阅读说明：本技术 二维正交振幅调制信号的格式识别方法、装置及存储介质 (Format identification method, device and storage medium for two-dimensional quadrature amplitude modulation signal ) 是由 揭水平 高明义 沈纲祥 邵卫东 符小东 马宗仰 于 2021-07-16 设计创作，主要内容包括：本申请实施例提供面向高速光通信模块应用的一种二维正交振幅调制信号的格式识别方法、装置及存储介质,通过利用基于信道估计的解调算法从信号解调得到二维正交振幅调制信号,并构建二维正交振幅调制信号的星座图；基于聚类算法,确定并基于星座图中星座云簇的数量,从多种信号格式中确定出二维正交振幅调制信号的信号格式；其中,多种信号格式包括：正交相移信号、8正交振幅调制信号、16正交振幅调制信号、32正交振幅调制信号和64正交振幅调制信号。本申请通过聚类算法以计算得到二维正交振幅调制信号的星座图中的星座云簇的数量,从而基于星座云簇数量,识别出二维正交振幅调制信号的信号调制格式,与现有技术相比,其识别鲁棒性较高。(The embodiment of the application provides a format identification method, a format identification device and a storage medium of a two-dimensional quadrature amplitude modulation signal for application of a high-speed optical communication module, wherein the two-dimensional quadrature amplitude modulation signal is obtained by demodulating the signal by using a demodulation algorithm based on channel estimation, and a constellation diagram of the two-dimensional quadrature amplitude modulation signal is constructed; determining a signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats based on a clustering algorithm and the number of constellation cloud clusters in a constellation diagram; wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal. According to the method and the device, the number of constellation cloud clusters in the constellation diagram of the two-dimensional quadrature amplitude modulation signal is obtained through a clustering algorithm, so that the signal modulation format of the two-dimensional quadrature amplitude modulation signal is identified based on the number of the constellation cloud clusters.)

1. A method for format recognition of a two-dimensional quadrature amplitude modulated signal, comprising:

demodulating a signal by using a demodulation algorithm based on channel estimation to obtain a two-dimensional quadrature amplitude modulation signal, and constructing a constellation diagram of the two-dimensional quadrature amplitude modulation signal;

determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm;

determining a signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram;

wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal.

2. The format recognition method of claim 1, wherein the determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm comprises:

determining a truncation distance between data points of the two-dimensional quadrature amplitude modulation signal, and calculating the convergence of each data point of the two-dimensional quadrature amplitude modulation signal;

for each data point in the data points, determining an associated data point corresponding to each data point according to the convergence of each data point, and calculating the minimum distance between each data and the associated data point corresponding to each data point; wherein the associated data point is a data point having a higher convergence than each of the data points;

and constructing a two-dimensional bar graph according to the minimum distance and the convergence of each data point, and calculating the number of constellation cloud clusters according to the two-dimensional bar graph.

3. The method of claim 2, wherein the calculating the convergence of each data point of the two-dimensional quadrature amplitude modulation signal comprises:

calculating the convergence of each data point according to a Gaussian kernel formula;

the gaussian kernel formula is expressed as:

where ρ is_iExpressed as the convergence of the ith data point of the N data points, D_ijExpressed as the Euclidean distance between the ith and jth data points of the N data points_DcRepresenting a preset truncation distance.

4. The format recognition method of claim 2, wherein for each of the data points, determining the associated data point corresponding to each data point according to the convergence of each data point, and before calculating the minimum distance between each data point and its corresponding associated data point, the method comprises:

carrying out high-density point removal processing on the data points according to the data points to obtain candidate data points;

for each candidate data point of the data points, a minimum distance between each candidate data point data and its corresponding associated data point is calculated.

5. The format recognition method of claim 3, wherein the processing of high density point removal on the data points according to each data point to obtain candidate data points comprises:

determining a cluster point and a non-cluster point in each data point;

calculating the distance from each non-cluster point to each cluster point;

carrying out zero setting processing on the convergence of the non-cluster center points with the distance smaller than a preset distance threshold value, and carrying out descending sequencing on the convergence of all the processed data points;

and selecting a preset number of data points as candidate data points according to the descending sorting result.

6. The format recognition method of claim 2, wherein determining the truncation distance between data points of the two-dimensional quadrature amplitude modulated signal comprises:

and determining the truncation distance according to the Euclidean distance between each data point and other data points and the number of the data points.

7. The format recognition method according to claim 2, wherein the two-dimensional bar graph is used for representing the association relationship between the product of the minimum distance and the convergence of each data point and each data point;

the calculating the number of constellation cloud clusters according to the two-dimensional bar graph comprises:

carrying out differential processing on the product of the minimum distance and the convergence of adjacent data points in the two-dimensional bar graph to obtain a processed two-dimensional bar graph;

and determining the number of the constellation cloud clusters according to the differential peak value in the processed two-dimensional bar graph.

8. An apparatus for format recognition of a two-dimensional quadrature amplitude modulated signal, comprising:

the demodulation module is used for demodulating a signal by using a demodulation algorithm based on channel estimation to obtain a two-dimensional quadrature amplitude modulation signal and constructing a constellation diagram of the two-dimensional quadrature amplitude modulation signal;

the clustering module is used for determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm;

the identification module is used for determining the signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram;

wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal.

9. An electronic device, comprising: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method for format recognition of a two-dimensional quadrature amplitude modulated signal as claimed in any of claims 1-7.

10. A computer-readable storage medium having stored thereon computer-executable instructions which, when executed by a processor, implement a method for format recognition of a two-dimensional quadrature amplitude modulated signal as claimed in any one of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, is adapted to carry out the method for format recognition of a two-dimensional quadrature amplitude modulated signal as claimed in any one of the claims 1 to 7.

Technical Field

The present application relates to the field of information transmission technologies, and in particular, to a format identification method and apparatus for a two-dimensional quadrature amplitude modulation signal, and a storage medium.

Background

The signal modulation format recognition technology plays an important role in the field of wireless communication, and particularly, with the development of coherent optical communication technology supporting multiple modulation signals, research is gradually carried out on the aspect of signal modulation format recognition in the field of optical communication.

In the prior art, supervised learning algorithms are generally used to identify the modulation format of the signal, for example, using a K-means clustering algorithm to obtain the number of centroids and estimate the order of the modulation format.

However, in the existing method, because the supervised learning algorithm is used, the supervised learning algorithm needs to be trained in advance, a large amount of training resources are needed in the process, and meanwhile, the training effect is related to the selection of the initial centroid, so that the resource consumption is high, and the recognition robustness is low.

Disclosure of Invention

The embodiment of the application provides a format identification method and device based on a two-dimensional quadrature amplitude modulation signal and a storage medium, which are used for improving the format identification accuracy of the two-dimensional quadrature amplitude modulation signal.

In a first aspect, the present application provides a format recognition method for a two-dimensional quadrature amplitude modulation signal, including:

demodulating a signal by using a demodulation algorithm based on channel estimation to obtain a two-dimensional quadrature amplitude modulation signal, and constructing a constellation diagram of the two-dimensional quadrature amplitude modulation signal;

determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm;

determining a signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram;

wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal.

Optionally, the determining the number of constellation cloud clusters in the constellation map based on a clustering algorithm includes:

determining a truncation distance between data points of the two-dimensional quadrature amplitude modulation signal, and calculating the convergence of each data point of the two-dimensional quadrature amplitude modulation signal;

for each data point in the data points, determining an associated data point corresponding to each data point according to the convergence of each data point, and calculating the minimum distance between each data and the associated data point corresponding to each data point; wherein the associated data point is a data point having a higher convergence than each of the data points;

and constructing a two-dimensional bar graph according to the minimum distance and the convergence of each data point, and calculating the number of constellation cloud clusters according to the two-dimensional bar graph.

Optionally, the calculating a convergence of each data point of the two-dimensional quadrature amplitude modulation signal includes:

calculating the convergence of each data point according to a Gaussian kernel formula;

the gaussian kernel formula is expressed as:

where ρ is_iExpressed as the convergence of the ith data point of the N data points, D_ijExpressed as the Euclidean distance between the ith and jth data points of the N data points, D_cRepresenting a preset truncation distance.

Optionally, for each data point in the data points, determining, according to the convergence of the data points, an associated data point corresponding to each data point, and before calculating the minimum distance between each data and its corresponding associated data point, the method includes:

carrying out high-density point removal processing on the data points according to the data points to obtain candidate data points;

for each candidate data point of the data points, a minimum distance between each candidate data point data and its corresponding associated data point is calculated.

Optionally, the performing high-density point removal processing on the data points according to each data point to obtain candidate data points includes:

determining a cluster point and a non-cluster point in each data point;

calculating the distance from each non-cluster point to each cluster point;

carrying out zero setting processing on the convergence of the non-cluster center points with the distance larger than a preset distance threshold value, and carrying out descending sequencing on the convergence of all the processed data points;

and selecting a preset number of data points as candidate data points according to the descending sorting result.

Optionally, the determining a truncation distance between data points of the two-dimensional quadrature amplitude modulation signal comprises:

and determining the truncation distance according to the Euclidean distance between each data point and other data points and the number of the data points.

Optionally, the two-dimensional bar graph is used for representing the association relationship between the product of the minimum distance and the convergence of each data point and each data point;

the calculating the number of constellation cloud clusters according to the two-dimensional bar graph comprises:

carrying out differential processing on the product of the minimum distance and the convergence of adjacent data points in the two-dimensional bar graph to obtain a processed two-dimensional bar graph;

and determining the number of the constellation cloud clusters according to the differential peak value in the processed two-dimensional bar graph.

In a second aspect, the present application provides a format recognition apparatus for a two-dimensional quadrature amplitude modulation signal, comprising:

the demodulation module is used for demodulating a signal by using a demodulation algorithm based on channel estimation to obtain a two-dimensional quadrature amplitude modulation signal and constructing a constellation diagram of the two-dimensional quadrature amplitude modulation signal;

the clustering module is used for determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm;

the identification module is used for determining the signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram;

wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal.

In a third aspect, the present application provides an electronic device, comprising: at least one processor and memory;

the memory stores computer-executable instructions;

the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method for format recognition of a two-dimensional quadrature amplitude modulated signal as claimed in any of claims 1-7.

In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the format recognition method of the two-dimensional quadrature amplitude modulation signal according to any one of the first aspect is implemented.

In a fifth aspect, the present application provides a computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the format recognition method for a two-dimensional quadrature amplitude modulated signal according to any one of the first aspect.

The embodiment of the application provides a format identification method, a device and a storage medium of a two-dimensional quadrature amplitude modulation signal, which are used for obtaining the two-dimensional quadrature amplitude modulation signal from signal demodulation by utilizing a demodulation algorithm based on channel estimation and constructing a constellation diagram of the two-dimensional quadrature amplitude modulation signal; determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm; determining a signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram; wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal. According to the method and the device, the number of constellation cloud clusters in the constellation diagram of the two-dimensional quadrature amplitude modulation signal is obtained through a clustering algorithm, so that the signal modulation format of the two-dimensional quadrature amplitude modulation signal is identified based on the number of the constellation cloud clusters.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

FIG. 1 is a schematic diagram of a network architecture on which the present application is based;

fig. 2 is a schematic flowchart of a format recognition method for a two-dimensional quadrature amplitude modulation signal according to the present application;

FIG. 3 is a two-dimensional bar graph of a 64-dimensional quadrature amplitude modulated signal as provided herein;

FIG. 4 is a schematic two-dimensional bar graph provided herein after expansion of the two-dimensional bar graph shown in FIG. 3;

FIG. 5 is a schematic two-dimensional bar graph obtained by performing differential processing on the two-dimensional bar graph shown in FIG. 4 according to the present disclosure;

FIG. 6 is a graph illustrating the recognition rate of a signal in multiple formats compared to the OSNR (dB);

fig. 7 is a schematic structural diagram of a format recognition apparatus for a two-dimensional qam signal according to the present application;

fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of systems and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The signal modulation format identification technology has very important functions in the field of wireless communication, for example, in the aspect of military electronics, the stolen signals are subjected to modulation format identification so as to assist in recovering the stolen signals; for example, in the aspect of civil communication, by identifying the signal modulation format in the link adaptive system, the distribution of redundant control information related to the modulation format can be reduced, and the spectrum utilization rate of the link adaptive system can be improved.

In addition, with the development of coherent optical communication technology supporting multiple modulation signals, research on signal modulation format recognition in the optical communication field is gradually carried out, and the role and application thereof are embodied in the following two aspects:

in a first aspect, the DSP phase recovery algorithm in the coherent optical receiver is signal modulation format dependent. Therefore, the signal modulation format information can be predicted by using the signal modulation format identification method, and then the signal can be effectively recovered by using the obtained signal modulation format information.

Secondly, the optical communication network is evolving from static rigid to dynamic intelligent, and the signal modulation format recognition technology can provide physical layer signal modulation information for the intelligent optical network switching node in real time. Signal modulation format identification is a necessary condition for realizing adaptive intelligent signal judgment.

In order to realize the identification of the signal format, in the prior art, a supervised learning algorithm is generally adopted to identify the modulation format of the signal, for example, a K-means clustering algorithm is used to obtain the number of centroids and estimate the order of the modulation format.

However, in the existing method, because the supervised learning algorithm is used, the supervised learning algorithm needs to be trained in advance, a large amount of training resources are needed in the process, and meanwhile, the training effect is related to the selection of the initial centroid, so that the resource consumption is high, and the recognition robustness is low.

In view of the above problems, the inventors found that the number of constellation cloud clusters in a constellation diagram of a two-dimensional quadrature amplitude modulation signal can be calculated through a clustering algorithm, so that a signal modulation format of the two-dimensional quadrature amplitude modulation signal is identified based on the number of constellation cloud clusters.

The method provided by the present application will be described below with reference to different implementations.

Referring to fig. 1, fig. 1 is a schematic diagram of a network architecture on which the present application is based, where the network architecture shown in fig. 1 can be specifically applied to a high-speed optical communication module, and may include a signal generator 1, a signal receiver 2, and a format recognition device 3 for a two-dimensional quadrature amplitude modulation signal.

The signal generator 1 may specifically include a combination of various instruments and devices, such as a waveform generator, an external cavity laser, a mach-zehnder modulator, a variable optical attenuator, and a erbium-doped amplifier. The waveform generator, the external cavity laser and the Mach-Zehnder modulator can be used for loading and converting a signal to be transmitted into an optical signal so as to obtain a modulated optical signal; and the variable optical attenuator and the erbium-doped amplifier may be used to vary the optical signal-to-noise ratio of the modulated optical signal. Of course, the above mentioned instruments are only examples, and in practical use, they may be other types and other functional instruments.

Through the signal generator 1, the signal will be converted into a modulated optical signal and sent to the signal receiver 2. The signal receiver 2 includes instruments such as a variable optical attenuator, an oscilloscope, and a photodetector, and is used for receiving and collecting the modulated optical signal from the signal generator 1, and collecting and modulating the modulated optical signal.

The format recognition device 3 for the two-dimensional quadrature amplitude modulation signal provided by the present application is integrated in the signal receiver 2, and is configured to perform format recognition processing on the two-dimensional quadrature amplitude modulation signal acquired and modulated by the signal receiver 2 based on the format recognition method provided by the present application, so as to facilitate subsequent processing such as signal recovery according to the signal format obtained by recognition.

Example one

Fig. 2 is a schematic flowchart of a format identification method for a two-dimensional quadrature amplitude modulation signal provided in the present application, as shown in fig. 2, the method includes:

step 201, demodulating a signal by using a demodulation algorithm based on channel estimation to obtain a two-dimensional quadrature amplitude modulation signal, and constructing a constellation diagram of the two-dimensional quadrature amplitude modulation signal.

Step 202, determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm.

Step 203, determining a signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram; wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal.

It should be noted that the format identification method for the two-dimensional quadrature amplitude modulation signal provided in the present application is specifically directed to format identification processing of the two-dimensional quadrature amplitude modulation signal in a high-speed optical communication module application, and the format identification apparatus may be installed or integrated in the signal receiver 2 in a network architecture as shown in fig. 1.

In the identification method provided in the present embodiment, a two-dimensional quadrature amplitude modulation signal is first demodulated by channel estimation irrespective of the modulation format, and then the constellation of the signal is identified. The constellation diagram of the two-dimensional quadrature amplitude modulation signal has M constellation cloud clusters, and the number M of the constellation cloud clusters can be used as the identification characteristic of the modulation format identification, and the modulation format of the signal is judged.

Specifically, first, the format recognition device demodulates a signal by using a demodulation algorithm based on channel estimation to obtain a two-dimensional quadrature amplitude modulation signal, and constructs a constellation diagram of the two-dimensional quadrature amplitude modulation signal. The format recognition device carries out channel estimation on the received two-dimensional quadrature amplitude modulation signal independent of the modulation format, and normalizes the processed data to realize the demodulation processing of the signal. The constellation diagram is a schematic diagram that represents digital signals on a complex plane to visually represent the signals and the relationship between the signals.

Then, the format recognition device will determine the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm.

In alternative embodiments, step 202 may specifically include the following steps:

step 2021, determining a truncation distance between data points of the two-dimensional quadrature amplitude modulation signal, and calculating a convergence of each data point of the two-dimensional quadrature amplitude modulation signal;

step 2022, for each data point in the data points, determining an associated data point corresponding to each data point according to the convergence of each data point, and calculating a minimum distance between each data point and the associated data point corresponding to each data point; wherein the associated data point is a data point having a higher convergence than each of the data points;

step 2023, constructing a two-dimensional bar graph according to the minimum distance and convergence of each data point, and calculating the number of constellation cloud clusters according to the two-dimensional bar graph.

The determination of the truncation distance in step 2021 may generally be based on the modulation format. In an alternative embodiment, the truncation distance may also be determined based on the euclidean distance between each data point and other data points and the number of data points.

Specifically, for a two-dimensional quadrature amplitude modulation signal, assuming that it includes N data points, for any one of the N data points, its euclidean distance to the remaining N-1 data points can be calculated using the following formula (1).

Wherein x_i＝(x_i1,x_i2,…,x_im) And x_j＝(x_j1,x_j2,…,x_jm) Is any two data points in the N m-dimensional data. D_ijFor representing x_iAnd x_jThe euclidean distance between them.

By calculating each data point, the Euclidean distance between several data points can be obtained, wherein, D is used for_ijAnd D_jiAre identical, i.e., it is known that half of the Euclidean distances are identical, i.e., N (N-1) Euclidean distances are de-duplicated to obtain N (N-1)/2 Euclidean distances, and then the Euclidean distances are sorted from small to large to obtain a distance sequence, e.g., D₁≤D₂≤D₃…≤D_N(N-1)/2At the k-th distance D in the sequence_kFrom the cut-off distance D_cCorrelation, based on the distance sequence, with k distances less than D_kSetting k to be 1% to 2% of N (N-1)/2, the value of k and D can be obtained from the sequence_kOn the basis of which a weighting coefficient of 0.3 is multiplied to obtain D_cThe value of (a).

The determination of the degree of convergence in step 2021 may then be made based on a gaussian kernel formula. In an alternative embodiment, the convergence of each data point may be first calculated according to gaussian kernel formula (2);

where ρ is_iExpressed as the convergence of the ith data point of the N data points, D_ijExpressed as the Euclidean distance between the ith and jth data points of the N data points, D_cThe truncation distance is indicated.

Subsequently, after the determination of the convergence of the data points is completed, the minimum distance for each data point will also be calculated. The calculation of the minimum distance at each data point may determine the associated data point associated therewith, where an associated data point is a data point having a higher convergence than each of the data points.

For example, for the ith data point, first, according to the convergence of each data point, the convergence greater than ρ is selected_iThe data point of (a) as the associated data point of the ith data point; then, the distance between each associated data point and the ith data point is respectively calculated, wherein the minimum distance is the minimum distance delta of the ith data point.

Further, in order to make the calculation of the minimum distance more accurate, the accuracy of signal format identification is also ensured. For the signal, the points with higher convergence around the constellation cloud cluster center will have an influence on the distance calculation of the data points, and in order to reduce this influence, before step 2022, further include:

301, performing high-density point removal processing on data points according to the data points to obtain candidate data points;

step 302, for each candidate data point in the data points, calculating a minimum distance between each candidate data point data and its corresponding associated data point.

Step 301 may specifically include determining a cluster point and a non-cluster point in each data point; calculating the distance from each non-cluster point to each cluster point; carrying out zero setting processing on the convergence of the non-cluster center points with the distance larger than a preset distance threshold value, and carrying out descending sequencing on the convergence of all the processed data points; and selecting a preset number of data points as candidate data points according to the descending sorting result.

Further, the partition for clustered and non-clustered points can be determined by the euclidean distance between the data points: sorting the convergence values of all the data points in a descending order to obtain rho₁≥ρ₂≥ρ₃...≥ρ_N. The point with higher convergence is a cluster center point; the rest are non-cluster center points.

For the processing of removing the high-density points, in order to avoid that the points with higher convergence around the constellation cloud cluster center will have an influence on the distance calculation of the data points, two circles with radii as the intercept distance and the preset distance threshold may be drawn on the constellation diagram with each cluster center point as the center of the circle, where the preset distance threshold may be one eighth of the intercept distance. Then, the convergence of each non-cluster center point located in the circle where the preset distance threshold is located is set to be zero. And then, sorting the convergence of the data points in a descending order again, and selecting a preset number of data points from the sorted data points as candidate data points, such as 2000 data points, to calculate the minimum distance.

For any candidate data point, as described in step 302, the other candidate data points with higher aggregation level than any candidate data point in the candidate data points are used as the associated data points of any candidate data point, and then the distance between each associated data point and any candidate data point is calculated, i.e. a plurality of distances are obtained; of these distances, the smallest one serves as the smallest distance for the any one candidate data point.

And then, the format recognition device constructs a two-dimensional bar graph according to the minimum distance and the convergence of each data point, and calculates the number of constellation cloud clusters according to the two-dimensional bar graph.

The two-dimensional bar graph is used for representing the association relation between the product of the minimum distance and the convergence of each data point and each data point; performing differential processing on the product of the minimum distance and the convergence of adjacent data points in the two-dimensional bar graph to obtain a processed two-dimensional bar graph; and determining the number of the constellation cloud clusters according to the differential peak value in the processed two-dimensional bar graph.

FIG. 3 is a two-dimensional bar graph of a 64-dimensional quadrature amplitude modulated signal as provided herein; wherein the abscissa n is used to represent a data point; the ordinate R is used to represent the product of the minimum distance and convergence of the data points, i.e., R_i＝ρ_iδ_i. From the two-dimensional bar chart shown in fig. 3, it can be known that the larger R variation between the constellation cloud cluster center and other constellation cloud cluster points is the demarcation point for distinguishing the cloud clusters.

In order to make the change more obvious, the two-dimensional bar graph shown in fig. 4 can be obtained by performing logarithmic function processing on the two-dimensional bar graph shown in fig. 3 to enlarge the R change. FIG. 4 is a schematic two-dimensional bar graph provided herein after expansion of the two-dimensional bar graph shown in FIG. 3; unlike fig. 3, the ordinate in fig. 4 is lg (r).

Then, in order to obtain the number of constellation cloud cluster centers, the product of the minimum distance and the convergence of adjacent data points of the two-dimensional bar graph in fig. 4 is subjected to difference processing, so as to obtain the processed two-dimensional bar graph shown in fig. 5. FIG. 5 is a schematic two-dimensional bar graph obtained by performing differential processing on the two-dimensional bar graph shown in FIG. 4 according to the present disclosure; as shown in FIG. 5, the ordinate in the figure is R_i'＝log(R_i-1)-log(R_i). As can be seen from reading the figure, the maximum R' value corresponds to n-65, and the number M-n-1-64 of constellation cloud centers.

Finally, the format recognition device determines the signal format of the two-dimensional quadrature amplitude modulation signal, namely a two-dimensional bar graph of the 64 two-dimensional quadrature amplitude modulation signal, from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram.

The format identification method applied to the high-speed optical communication module provided by the present application can have a better identification rate for the format identification of signals, and fig. 6 is a schematic diagram of the identification rate of signals of a plurality of formats provided by the present application in comparison with the optical signal to noise ratio osnr (db), as shown in fig. 6, and the signal formats of the signals include quadrature phase shift signal (4-QAM), 8 quadrature amplitude modulation signal (8-QAM), 16 quadrature amplitude modulation signal (16-QAM), 32 quadrature amplitude modulation signal (32-QAM) and 64 quadrature amplitude modulation signal (64-QAM).

Specifically, in the process of obtaining fig. 6 through testing, the length of the test sample of each osnr is 8192. To evaluate the effect, the signal recognition rates of the 4, 8, 16, 32 and 64 quadrature amplitude modulation signals were tested, as shown in fig. 6. In fig. 6, the dotted line and the solid line indicate the results with and without removing the high density dots, respectively. As shown by the dashed curve in fig. 6, by removing the points with higher density, there is a higher recognition rate of the signal for the signal with low optical signal-to-noise ratio.

The embodiment of the application provides a format identification method of a two-dimensional quadrature amplitude modulation signal applied to a high-speed optical communication module, which is characterized in that the two-dimensional quadrature amplitude modulation signal is obtained by demodulating the signal by using a demodulation algorithm based on channel estimation, and a constellation diagram of the two-dimensional quadrature amplitude modulation signal is constructed; determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm; determining a signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram; wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal. According to the method, the number of constellation cloud clusters in the constellation diagram of the two-dimensional quadrature amplitude modulation signal is obtained through a clustering algorithm, so that the signal modulation format of the two-dimensional quadrature amplitude modulation signal is identified based on the number of the constellation cloud clusters.

Example two

On the basis of the first embodiment, the second embodiment provides a format recognition apparatus for a two-dimensional quadrature amplitude modulation signal, fig. 7 is a schematic structural diagram of the format recognition apparatus for a two-dimensional quadrature amplitude modulation signal provided in the present application, and as shown in fig. 7, the format recognition apparatus for a two-dimensional quadrature amplitude modulation signal includes:

a demodulation module 710, configured to demodulate a signal with a demodulation algorithm based on channel estimation to obtain a two-dimensional quadrature amplitude modulation signal, and construct a constellation diagram of the two-dimensional quadrature amplitude modulation signal;

a clustering module 720, configured to determine the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm;

an identifying module 730, configured to determine a signal format of the two-dimensional quadrature amplitude modulation signal from multiple signal formats according to the number of constellation cloud clusters in the constellation diagram;

wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal.

Optionally, the clustering module 720 is specifically configured to:

determining a truncation distance between data points of the two-dimensional quadrature amplitude modulation signal, and calculating the convergence of each data point of the two-dimensional quadrature amplitude modulation signal; for each data point in the data points, determining an associated data point corresponding to each data point according to the convergence of each data point, and calculating the minimum distance between each data and the associated data point corresponding to each data point; wherein the associated data point is a data point having a higher convergence than each of the data points; and constructing a two-dimensional bar graph according to the minimum distance and the convergence of each data point, and calculating the number of constellation cloud clusters according to the two-dimensional bar graph.

Optionally, the clustering module 720 is specifically configured to:

calculating the convergence of each data point according to a Gaussian kernel formula;

the gaussian kernel formula is expressed as:

where ρ is_iExpressed as the convergence of the ith data point in the N data points, and Dij expressed as the Euclidean distance between the ith data point and the jth data point in the N data points, wherein_DcRepresenting a preset truncation distance.

Optionally, the clustering module 720 is further specifically configured to:

carrying out high-density point removal processing on the data points according to the data points to obtain candidate data points; for each candidate data point of the data points, a minimum distance between each candidate data point data and its corresponding associated data point is calculated.

Optionally, the clustering module 720 is specifically configured to:

determining a cluster point and a non-cluster point in each data point; calculating the distance from each non-cluster point to each cluster point; carrying out zero setting processing on the convergence of the non-cluster center points with the distance larger than a preset distance threshold value, and carrying out descending sequencing on the convergence of all the processed data points; and selecting a preset number of data points as candidate data points according to the descending sorting result.

Optionally, the clustering module 720 is specifically configured to: and determining the truncation distance according to the Euclidean distance between each data point and other data points and the number of the data points.

Optionally, the two-dimensional bar graph is used for representing the association relationship between the product of the minimum distance and the convergence of each data point and each data point;

the clustering module 720 is specifically configured to: carrying out differential processing on the product of the minimum distance and the convergence of adjacent data points in the two-dimensional bar graph to obtain a processed two-dimensional bar graph; and determining the number of the constellation cloud clusters according to the differential peak value in the processed two-dimensional bar graph.

The embodiment of the application provides a format recognition device of a two-dimensional quadrature amplitude modulation signal applied to a high-speed optical communication module, which is used for obtaining the two-dimensional quadrature amplitude modulation signal from signal demodulation by utilizing a demodulation algorithm based on channel estimation and constructing a constellation diagram of the two-dimensional quadrature amplitude modulation signal; determining the number of constellation cloud clusters in the constellation diagram based on a clustering algorithm; determining a signal format of the two-dimensional quadrature amplitude modulation signal from a plurality of signal formats according to the number of constellation cloud clusters in the constellation diagram; wherein the plurality of signal formats includes: a quadrature phase shift signal, an 8 quadrature amplitude modulation signal, a 16 quadrature amplitude modulation signal, a 32 quadrature amplitude modulation signal, and a 64 quadrature amplitude modulation signal. According to the method and the device, the number of constellation cloud clusters in the constellation diagram of the two-dimensional quadrature amplitude modulation signal is obtained through a clustering algorithm, so that the signal modulation format of the two-dimensional quadrature amplitude modulation signal is identified based on the number of the constellation cloud clusters.

EXAMPLE III

Fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, and as shown in fig. 8, an embodiment of the present application further provides an electronic device 1400, which includes: memory 1401, processor 1402, and computer programs.

Wherein the computer program is stored in the memory 1401 and configured to be executed by the processor 1402 to implement the format recognition method of the two-dimensional quadrature amplitude modulation signal provided by any one of the embodiments of the present application. The related descriptions and effects corresponding to the steps in the drawings can be correspondingly understood, and redundant description is not repeated here.

In this embodiment, the memory 1401 and the processor 1402 are connected by a bus.

Example four

The embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the format recognition method for a two-dimensional quadrature amplitude modulation signal provided in any one of the embodiments of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, a division of modules is merely a logical division, and an actual implementation may have another division, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or modules, and may be in an electrical, mechanical or other form.

Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.

Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable question answering system, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

Furthermore, the present application provides a computer program product comprising a computer program which, when being executed by a processor, realizes the method for format recognition of a two-dimensional quadrature amplitude modulated signal as described above.

Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.