技术被广泛应用。The parameters of optical signal-to-noise ratio (OSNR) and laser linewidth are extremely important for the design of transmission systems in elastic optical networks, and monitoring of laser linewidth is also helpful in diagnosing sudden anomalies in lasers. In addition, modulation format identification (MFI) is particularly important for the selection of receiver algorithms in elastic optical networks. With the development of digital signal processing (DSP) technology, optical performance monitoring has shifted from the optical domain to the electrical domain, which further reduces the monitoring cost. To achieve high-precision performance monitoring, machine learning techniques are widely used.
在相干光通信系统中,OSNR和激光器线宽是度量幅度噪声和相位噪声的两大关键指标,但是现有技术没有针对概率整形波的检测方法,同时并不能对激光器线宽进行识别。In coherent optical communication systems, OSNR and laser linewidth are two key indicators for measuring amplitude noise and phase noise. However, there is no detection method for probability shaped waves in the prior art, and laser linewidth cannot be identified at the same time.
发明内容SUMMARY OF THE INVENTION
本发明实施例提供了一种概率整形信号的双阶段检测模型训练方法、识别方法及装置,以消除或改善现有技术中存在的一个或更多个缺陷,解决现有技术无法有效识别概率整形波以及无法对激光器线宽进行识别的问题。The embodiments of the present invention provide a two-stage detection model training method, identification method and device for probabilistic shaping signals, so as to eliminate or improve one or more defects existing in the prior art, and solve the problem that the prior art cannot effectively identify probabilistic shaping signals wave and the inability to identify the laser linewidth.
本发明的技术方案如下:The technical scheme of the present invention is as follows:
一方面,本发明提供一种概率整形信号的双阶段检测模型训练方法,包括:In one aspect, the present invention provides a two-stage detection model training method for probabilistic shaped signals, including:
获取训练样本集,所述训练样本集中包含多个样本,各样本包括在多种信号调制格式下,由弹性光网络接收端的DSP模块通过盲均衡算法处理得到的幅度柱状图;各样本还包括相位噪声;标记每个样本对应的光信噪比、调制格式以及激光器线宽作为标签;其中,所述弹性光网络采用洛伦兹线型的信号激光器和本振激光器;Acquire a training sample set, the training sample set includes multiple samples, and each sample includes an amplitude histogram obtained by processing a DSP module at the receiving end of the elastic optical network through a blind equalization algorithm under various signal modulation formats; each sample also includes a phase Noise; mark the optical signal-to-noise ratio, modulation format and laser line width corresponding to each sample as a label; wherein, the elastic optical network adopts a Lorentzian line-type signal laser and a local oscillator laser;
获取第一初始模型和第二初始模型,所述第一初始模型和所述第二初始模型为ANN(Artificial Neural Network,人工神经网络)网络;其中,所述第一初始模型中包含输入层、共享隐藏层、分别连接所述共享隐藏层的第一特定隐藏层和第二特定隐藏层、所述第一特定隐藏层用于识别调制格式并连接第一输出层,所述第二特定隐藏层用于识别光信噪比并连接第二输出层;Obtain a first initial model and a second initial model, where the first initial model and the second initial model are ANN (Artificial Neural Network, artificial neural network) networks; wherein, the first initial model includes an input layer, A shared hidden layer, a first specific hidden layer and a second specific hidden layer respectively connecting the shared hidden layer, the first specific hidden layer is used to identify the modulation format and connect the first output layer, the second specific hidden layer Used to identify the optical signal-to-noise ratio and connect the second output layer;
以各样本的幅度柱状图为输入,以的光信噪比和调制格式为输出,采用所述训练样本集对所述第一初始模型进行训练,得到光信噪比及调制格式识别模型;Taking the amplitude histogram of each sample as the input, taking the optical signal-to-noise ratio and the modulation format as the output, using the training sample set to train the first initial model to obtain the optical signal-to-noise ratio and modulation format recognition model;
以各样本在不同符号间隔下相位噪声增量的均方根值和平均值形成的序列为输入,以激光器线宽为输出,采用所述训练样本集对所述第二初始模型进行训练,得到激光器线宽识别模型。Taking the sequence formed by the root mean square value and the average value of the phase noise increments of each sample at different symbol intervals as input, and taking the laser linewidth as output, the second initial model is trained by using the training sample set, and the result is obtained: Laser linewidth identification model.
在一些实施例中,所述训练样本集中的样本是在QPSK、16QAM、PS-16QAM、64QAM和PS-64QAM五种调制格式下产生的。In some embodiments, the samples in the training sample set are generated under five modulation formats of QPSK, 16QAM, PS-16QAM, 64QAM and PS-64QAM.
在一些实施例中,所述训练样本集在激光器线宽50KHz至500KHz的范围内,按照50KHz为步长进行幅度柱状图采样,同时,对于采用QPSK调制格式的弹性光网络,在光信噪比10至25dB范围内对每个参数分别进行幅度柱状图采样;对于采用16QAM和PS-16QAM调制格式的弹性光网络,在光信噪比15至30dB范围内对每个参数分别进行幅度柱状图采样;对于采用64QAM和PS-64QAM调制格式的弹性光网络,在光信噪比20至35dB范围内对每个参数分别进行幅度柱状图采样;对于每一个激光器线宽、调制格式和光信噪比的组合分别采集第一设定数量个样本。In some embodiments, the training sample set is in the range of the laser line width of 50KHz to 500KHz, and the amplitude histogram is sampled according to the step size of 50KHz. At the same time, for the elastic optical network using the QPSK modulation format, the optical signal-to-noise ratio is Amplitude histogram sampling for each parameter in the range of 10 to 25dB; amplitude histogram sampling for each parameter in the range of optical signal-to-
在一些实施例中,所述幅度柱状图包含双偏振态信号的幅度柱状图。In some embodiments, the amplitude histogram includes an amplitude histogram of a dual polarization state signal.
在一些实施例中,所述训练样本集在激光器线宽50KHz至500KHz的范围内,按照50KHz为步长进行相位噪声采集,同时,对于采用QPSK、16QAM和PS-16QAM调制格式的弹性光网络,在光信噪比20至30dB范围内对每个参数分别进行相位噪声采集;对于采用PS-64QAM和64QAM调制格式的弹性光网络,在光信噪比30至40dB范围内对每个参数分别进行相位噪声采集;对于每一个激光器线宽、调制格式和光信噪比的组合分别采集第二设定数量个样本。In some embodiments, the training sample set is in the range of the laser line width of 50KHz to 500KHz, and the phase noise is collected according to the step size of 50KHz. At the same time, for the elastic optical network using the QPSK, 16QAM and PS-16QAM modulation formats, The phase noise is collected separately for each parameter in the range of optical signal-to-noise ratio of 20 to 30dB; for elastic optical networks using PS-64QAM and 64QAM modulation formats, each parameter is collected in the range of optical signal-to-noise ratio of 30 to 40dB. Phase noise collection; separately collect a second set number of samples for each combination of laser linewidth, modulation format, and optical signal-to-noise ratio.
在一些实施例中,以各样本在不同符号间隔下相位噪声增量的均方根值和平均值形成的序列为输入,包括:In some embodiments, the input is a sequence formed by the root mean square value and the average value of the phase noise increments of each sample at different symbol intervals, including:
对于采用洛伦兹线型的信号激光器和本振激光器,相位噪声是一个维纳过程,表示为:For signal lasers and local oscillator lasers using the Lorentzian line type, the phase noise is a Wiener process, expressed as:
其中,w(n)是服从高斯分布的序列,w(n)的均值为0,方差为的表达式为:Among them, w(n) is a sequence that obeys a Gaussian distribution, the mean of w(n) is 0, and the variance is The expression is:
其中,Δv是信号和本振激光器的半高全宽之和,τs是相邻信号的时间间隔;where Δv is the sum of the full width at half maximum of the signal and the LO, and τ s is the time interval between adjacent signals;
则所述相位噪声增量的表达式为:Then the phase noise increments The expression is:
其中,abs(·)为绝对值函数,N表示符号间隔数,n表示时序数;Among them, abs( ) is the absolute value function, N represents the number of symbol intervals, and n represents the number of time series;
计算多个设定的符号间隔数对应的相位噪声增量,并获取每个符号间隔数对应的均方根值和平均值;Calculate the phase noise increments corresponding to multiple set symbol intervals, and obtain the root mean square value and average value corresponding to each symbol interval;
将各符号间隔数对应的相位噪声增量的均方根值和平均值组成序列,作为第二初始模型的输入。The root mean square value and the average value of the phase noise increment corresponding to each symbol interval number form a sequence, which is used as the input of the second initial model.
在一些实施例中,计算多个设定的符号间隔数对应的相位噪声增量中,多个设定的符号间隔数包括2、5和10。In some embodiments, in calculating the phase noise increment corresponding to the plurality of preset numbers of symbol intervals, the multiple preset numbers of symbol intervals include 2, 5, and 10.
另一方面,本发明还提供一种概率整形信号的双阶段检测方法,包括:On the other hand, the present invention also provides a two-stage detection method for a probability shaped signal, comprising:
获取待检测信号的经盲均衡算法处理得到的待测幅度柱状图,将所述待测幅度柱状图输入上述概率整形信号的双阶段检测模型训练方法中的光信噪比及调制格式识别模型,并输出所述待检测信号的光信噪比和调制格式识别结果;Obtaining a histogram of the amplitude to be detected obtained by processing the blind equalization algorithm of the signal to be detected, and inputting the histogram of the amplitude to be detected into the optical signal-to-noise ratio and modulation format recognition model in the above-mentioned two-stage detection model training method for probabilistic shaped signals, and output the optical signal-to-noise ratio and modulation format identification result of the signal to be detected;
获取待检测信号在不同符号间隔下相位噪声增量的均方根值和平均值,并组成待检测序列,将所述待检测序列输入上述概率整形信号的双阶段检测模型训练方法中的激光器线宽识别模型,并输出激光器线型识别结果。Obtain the root mean square value and average value of the phase noise increment of the signal to be detected at different symbol intervals, and form a sequence to be detected, and input the sequence to be detected into the laser line in the above-mentioned two-stage detection model training method for probability-shaping signals Wide recognition model and output laser line type recognition results.
另一方面,本发明还提供一种电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,所述处理器执行所述程序时实现上述方法的步骤。In another aspect, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of the above method when the processor executes the program.
另一方面,本发明还提供一种计算机可读存储介质,其上存储有计算机程序,该程序被处理器执行时实现上述方法的步骤。In another aspect, the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps of the above method.
本发明的有益效果是:The beneficial effects of the present invention are:
本发明所述概率整形信号的双阶段检测模型训练方法、识别方法及装置中,在信号接收端通过DSP模块处理得到的电域信号,基于盲均衡算法处理后的幅度柱状图,通过机器学习的方式构建幅度柱状图至光信噪比和调制格式的映射,以实现对概率整形信号的光信噪比和调制格式的识别。同时,由于信号激光器和本振激光器在洛伦兹线型下的相位噪声是微纳过程,且相位噪声增量的方差与激光器线宽是线性关系的特性,通过机器学习的方式构建相位噪声增量的均方根植和平均值映射至激光器线宽,以实现对激光器线宽的识别。In the two-stage detection model training method, identification method and device of the probability shaping signal of the present invention, the electrical domain signal obtained by processing the DSP module at the signal receiving end is based on the amplitude histogram processed by the blind equalization algorithm, and is processed by machine learning. The mapping of the amplitude histogram to the optical signal-to-noise ratio and the modulation format is constructed in a manner to realize the identification of the optical signal-to-noise ratio and the modulation format of the probability shaped signal. At the same time, since the phase noise of the signal laser and the local oscillator laser under the Lorentzian line type is a micro-nano process, and the variance of the phase noise increment is linearly related to the laser linewidth, the phase noise increment is constructed by machine learning. The rms and average values of the quantities are mapped to the laser linewidth to enable identification of the laser linewidth.
进一步的,通过引入不同符号间隔下相位噪声增量的均方根值和平均值形成序列,并通过机器学习的方式构建该序列与激光器线宽的映射关系,能够有效提高识别的泛化能力。Further, by introducing the root mean square value and average value of phase noise increments at different symbol intervals to form a sequence, and constructing the mapping relationship between the sequence and the laser linewidth by means of machine learning, the generalization ability of recognition can be effectively improved.
本发明的附加优点、目的,以及特征将在下面的描述中将部分地加以阐述,且将对于本领域普通技术人员在研究下文后部分地变得明显,或者可以根据本发明的实践而获知。本发明的目的和其它优点可以通过在书面说明及其权利要求书以及附图中具体指出的结构实现到并获得。Additional advantages, objects, and features of the present invention will be set forth in part in the description that follows, and in part will become apparent to those of ordinary skill in the art upon study of the following, or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
本领域技术人员将会理解的是,能够用本发明实现的目的和优点不限于以上具体所述,并且根据以下详细说明将更清楚地理解本发明能够实现的上述和其他目的。Those skilled in the art will appreciate that the objects and advantages that can be achieved with the present invention are not limited to those specifically described above, and that the above and other objects that can be achieved by the present invention will be more clearly understood from the following detailed description.
附图说明Description of drawings
此处所说明的附图用来提供对本发明的进一步理解,构成本申请的一部分,并不构成对本发明的限定。在附图中:The accompanying drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present application, and do not constitute a limitation to the present invention. In the attached image:
图1为本发明一实施例所述概率整形信号的双阶段检测模型训练方法流程示意图;1 is a schematic flowchart of a method for training a two-stage detection model for probabilistic shaped signals according to an embodiment of the present invention;
图2为本发明一实施例所述概率整形信号的双阶段检测模型训练方法流的逻辑示意图;FIG. 2 is a logical schematic diagram of a method flow for training a two-stage detection model for probabilistic shaped signals according to an embodiment of the present invention;
图3为本发明一实施例所述概率整形信号的双阶段检测模型训练方法中第一初始模型结构示意图;3 is a schematic structural diagram of a first initial model in a method for training a two-stage detection model for probabilistic shaped signals according to an embodiment of the present invention;
图4为本发明一实施例所述概率整形信号的双阶段检测模型训练方法中第二初始模型结构示意图;4 is a schematic structural diagram of a second initial model in the two-stage detection model training method for probabilistic shaped signals according to an embodiment of the present invention;
图5为不同光信噪比不同调制下信号的幅度分布柱状图;Fig. 5 is a histogram of amplitude distribution of signals under different modulations with different optical signal-to-noise ratios;
图6为不同激光器线宽下相位噪声曲线图;Fig. 6 is the phase noise curve graph under different laser linewidths;
图7为本发明一实施例所述概率整形信号的双阶段光信噪比与激光器线宽监测方案对光信噪比的估计结果;7 is an estimation result of the optical signal-to-noise ratio of the two-stage optical signal-to-noise ratio of the probability-shaping signal and the laser linewidth monitoring scheme according to an embodiment of the present invention;
图8为本发明一实施例所述概率整形信号的双阶段光信噪比与激光器线宽监测方案对激光器线宽的估计结果。FIG. 8 is an estimation result of the laser linewidth by the two-stage optical signal-to-noise ratio of the probability-shaping signal and the laser linewidth monitoring scheme according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚明白,下面结合实施方式和附图,对本发明做进一步详细说明。在此,本发明的示意性实施方式及其说明用于解释本发明,但并不作为对本发明的限定。In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.
在此,还需要说明的是,为了避免因不必要的细节而模糊了本发明,在附图中仅仅示出了与根据本发明的方案密切相关的结构和/或处理步骤,而省略了与本发明关系不大的其他细节。Here, it should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only the structures and/or processing steps closely related to the solution according to the present invention are shown in the drawings, and the related structures and/or processing steps are omitted. Other details not relevant to the invention.
应该强调,术语“包括/包含”在本文使用时指特征、要素、步骤或组件的存在,但并不排除一个或更多个其它特征、要素、步骤或组件的存在或附加。It should be emphasized that the term "comprising/comprising" when used herein refers to the presence of a feature, element, step or component, but does not exclude the presence or addition of one or more other features, elements, steps or components.
在此,还需要说明的是,如果没有特殊说明,术语“连接”在本文不仅可以指直接连接,也可以表示存在中间物的间接连接。Here, it should also be noted that, if there is no special description, the term "connection" herein may not only refer to direct connection, but also to indicate indirect connection with intermediates.
为了高效检出概率整形信号的调制格式、光信噪比和激光器线宽,通过数字信号处理模块(DSP模块)将光性能检测由光域转向电域,使检测成本下降,性能提高。In order to efficiently detect the modulation format, optical signal-to-noise ratio and laser linewidth of the probability shaping signal, the optical performance detection is shifted from the optical domain to the electrical domain through the digital signal processing module (DSP module), which reduces the detection cost and improves the performance.
具体的,本发明提供一种概率整形信号的双阶段检测模型训练方法,如图1所示,包括步骤S101~S104:Specifically, the present invention provides a two-stage detection model training method for probabilistic shaped signals, as shown in FIG. 1 , including steps S101 to S104:
步骤S101:获取训练样本集,训练样本集中包含多个样本,各样本包括在多种信号调制格式下,由弹性光网络接收端的DSP模块通过盲均衡算法处理得到的幅度柱状图;各样本还包括相位噪声;标记每个样本对应的光信噪比、调制格式以及激光器线宽作为标签;其中,弹性光网络采用洛伦兹线型的信号激光器和本振激光器;Step S101: Acquire a training sample set, the training sample set includes multiple samples, and each sample includes an amplitude histogram obtained by processing a DSP module at the receiving end of the elastic optical network through a blind equalization algorithm under a variety of signal modulation formats; each sample also includes Phase noise; mark the optical signal-to-noise ratio, modulation format and laser line width corresponding to each sample as a label; among them, the elastic optical network adopts Lorentzian line type signal laser and local oscillator laser;
步骤S102:获取第一初始模型和第二初始模型,第一初始模型和第二初始模型为ANN网络;其中,第一初始模型中包含输入层、共享隐藏层、分别连接共享隐藏层的第一特定隐藏层和第二特定隐藏层、第一特定隐藏层用于识别调制格式并连接第一输出层,第二特定隐藏层用于识别光信噪比并连接第二输出层。Step S102: Obtain a first initial model and a second initial model, where the first initial model and the second initial model are ANN networks; wherein, the first initial model includes an input layer, a shared hidden layer, and a first initial model that is respectively connected to the shared hidden layer. The specific hidden layer and the second specific hidden layer, the first specific hidden layer is used to identify the modulation format and connect the first output layer, and the second specific hidden layer is used to identify the optical signal-to-noise ratio and connect the second output layer.
骤S103:以各样本的幅度柱状图为输入,以的光信噪比和调制格式为输出,采用训练样本集对第一初始模型进行训练,得到光信噪比及调制格式识别模型。Step S103: Take the amplitude histogram of each sample as input, and take the OSR and modulation format as output, use the training sample set to train the first initial model, and obtain the optical signal-to-noise ratio and modulation format recognition model.
骤S104:以各样本在不同符号间隔下相位噪声增量的均方根值和平均值形成的序列为输入,以激光器线宽为输出,采用训练样本集对第二初始模型进行训练,得到激光器线宽识别模型。Step S104: Take the sequence formed by the root mean square value and the average value of the phase noise increments of each sample at different symbol intervals as input, take the laser line width as output, use the training sample set to train the second initial model, and obtain the laser Line width recognition model.
在本实施例中,采用光网络中经DSP模块处理得到的电信号对光信噪比、调制格式以及激光器线宽进行识别,通过引入机器学习,构建映射关系,实现高效识别。具体的,本实施例构建两阶段的识别模型,通过构建两组映射关系识别光信噪比、调制格式以及激光器线宽。In this embodiment, the optical signal-to-noise ratio, modulation format and laser linewidth are identified by using the electrical signal processed by the DSP module in the optical network, and machine learning is introduced to construct a mapping relationship to achieve efficient identification. Specifically, in this embodiment, a two-stage identification model is constructed, and the optical signal-to-noise ratio, modulation format, and laser linewidth are identified by constructing two sets of mapping relationships.
在步骤S101中,发射端经概率整形后得到的光信号,通过光纤传输,并采用光纤放大器放大,引入了放大器自发辐射噪声(ASE noise)之后,在接收端通过集成相干接收机接收,并经行数字信号处理。获取通过盲均衡算法处理得到幅度柱状图作为识别光信噪比和调制格式的参数。具体的,可以采用恒模算法处理后信号分布的幅度柱状图。同时,采用经盲相位搜索算法计算获取的相位噪声作为识别激光线宽的参数。In step S101, the optical signal obtained by the probability shaping at the transmitting end is transmitted through an optical fiber, and amplified by an optical fiber amplifier. digital signal processing. The amplitude histogram obtained through blind equalization algorithm processing is used as a parameter for identifying optical signal-to-noise ratio and modulation format. Specifically, the amplitude histogram of the signal distribution after processing by the constant modulus algorithm can be used. At the same time, the phase noise calculated by the blind phase search algorithm is used as the parameter for identifying the laser linewidth.
通过现有数据进行筛选或者构建实验平台采集样本数据,并构成训练样本集。示例性的,可以搭建实验平台,在发射端包括激光器、双偏IQ调制器、多个长度的单模光纤;在接收端,通过掺铒光纤放大器(EDFA)放大,经集成相干接收机进行数据光信号接收后,通过模数转换器处理,通过数字信号处理,通过上采样、色散补偿、IQ均衡、恒模算法处理后,得到幅度柱状图。进一步通过盲相位搜索算法计算获取相位噪声。Filter the existing data or build an experimental platform to collect sample data and form a training sample set. Exemplarily, an experimental platform can be built, which includes lasers, double-biased IQ modulators, and single-mode fibers of multiple lengths at the transmitting end; After the optical signal is received, it is processed through an analog-to-digital converter, through digital signal processing, through upsampling, dispersion compensation, IQ equalization, and constant modulus algorithm to obtain an amplitude histogram. The phase noise is further obtained by calculating the blind phase search algorithm.
在构建样本数据集的过程中,为了提高泛化能力以及识别精度,在多种调制模式下进行数据采集,在一些实施例中,训练样本集中的样本是在QPSK、16QAM、PS-16QAM、64QAM和PS-64QAM五种调制格式下产生的。还可以设置多种长度的单模光纤,例如80KM、160KM和240KM。In the process of constructing the sample data set, in order to improve the generalization ability and the recognition accuracy, data collection is performed in various modulation modes. In some embodiments, the samples in the training sample set are QPSK, 16QAM, PS-16QAM, 64QAM and PS-64QAM under five modulation formats. Various lengths of single-mode fiber can also be set, such as 80KM, 160KM and 240KM.
在一些实施例中,训练样本集在激光器线宽50KHz至500KHz的范围内,按照50KHz为步长进行幅度柱状图采样,同时,对于采用QPSK调制格式的弹性光网络,在光信噪比10至25dB范围内对每个参数分别进行幅度柱状图采样;对于采用16QAM和PS-16QAM调制格式的弹性光网络,在光信噪比15至30dB范围内对每个参数分别进行幅度柱状图采样;对于采用64QAM和PS-64QAM调制格式的弹性光网络,在光信噪比20至35dB范围内对每个参数分别进行幅度柱状图采样;对于每一个激光器线宽、调制格式和光信噪比的组合分别采集第一设定数量个样本。进一步的,幅度柱状图为包含双偏振态信号的幅度柱状图。这样,训练样本集中至少有8000(5×16×10×5×2)个幅度柱状图样本。这里数据集中的8000各样本(5×16×10×5×2)是在5种调制格式,16个信噪比,10个线宽以及2个偏振态的排列组合下收集数据,并且每个情况下收集5组数据。In some embodiments, the training sample set is in the range of the laser line width of 50KHz to 500KHz, and the amplitude histogram is sampled according to the step size of 50KHz. Meanwhile, for the elastic optical network using the QPSK modulation format, the optical signal-to-noise ratio is 10 to Amplitude histogram sampling is performed for each parameter within the range of 25dB; for elastic optical networks using 16QAM and PS-16QAM modulation formats, amplitude histogram sampling is performed for each parameter within the optical signal-to-noise ratio range of 15 to 30dB; Using elastic optical networks with 64QAM and PS-64QAM modulation formats, amplitude histogram sampling is performed for each parameter in the range of optical signal-to-
在一些实施例中,可以将训练样本集再按照设定比例进一步划分为训练集和测试集,训练集用于训练更新模型参数,测试集用于测试和优化模型参数。示例性的,随机选择90%的幅度柱状图样本作为训练集,另10%的幅度柱状图样本作为测试集。In some embodiments, the training sample set may be further divided into a training set and a test set according to a set ratio, the training set is used for training and updating model parameters, and the test set is used for testing and optimizing model parameters. Exemplarily, 90% of the amplitude histogram samples are randomly selected as the training set, and the other 10% of the amplitude histogram samples are selected as the test set.
进一步的,对用于激光器线宽识别的相位噪声,训练样本集在激光器线宽50KHz至500KHz的范围内,按照50KHz为步长进行相位噪声采集,同时,对于采用QPSK、16QAM和PS-16QAM调制格式的弹性光网络,在光信噪比20至30dB范围内对每个参数分别进行相位噪声采集;对于采用PS-64QAM和64QAM调制格式的弹性光网络,在光信噪比30至40dB范围内对每个参数分别进行相位噪声采集;对于每一个激光器线宽、调制格式和光信噪比的组合分别采集第二设定数量个样本。这样,至少采集2200(4×5×11×10)个相位噪声作为数据集,在一些实施例中,70%的数据被随机选取作为训练集,其余的作为测试集。Further, for the phase noise used for laser linewidth identification, the training sample set is in the range of the laser linewidth from 50KHz to 500KHz, and the phase noise is collected according to the step size of 50KHz. For the elastic optical network in the optical signal-to-noise ratio range of 20 to 30dB, the phase noise of each parameter is collected separately; for the elastic optical network with PS-64QAM and 64QAM modulation formats, the optical signal-to-noise ratio is in the range of 30 to 40dB. Phase noise collection is performed separately for each parameter; for each combination of laser linewidth, modulation format and optical signal-to-noise ratio, a second set number of samples are collected respectively. In this way, at least 2200 (4×5×11×10) phase noises are collected as data sets, and in some embodiments, 70% of the data are randomly selected as training sets and the rest as test sets.
最后,对于每个样本,标记相应的调制格式、光信噪比和激光器线宽作为标签。Finally, for each sample, the corresponding modulation format, optical signal-to-noise ratio and laser linewidth are marked as labels.
在步骤S102中,限定了第一初始模型和第二初始模型为ANN网络,ANN网络是由具有适应性的简单单元组成的广泛并行互联的网络,它的组织能够模拟生物神经系统对真实世界物体所作出交互反应。在本实施例中,具体限定了基于多任务学习的第一初始模型的结构,由于第一初始模型需要利用幅度柱状图同时识别光信噪比和调制格式,因此,第一初始模型的结构在共享隐藏层之后分别针对两个识别任务构建特定隐藏层和输出层。而第二初始模型除了输入层和输出层之外,可以包含多个隐藏层。In step S102, the first initial model and the second initial model are defined as an ANN network. The ANN network is an extensive parallel interconnected network composed of simple adaptive units, and its organization can simulate the biological nervous system to real-world objects. interactive response. In this embodiment, the structure of the first initial model based on multi-task learning is specifically limited. Since the first initial model needs to use the amplitude histogram to simultaneously identify the optical signal-to-noise ratio and the modulation format, the structure of the first initial model is as follows: After sharing the hidden layers, specific hidden layers and output layers are constructed for the two recognition tasks, respectively. In addition to the input layer and the output layer, the second initial model can contain multiple hidden layers.
在步骤S103中,对于第一初始模型的训练,可以基于误差反向传播进行网络更新,具体利用损失函数与随机梯度下降的方法进行处理。In step S103, for the training of the first initial model, network update may be performed based on error backpropagation, and specifically, the method of loss function and stochastic gradient descent is used for processing.
在步骤S104中,当信号激光器和本振激光器是洛伦兹线型时,相位噪声是一个维纳过程,由于维纳过程是独立增量过程,相位噪声增量的方差与激光器线宽是线性关系,因此监测激光器线宽的关键是监测相位噪声增量的方差。In step S104, when the signal laser and the local oscillator laser are of the Lorentzian line type, the phase noise is a Wiener process. Since the Wiener process is an independent incremental process, the variance of the phase noise increment has a linear relationship with the laser linewidth. Therefore, the key to monitoring the laser linewidth is to monitor the variance of the phase noise increment.
在一些实施例中,以各样本在不同符号间隔下相位噪声增量的均方根值和平均值形成的序列为输入,包括:In some embodiments, the input is a sequence formed by the root mean square value and the average value of the phase noise increments of each sample at different symbol intervals, including:
对于采用洛伦兹线型的信号激光器和本振激光器,相位噪声是一个维纳过程,表示为:For signal lasers and local oscillator lasers using the Lorentzian line type, the phase noise is a Wiener process, expressed as:
其中,w(n)是服从高斯分布的序列,w(n)的均值为0,方差为的表达式为:Among them, w(n) is a sequence that obeys a Gaussian distribution, the mean of w(n) is 0, and the variance is The expression is:
其中,Δv是信号和本振激光器的半高全宽之和,τs是相邻信号的时间间隔。where Δv is the sum of the full width at half maximum of the signal and the LO, and τs is the time interval between adjacent signals.
则相位噪声增量的表达式为:Then the phase noise increment The expression is:
其中,abs(·)为绝对值函数,N表示符号间隔数,n表示时序数。Among them, abs( ) is the absolute value function, N represents the number of symbol intervals, and n represents the number of time series.
计算多个设定的符号间隔数对应的相位噪声增量,并获取每个符号间隔数对应的均方根值和平均值;将各符号间隔数对应的相位噪声增量的均方根值和平均值组成序列,作为第二初始模型的输入。Calculate the phase noise increments corresponding to a number of set symbol intervals, and obtain the root mean square value and average value corresponding to each symbol interval number; The averages make up the series and serve as input to the second initial model.
具体的,在本事实例中,在集成相干接收机中相位噪声可以通过载波相位恢复(CPR)的结果估计得到,具体可以采用盲相位搜索的方式处理,得到相位噪声后通过式(3)计算的绝对值。即为N个w(n)的叠加,w(n)是服从高斯分布的序列,w(n)的均值为0,方差为在绝对值函数的作用下,得到了方差叠加后的参数。Specifically, in this example, the phase noise in the integrated coherent receiver can be estimated by the result of carrier phase recovery (CPR). Specifically, it can be processed by blind phase search. After the phase noise is obtained, it can be calculated by formula (3) the absolute value of . That is, the superposition of N w(n), w(n) is a sequence obeying a Gaussian distribution, the mean of w(n) is 0, and the variance is Under the action of the absolute value function, the parameters after the variance is superimposed are obtained.
由于在不同激光器线宽下,准确预测线宽时所需要的N值不同。为了使该方案能够在多个线宽下都能准确预测线宽,本实施例需要取多个N值,利用不同符号间隔下相位噪声增量的数据作为ANN网络的输入来提高方案的鲁棒性。经过参数优化后N的取值为2、5和10,也即计算多个设定的符号间隔数对应的相位噪声增量中,多个设定的符号间隔数包括2、5和10。由于增量是均值为零的高斯分布,故计算增量时只需要得到绝对值信息。Due to the different laser linewidths, the N values required to accurately predict the linewidth are different. In order to enable the scheme to accurately predict the linewidth under multiple linewidths, this embodiment needs to take multiple N values, and use the phase noise increment data at different symbol intervals as the input of the ANN network to improve the robustness of the scheme sex. After parameter optimization, the values of N are 2, 5, and 10, that is, in the calculation of phase noise increments corresponding to multiple preset numbers of symbol intervals, the multiple preset numbers of symbol intervals include 2, 5, and 10. Since the increment is a Gaussian distribution with zero mean, only the absolute value information needs to be obtained when calculating the increment.
在对第二初始模型的训练过程中,可以基于误差反向传播进行网络更新,具体利用损失函数与随机梯度下降的方法进行处理。During the training process of the second initial model, the network can be updated based on error backpropagation, and specifically, the method of loss function and stochastic gradient descent is used for processing.
另一方面,本发明还提供一种概率整形信号的双阶段检测方法,包括步骤S201~S202:On the other hand, the present invention also provides a two-stage detection method for a probability shaped signal, including steps S201-S202:
步骤S201:获取待检测信号的经盲均衡算法处理得到的待测幅度柱状图,将待测幅度柱状图输入上述步骤S101~S104所述概率整形信号的双阶段检测模型训练方法中的光信噪比及调制格式识别模型,并输出待检测信号的光信噪比和调制格式识别结果。Step S201: Obtain a histogram of the amplitude to be measured obtained by processing the blind equalization algorithm of the signal to be detected, and input the histogram of the amplitude to be measured into the optical signal noise in the two-stage detection model training method for probabilistic shaped signals described in steps S101 to S104 above. Compare with the modulation format recognition model, and output the optical signal-to-noise ratio and modulation format recognition results of the signal to be detected.
步骤S202:获取待检测信号在不同符号间隔下相位噪声增量的均方根值和平均值,并组成待检测序列,将所述待检测序列输入上述步骤S101~S104所述概率整形信号的双阶段检测模型训练方法中的激光器线宽识别模型,并输出激光器线型识别结果。Step S202: Obtain the root mean square value and the average value of the phase noise increments of the signal to be detected at different symbol intervals, and form a sequence to be detected, and input the sequence to be detected into the double signal of the probability shaping signal in the above steps S101-S104. The laser line width recognition model in the model training method is detected in stages, and the laser line shape recognition result is output.
在本实施例中,待检测信号在接收端通过集成相干接收机接收后,进行DSP处理,对于采用盲均衡算法处理得到的待测幅度柱状图,输入至步骤S101~S104预训练得到的光信噪比及调制格式识别模型,以检出光信噪比和调制格式。进一步的,按照步骤S101~S104预训练的激光器线宽识别模型的输入参数格式和要求。In this embodiment, after the signal to be detected is received by the integrated coherent receiver at the receiving end, DSP processing is performed, and the histogram of the amplitude to be detected obtained by processing the blind equalization algorithm is input to the optical signal obtained by pre-training in steps S101 to S104. Noise ratio and modulation format recognition model to detect optical signal-to-noise ratio and modulation format. Further, the input parameter format and requirements of the pre-trained laser linewidth identification model according to steps S101 to S104.
另一方面,本发明还提供一种电子设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,所述处理器执行所述程序时实现上述方法的步骤。In another aspect, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of the above method when the processor executes the program.
另一方面,本发明还提供一种计算机可读存储介质,其上存储有计算机程序,该程序被处理器执行时实现上述方法的步骤。In another aspect, the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the steps of the above method.
下面结合一具体实施例对本发明进行说明:Below in conjunction with a specific embodiment, the present invention will be described:
本实施例提供一种对于概率整形信号的双阶段光信噪比与激光器线宽监测方案。This embodiment provides a two-stage optical signal-to-noise ratio and laser linewidth monitoring solution for probability shaped signals.
双阶段光信噪比与激光器线宽监测方案如图2所示。在第一阶段,利用基于多任务学习的ANN网络实现光信噪比估计和调制格式识别。第一阶段位于盲均衡算法后,均衡后信号的幅度柱状图被输入到基于多任务学习的神经网络中。该神经网络的结构如图3所示,其中共享层可以同时学习调制格式(MFI)和光信噪比(OSNR)估计两个任务的共性,因此可以提升结果的准确度并降低网络结构的复杂度。第二阶段则位于载波相位恢复(CPR)之后,利用一个简单的ANN网络来实现激光器线宽的估计,该ANN网络的结构如图4所示。The two-stage optical signal-to-noise ratio and laser linewidth monitoring scheme is shown in Figure 2. In the first stage, optical signal-to-noise ratio estimation and modulation format identification are realized by using ANN network based on multi-task learning. The first stage is located after the blind equalization algorithm, and the amplitude histogram of the equalized signal is input into the neural network based on multi-task learning. The structure of the neural network is shown in Figure 3, where the shared layer can simultaneously learn the modulation format (MFI) and the optical signal-to-noise ratio (OSNR) to estimate the commonality of the two tasks, thus improving the accuracy of the results and reducing the complexity of the network structure . The second stage is located after carrier phase recovery (CPR). A simple ANN network is used to estimate the laser linewidth. The structure of the ANN network is shown in Figure 4.
具体的,在第一阶段利用一个多任务学习的ANN网络将幅度柱状图映射至光信噪比和调制格式。在第二阶段用一个ANN网络将相位噪声增量的均方根植和平均值映射至激光器线宽。Specifically, a multi-task learning ANN network is used in the first stage to map the amplitude histogram to the optical signal-to-noise ratio and modulation format. An ANN network is used in the second stage to map the rms and average values of the phase noise increments to the laser linewidth.
在训练过程中,首先建立训练样本集。本实施例中,可以根据现有数据库中已有的数据进行筛选建立,也可以搭建实验平台进行模拟运行并采集数据。示例性的,建立实验平台包括:发射端的激光器(1550nm)和双偏IQ调制器,80km标准单模光纤,在接收端设置掺铒光纤放大器(EDFA),引入了放大器自发辐射噪声(ASE noise),通过集成相干接收机获取光信号,并进行模数转换,在上采样之后进行数字处理,至少包括色散补偿、IQ均衡和恒模算法处理,经恒模算法处理后得到双偏振态信号的幅度柱状图。后经盲相位搜索得到相位噪声。During the training process, a training sample set is first established. In this embodiment, screening and establishment may be performed according to the existing data in the existing database, or an experimental platform may be established to perform simulated operation and collect data. Exemplarily, the establishment of the experimental platform includes: a laser (1550nm) and a double-biased IQ modulator at the transmitting end, a standard single-mode fiber of 80 km, an Erbium-Doped Fiber Amplifier (EDFA) at the receiving end, and introduced amplifier spontaneous emission noise (ASE noise) , obtain the optical signal through the integrated coherent receiver, perform analog-to-digital conversion, and perform digital processing after up-sampling, including at least dispersion compensation, IQ equalization and constant modulus algorithm processing, after the constant modulus algorithm processing, the amplitude of the dual polarization state signal is obtained Histogram. The phase noise is then obtained by blind phase search.
对于幅度柱状图,训练样本集在激光器线宽50KHz至500KHz的范围内,按照50KHz为步长进行幅度柱状图采样,同时,对于采用QPSK调制格式的弹性光网络,在光信噪比10至25dB范围内对每个参数分别进行幅度柱状图采样;对于采用16QAM和PS-16QAM调制格式的弹性光网络,在光信噪比15至30dB范围内对每个参数分别进行幅度柱状图采样;对于采用64QAM和PS-64QAM调制格式的弹性光网络,在光信噪比20至35dB范围内对每个参数分别进行幅度柱状图采样;对于每一个激光器线宽、调制格式和光信噪比的组合分别采集第一设定数量个样本。进一步的,幅度柱状图为包含双偏振态信号的幅度柱状图。这样,训练样本集中至少有8000(5×16×10×5×2)个幅度柱状图样本。随机选择90%的幅度柱状图样本作为训练集,另10%的幅度柱状图样本作为测试集。对于每个样本的幅度柱状图添加对应的光信噪比和调制格式作为标签。For the amplitude histogram, the training sample set is in the range of the laser line width of 50KHz to 500KHz, and the amplitude histogram is sampled according to the step size of 50KHz. At the same time, for the elastic optical network using the QPSK modulation format, the optical signal to noise ratio is 10 to 25dB. The amplitude histogram is sampled separately for each parameter within the range; for the elastic optical network using 16QAM and PS-16QAM modulation formats, the amplitude histogram is sampled for each parameter within the optical signal-to-noise ratio range of 15 to 30dB; For elastic optical networks in 64QAM and PS-64QAM modulation formats, amplitude histogram sampling is performed for each parameter in the range of optical signal-to-noise ratio of 20 to 35 dB; separately for each combination of laser linewidth, modulation format and optical signal-to-noise ratio The first set number of samples. Further, the amplitude histogram is an amplitude histogram including signals of dual polarization states. In this way, there are at least 8000 (5×16×10×5×2) magnitude histogram samples in the training sample set. 90% of the amplitude histogram samples are randomly selected as the training set, and the other 10% of the amplitude histogram samples are selected as the test set. Add the corresponding optical signal-to-noise ratio and modulation format as labels for the amplitude histogram of each sample.
对用于激光器线宽识别的相位噪声,训练样本集在激光器线宽50KHz至500KHz的范围内,按照50KHz为步长进行相位噪声采集,同时,对于采用QPSK、16QAM和PS-16QAM调制格式的弹性光网络,在光信噪比20至30dB范围内对每个参数分别进行相位噪声采集;对于采用PS-64QAM和64QAM调制格式的弹性光网络,在光信噪比30至40dB范围内对每个参数分别进行相位噪声采集;对于每一个激光器线宽、调制格式和光信噪比的组合分别采集第二设定数量个样本。这样,至少采集2200(4×5×11×10)个相位噪声作为数据集,在一些实施例中,70%的数据被随机选取作为训练集,其余的作为测试集。本实施例中,采用的激光器和本振激光器为洛伦兹线型,所以相位噪声是维纳过程,参照上述式1至3的说明,计算不同符号间隔数对应的相位噪声增量的均方根值和平均值组成序列,作为第二阶段ANN网络的输入,并以对应的激光线宽作为输出。For the phase noise used for laser linewidth identification, the training sample set is in the range of the laser linewidth from 50KHz to 500KHz, and the phase noise is collected according to the step size of 50KHz. For optical networks, phase noise collection is performed for each parameter in the range of optical signal-to-noise ratio of 20 to 30dB; for elastic optical networks using PS-64QAM and 64QAM modulation formats, each of the The parameters are separately collected for phase noise; for each combination of laser linewidth, modulation format and optical signal-to-noise ratio, a second set number of samples are collected respectively. In this way, at least 2200 (4×5×11×10) phase noises are collected as data sets, and in some embodiments, 70% of the data are randomly selected as training sets and the rest as test sets. In this embodiment, the laser and the local oscillator laser used are Lorentzian line type, so the phase noise is a Wiener process. Referring to the description of the above equations 1 to 3, calculate the mean square of the phase noise increment corresponding to different symbol intervals The root value and the mean value form a sequence, which is used as the input of the second-stage ANN network, and the corresponding laser linewidth is used as the output.
利用训练样本集对第一阶段的ANN网络和第二阶段的ANN网络分别进行训练,得到光信噪比及调制格式识别模型,和激光器线宽识别模型。The ANN network in the first stage and the ANN network in the second stage are respectively trained using the training sample set to obtain the optical signal-to-noise ratio and modulation format recognition model, and the laser linewidth recognition model.
进一步的,为了研究本实施例提出的对于概率整形信号的双阶段光信噪比与激光器线宽监测方案的性能,搭建了一个80km传输的相干光通信系统仿真,传输了QPSK、16QAM、概率整形(PS)-16QAM、64QAM、PS-64QAM五种信号,采集幅度分布柱状图和相位噪声增量的均方根值、均值,以按照相应格式输入预训练得到的光信噪比及调制格式识别模型和激光器线宽识别模型。Further, in order to study the performance of the two-stage optical signal-to-noise ratio and laser linewidth monitoring scheme for probability-shaping signals proposed in this embodiment, an 80km transmission coherent optical communication system simulation is built, and QPSK, 16QAM, probability shaping (PS)-16QAM, 64QAM, PS-64QAM five kinds of signals, collect the amplitude distribution histogram and the root mean square value and mean value of the phase noise increment, and input the pre-trained optical signal-to-noise ratio and modulation format identification according to the corresponding format Model and Laser Linewidth Identification Model.
如图5所示,信号的幅度柱状图的形状与调制格式和信噪比都相关。如图6所示,相位噪声的变化与线宽大小相关。仿真结果表明调制格式识别的准确度是100%,OSNR和线宽监测误差的方均根误差(RMSE)分别是0.2dB and 18KHz。其中,预测OSNR和实际OSNR的曲线如图7所示,预测linewidth的值和实际linewidth的值如图8所示。As shown in Figure 5, the shape of the amplitude histogram of the signal is related to both the modulation format and the signal-to-noise ratio. As shown in Figure 6, the change in phase noise is related to the size of the line width. The simulation results show that the accuracy of modulation format identification is 100%, and the root mean square error (RMSE) of OSNR and linewidth monitoring errors are 0.2dB and 18KHz, respectively. Among them, the curves of predicted OSNR and actual OSNR are shown in FIG. 7 , and the value of predicted linewidth and the value of actual linewidth are shown in FIG. 8 .
本发明所述概率整形信号的双阶段检测模型训练方法、识别方法及装置中,在信号接收端通过DSP模块处理得到的电域信号,基于盲均衡算法处理后的幅度柱状图,通过机器学习的方式构建幅度柱状图至光信噪比和调制格式的映射,以实现对概率整形信号的光信噪比和调制格式的识别。同时,由于信号激光器和本振激光器在洛伦兹线型下的相位噪声是微纳过程,且相位噪声增量的方差与激光器线宽是线性关系的特性,通过机器学习的方式构建相位噪声增量的均方根植和平均值映射至激光器线宽,以实现对激光器线宽的识别。In the two-stage detection model training method, identification method and device of the probability shaping signal of the present invention, the electrical domain signal obtained by processing the DSP module at the signal receiving end is based on the amplitude histogram processed by the blind equalization algorithm, and is processed by machine learning. The mapping of the amplitude histogram to the optical signal-to-noise ratio and modulation format is constructed in a manner to realize the identification of the optical signal-to-noise ratio and the modulation format of the probability shaped signal. At the same time, since the phase noise of the signal laser and the local oscillator laser under the Lorentzian line type is a micro-nano process, and the variance of the phase noise increment is linearly related to the laser linewidth, the phase noise increment is constructed by machine learning. The rms and average values of the quantities are mapped to the laser linewidth to enable identification of the laser linewidth.
进一步的,通过引入不同符号间隔下相位噪声增量的均方根值和平均值形成序列,并通过机器学习的方式构建该序列与激光器线宽的映射关系,能够有效提高识别的泛化能力。Further, by introducing the root mean square value and average value of phase noise increments at different symbol intervals to form a sequence, and constructing the mapping relationship between the sequence and the laser linewidth by means of machine learning, the generalization ability of recognition can be effectively improved.
本领域普通技术人员应该可以明白,结合本文中所公开的实施方式描述的各示例性的组成部分、系统和方法,能够以硬件、软件或者二者的结合来实现。具体究竟以硬件还是软件方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本发明的范围。当以硬件方式实现时,其可以例如是电子电路、专用集成电路(ASIC)、适当的固件、插件、功能卡等等。当以软件方式实现时,本发明的元素是被用于执行所需任务的程序或者代码段。程序或者代码段可以存储在机器可读介质中,或者通过载波中携带的数据信号在传输介质或者通信链路上传送。“机器可读介质”可以包括能够存储或传输信息的任何介质。机器可读介质的例子包括电子电路、半导体存储器设备、ROM、闪存、可擦除ROM(EROM)、软盘、CD-ROM、光盘、硬盘、光纤介质、射频(RF)链路,等等。代码段可以经由诸如因特网、内联网等的计算机网络被下载。It should be understood by those of ordinary skill in the art that the various exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is implemented in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, elements of the invention are programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted over a transmission medium or communication link by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments may be downloaded via a computer network such as the Internet, an intranet, or the like.
还需要说明的是,本发明中提及的示例性实施例,基于一系列的步骤或者装置描述一些方法或系统。但是,本发明不局限于上述步骤的顺序,也就是说,可以按照实施例中提及的顺序执行步骤,也可以不同于实施例中的顺序,或者若干步骤同时执行。It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be different from the order in the embodiments, or several steps may be performed simultaneously.
本发明中,针对一个实施方式描述和/或例示的特征,可以在一个或更多个其它实施方式中以相同方式或以类似方式使用,和/或与其他实施方式的特征相结合或代替其他实施方式的特征。In the present invention, features described and/or illustrated with respect to one embodiment may be used in the same or similar manner in one or more other embodiments, and/or in combination with or in place of features of other embodiments Features of the implementation.
以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明实施例可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, various modifications and changes may be made to the embodiments of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.
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