阅读说明：本技术 一种线性化模型的分段点确定方法及装置 (Segmentation point determination method and device of linearized model ) 是由 张永丽 伍坚 于 2020-05-12 设计创作，主要内容包括：本发明实施例提供一种线性化模型的分段点确定方法及装置,用以提高确定功率放大器的最优分段点的效率。该方法中,功率放大器管理设备获取功率放大器的输入和实际输出信号,以及分段点总数；依次在输入信号中的采样点中确定每个分段点,以保证通过每个分段点对应的分段集合得到的线性化模型得到的预测输出信号与所述实际输出信号的误差最小。该方法可以通过分步选取方式确定最优分段点,不仅可以提高选取最优分段点效率,还可以进一步提高分段线性模型的准确性,最终保证功率放大器的功放特性。(The embodiment of the invention provides a method and a device for determining a segmentation point of a linear model, which are used for improving the efficiency of determining the optimal segmentation point of a power amplifier. In the method, a power amplifier management device acquires input and actual output signals of a power amplifier and the total number of segmentation points; and determining each segmentation point in the sampling points in the input signal in sequence to ensure that the error between the predicted output signal obtained by the linearized model obtained by the segmentation set corresponding to each segmentation point and the actual output signal is minimum. The method can determine the optimal segmentation point through a step-by-step selection mode, not only can improve the efficiency of selecting the optimal segmentation point, but also can further improve the accuracy of the piecewise linear model, and finally ensures the power amplifier characteristic of the power amplifier.)

1. A method for determining segmentation points of a linearized model, comprising:

obtaining an input signal x for a power amplifier_nAnd the corresponding actual output signal y_nAnd determining the total number K of the preset segmentation points;

for each sampling point C in a plurality of sampling points of the input signal in turn_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; the first segment set comprises sampling points from a first sampling point to a1 st segment point to-be-selected point in the input signal;

determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point;

for each of the plurality of first sampling points D in turn_jPerforming the following step, wherein the plurality of first sampling points D_jIs a sampling point after the kth segment point, K is a positive integer less than K, j is an integer from 1 to N, N is the number of the first sampling points: the first sampling point D_jAs a (k + 1) th segment point candidate point, adjusting the linearization model of the power amplifier according to a (k + 1) th segment set corresponding to the (k + 1) th segment point candidate point and a preset memory depth of the (k + 1) th segment set to obtain a (k + 1) th linearization model; predicting a (k + 1) th predicted output signal of the input signal according to the (k + 1) th linearized model, and calculating an error between the (k + 1) th predicted output signal and the actual output signal; wherein the k +1 th segment set comprises the first sampling point in the input signal to the first segment setSampling points between the (k + 1) th segmentation point and the points to be selected;

and determining the sampling point with the minimum corresponding error in the plurality of first sampling points as the (k + 1) th segmentation point.

2. The method of claim 1, wherein the adjusting the linearized model of the power amplifier according to the first segment set corresponding to the 1 st segment point candidate point and a preset memory depth of the first segment set to obtain a first linearized model comprises:

establishing a piecewise function model corresponding to the first segmentation set according to the 1 st segmentation point candidate point, the first segmentation set and the preset memory depth of the first segmentation set; the segment function model corresponding to the first segment set is used for representing the corresponding relation among the 1 st segment point to-be-selected point, the first segment set and the preset memory depth of the first segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.

3. The method of claim 1, wherein said calculating an error between said first predicted output signal and said actual output signal satisfies the following equation:y'_(n)representing said first prediction output signal, y_nRepresenting the actual output signal.

4. The method of claim 1, wherein the linearized model of the power amplifier satisfies the following equation:

wherein x is_nRepresenting the input signal, y_nRepresenting the prediction output signal, k the kth segmentation point, M the memory depth, beta_kAnd representing a segmentation set corresponding to the kth segmentation point to be selected.

5. An apparatus for determining a segmentation point of a linearized model, comprising:

an acquisition unit for acquiring an input signal x of a power amplifier_nAnd the corresponding actual output signal y_nAnd determining the total number K of the preset segmentation points;

a processing unit for sequentially aiming at each of a plurality of sampling points C of the input signal_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; the first segment set comprises sampling points from a first sampling point to a1 st segment point to-be-selected point in the input signal;

determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point;

the processing unit is further configured to sequentially target each of the plurality of first sampling points D_jPerforming the following step, wherein the plurality of first sampling points D_jIs a sampling point after the kth segment point, K is a positive integer less than K, j is an integer from 1 to N, N is the number of the first sampling points: the first sampling point D_jAs a (k + 1) th segment point candidate point, adjusting the linearization model of the power amplifier according to a (k + 1) th segment set corresponding to the (k + 1) th segment point candidate point and a preset memory depth of the (k + 1) th segment set to obtain a (k + 1) th lineA sexual model; predicting a (k + 1) th predicted output signal of the input signal according to the (k + 1) th linearized model, and calculating an error between the (k + 1) th predicted output signal and the actual output signal; the (k + 1) th segmentation set comprises sampling points from a first sampling point in the input signal to a (k + 1) th segmentation point candidate point;

and determining the sampling point with the minimum corresponding error in the plurality of first sampling points as the (k + 1) th segmentation point.

6. The apparatus of claim 5, wherein the processing unit, when adjusting the linearized model of the power amplifier according to the first segmentation set corresponding to the 1 st segmentation point candidate, and a preset memory depth of the first segmentation set, to obtain the first linearized model, is specifically configured to:

establishing a piecewise function model corresponding to the first segmentation set according to the 1 st segmentation point candidate point, the first segmentation set and the preset memory depth of the first segmentation set; the segment function model corresponding to the first segment set is used for representing the corresponding relation among the 1 st segment point to-be-selected point, the first segment set and the preset memory depth of the first segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.

7. The apparatus of claim 5, wherein the calculating the error between the first predicted output signal and the actual output signal satisfies the following equation:y'_(n)representing said first prediction output signal, y_nRepresenting the actual output signal.

8. The apparatus of claim 5, wherein the linearized model of the power amplifier satisfies the following equation:

wherein x is_nRepresenting the input signal, y_nRepresenting the prediction output signal, k the kth segmentation point, M the memory depth, beta_kAnd representing a segmentation set corresponding to the kth segmentation point to be selected.

9. A computer-readable storage medium, in which a computer program is stored which, when run on an index monitoring device, causes the index monitoring device to carry out the method according to any one of claims 1-4.

10. A chip for reading a computer program stored in a memory for performing the method according to any one of claims 1 to 4.

Technical Field

The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for determining a segmentation point of a linearized model.

Background

The power amplifier is an important component of a base station transmission system, and in a signal transmission link, the linearization degree of the power amplifier directly influences the transmission quality of a signal. In order to reduce the power consumption of the base station system, the efficiency requirement of the power amplifier is higher and higher.

Power amplifiers typically operate in a non-linear region, which results in amplitude and phase distortion of the output signal of the power amplifier due to differences in the instantaneous amplitude of the input signal. At present, the communication field can solve the linearization problem of the power amplifier through a predistortion model based on a piecewise linear function. The piecewise linear function is a linear sub-function which approximately expresses a nonlinear function on a partial domain, and a predistortion model of the piecewise linear function is generally composed of a set of continuous slice function models which are linearly represented by basis functions with saturation characteristics. The piecewise linear function has the advantages of free parameter configuration and low calculation complexity, so that the power amplifier characteristic can be well described. Generally, the piecewise linear function model comprises a Volterra series model, and a commonly used polynomial model MP model and a GMP model obtained based on the Volterra series model change.

In the prior art, a model of a piecewise linear function used by a power amplifier includes a plurality of piecewise point functions, and different piecewise point functions can describe power amplifier characteristics of the power amplifier in the piecewise point. However, in the polynomial model, a polynomial model with a higher nonlinear order is required to improve the nonlinearity of the power amplifier, which results in an increase in the number of parameters thereof, and the model solution involved in the configuration process of the segmentation points becomes more complicated, so that the segmentation points are generally uniformly configured, but this configuration cannot effectively utilize the behavior characteristics of the power amplifier, the efficiency is low, and the actual performance is not proportional to the number of the segmentation points. Therefore, in order to enable the model of the piecewise linear function to better describe the power amplifier characteristics, an optimal piecewise point configuration of the power amplifier needs to be found. The time length required by the conventional traversal method increases exponentially with the increase of the number of the segments, so that the complexity of the segment point selection time is also large.

Disclosure of Invention

The application provides a method and a device for determining a section point of a linearization model, which are used for improving the efficiency of determining the optimal section point of the linearization model corresponding to a power amplifier.

The embodiment of the invention provides the following specific technical scheme:

in a first aspect, an embodiment of the present application provides a method for determining a segmentation point of a linearized model, where the method specifically includes the following steps:

deriving the output of a power amplifierIncoming signal x_nAnd the corresponding actual output signal y_nAnd determining the total number K of the preset segmentation points;

for each sampling point C in a plurality of sampling points of the input signal in turn_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; the first segment set comprises sampling points from a first sampling point to a1 st segment point to-be-selected point in the input signal;

determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point;

for each of the plurality of first sampling points D in turn_jPerforming the following step, wherein the plurality of first sampling points D_jIs a sampling point after the kth segment point, K is a positive integer less than K, j is an integer from 1 to N, N is the number of the first sampling points: the first sampling point D_jAs a (k + 1) th segment point candidate point, adjusting the linearization model of the power amplifier according to a (k + 1) th segment set corresponding to the (k + 1) th segment point candidate point and a preset memory depth of the (k + 1) th segment set to obtain a (k + 1) th linearization model; predicting a (k + 1) th predicted output signal of the input signal according to the (k + 1) th linearized model, and calculating an error between the (k + 1) th predicted output signal and the actual output signal; the (k + 1) th segmentation set comprises sampling points from a first sampling point in the input signal to a (k + 1) th segmentation point candidate point;

and determining the sampling point with the minimum corresponding error in the plurality of first sampling points as the (k + 1) th segmentation point.

In a possible implementation manner, the adjusting the linearized model of the power amplifier according to the first segment set corresponding to the 1 st segment point candidate and a preset memory depth of the first segment set to obtain a first linearized model includes:

establishing a piecewise function model corresponding to the first segmentation set according to the 1 st segmentation point candidate point, the first segmentation set and the preset memory depth of the first segmentation set; the segment function model corresponding to the first segment set is used for representing the corresponding relation among the 1 st segment point to-be-selected point, the first segment set and the preset memory depth of the first segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.

In one possible embodiment, the calculating the error between the first predicted output signal and the actual output signal satisfies the following equation:y′_(n)representing said first prediction output signal, y_nRepresenting the actual output signal.

In one possible embodiment, the linearized model of the power amplifier satisfies the following equation:

wherein x is_nRepresenting the input signal, y_nRepresenting the prediction output signal, k the kth segmentation point, M the memory depth, beta_kAnd representing a segmentation set corresponding to the kth segmentation point to be selected.

In a second aspect, an embodiment of the present application provides an apparatus for determining a segmentation point of a linearized model, including:

an acquisition unit for acquiring an input signal x of a power amplifier_nAnd the corresponding actual output signal y_nAnd determining the total number K of the preset segmentation points;

a processing unit for sequentially aiming at each of a plurality of sampling points C of the input signal_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; the first segment set comprises sampling points from a first sampling point to a1 st segment point to-be-selected point in the input signal;

determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point;

the processing unit is further configured to sequentially target each of the plurality of first sampling points D_jPerforming the following step, wherein the plurality of first sampling points D_jIs a sampling point after the kth segment point, K is a positive integer less than K, j is an integer from 1 to N, N is the number of the first sampling points: the first sampling point D_jAs a (k + 1) th segment point candidate point, adjusting the linearization model of the power amplifier according to a (k + 1) th segment set corresponding to the (k + 1) th segment point candidate point and a preset memory depth of the (k + 1) th segment set to obtain a (k + 1) th linearization model; predicting a (k + 1) th predicted output signal of the input signal according to the (k + 1) th linearized model, and calculating an error between the (k + 1) th predicted output signal and the actual output signal; the (k + 1) th segmentation set comprises sampling points from a first sampling point in the input signal to a (k + 1) th segmentation point candidate point;

and determining the sampling point with the minimum corresponding error in the plurality of first sampling points as the (k + 1) th segmentation point.

In a possible implementation manner, when the processing unit adjusts the linearized model of the power amplifier according to the first segmentation set corresponding to the 1 st segmentation point candidate, and the preset memory depth of the first segmentation set, to obtain a first linearized model, the processing unit is specifically configured to:

establishing a piecewise function model corresponding to the first segmentation set according to the 1 st segmentation point candidate point, the first segmentation set and the preset memory depth of the first segmentation set; the segment function model corresponding to the first segment set is used for representing the corresponding relation among the 1 st segment point to-be-selected point, the first segment set and the preset memory depth of the first segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.

In one possible embodiment, the calculating the error between the first predicted output signal and the actual output signal satisfies the following equation:y′_(n)representing said first prediction output signal, y_nRepresenting the actual output signal.

In one possible embodiment, the linearized model of the power amplifier satisfies the following equation:

wherein x is_nRepresenting the input signal, y_nRepresenting the prediction output signal, k the kth segmentation point, M the memory depth, beta_kAnd representing a segmentation set corresponding to the kth segmentation point to be selected.

In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including: the computer-readable storage medium has stored thereon a computer program which, when run on an electronic device, causes the electronic device to perform any one of the possible implementations of any of the above aspects.

In a fourth aspect, embodiments of the present application provide a computer program comprising instructions that, when executed on a computer, cause the computer to perform any one of the possible implementations of any one of the above aspects.

In a fifth aspect, the present application provides a chip, where the chip is configured to read a computer program stored in a memory, and perform any one of the possible implementations of the foregoing aspects.

In the technical scheme of the embodiment of the application, the power amplifier management equipment acquires an input signal and a corresponding actual output signal of a power amplifier and determines the total number K of preset segmentation points; secondly, for each sampling point C in a plurality of sampling points of the input signal in turn_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point; further, the same steps as the 1 st segmentation point are sequentially executed for each of the plurality of first sampling points, and the 2 nd segmentation point is determined until the K segmentation points are determined. The method determines the optimal segmentation point corresponding to each set through the step-by-step selection mode, reduces the time for selecting the segmentation point, can improve the efficiency of selecting the optimal segmentation point, can further improve the accuracy of the piecewise linear model, and finally ensures the power amplifier characteristic of the power amplifier.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.

FIG. 1 is a schematic diagram of an internal structure of a base station transmitting system according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating a segmentation point determination method for a linearized model according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart illustrating steps of one embodiment of the present invention;

fig. 4 is a schematic diagram of a power amplifier management apparatus according to an embodiment of the present invention;

fig. 5 is a schematic diagram of a power amplifier management apparatus according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. 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 application.

The embodiment of the application provides a segmentation point determination method of a linear model, which is used for improving the efficiency of determining the optimal segmentation point of a power amplifier. The method and the device are based on the same inventive concept, and because the principles of solving the problems of the method and the device are similar, the implementation of the device and the method can be mutually referred, and repeated parts are not repeated.

In the technical scheme of the embodiment of the application, the power amplifier management equipment acquires input and actual output signals of the power amplifier and the total number of segmentation points; the power amplifier management equipment determines each segmentation point in sampling points in the input signal in sequence so as to ensure that the error between the predicted output signal obtained by the linearization model obtained by the segmentation set corresponding to each segmentation point and the actual output signal is minimum. The method can determine the optimal segmentation point through a step-by-step selection mode, not only can improve the efficiency of selecting the optimal segmentation point, but also can further improve the accuracy of the piecewise linear model, and finally ensures the power amplifier characteristic of the power amplifier.

Some terms in the embodiments of the present application will be explained below to facilitate understanding by those skilled in the art.

1. And the segmentation point is used for dividing a group of data points, and one segmentation point configuration corresponds to one grouping condition. Because the linear function model comprises a plurality of variables, the multivariate is classified and grouped, and a corresponding piecewise function model is established. Therefore, by configuring the optimal segmentation point, a corresponding optimal segmentation function model can be established, and the linearization function model is adjusted, so that the linearization function model can describe the characteristics of the power amplifier more effectively.

2. The memory depth is that in a broadband communication system, a wider input signal bandwidth easily enables the power amplifier to have a certain memory effect. As the input signal bandwidth increases, the memory effect of the power amplifier will also tend to be significant.

3. A plurality, denoted as at least two.

4. And/or, describing the association relationship of the associated object, indicating that there may be three relationships, e.g., a and/or B, which may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "three types" generally indicates that the former and latter associated objects are in an "or" relationship.

Embodiments of the present application will be described below with reference to the drawings.

As shown in fig. 1, in a base station 100, a plurality of power amplifiers 102 are important components of its transmission system. In a signal transmitting link, the linearization degree of a power amplifier directly influences the transmitting quality of a signal. The input unit 101 inputs a signal, the signal is processed by the power amplifier 102, and the processed signal is sent out again by the output unit 103. The base station 100 further includes a power amplifier management device 104, where the power amplifier management device 104 can adjust a linearization model of the power amplifier, so as to control and manage the power amplifier 102, so as to ensure the power amplification characteristic of the power amplifier 102.

However, with the development of 5G communication technology, large-scale antenna technology is widely used, and the base station size is also getting larger. In order to reduce system power consumption, the efficiency of power amplifiers is also increasing. The AM-AM curve (the amplitude distortion curve of the output signal versus the input signal) of a high efficiency amplifier is no longer a monotonic characteristic, with one or several "pits" of different depths appearing in the curve. The traditional GMP model is widely applied to the field of digital predistortion, but aiming at high-efficiency power amplifier, the matching degree of the GMP model and the power amplifier is reduced. The predistortion model based on the piecewise linear function has the advantages of free parameter configuration and low calculation complexity, but the general uniform segmentation mode cannot effectively utilize the behavior characteristics of the power amplifier, the efficiency is low, and the actual performance is not in direct proportion to the number of segments.

However, in practical situations, the power amplifier usually works in a non-linear region to improve the efficiency of the power amplifier, thereby causing amplitude and phase distortion of the output signal of the power amplifier due to the difference of the instantaneous amplitudes of the input signal. Meanwhile, the wider signal bandwidth also easily enables the power amplifier to have a certain memory effect.

In order to ensure the integrity of the transmitted signal and aim at the problem of power amplifier linearization, digital predistortion is a widely applied power amplifier linearization technique. Input signal x of hypothetical power amplifier_nThe output signal is y_nThe Volterra series model for describing the power amplifier characteristics is shown as follows:

the Volterra series model has a deep theoretical basis, parameters are easy and convenient to extract, model parameters are easy to solve through methods such as least square, and commonly used polynomial models such as MP models and GMP models are all formed based on changes of the Volterra model.

The regular piecewise linear function model is a continuous piecewise function model linearly represented by a set of basis functions with saturation characteristics. For the input of specific frequency, the output frequency of each basic function of the function model is complex, and theoretically, the whole frequency domain can be covered.

The output frequency of the basis function of the power series equation is only the simple relationship between the sum and the difference of the frequency multiplication of the input frequency, so the order of the signal intermodulation component (i.e. the spread width of the spectrum) which can be fitted by the polynomial model is related to the order of the power series equation. When the order is lower, the power series equation can fit fewer components, and the piecewise function model can fit all components theoretically no matter the number of sectors, but the fitting accuracy is different, which also becomes a significant characteristic of the piecewise function model.

In general, the mathematical expression of the piecewise linear function is as follows:

in order to describe the behavior characteristics of the power amplifier, the above formula is expanded to a complex domain, and the following linear model parameters are obtained:

where M is the memory depth, K is the number of segments, [ beta ]₁,β₂,...,β_K]Is the corresponding segmentation point.

In theory, the segmentation points of the model can be freely selected, but in the existing piecewise linear model, the segmentation points are usually uniformly selected by default in order to simplify the solving process of the algorithm. The uniformly selected segmentation points ignore the distortion characteristics of the power amplifier to signals with different amplitudes, and in order to enable the piecewise linear model to better describe the characteristics of the power amplifier under the configuration of the same memory depth and segmentation number, an optimal segmentation point configuration corresponding to the power amplifier needs to be found. The total time required by the traversal method increases exponentially with the increase of the number of the segments, so the time complexity for selecting the segment points is higher.

In order to solve the above problem, an embodiment of the present application provides a segmentation point determination method for a linearized model. The method can be applied to a power amplifier in a base station as shown in fig. 1, and the following describes in detail a flow of a segmentation point determination method of a linearization model provided by an embodiment of the present application with reference to fig. 2.

S201: power amplifier management device obtaining input signal x of power amplifier_nAnd the corresponding actual output signal y_nAnd determining the preset total number K of the segmentation points.

S202: the power amplifier management device determines a1 st segment point among a plurality of sampling points of the input signal.

In one embodiment, the power amplifier management device may perform S202 through the following steps a1 and a 2.

A1: for each sampling point C in a plurality of sampling points of the input signal in turn_iWherein i is an integer from 1 to Q, Q is the number of the plurality of sampling points, and the following steps are executed:

a 1: each sampling point C_iAnd as a1 st segmentation point candidate point, adjusting the linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model. The first segment set comprises sampling points from a first sampling point to the 1 st segment point to-be-selected point in the input signal.

In an embodiment, the adjusting the linearized model of the power amplifier according to the first segment set corresponding to the 1 st segment point candidate and a preset memory depth of the first segment set to obtain a first linearized model includes:

establishing a piecewise function model corresponding to the first segmentation set according to the 1 st segmentation point candidate point, the first segmentation set and the preset memory depth of the first segmentation set; the segment function model corresponding to the first segment set is used for representing the corresponding relation among the 1 st segment point to-be-selected point, the first segment set and the preset memory depth of the first segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.

Optionally, the linearized model of the power amplifier satisfies the following formula:

wherein x is_nRepresenting the input signal, y_nRepresenting the prediction output signal, k the kth segmentation point, M the memory depth, beta_kAnd representing a segmentation set corresponding to the kth segmentation point to be selected.

a 2: a first predicted output signal of the input signal is predicted according to the first linearized model.

Optionally, the input signal is predicted according to the formula (3) to obtain a predicted output signal of the input signal.

a 3: an error between the first predicted output signal and the actual output signal is calculated.

Optionally, an error between the first prediction output signal and the actual output signal satisfies the following formula:y'_(n)representing said first prediction output signal, y_nRepresenting the actual output signal.

A2: and determining the sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point.

S203: the power amplifier management device determines other segment points subsequent to the 1 st segment point among a plurality of first sample points, wherein the plurality of first sample points are subsequent to the kth segment point, and K is a positive integer smaller than K.

In one embodiment, the power amplifier management device may perform S203 through the following steps B1 and B2.

B1: for each of the plurality of first sampling points D in turn_jPerforming the following steps, wherein j is an integer from 1 to N, and N is the number of the plurality of first sampling points:

b 1: the first sampling point D_jAnd adjusting the linearization model of the power amplifier as a (k + 1) th segmentation point candidate point according to a (k + 1) th segmentation set corresponding to the (k + 1) th segmentation point candidate point and a preset memory depth of the (k + 1) th segmentation set to obtain a (k + 1) th linearization model. The (k + 1) th segment set comprises sampling points from a first sampling point to a (k + 1) th segment point candidate point in the input signal.

In an embodiment, the adjusting the linearization model of the power amplifier according to a k +1 th segment set corresponding to the k +1 th segment point to be selected and a preset memory depth of the k +1 th segment set to obtain a k +1 th linearization model includes:

according to a to-be-selected point of a (k + 1) th segmentation point, the (k + 1) th segmentation set and a preset memory depth of the (k + 1) th segmentation set, establishing a segmentation function model corresponding to the (k + 1) th segmentation set; the segment function model corresponding to the (k + 1) th segment set is used for representing the corresponding relation among the (k + 1) th segment point to be selected, the (k + 1) th segment set and the preset memory depth of the (k + 1) th segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a (k + 1) th linearization model.

b 2: predicting a (k + 1) th predicted output signal of the input signal according to the (k + 1) th linearized model.

Optionally, the input signal is predicted according to the formula (3) to obtain a (k + 1) th predicted output signal of the input signal.

b 3: calculating an error between the (k + 1) th predicted output signal and the actual output signal.

Optionally, an error between the (k + 1) th predicted output signal and the actual output signal is calculated, and the following formula is satisfied:y'_(n)representing said (k + 1) th prediction output signal, y_nRepresenting the actual output signal.

B2: and determining the sampling point with the minimum corresponding error in the plurality of first sampling points as the (k + 1) th segmentation point.

The embodiment of the application provides a method for determining segmentation points of a linear model, so that time complexity and total number of the segmentation points are in a linear relation. The method can not only freely configure a segmentation mode aiming at the characteristics of the power amplifier, but also enhance the matching degree of the linearization model and the power amplifier; the segmentation point selection strategy can greatly reduce the computation time consumption of the segmentation points and improve the efficiency of determining the optimal segmentation points of the linear model corresponding to the power amplifier; in addition, the power amplifier management device can adjust the power amplifier according to the linearization model corresponding to the optimal segmentation point so as to optimize the characteristics of the power amplifier.

Based on the embodiment shown in fig. 2, the present application further provides an example of a segmentation point determination method for a linearized model, so that the time length for selecting a segmentation point is linearly related to the total number of the segmentation points, and a specific flow of steps of an implementation method of a segmentation point selection algorithm is shown in fig. 3:

step 1: power amplifier management device obtaining input signal x of power amplifier_(n)And the actual output signal y_(n)。

Step 2: the power amplifier management equipment sequentially selects a point x to be selected of the kth segmentation point_iAnd determining the kth segmentation point candidate point x_iCorresponding set of k segments beta_k＝(x₁,....,x_i)。

Step 3: the power amplifier management equipment acquires the preset memory depth M of the kth segment set_kA kth segment set beta corresponding to the kth segment point to be selected_kEstablishing the kth segmentation point candidate point x_iA corresponding k-th segmented model is adjusted according to the k-th segmented model to obtain a k-th segmented point candidate point x_iThe corresponding k-th linearized function model.

Step 4: the power amplifier management equipment determines the point x to be selected according to the k-th linearized function model_iCorresponding kth prediction output signal y'_(n)。

Step 5: the power amplifier management device calculating the k-th predicted output signal y'_(n)And the actual output signal y_(n)The error of (2).

Step 6: the power amplifier management equipment judges the kth sectional point candidate point x_iWhether it is the last candidate point of the kth segmentation point.

Step 7: if said x_iIf the segment point is not the last candidate point of the kth segment point, i is made to be i +1, and the step2 is returned to execute the following steps again.

Step 8: if x_iIs the last candidate point of the kth segmentation point, and determines the candidate point x corresponding to the minimum error_ksAs the kth segmentation point (i.e., the kth optimal segmentation point), x_ksAnd selecting one candidate point in the k-th segmentation point candidate points.

Step 9: the power amplifier management device determines whether a current K is equal to K, which is a total number of segmentation points.

If K is equal to K, the selection of the segmentation points is finished, and the total optimal segmentation point is x_1s,x₁₂,......x_Ks。x_1sDenotes the 1 st segmentation point, x_2sRepresents the 2 nd segmentation point_KsRepresenting the K-th segmentation point.

If K is not equal to K, let K be K +1, return to step2 to execute the following steps again.

From the above steps, it is easy to see that the time complexity of the selection method with respect to the number of segmentation points is o (k).

Based on the same technical concept, the embodiment of the present application further provides an apparatus for determining a segmentation point of a linearized model, and the structure of the apparatus is shown in fig. 4. Comprises an acquisition unit 401 and a processing unit 402. The device can be applied to the power amplifier management equipment shown in fig. 1, and can realize the segmentation point determination method of the linearization model shown in fig. 2. The functions of the various units in the apparatus 400 are described below.

An obtaining unit 401 for obtaining an input signal x of the power amplifier_nAnd the corresponding actual output signal y_nAnd determining the total number K of the preset segmentation points;

a processing unit 402 for sequentially aiming at each of a plurality of sampling points C of the input signal_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; the first segment set comprises sampling points from a first sampling point to a1 st segment point to-be-selected point in the input signal;

determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point;

the processing unit 402 is further configured to sequentially target each of the plurality of first sampling points D_jPerforming the following step, wherein the plurality of first sampling points D_jIs a sampling point after the kth segment point, K is a positive integer less than K, j is an integer from 1 to N, N is the number of the first sampling points: the first sampling point D_jAs the (k + 1) th segment point candidate point, according to the (k + 1) th segment set corresponding to the (k + 1) th segment point candidate point and the preset mark of the (k + 1) th segment setMemory depth, namely adjusting a linearization model of the power amplifier to obtain a k +1 th linearization model; predicting a (k + 1) th predicted output signal of the input signal according to the (k + 1) th linearized model, and calculating an error between the (k + 1) th predicted output signal and the actual output signal; the (k + 1) th segmentation set comprises sampling points from a first sampling point in the input signal to a (k + 1) th segmentation point candidate point;

and determining the sampling point with the minimum corresponding error in the plurality of first sampling points as the (k + 1) th segmentation point.

In an embodiment, when the processing unit 402 adjusts the linearized model of the power amplifier according to the first segmentation set corresponding to the 1 st segmentation point candidate, and the preset memory depth of the first segmentation set, to obtain a first linearized model, the processing unit is specifically configured to:

establishing a piecewise function model corresponding to the first segmentation set according to the 1 st segmentation point candidate point, the first segmentation set and the preset memory depth of the first segmentation set; the segment function model corresponding to the first segment set is used for representing the corresponding relation among the 1 st segment point to-be-selected point, the first segment set and the preset memory depth of the first segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.

In one embodiment, the calculating the error between the first predicted output signal and the actual output signal satisfies the following equation:y′_(n)representing said first prediction output signal, y_nRepresenting the actual output signal.

In one embodiment, the linearized model of the power amplifier satisfies the following equation:

wherein x is_nRepresenting the input signal, y_nRepresenting the prediction output signal, k the kth segmentation point, M the memory depth, beta_kAnd representing a segmentation set corresponding to the kth segmentation point to be selected.

Based on the same technical concept, the embodiment of the present application further provides an apparatus for determining a segmentation point of a linearization model, which may be applied to the power amplifier management apparatus shown in fig. 1 and may implement a method for determining a segmentation point of a linearization model shown in fig. 2. Referring to fig. 5, the monitoring apparatus includes: communication module 501, processor 502, memory 503. The communication module 501, the processor 502 and the memory 503 are connected to each other.

Optionally, the communication module 501, the processor 502 and the memory 503 are connected to each other through a bus 504. The bus 504 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc.

A communication module 501 for obtaining an input signal x of a power amplifier_nAnd the corresponding actual output signal y_nAnd determining the total number K of the preset segmentation points;

the communication module 501 is configured to obtain an input signal x of a power amplifier_nAnd the corresponding actual output signal y_nAnd determining the total number K of the preset segmentation points;

the processor 502 is configured to sequentially target each sampling point C of a plurality of sampling points of the input signal_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, linearizing the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation setAdjusting the model to obtain a first linearized model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; the first segment set comprises sampling points from a first sampling point to a1 st segment point to-be-selected point in the input signal;

determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point;

the processor 502 is further configured to sequentially identify each of the plurality of first sampling points D_jPerforming the following step, wherein the plurality of first sampling points D_jIs a sampling point after the kth segment point, K is a positive integer less than K, j is an integer from 1 to N, N is the number of the first sampling points: the first sampling point D_jAs a (k + 1) th segment point candidate point, adjusting the linearization model of the power amplifier according to a (k + 1) th segment set corresponding to the (k + 1) th segment point candidate point and a preset memory depth of the (k + 1) th segment set to obtain a (k + 1) th linearization model; predicting a (k + 1) th predicted output signal of the input signal according to the (k + 1) th linearized model, and calculating an error between the (k + 1) th predicted output signal and the actual output signal; the (k + 1) th segmentation set comprises sampling points from a first sampling point in the input signal to a (k + 1) th segmentation point candidate point;

and determining the sampling point with the minimum corresponding error in the plurality of first sampling points as the (k + 1) th segmentation point.

In an embodiment, when the processor 502 adjusts the linearized model of the power amplifier according to the first segmentation set corresponding to the 1 st segmentation point candidate, and the preset memory depth of the first segmentation set, to obtain the first linearized model, the processor is specifically configured to:

establishing a piecewise function model corresponding to the first segmentation set according to the 1 st segmentation point candidate point, the first segmentation set and the preset memory depth of the first segmentation set; the segment function model corresponding to the first segment set is used for representing the corresponding relation among the 1 st segment point to-be-selected point, the first segment set and the preset memory depth of the first segment set;

and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.

In one embodiment, the calculating the error between the first predicted output signal and the actual output signal satisfies the following equation:y′_(n)representing said first prediction output signal, y_nRepresenting the actual output signal.

In one embodiment, the linearized model of the power amplifier satisfies the following equation:

wherein x is_nRepresenting the input signal, y_nRepresenting the prediction output signal, k the kth segmentation point, M the memory depth, beta_kAnd representing a segmentation set corresponding to the kth segmentation point to be selected.

Based on the above embodiments, the present application further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a computer, the computer program causes the computer to execute a method for determining a segmentation point of a linearized model, which is provided by the embodiment shown in fig. 2.

Based on the above embodiments, the present application further provides a chip, where the chip is configured to read a computer program stored in a memory, and implement the method for determining the segmentation point of the linearized model provided in the embodiment shown in fig. 2.

Based on the foregoing embodiments, an embodiment of the present application provides a chip system, where the chip system includes a processor, and is used to support a computer device to implement the functions of the device in the embodiment shown in fig. 4. In one possible design, the system-on-chip further includes a memory for storing programs and data necessary for the computer device. The chip system may be constituted by a chip, or may include a chip and other discrete devices.

In the technical scheme of the embodiment of the application, the power amplifier management equipment acquires an input signal and a corresponding actual output signal of a power amplifier and determines the total number K of preset segmentation points; secondly, for each sampling point C in a plurality of sampling points of the input signal in turn_iPerforming the following steps, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: the sampling point C_iAs a1 st segmentation point candidate point, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point candidate point and a preset memory depth of the first segmentation set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearized model and calculating an error between the first predicted output signal and the actual output signal; determining a sampling point with the minimum corresponding error in the plurality of sampling points as the 1 st segmentation point; further, the same steps as the 1 st segmentation point are sequentially executed for each of the plurality of first sampling points, and the 2 nd segmentation point is determined until the K segmentation points are determined. The method determines the optimal segmentation point corresponding to each set through the step-by-step selection mode, reduces the time for selecting the segmentation point, can improve the efficiency of selecting the optimal segmentation point, can further improve the accuracy of the piecewise linear model, and finally ensures the power amplifier characteristic of the power amplifier.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.