阅读说明：本技术 一种误差向量确定及非线性信号纠正方法、装置 (Error vector determination and nonlinear signal correction method and device ) 是由 席赵洋 陈金树 于 2020-11-05 设计创作，主要内容包括：本发明提供一种误差向量确定及非线性信号纠正方法、装置,误差向量确定方法包括：接收待解析数据,待解析数据包括待解析数据的调制方式以及待解析数据的数据信息；根据目标聚类算法,对待解析数据映射到对应星座图上的数据点进行聚类,得到多个聚类中心,待解析数据对应的星座图根据调制方式确定；对聚类中心按照半径长度进行排序,对处于不同半径长度的聚类中心按照半径长度进行递归判决,确定聚类中心对应的星座图标准点；根据聚类中心以及对应星座图标准点,确定每一个聚类中心和对应星座图标准点之间的误差向量,误差向量用于进行非线性信号纠正。实施本方法,便于后续的数据根据求取的误差向量进行纠正,提高非线性纠正的准确性。(The invention provides an error vector determination and nonlinear signal correction method and device, wherein the error vector determination method comprises the following steps: receiving data to be analyzed, wherein the data to be analyzed comprises a modulation mode of the data to be analyzed and data information of the data to be analyzed; clustering data points of the data to be analyzed mapped to the corresponding constellation diagram according to a target clustering algorithm to obtain a plurality of clustering centers, wherein the constellation diagram corresponding to the data to be analyzed is determined according to a modulation mode; sorting the clustering centers according to the radius lengths, carrying out recursive judgment on the clustering centers with different radius lengths according to the radius lengths, and determining the constellation icon reference points corresponding to the clustering centers; and determining an error vector between each cluster center and the corresponding constellation icon quasi point according to the cluster centers and the corresponding constellation icon quasi points, wherein the error vector is used for correcting nonlinear signals. By implementing the method, the subsequent data can be corrected conveniently according to the solved error vector, and the accuracy of nonlinear correction is improved.)

1. A method for determining an error vector, comprising the steps of:

receiving data to be analyzed, wherein the data to be analyzed comprises a modulation mode of the data to be analyzed and data information of the data to be analyzed;

clustering data points of the data to be analyzed, which are mapped to corresponding constellation diagrams, according to a target clustering algorithm to obtain a plurality of clustering centers, wherein the constellation diagrams corresponding to the data to be analyzed are determined according to the modulation mode;

sorting the clustering centers according to radius lengths, carrying out recursive judgment on the clustering centers with different radius lengths according to the radius lengths, and determining constellation icon reference points corresponding to the clustering centers;

and determining an error vector between each cluster center and the corresponding constellation icon quasi point according to the cluster centers and the corresponding constellation icon quasi points, wherein the error vector is used for correcting nonlinear signals.

2. The method of claim 1, wherein the sorting of the cluster centers according to radius lengths, and the recursive determination of the cluster centers with different radius lengths according to radius lengths, and the determining of the constellation icon quasi-point corresponding to the cluster center comprises:

acquiring an average phase error between a constellation diagram standard point at a previous radius and a corresponding clustering center;

carrying out phase correction on the clustering center at the current radius according to the average phase error between the constellation diagram standard point at the previous radius and the corresponding clustering center to obtain the corrected clustering center, wherein the radius length of the previous radius is smaller than that of the current radius;

and determining a constellation icon quasi point corresponding to the corrected clustering center according to the corrected clustering center.

3. The method of claim 2, wherein performing a phase correction on the cluster center at the current radius according to an average phase error between the constellation standard point at the previous radius and the corresponding cluster center to obtain a corrected cluster center, comprises:

receiving at least one correction parameter;

and correcting the clustering centers by utilizing the average phase errors and the correction parameters according to the preset sequence and the size of the radius length of the constellation icon quasi points corresponding to different clustering centers in the constellation diagram.

4. The method according to claim 1, wherein the modulation scheme of the data to be analyzed includes any one or more of 16APSK, 32APSK, and 16 QAM.

5. The method of claim 1, wherein the target clustering algorithm comprises: and the initial clustering points of the K-means clustering algorithm are the standard points of the constellation diagram corresponding to the data to be analyzed, and the clustering number is the number of the standard points of the constellation diagram corresponding to the data to be analyzed.

6. A method for correcting a nonlinear signal, comprising the steps of:

receiving data to be corrected, wherein the data to be corrected comprises a modulation mode of the data to be corrected and data information of the data to be corrected;

determining an error vector between each cluster center corresponding to the modulation mode of the data to be corrected and a quasi point of a constellation icon by using the error vector determination method according to any one of claims 1 to 5;

and correcting the data points of the data to be corrected according to the error vector between each cluster center and the quasi point of the constellation icon.

7. The method of claim 6, further comprising: and performing linear equalization on the corrected data points to be corrected and the corresponding constellation icon standard points.

8. An error vector determination apparatus, comprising:

the data receiving module to be analyzed is used for receiving data to be analyzed, and the data to be analyzed comprises a modulation mode of the data to be analyzed and data information of the data to be analyzed;

the clustering module is used for clustering the data points mapped to the corresponding constellation diagram by the data to be analyzed according to a target clustering algorithm to obtain a plurality of clustering centers, and the constellation diagram corresponding to the data to be analyzed is determined according to the modulation mode;

the constellation icon quasi-point determining module is used for sequencing the clustering centers according to the radius lengths, carrying out recursive judgment on the clustering centers with different radius lengths according to the radius lengths and determining the constellation icon quasi-points corresponding to the clustering centers;

and the error vector determining module is used for determining an error vector between each clustering center and the corresponding constellation icon quasi point according to the clustering centers and the corresponding constellation icon quasi points, and the error vectors are used for carrying out nonlinear signal correction.

9. A nonlinear signal correction apparatus, comprising:

the device comprises a to-be-corrected data receiving module, a correcting module and a correcting module, wherein the to-be-corrected data receiving module is used for receiving to-be-corrected data, and the to-be-corrected data comprises a modulation mode of the to-be-corrected data and data information of the to-be-corrected data;

an error vector obtaining module, configured to determine an error vector between each cluster center and a constellation icon quasi-point corresponding to a modulation mode of data to be corrected by using the error vector determination method according to any one of claims 1 to 5;

and the correcting module is used for correcting the data points of the data to be corrected according to the error vector between each cluster center and the quasi point of the constellation icon.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the error vector determination method of any one of claims 1 to 5 or the nonlinear signal correction method of claim 6 or 7 are implemented when the program is executed by the processor.

11. A storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the error vector determination method of any one of claims 1 to 5 or the non-linear signal correction method of claim 6 or 7.

Technical Field

The invention relates to the field of nonlinear signal processing, in particular to a method and a device for determining an error vector and correcting a nonlinear signal.

Background

Nonlinear signal interference caused by gain compression brought by high-power amplifiers in signal transmission is one of the most major problems troubling designers of transmitters and receivers in the field of communications. A plurality of nonlinear signal processing methods including digital predistortion, power backspacing, feedforward and the like are adopted at a transmitting end, the nonlinear processing method at a receiving end is less researched at present, and particularly in the field of remote sensing satellite communication, a satellite transmitting end does not comprise a nonlinear processing module, so that a nonlinear blind equalization module is necessary to be added at the receiving end.

In the related art, the satellite receiving end generally does not include a nonlinear processing module, and only can use a lower-order modulation mode, such as BPSK, QPSK, 8PSK, and the like. Even if a nonlinear processing module exists, in the aspect of nonlinear processing, a receiving end mostly adopts a simplified form of a Volterra series and an FIR structure for nonlinear correction, and when the severe condition of nonlinear distortion is small, the nonlinear processing method can better process the nonlinear distortion, but when the severe condition of nonlinear distortion is large, the accuracy of nonlinear correction is low.

Disclosure of Invention

In view of this, embodiments of the present invention provide a method and an apparatus for determining an error vector and correcting a nonlinear signal to solve the problem of low correction accuracy when the severe condition of nonlinear distortion is large.

According to a first aspect, an embodiment of the present invention provides an error vector determining method, including the following steps: receiving data to be analyzed, wherein the data to be analyzed comprises a modulation mode of the data to be analyzed and data information of the data to be analyzed; clustering data points of the data to be analyzed, which are mapped to corresponding constellation diagrams, according to a target clustering algorithm to obtain a plurality of clustering centers, wherein the constellation diagrams corresponding to the data to be analyzed are determined according to the modulation mode; sorting the clustering centers according to radius lengths, carrying out recursive judgment on the clustering centers with different radius lengths according to the radius lengths, and determining constellation icon reference points corresponding to the clustering centers; and determining an error vector between each cluster center and the corresponding constellation icon quasi point according to the cluster centers and the corresponding constellation icon quasi points, wherein the error vector is used for correcting nonlinear signals.

Optionally, the sorting the cluster centers according to radius lengths, performing recursive decision on the cluster centers with different radius lengths according to radius lengths, and determining the constellation icon reference points corresponding to the cluster centers includes: acquiring an average phase error between a constellation diagram standard point at a previous radius and a corresponding clustering center; carrying out phase correction on the clustering center at the current radius according to the average phase error between the constellation diagram standard point at the previous radius and the corresponding clustering center to obtain the corrected clustering center, wherein the radius length of the previous radius is smaller than that of the current radius; and determining a constellation icon quasi point corresponding to the corrected clustering center according to the corrected clustering center.

Optionally, performing phase correction on the cluster center at the current radius according to an average phase error between the constellation diagram standard point at the previous radius and the corresponding cluster center, to obtain a corrected cluster center, including: receiving at least one correction parameter; and correcting the clustering centers by utilizing the average phase errors and the correction parameters according to the preset sequence and the size of the radius length of the constellation icon quasi points corresponding to different clustering centers in the constellation diagram.

Optionally, the modulation scheme of the data to be analyzed includes any one or more of 16APSK, 32APSK, and 16 QAM.

Optionally, the target clustering algorithm includes: and the initial clustering points of the K-means clustering algorithm are the standard points of the constellation diagram corresponding to the data to be analyzed, and the clustering number is the number of the standard points of the constellation diagram corresponding to the data to be analyzed.

According to a second aspect, an embodiment of the present invention provides a nonlinear signal correction method, including the following steps: receiving data to be corrected, wherein the data to be corrected comprises a modulation mode of the data to be corrected and data information of the data to be corrected; determining an error vector between each cluster center corresponding to the modulation mode of the data to be corrected and a quasi point of a constellation icon by using an error vector determination method according to the first aspect or any embodiment of the first aspect; and correcting the data points of the data to be corrected according to the error vector between each cluster center and the quasi point of the constellation icon.

Optionally, the nonlinear signal correction method further includes: and performing linear equalization on the corrected data points to be corrected and the corresponding constellation icon standard points.

According to a third aspect, an embodiment of the present invention provides an error vector determination apparatus, including: the data receiving module to be analyzed is used for receiving data to be analyzed, and the data to be analyzed comprises a modulation mode of the data to be analyzed and data information of the data to be analyzed; the clustering module is used for clustering the data points mapped to the corresponding constellation diagram by the data to be analyzed according to a target clustering algorithm to obtain a plurality of clustering centers, and the constellation diagram corresponding to the data to be analyzed is determined according to the modulation mode; the constellation icon quasi-point determining module is used for sequencing the clustering centers according to the radius lengths, carrying out recursive judgment on the clustering centers with different radius lengths according to the radius lengths and determining the constellation icon quasi-points corresponding to the clustering centers; and the error vector determining module is used for determining an error vector between each clustering center and the corresponding constellation icon quasi point according to the clustering centers and the corresponding constellation icon quasi points, and the error vectors are used for carrying out nonlinear signal correction.

According to a fourth aspect, an embodiment of the present invention provides a nonlinear signal correction apparatus, including: the device comprises a to-be-corrected data receiving module, a correcting module and a correcting module, wherein the to-be-corrected data receiving module is used for receiving to-be-corrected data, and the to-be-corrected data comprises a modulation mode of the to-be-corrected data and data information of the to-be-corrected data; an error vector obtaining module, configured to determine an error vector between each cluster center and a constellation icon quasi-point corresponding to a modulation mode of data to be corrected by using the error vector determination method according to the first aspect or any implementation manner of the first aspect; and the correcting module is used for correcting the data points of the data to be corrected according to the error vector between each cluster center and the quasi point of the constellation icon.

According to a fifth aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the error vector determination method according to the first aspect or any of the embodiments of the first aspect when executing the program; or the step of the nonlinear signal correction method according to the second aspect or any embodiment of the second aspect.

According to a sixth aspect, an embodiment of the present invention provides a storage medium, on which computer instructions are stored, and the instructions, when executed by a processor, implement the error vector determination method according to the first aspect or any embodiment of the first aspect; or the step of the nonlinear signal correction method according to the second aspect or any embodiment of the second aspect.

The technical scheme of the invention has the following advantages:

in the error vector determination method provided by this embodiment, data points of data to be analyzed are clustered by a target clustering algorithm, clustering centers are sorted according to radius lengths, clustering centers at different radius lengths are recursively judged according to radius lengths, constellation icon standard points corresponding to the clustering centers are determined, and error vectors of the clustering centers and corresponding standard points are obtained, so that subsequent data can be corrected according to the obtained error vectors.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a specific example of an error vector determination method in an embodiment of the present invention;

FIG. 2 is a diagram of an exemplary error vector determination method according to an embodiment of the present invention;

FIG. 3 is a flowchart illustrating a specific example of a nonlinear signal correction method according to an embodiment of the present invention;

FIG. 4 is a schematic block diagram of a specific example of an error vector determination apparatus according to an embodiment of the present invention;

FIG. 5 is a schematic block diagram of a specific example of a nonlinear signal correction apparatus according to an embodiment of the present invention;

fig. 6 is a schematic block diagram of a specific example of an electronic device in the embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

The present embodiment provides an error vector determination method, which may be used for satellite signal processing, and may also be applied to a series of devices including constellation mitigation, such as a mobile phone, and may be implemented by using software, as shown in fig. 1, and includes the following steps:

s101, receiving data to be analyzed, wherein the data to be analyzed comprises a modulation mode of the data to be analyzed and data information of the data to be analyzed.

For example, the data to be analyzed may be data carried by satellite signals captured at intervals, or data carried by signals received by the mobile phone from the base station at intervals, which is generally 1024 data points. The header of each frame of data to be analyzed includes the modulation mode of the data to be analyzed, such as 16APSK, 32APSK, etc. And the data information of the data to be analyzed is used for mapping the data to be analyzed to the corresponding constellation diagram to form a corresponding data point.

And S102, clustering data points of the data to be analyzed, which are mapped to the corresponding constellation diagrams, according to a target clustering algorithm to obtain a plurality of clustering centers, wherein the constellation diagrams corresponding to the data to be analyzed are determined according to a modulation mode.

For example, the target clustering algorithm sets initial clustering centers according to the modulation mode of the data to be analyzed, so that the number of formed clustering centers is consistent with the number of constellation quasi-points corresponding to the modulation mode, for example, for the data to be analyzed in the 32APSK modulation mode, the number of the set initial clustering centers may be 32. The target clustering algorithm may be K-means and its derivative algorithms, for example, it may be K-means clustering algorithm, and the present embodiment does not limit the type of the target clustering algorithm, and those skilled in the art can determine it as required.

S103, sequencing the clustering centers according to the radius lengths, carrying out recursive judgment on the clustering centers with different radius lengths according to the radius lengths, and determining the constellation icon reference points corresponding to the clustering centers.

For example, for different modulation schemes, the constellation icon quasi-points may be located on a plurality of different radius lengths, for example, 32APSK modulation scheme, as shown in fig. 2, the constellation icon quasi-points are respectively located on three different radii, a first radius includes 4 constellation icon quasi-points, a second radius includes 12 constellation icon quasi-points, and a third radius includes 16 constellation icon quasi-points. The specific process of sorting the cluster centers according to the radius lengths may be sorting according to the radius lengths of the cluster centers in a constellation diagram, then determining the number of the cluster centers on each radius in the modulation mode, and dividing the sorting according to the number to determine which cluster centers are on the same radius.

The specific way of sorting the cluster centers according to the radius lengths may be: assume that a cluster center C ═ C is obtained₁，c₂，…，c_kAnd point X ═ X according to the constellation diagram₁，x₂，…，x_kN radii in total and knowing the number M ═ M of constellation standard points on each radius in the constellation obtained according to the DVBS-2 communication protocol₁，m₂，…，m_nAnd then sorting:

X＝sort(X.begin，X.end，cmp(|x_i|＜|x_j|))

C＝sort(C.begin，C.end，cmp(|c_i|＜|c_j|))

after sorting, dividing the M into clusters from beginning to end according to the clustering centers/constellation icon quasi points in each radius:

X＝{X₁，X₂，…，X_n}

C＝{C₁，C₂，…，C_n}

at this time, X₁And C₁Corresponds to, X₁Representing a set of constellation quasi-points lying on radius 1, C₁The cluster centers at the nth radius.

The recursive decision of the clustering centers with different radius lengths according to the radius lengths can be realized by obtaining the average phase error between the constellation diagram standard point with the previous radius and the corresponding clustering center; carrying out phase correction on the clustering center at the current radius according to the average phase error between the constellation diagram standard point at the previous radius and the corresponding clustering center to obtain the corrected clustering center, wherein the radius length of the previous radius is smaller than that of the current radius; and determining a constellation icon quasi point corresponding to the corrected clustering center according to the corrected clustering center.

Taking the 32APSK modulation scheme as an example, including three different radii, when determining the average phase error between the constellation diagram standard point at the second radius and the corresponding cluster center, performing phase correction on the clustering center on the second radius through the average phase error between the constellation diagram standard point on the first radius and the corresponding clustering center to obtain the position of the corrected clustering center, determining the constellation diagram standard point closest to the position of the corrected clustering center, then, the average phase error is determined through the constellation icon standard point with the position of the corrected cluster center closest to the position of the constellation icon standard point and the cluster center before correction, and finally, solving the average phase error of all the clusters on the second radius as the average phase error of the second radius to correct the cluster center on the third radius.

The specific process can be as follows:

index＝pdist(X_i，re_angle(C_i，α_i-1))，1≤i≤n；(1)

wherein the content of the first and second substances,the Re _ angle function is mainly for C_iElement corrected phase, alpha_i-1Is according to C_i-1Cluster center and corresponding X in (1)_i-1Average phase error of quasi-point of constellation diagram in C_iThe cluster center in (1) is corrected. In the error vector determination method provided by this embodiment, as the radius of the standard points in the constellation increases, the standard points are more and more dense, and the probability of misjudgment caused by phase deviation or intersymbol crosstalk occursThe larger the average phase error between the cluster center with the shortest radius and the standard point is, the cluster center with the second shortest radius is corrected, so that the correction accuracy is ensured, in addition, the phase error between the previous radius and the next radius in the constellation map is closest, the cluster center with the next radius is corrected according to the average phase error between the cluster center with the previous radius and the standard point, and the accuracy of the constellation map standard point judgment is improved.

The method for determining the constellation icon reference point corresponding to the cluster center may be a distance hard decision method, that is, a distance between the cluster center and the constellation standard points in the same cluster after being sorted is calculated, and the constellation icon reference point with the smallest distance is selected as the standard point corresponding to the cluster center.

index＝pdist(X_i，C_i)，1≤i≤n

pdist () computes the distance of each element of the input object and returns X in the closest distance_iIndex in (1), index is indicated at X_iStandard point of constellation diagram in (1) and (C)_iAnd the middle clustering centers are in one-to-one correspondence.

And S104, determining an error vector between each cluster center and the corresponding constellation icon quasi point according to the cluster centers and the corresponding constellation icon quasi points, wherein the error vector is used for carrying out nonlinear signal correction.

Illustratively, the phase difference between the cluster center and the quasi point of the corresponding constellation icon can be obtained according to the way of obtaining the error vector between the cluster center and the quasi point of the corresponding constellation icon. Taking a 32APSK modulation mode as an example, the phase difference between each cluster center and the corresponding constellation icon quasi-point is obtained to obtain 32 error vectors.

In the error vector determination method provided by this embodiment, data points of data to be analyzed are clustered by a target clustering algorithm, clustering centers are sorted according to radius lengths, clustering centers at different radius lengths are recursively judged according to radius lengths, constellation icon standard points corresponding to the clustering centers are determined, and error vectors of the clustering centers and corresponding standard points are obtained, so that subsequent data can be corrected according to the obtained error vectors.

As an optional implementation manner of this embodiment, performing phase correction on a cluster center at a current radius according to an average phase error between a constellation diagram standard point at a previous radius and a corresponding cluster center to obtain a corrected cluster center, includes: receiving at least one correction parameter; and correcting the clustering centers by utilizing the average phase errors and the correction parameters according to the preset sequence and the size of the radius length of the constellation icon quasi points corresponding to different clustering centers in the constellation diagram.

Illustratively, the preset order may be an order of short to long according to the radius length. For different modulation modes, different numbers of correction parameters may be set, and specifically, when a constellation diagram corresponding to the modulation mode includes N numbers of radii, the correction parameters may be set to N numbers. For example, if the 32APSK modulation scheme has three different radius lengths, the correction parameters may be three, and the three correction parameters may respectively correspond to the correction of the phase of the cluster center located at the first radius, the phase of the cluster center located at the second radius, and the phase of the cluster center located at the third radius. After the first calculation processing is carried out on the clustering center on the first radius, a first average phase error is obtained, and when the second calculation processing is carried out on the clustering center on the second radius, the first average phase error and the first correction parameter are superposed to correct the clustering center on the second radius, and a second average phase error is obtained; and when the third calculation processing is performed on the cluster center on the third radius, correcting the cluster center on the third radius by superposing the second mean phase error and the second correction parameter.

Formula (1) may be replaced as in this embodiment:

index＝pdist(X_i，re_angle(C_i，α_i-1，β_i))，1≤i≤n；

wherein, beta_iTo be in pair at C_iThe cluster center in (1) is corrected. The error vector determination method provided by the embodiment can receive correction parameters, and the correction parameters can be adjusted according to experience or actual conditions of engineering personnel, so that the problem of phase ambiguity is solved.

As an optional implementation manner of this embodiment, the modulation scheme of the data to be analyzed includes any one or more of 16APSK, 32APSK, and 16 QAM.

As an optional implementation manner of this embodiment, the target clustering algorithm includes: and the initial clustering points of the K-means clustering algorithm are the standard points of the constellation diagram corresponding to the data to be analyzed, and the clustering number is the number of the standard points of the constellation diagram corresponding to the data to be analyzed.

Illustratively, the specific process of the K-means clustering algorithm comprises the following steps: determining the number of clustering centers and a standard constellation point coordinate as initialized clustering centers through a modulation mode of data to be analyzed, and clustering according to the distance between each data point in the data to be analyzed and the initialized clustering centers, wherein in the embodiment, an iteration cut-off condition of a clustering algorithm is preset and two cut-off conditions are taken as bases, wherein one mode is that the number of internal circulation times is taken as a basis, clustering can be stopped when the number of circulation times exceeds a first threshold value, and the first threshold value can be 5; the second method is to stop clustering according to the accumulated cluster center deviation value after iteration when the accumulated cluster center deviation value is smaller than a second threshold value, which may be 0.01. When the first mode is adopted, the stability is higher. In practical application, two modes can be used jointly, wherein the first mode is used as an outer threshold, and the second mode is used as an inner threshold.

The method specifically comprises the following steps: assume a sample X of data points of the data to be analyzed, containing n objects X ═ X₁，X₂，…，X_nAnd each object has m-dimensional attributes, and the K-means algorithm aims to gather n objects into specified K class clusters according to the similarity among the objects, wherein each object belongs to one and only belongs to one class cluster with the minimum distance from the center of the class cluster. For theK-means, first of all, K cluster centers { C need to be initialized₁，C₂，…，C_k1 < k ≦ n, and then calculating the Euclidean distance from each object to the center of each cluster, as shown in the formula:

the algorithm flow comprises the following steps:

in the above formula, Xi represents that the i-th object 1. ltoreq. i. ltoreq.n, C_jJ is more than or equal to j and is more than or equal to k and X of the jth cluster center_itRespectively representing the t-th attribute, C, of the i-th object_jtThe t-th attribute representing the j-th cluster center.

Sequentially comparing the distance from each object to each cluster center, and distributing the objects to the cluster of the cluster center closest to the object to obtain k clusters { S }₁，S₂，…，S_kThe calculation method of the clustering center is as follows:

in the above formula C_LRepresenting the L-th clustering center, L is more than or equal to 1 and less than or equal to k, and S_LI represents the number X of objects in the L-th class cluster_iRepresents the ith object in the Lth class cluster, and is more than or equal to 1 and less than or equal to i and less than or equal to | S_L|

The main algorithm flow is as follows:

inputting: sample set D ═ x₁，x₂，…，x_m}; cluster number K, and initialization vector concell ═ μ₁，μ₂，…，μ_k}，

1：Repeat

2: order to

3：for j＝1，2，…，m do

4: calculating a sample x_jAnd each mean vector mu₁(1≤i ≦ k): d_ji＝||x_j-μ_i||₂；

5: determining x from the nearest mean vector_jCluster marking of (2): alpha is alpha_j＝argmin_{i∈{1，2，3，…，k}}d_ji；

6: sample x_jDividing into corresponding clusters:

7：end for

8：：for i＝1，2，…，k do

9: calculating a new mean vector:

11: according to the set cut-off condition (mean value is not updated or cycle times)

And (3) outputting: mean vector C ═ μ₁，μ₂，…，μ_k}。

The error vector determination method provided by this embodiment can effectively perform clustering operation on data points of data to be analyzed through a clustering algorithm, can effectively use all data information, can perform data extraction in an actual operation by taking a frame as a unit, has an optimal solution since a K-means clustering algorithm is adopted and the essence of the K-means clustering algorithm is still the minimum value obtained by a plane belonging to an MES, and has a certain convergence at the end of the algorithm, so that the algorithm is stable, the stability of the whole signal correction process is improved, and since an initial clustering point of the K-means clustering algorithm is a constellation icon quasi point corresponding to the data to be analyzed, the acceptable degree of nonlinearity is very high, and in the case of small intersymbol crosstalk, the nonlinear interference of a large degree can be supported, and the processing of a nonlinear signal can be effectively guaranteed to be completed within a limited time complexity, time complexity is reduced.

The embodiment of the present invention provides a nonlinear signal correction method, which may be used for satellite signal processing, and may also be applied to a series of devices including constellation map mitigation, such as a mobile phone, and the nonlinear signal correction method in the embodiment may be implemented by using an FPGA, as shown in fig. 3, and includes the following steps:

s201, receiving data to be corrected, wherein the data to be corrected comprises a modulation mode of the data to be corrected and data information of the data to be corrected.

For example, the data to be corrected may be real-time data carried by a satellite signal, or may also be real-time data carried by a signal received by a mobile phone from a base station. The frame header of each frame of data to be corrected includes the modulation mode of the data to be corrected, such as 16APSK, 32APSK, and the like. The data information is used for mapping the data to be corrected to the corresponding constellation diagram to form the corresponding data point.

S202, determining an error vector between each cluster center corresponding to the modulation mode of the data to be corrected and a quasi point of a constellation icon by using the error vector determination method in the embodiment;

for example, when data to be corrected is received, an error vector between each cluster center and a constellation icon quasi point in the modulation mode corresponding to the data to be corrected, which is determined by the error vector determination method in the above embodiment, is obtained, at this time, an error vector between each cluster center and a constellation icon quasi point in the modulation mode corresponding to the data to be corrected is obtained, and the obtained form of the error vector between each cluster center and a constellation icon quasi point in the modulation mode corresponding to the data to be corrected may be a form in which the cluster center, the corresponding constellation icon quasi point, and the error vector are taken as a triplet.

And S203, correcting the data points of the data to be corrected according to the error vector between each cluster center and the quasi point of the constellation icon.

For example, according to the error vector between each cluster center and the quasi point of the constellation icon, the data point of the data to be corrected may be corrected by taking the cluster center as the standard point to make a decision, and then performing error vector addition correction after the decision.

According to the nonlinear signal correction method provided by the embodiment, the error vector determined in the error vector determination method is obtained, so that the real-time signal is quickly corrected, the processes of receiving data to be corrected and adding the error vector are realized through the real-time FPGA, no extra multiplier is added, the real-time requirement of the data to be corrected is lower, the two processes are performed in parallel, and the consumption of FPGA internal resources is reduced.

As an optional implementation manner of this embodiment, the nonlinear signal correction method further includes: and performing linear equalization on the corrected data points to be corrected and the corresponding constellation icon standard points.

Illustratively, the linear equalization may be performed by blind adaptive equalization using an LMS algorithm of an FIR structure, the order of the FIR structure is 7, and the LMS algorithm performs real-time parameter update. The LMS algorithm is actually an application of the adaptive filtering algorithm in the present embodiment.

The LMS algorithm structure:

the inputs are X (n) ═ x (n), x (n-1), …, x (n-L)]^TAdaptive weight coefficient w (n) ═ w₁(n)，w₂(n)，…，w_L(n)]^TThe output calculation formula at this time is

y(n)＝W(n)^T*X(n)

The error signal e (n) is:

e(n)＝d(n)-y(n)＝d(n)-W(n)^T*X(n)；

wherein d (n) is the desired output;

the LMS algorithm loss function J (n) is:

J(n)＝e(n)²；

the following can be obtained:

the nonlinear signal correction method provided by the embodiment performs adaptive equalization by using the LMS algorithm, and can save computing resources due to the low cost of the LMS algorithm.

The present embodiment provides an error vector determination apparatus, as shown in fig. 4, including:

a to-be-analyzed data receiving module 301, configured to receive to-be-analyzed data, where the to-be-analyzed data includes a modulation mode of the to-be-analyzed data and data information of the to-be-analyzed data; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

The clustering module 302 is configured to sort the clustering centers according to radius lengths, perform recursive decision on the clustering centers with different radius lengths according to the radius lengths, and determine constellation icon quasi points corresponding to the clustering centers; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

A constellation icon quasi-point determining module 303, configured to sort the cluster centers according to radius lengths, perform recursive decision on cluster centers with different radius lengths according to radius lengths, and determine constellation icon quasi-points corresponding to the cluster centers; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

An error vector determining module 304, configured to determine an error vector between each cluster center and the corresponding constellation quasi-point according to the cluster centers and the corresponding constellation quasi-points, where the error vector is used for performing nonlinear signal correction. For details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

As an optional implementation manner of this embodiment, the constellation icon quasi-point determining module 303 includes:

the average phase error acquisition module is used for acquiring the average phase error between the constellation diagram standard point in the previous radius and the corresponding clustering center; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

The correction clustering center determining module is used for carrying out phase correction on the clustering center at the current radius according to the phase error between the constellation diagram standard point at the previous radius and the corresponding clustering center to obtain the corrected clustering center, wherein the radius length of the previous radius is smaller than that of the current radius; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

The constellation icon quasi point determining module is used for determining constellation icon quasi points corresponding to the corrected clustering centers according to the corrected clustering centers; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

As an optional implementation manner of this embodiment, the module for determining a corrected cluster center includes:

the parameter receiving module is used for receiving at least one correction parameter; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

And the correcting module is used for correcting the clustering centers by utilizing the plurality of average phase errors and the correcting parameters according to the preset sequence and the size of the radius length of the constellation icon quasi-points corresponding to different clustering centers in the constellation diagram. For details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

As an optional implementation manner of this embodiment, the modulation scheme of the data to be analyzed includes any one or more of 16APSK, 32APSK, and 16 QAM. For details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

As an optional implementation manner of this embodiment, the target clustering algorithm includes: and the initial clustering points of the K-means clustering algorithm are the standard points of the constellation diagram corresponding to the data to be analyzed, and the clustering number is the number of the standard points of the constellation diagram corresponding to the data to be analyzed. For details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

The present embodiment provides a nonlinear signal correction apparatus, as shown in fig. 5, including:

a to-be-corrected data receiving module 401, configured to receive to-be-corrected data, where the to-be-corrected data includes a modulation mode of the to-be-corrected data and data information of the to-be-corrected data; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

An error vector obtaining module 402, configured to determine an error vector between each cluster center and a constellation icon quasi point corresponding to a modulation mode of data to be corrected by using the error vector determining method described in the foregoing embodiment; for details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

And a correcting module 403, configured to correct the data point of the data to be corrected according to the error vector between each cluster center and the quasi point of the constellation icon. For details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

As an optional implementation manner of this embodiment, the apparatus further includes: and the balancing module is used for performing linear balancing on the corrected data points to be corrected and the corresponding constellation icon standard points. For details, reference is made to the corresponding parts of the above embodiments, and details are not repeated here.

The embodiment of the present application also provides an electronic device, as shown in fig. 5, including a processor 510 and a memory 520, where the processor 510 and the memory 520 may be connected by a bus or in other manners.

Processor 510 may be a Central Processing Unit (CPU). The Processor 510 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.

The memory 520, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the error vector determination method or the non-linear signal correction method in the embodiments of the present invention. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions, and modules stored in the memory.

The memory 520 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 520 may optionally include memory located remotely from the processor, which may be connected to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory 520 and, when executed by the processor 510, perform an error vector determination method as in the embodiment of fig. 1 or a non-linear signal correction method as in the embodiment of fig. 2.

The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.

The present embodiments also provide a computer storage medium storing computer-executable instructions that can perform the error vector determination method in the embodiment shown in fig. 1 or the nonlinear signal correction method in the embodiment shown in fig. 2. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.

It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.