CN113659936B - Segmentation point determination method and device for linearization model - Google Patents
Segmentation point determination method and device for linearization model Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a device for determining a segmentation point of a linearization model, which are used for improving the efficiency of determining an 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 total number of segmentation points; and determining each segment point in the sampling points in the input signal in turn so as to ensure that the error between the predicted output signal obtained by the linearization model obtained by the segment set corresponding to each segment point and the actual output signal is minimum. The method can determine the optimal segmentation point in a step-by-step selection mode, so that the efficiency of selecting the optimal segmentation point can be improved, the accuracy of the piecewise linear model can be further improved, and finally the power amplification characteristic of the power amplifier is guaranteed.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a method and an apparatus for determining a segmentation point of a linearization model.
Background
The power amplifier is an important component of a base station transmitting system, and in a signal transmitting link, the linearization degree of the power amplifier directly influences the transmitting quality of a signal. In order to reduce the power consumption of the base station system, the efficiency of the power amplifier is also increasingly required.
The power amplifier typically operates in a nonlinear region, which results in distortion of the output signal of the power amplifier in amplitude and phase due to the difference in instantaneous amplitude of the input signal. Currently, the communication field can solve the linearization problem of the power amplifier through a predistortion model based on piecewise linear functions. Wherein the piecewise linear function approximates a nonlinear function over a partial domain as a number of linear sub-functions, and the predistortion model of the piecewise linear function is typically composed of a set of continuous piecewise function models with linear representations of basis functions having 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. Typically the piecewise linear function model includes a Volterra series model, and commonly used polynomial models MP and GMP models derived based on changes in the Volterra series model.
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 the power amplifier characteristics of the power amplifier in the piecewise points. However, in the polynomial model, the polynomial model with higher nonlinear order is needed to improve the nonlinearity of the power amplifier, so that the number of parameters of the polynomial model increases, and model solving involved in the process of configuration of the segmentation points also becomes more complex, so that the segmentation points are uniformly configured in general, but the configuration mode cannot effectively utilize the behavior characteristics of the power amplifier, and the efficiency is lower and the actual performance is not proportional to the segmentation number. Therefore, in order to enable a model of the piecewise linear function to better describe the power amplifier characteristics, it is necessary to find the optimal piecewise point configuration of the power amplifier. The required length of the conventional traversal method increases exponentially with the increase of the number of segments, so that the time complexity of selecting the segment points is also larger.
Disclosure of Invention
The application provides a method and a device for determining a segmentation point of a linearization model, which are used for improving the efficiency of determining an optimal segmentation point of the linearization model corresponding to a power amplifier.
The specific technical scheme provided by the embodiment of the invention is as follows:
in a first aspect, an embodiment of the present application provides a method for determining a segmentation point of a linearization model, where the method specifically includes the following steps:
acquiring an input signal x of a power amplifier n And the corresponding actual output signal y n Determining a preset total number K of segmentation points;
for each sampling point C of the plurality of sampling points of the input signal in turn i The following steps are executed, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: sampling point C i As a 1 st segmentation point to be selected, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point to be selected 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 linearization model, and calculating an error between the first predicted output signal and the actual output signal; wherein the first set of segments comprises Sampling points from the first sampling point to the 1 st segment point to be selected in the input signal;
determining a sampling point with the minimum corresponding error from the plurality of sampling points as the 1 st segmentation point;
for each first sampling point D in the plurality of first sampling points in turn j Executing the following steps, wherein the plurality of first sampling points D j For the sampling points after the kth segment point, K is a positive integer smaller than K, j is an integer from 1 to N, and N is the number of the plurality of first sampling points: the first sampling point D j As a k+1th segmentation point to be selected, adjusting a linearization model of the power amplifier according to a k+1th segmentation set corresponding to the k+1th segmentation point to be selected and a preset memory depth of the k+1th segmentation set to obtain a k+1th linearization model; predicting a k+1 th predicted output signal of the input signal according to the k+1 th linearization model, and calculating an error between the k+1 th predicted output signal and the actual output signal; the k+1th segment set comprises sampling points from a first sampling point to a point to be selected of the k+1th segment point in the input signal;
and determining a sampling point with the minimum corresponding error from the first sampling points as the (k+1) th segmentation point.
In a possible implementation manner, the adjusting the linearization model of the power amplifier according to the first segment set corresponding to the 1 st segment point to-be-selected point and the preset memory depth of the first segment set to obtain a first linearization model includes:
establishing a piecewise function model corresponding to the first piecewise set according to the 1 st piecewise point to be selected, the first piecewise set and a preset memory depth of the first piecewise set; the piecewise function model corresponding to the first piecewise set is used for representing the corresponding relation among the 1 st piecewise point to be selected, the first piecewise set and the preset memory depth of the first piecewise set;
and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.
In a possible implementation, the calculating the error between the first predicted output signal and the actual output signal satisfies the following formula:
y′ (n) representing the first predicted output signal, y n Representing the actual output signal.
In one possible embodiment, the linearization model of the power amplifier satisfies the following formula:
In a second aspect, an embodiment of the present application provides a piecewise point determining apparatus of a linearization model, including:
an acquisition unit for acquiring an input signal x of the power amplifier n And the corresponding actual output signal y n Determining a preset total number K of segmentation points;
a processing unit for sequentially aiming at each sampling point C in a plurality of sampling points of the input signal i The following steps are executed, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: sampling point C i As a 1 st segmentation point to be selected, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point to be selected 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 linearization model, and calculating an error between the first predicted output signal and the actual output signal; wherein the first set of segments comprises the input Entering a sampling point from the first sampling point to a point to be selected of the 1 st segment point in the signal;
determining a sampling point with the minimum corresponding error from the plurality of sampling points as the 1 st segmentation point;
the processing unit is further configured to, for each first sampling point D of the plurality of first sampling points in turn j Executing the following steps, wherein the plurality of first sampling points D j For the sampling points after the kth segment point, K is a positive integer smaller than K, j is an integer from 1 to N, and N is the number of the plurality of first sampling points: the first sampling point D j As a k+1th segmentation point to be selected, adjusting a linearization model of the power amplifier according to a k+1th segmentation set corresponding to the k+1th segmentation point to be selected and a preset memory depth of the k+1th segmentation set to obtain a k+1th linearization model; predicting a k+1 th predicted output signal of the input signal according to the k+1 th linearization model, and calculating an error between the k+1 th predicted output signal and the actual output signal; the k+1th segment set comprises sampling points from a first sampling point to a point to be selected of the k+1th segment point in the input signal;
And determining a sampling point with the minimum corresponding error from the first sampling points as the (k+1) th segmentation point.
In a possible implementation manner, the processing unit is specifically configured to, when adjusting the linearization model of the power amplifier according to the first segment set corresponding to the 1 st segment point to be selected and the preset memory depth of the first segment set to obtain a first linearization model:
establishing a piecewise function model corresponding to the first piecewise set according to the 1 st piecewise point to be selected, the first piecewise set and a preset memory depth of the first piecewise set; the piecewise function model corresponding to the first piecewise set is used for representing the corresponding relation among the 1 st piecewise point to be selected, the first piecewise set and the preset memory depth of the first piecewise set;
and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.
In a possible implementation, the calculating the error between the first predicted output signal and the actual output signal satisfies the following formula: 
y′ (n) Representing the first predicted output signal, y n Representing the actual output signal.
In one possible embodiment, the linearization model of the power amplifier satisfies the following formula:
In a third aspect, embodiments of the present application provide a computer-readable storage medium, comprising: the computer readable storage medium has stored therein a computer program which, when run on an electronic device, causes the electronic device to perform any one of the possible implementations of the above aspects.
In a fourth aspect, embodiments of the present application provide a computer program comprising instructions which, when run 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, embodiments of the present application provide a chip for reading a computer program stored in a memory, and performing any one of the possible implementations of the above aspect.
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 the power amplifier, and determines the total number K of preset segmentation points; next, according to Sub-for each sampling point C of a plurality of sampling points of the input signal i The following steps are executed, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: sampling point C i As a 1 st segmentation point to be selected, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point to be selected 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 linearization 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 from 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. According to the method, the optimal segmentation points corresponding to each set are determined in the step-by-step selection mode, so that the time for selecting the segmentation points is reduced, the efficiency for selecting the optimal segmentation points can be improved, the accuracy of a piecewise linear model can be further improved, and finally the power amplification characteristic of the power amplifier is guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
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 flow chart of a method for determining segmentation points of a linearization model according to an embodiment of the invention;
FIG. 3 is a schematic flow chart of steps of an 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
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings, wherein it is apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The embodiment of the application provides a segmentation point determining method of a linearization model, which is used for improving the efficiency of determining the optimal segmentation point of a power amplifier. The method and the device described in the present application are based on the same inventive concept, and because the principles of solving the problems by the method and the device are similar, the implementation of the device and the method can be referred to each other, and the repetition is not repeated.
In the technical scheme of the embodiment of the application, the power amplifier management equipment acquires the input and actual output signals of the power amplifier and the total number of segmentation points; the power amplifier management device sequentially determines each segment point in sampling points in an input signal so as to ensure that the error between a predicted output signal obtained by a linearization model obtained by a segment set corresponding to each segment point and the actual output signal is minimum. The method can determine the optimal segmentation point in a step-by-step selection mode, so that the efficiency of selecting the optimal segmentation point can be improved, the accuracy of the piecewise linear model can be further improved, and finally the power amplification characteristic of the power amplifier is guaranteed.
Some of the terms in the embodiments of the present application are explained below to facilitate understanding by those skilled in the art.
1. The segmentation points are used for dividing a group of data points, and one segmentation point configuration corresponds to one grouping condition. Since the linear function model includes a plurality of variables, the multivariate is categorized and grouped, and a corresponding piecewise function model is established. Therefore, by configuring the optimal piecewise points, a corresponding optimal piecewise function model can be established, and the linearization function model is further adjusted, so that the linearization function model can describe the characteristics of the power amplifier more effectively.
2. Memory depth, in a broadband communication system, a wider input signal bandwidth is easy to enable a power amplifier to have a certain memory effect. As the bandwidth of the input signal increases, the memory effect of the power amplifier will also tend to be significant.
3. A plurality, denoted at least two.
4. And/or, the association relationship describing the association object, the representation may have three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. The character "three kinds generally indicates that the front-rear association object is an or relationship.
Embodiments of the present application are described below with reference to the accompanying drawings.
As shown in fig. 1, in a base station 100, a plurality of power amplifiers 102 are an important component of its transmission system. In the signal transmitting link, the linearization degree of the power amplifier directly influences the transmitting quality of the signal. The signal is inputted from the input unit 101, processed by the power amplifier 102, and the processed signal is transmitted 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 size of the base station is also increasing. In order to reduce the system power consumption, the efficiency of the power amplifier is also increasing. The AM-AM curve (amplitude distortion curve of the output signal versus the input signal) of a high efficiency amplifier is no longer a monotonic characteristic, and one or several "pits" of different depths can appear in the curve. The traditional GMP model has wide application in the field of digital predistortion, but aiming at high-efficiency power amplification, the matching degree of the GMP model and the power amplification is reduced. The predistortion model based on piecewise linear function has the advantages of free parameter configuration and low calculation complexity, but the general uniform piecewise mode can not effectively utilize the behavior characteristics of the power amplifier, the efficiency is low, and the actual performance is not proportional to the number of segments.
In practical situations, however, the power amplifier generally operates in a nonlinear region to increase the efficiency of the power amplifier, so that the output signal of the power amplifier generates distortion of amplitude and phase due to the difference of instantaneous amplitudes of the input signals. 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, digital predistortion is a widely used power amplifier linearization technique for the problem of power amplifier linearization. Let it be assumed that the input signal x of the power amplifier n The output signal is y n The Volterra series model for describing the power amplifier characteristics is shown as follows:
the Volterra series model has a deep theoretical basis, is simple and convenient in parameter extraction, is easy to solve model parameters by a least squares method and the like, and common polynomial models such as an MP model and a GMP model are all changed based on the Volterra model.
The regular piecewise linear function model is a continuous piecewise function model that is linearly represented by a set of basis functions having saturation characteristics. For the input of specific frequencies, each basis function output frequency of the function model is complex, and theoretically, the whole frequency domain can be covered.
The fundamental function output frequency of the power series equation is simply the simple multiplication and sum-difference relation of the input frequency, so that the signal intermodulation component order (i.e. the frequency spectrum expansion width) 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 theoretically fit all components no matter how many sectors are, but the fitting precision is different, which also becomes a significant feature of the piecewise function model.
Generally, the mathematical expression of the piecewise linear function is as follows:
In order to describe the behavior characteristics of the power amplifier, the above method is expanded to a complex domain, and the obtained linearization model parameters are as follows:
wherein M is memory depth, K is segmentation number, [ beta ] 1 ,β 2 ,...,β K ]Is the corresponding segmentation point.
Theoretically, 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 neglect the distortion characteristics of the power amplifier on signals with different amplitudes, so that the segmentation linear model can better describe the characteristics of the power amplifier under the same configuration of the memory depth and the segmentation number, and the optimal segmentation point configuration corresponding to the power amplifier needs to be found. The total time length required by the traversal method increases exponentially with the increase of the number of segments, so the time complexity of selecting the segment points is also high.
In order to solve the above problems, the embodiment of the present application provides a segmentation point determination method of a linearization model. The method can be applied to a power amplifier in a base station as shown in fig. 1, and a detailed description is given below of a flow of a method for determining a segmentation point of a linearization model according to an embodiment of the application with reference to fig. 2.
S201: the power amplifier management device obtains the input signal x of the power amplifier n And the corresponding actual output signal y n And determining a preset total number K of 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 apparatus may perform S202 through the following steps A1 and A2.
A1: for each sampling point C of the plurality of sampling points of the input signal in turn i Wherein i is an integer between 1 and Q, Q is the number of the plurality of sampling points, and the following steps are executed:
a1: each sampling point C i And as a1 st segmentation point to be selected, adjusting the linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point to be selected 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 a1 st segment point to be selected point in the input signal.
In one embodiment, the adjusting the linearization model of the power amplifier according to the first segment set corresponding to the 1 st segment point to-be-selected point and the preset memory depth of the first segment set to obtain a first linearization model includes:
Establishing a piecewise function model corresponding to the first piecewise set according to the 1 st piecewise point to be selected, the first piecewise set and a preset memory depth of the first piecewise set; the piecewise function model corresponding to the first piecewise set is used for representing the corresponding relation among the 1 st piecewise point to be selected, the first piecewise set and the preset memory depth of the first piecewise 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 linearization model of the power amplifier satisfies the following formula:
wherein x is n Representing the input signal, y n Represents the predicted output signal, k represents the kthSegmentation point, M represents memory depth, beta k And representing the segment set corresponding to the k segment point to-be-selected point.
a2: a first predicted output signal of the input signal is predicted according to the first linearization model.
Optionally, the input signal is predicted according to the formula (3) to obtain a predicted output signal of the input signal.
a3: an error between the first predicted output signal and the actual output signal is calculated.
Optionally, an error between the first predicted output signal and the actual output signal satisfies the following formula:
y' (n) representing the first predicted output signal, y n Representing the actual output signal.
A2: and determining the sampling point with the minimum corresponding error from the plurality of sampling points as the 1 st segmentation point.
S203: the power amplifier management device determines other segmentation points after the 1 st segmentation point in a plurality of first sampling points, wherein the plurality of first sampling points are sampling points after the kth segmentation point, and K is a positive integer smaller than K.
In one embodiment, the power amplifier management apparatus may perform S203 through the following steps B1 and B2.
B1: for each first sampling point D in the plurality of first sampling points in turn j The method comprises the following steps of:
b1: the first sampling point D j And as a k+1 segmentation point to be selected, adjusting the linearization model of the power amplifier according to a k+1 segmentation set corresponding to the k+1 segmentation point to be selected and a preset memory depth of the k+1 segmentation set to obtain a k+1 linearization model. Wherein the k+1th segment set includes a first sampling point to a k+1th segment point to be selected point in the input signal Sampling points in between.
In one embodiment, the adjusting the linearization model of the power amplifier according to the kth+1 segment set corresponding to the kth+1 segment point to-be-selected point and the preset memory depth of the kth+1 segment set to obtain the kth+1 linearization model includes:
according to the k+1th segmentation point to be selected, the k+1th segmentation set and the preset memory depth of the k+1th segmentation set, and establishing a segmentation function model corresponding to the k+1th segmentation set; the piecewise function model corresponding to the k+1th piecewise set is used for representing the corresponding relation among the k+1th piecewise point to be selected, the k+1th piecewise set and the preset memory depth of the k+1th piecewise set;
and according to the established piecewise function model, adjusting the linearization model of the power amplifier to obtain a k+1 linearization model.
b2: and predicting a k+1 predictive output signal of the input signal according to the k+1 linearization model.
Optionally, the input signal is predicted according to the formula (3) to obtain a k+1 predicted output signal of the input signal.
b3: an error between the k+1-th predicted output signal and the actual output signal is calculated.
Optionally, calculating an error between the k+1-th predicted output signal and the actual output signal satisfies the following formula:
y' (n) representing the k+1 th predicted output signal, y n Representing the actual output signal.
B2: and determining a sampling point with the minimum corresponding error from the first sampling points as the (k+1) th segmentation point.
The embodiment of the application provides a piecewise point determining method of a linearization model, which enables time complexity and the total number of piecewise points to be in a linear relation. The method can freely configure a segmentation mode aiming at the characteristics of the power amplifier, and the matching degree of the linearization model and the power amplifier is enhanced; the segmentation point selection strategy can greatly reduce the calculation time consumption of the segmentation points, and improves the efficiency of determining the optimal segmentation points of the linearization 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 method for determining segmentation points of a linearization model, so that the time length of selecting the segmentation points and the total number of segmentation points form a linear relationship, and a specific flow of steps of an embodiment of a segmentation algorithm is shown in fig. 3:
Step1: the power amplifier management device obtains the input signal x of the power amplifier (n) And the actual output signal y (n) 。
Step2: the power amplifier management equipment sequentially selects the candidate points x of the kth segmentation point i And determining the candidate point x of the kth segment point i Corresponding kth segment set beta k ＝(x 1 ,....,x i )。
Step3: the power amplifier management device obtains a preset memory depth M of the kth segment set k The kth segment set beta corresponding to the kth segment point to-be-selected point k Establishing the k segmentation point to-be-selected point x i The corresponding kth segment model is used for adjusting the linearization function model according to the kth segment model to obtain the kth segment point to-be-selected point x i A corresponding kth linearization function model.
Step4: the power amplifier management device determines the candidate point x according to the kth linearization function model i Corresponding kth predicted output signal y' (n) 。
Step5: the power amplifier management device calculates the kth predicted output signal y' (n) And the actual output signal y (n) Is a function of the error of (a).
Step6: the power amplifier management device judges that the kth segment point is to be selectedPoint x i Whether the last candidate point of the kth segment point is the last candidate point.
Step7: if said x i If not the last candidate point of the kth segment point, i=i+1 is returned to step2 to execute the following steps again.
Step8: if x i The last candidate point of the kth segment point is used for determining the candidate point x corresponding to the minimum error ks As the kth segmentation point (i.e., the kth optimal segmentation point), x ks And selecting one of the k segmentation point candidates.
Step9: the power amplifier management device determines whether a current K is equal to K, the K being a total number of segmentation points.
If K is equal to K, the segment point selection is finished, and the total optimal segment point is x 1s ,x 12 ,......x Ks 。x 1s Represents the 1 st segmentation point, x 2s Express the 2 nd segment point Ks Representing the kth segmentation point.
If K is not equal to K, let k=k+1, return step2 and re-execute the following steps.
Through the above steps, it is easy to see that the time complexity of the selection method with respect to the segmentation point number is O (K).
Based on the same technical concept, the embodiment of the application also provides a device for determining the segmentation point of the linearization model, and the structure of the device is shown in fig. 4. Comprises an acquisition unit 401 and a processing unit 402. The apparatus can be applied to the power amplifier management device shown in fig. 1, and can implement 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 acquisition unit 401 for acquiring an input signal x of the power amplifier n And the corresponding actual output signal y n Determining a preset total number K of segmentation points;
a processing unit 402 for sequentially for each sampling point C of the plurality of sampling points of the input signal i Executing the following steps, wherein i is an integer between 1 and Q, Q is the sampling pointsNumber of: sampling point C i As a 1 st segmentation point to be selected, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point to be selected 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 linearization 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 a 1 st segment point to be selected in the input signal;
determining a sampling point with the minimum corresponding error from the plurality of sampling points as the 1 st segmentation point;
the processing unit 402 is further configured to, for each first sampling point D of the plurality of first sampling points in turn j Executing the following steps, wherein the plurality of first sampling points D j For the sampling points after the kth segment point, K is a positive integer smaller than K, j is an integer from 1 to N, and N is the number of the plurality of first sampling points: the first sampling point D j As a k+1th segmentation point to be selected, adjusting a linearization model of the power amplifier according to a k+1th segmentation set corresponding to the k+1th segmentation point to be selected and a preset memory depth of the k+1th segmentation set to obtain a k+1th linearization model; predicting a k+1 th predicted output signal of the input signal according to the k+1 th linearization model, and calculating an error between the k+1 th predicted output signal and the actual output signal; the k+1th segment set comprises sampling points from a first sampling point to a point to be selected of the k+1th segment point in the input signal;
and determining a sampling point with the minimum corresponding error from the first sampling points as the (k+1) th segmentation point.
In one embodiment, the processing unit 402 is configured to, when adjusting the linearization model of the power amplifier according to the first segment set corresponding to the 1 st segment point to be selected and the preset memory depth of the first segment set to obtain a first linearization model, specifically:
Establishing a piecewise function model corresponding to the first piecewise set according to the 1 st piecewise point to be selected, the first piecewise set and a preset memory depth of the first piecewise set; the piecewise function model corresponding to the first piecewise set is used for representing the corresponding relation among the 1 st piecewise point to be selected, the first piecewise set and the preset memory depth of the first piecewise 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 the first predicted output signal, y n Representing the actual output signal.
In one embodiment, the linearization model of the power amplifier satisfies the following equation:
Based on the same technical concept, the embodiment of the application also provides a device for determining the segmentation point of the linearization model, which can be applied to the power amplifier management device shown in fig. 1 and can realize a method for determining the segmentation point of the linearization model as shown in fig. 2. Referring to fig. 5, the monitoring device includes: a communication module 501, a processor 502, and a memory 503. Wherein 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 by a bus 504. The bus 504 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, or the like. The buses may be classified as address buses, data buses, control buses, etc.
A communication module 501 for acquiring an input signal x of the power amplifier n And the corresponding actual output signal y n Determining a preset total number K of segmentation points;
the communication module 501 is configured to obtain an input signal x of the power amplifier n And the corresponding actual output signal y n Determining a preset total number K of segmentation points;
the processor 502 is configured to, for each sampling point C of the plurality of sampling points of the input signal in turn i The following steps are executed, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: sampling point C i As a 1 st segmentation point to be selected, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point to be selected 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 linearization 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 a 1 st segment point to be selected in the input signal;
Determining a sampling point with the minimum corresponding error from the plurality of sampling points as the 1 st segmentation point;
the processor 502 is further configured to, for each of the plurality of first sampling points D in turn j Executing the following steps, wherein the plurality of first sampling points D j For sampling points after the kth segment point, K is a positive integer less than K, j is an integer from 1 to N, N is the plurality of the segmentsThe number of sampling points: the first sampling point D j As a k+1th segmentation point to be selected, adjusting a linearization model of the power amplifier according to a k+1th segmentation set corresponding to the k+1th segmentation point to be selected and a preset memory depth of the k+1th segmentation set to obtain a k+1th linearization model; predicting a k+1 th predicted output signal of the input signal according to the k+1 th linearization model, and calculating an error between the k+1 th predicted output signal and the actual output signal; the k+1th segment set comprises sampling points from a first sampling point to a point to be selected of the k+1th segment point in the input signal;
and determining a sampling point with the minimum corresponding error from the first sampling points as the (k+1) th segmentation point.
In one embodiment, the processor 502 is configured to, when adjusting the linearization model of the power amplifier according to the first segment set corresponding to the 1 st segment point to be selected and the preset memory depth of the first segment set to obtain a first linearization model, specifically:
establishing a piecewise function model corresponding to the first piecewise set according to the 1 st piecewise point to be selected, the first piecewise set and a preset memory depth of the first piecewise set; the piecewise function model corresponding to the first piecewise set is used for representing the corresponding relation among the 1 st piecewise point to be selected, the first piecewise set and the preset memory depth of the first piecewise 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 the first predicted output signal, y n Representing the actual output signal.
In one embodiment, the linearization model of the power amplifier satisfies the following equation:
Based on the above embodiments, the present application further provides a computer-readable storage medium having stored therein a computer program, which when executed by a computer, causes the computer to perform a method for piecewise point determination of a linearization model provided by the embodiment shown in fig. 2.
Based on the above implementation manner, the embodiment of the application further provides a chip, where the chip is configured to read a computer program stored in a memory, and implement a method for determining a segmentation point of a linearization model provided by the embodiment shown in fig. 2.
Based on the above embodiments, the present application provides a chip system including a processor for supporting a computer device to implement the functions of the device in the embodiment shown in fig. 4. In one possible design, the chip system further includes a memory for storing programs and data necessary for the computer device. The chip system can be composed of chips, and can also comprise chips 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 the power amplifier, and determines the total number K of preset segmentation points; next, for each sampling point C of the plurality of sampling points of the input signal in turn i The following steps are executed, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: sampling point C i As 1 st segment point to be selected, according to the 1 st segment point to be selected pointThe method comprises the steps of (1) adjusting a linearization model of the power amplifier to obtain a first linearization model, wherein the first segmentation set and the preset memory depth of the first segmentation set are used for adjusting the linearization model of the power amplifier; predicting a first predicted output signal of the input signal according to the first linearization 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 from 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. According to the method, the optimal segmentation points corresponding to each set are determined in the step-by-step selection mode, so that the time for selecting the segmentation points is reduced, the efficiency for selecting the optimal segmentation points can be improved, the accuracy of a piecewise linear model can be further improved, and finally the power amplification characteristic of the power amplifier is guaranteed.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. A method for determining piecewise points of a linearization model, comprising:
acquiring an input signal x of a power amplifier n And corresponding actual output signals yn, and determining the total number K of preset segmentation points;
the following steps are performed sequentially for each sampling point Ci of a plurality of sampling points of the input signal, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: taking the sampling points Ci as 1 st segment point to-be-selected points, and adjusting a linearization model of the power amplifier according to a first segment set corresponding to the 1 st segment point to-be-selected points and a preset memory depth of the first segment set to obtain a first linearization model; predicting a first predicted output signal of the input signal according to the first linearization 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 a 1 st segment point to be selected in the input signal; said calculating an error between said first predicted output signal and said actual output signal satisfying the following formula:
determining a sampling point with the minimum corresponding error from the plurality of sampling points as the 1 st segmentation point;
for each first sampling point D in the plurality of first sampling points in turn j Executing the following steps, wherein the plurality of first sampling points D j For the sampling points after the kth segment point, K is a positive integer smaller than K, j is an integer from 1 to N, and N is the number of the plurality of first sampling points: the first sampling point D j As a k+1th segmentation point to be selected, adjusting a linearization model of the power amplifier according to a k+1th segmentation set corresponding to the k+1th segmentation point to be selected and a preset memory depth of the k+1th segmentation set to obtain a k+1th linearization model; predicting a k+1 th predicted output signal of the input signal according to the k+1 th linearization model, and calculating an error between the k+1 th predicted output signal and the actual output signal; the k+1th segment set comprises sampling points from a first sampling point to a point to be selected of the k+1th segment point in the input signal;
and determining a sampling point with the minimum corresponding error from the first sampling points as the (k+1) th segmentation point.
2. The method of claim 1, wherein the adjusting the linearization model of the power amplifier according to the first segment set corresponding to the 1 st segment point candidate point and the preset memory depth of the first segment set to obtain the first linearization model includes:
establishing a piecewise function model corresponding to the first piecewise set according to the 1 st piecewise point to be selected, the first piecewise set and a preset memory depth of the first piecewise set; the piecewise function model corresponding to the first piecewise set is used for representing the corresponding relation among the 1 st piecewise point to be selected, the first piecewise set and the preset memory depth of the first piecewise 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 the linearization model of the power amplifier satisfies the following equation:
wherein x is n Represents the input signal, y' (n) represents the first predicted output signal, K represents the kth segmentation point, K is the total number of segmentation points, M represents the value of the memory depth, M is the maximum value of the memory depth, beta k Representing a segmented set corresponding to a k segmented point to be selected, wherein the symbol "| … |" is an absolute value symbol, a m And c km Is a parameter of the model.
4. A piecewise point determining apparatus for a linearization model, comprising:
an acquisition unit for acquiring an input signal x of the power amplifier n And corresponding actual output signals yn, and determining the total number K of preset segmentation points;
a processing unit for sequentially aiming at each sampling point C in a plurality of sampling points of the input signal i The following steps are executed, wherein i is an integer between 1 and Q, and Q is the number of the plurality of sampling points: sampling point C i As a 1 st segmentation point to be selected, adjusting a linearization model of the power amplifier according to a first segmentation set corresponding to the 1 st segmentation point to be selected 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 linearization 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 a 1 st segment point to be selected in the input signal; said calculating an error between said first predicted output signal and said actual output signal satisfying the following formula:
determining a sampling point with the minimum corresponding error from the plurality of sampling points as the 1 st segmentation point;
the processing unit is further configured to, for each first sampling point D of the plurality of first sampling points in turn j Executing the following steps, wherein the plurality of first sampling points D j For the sampling points after the kth segment point, K is a positive integer smaller than K, j is an integer from 1 to N, and N is the number of the plurality of first sampling points: the first sampling point D j As a k+1th segmentation point to be selected, adjusting a linearization model of the power amplifier according to a k+1th segmentation set corresponding to the k+1th segmentation point to be selected and a preset memory depth of the k+1th segmentation set to obtain a k+1th linearization model; predicting a k+1 th predicted output signal of the input signal according to the k+1 th linearization model, and calculating an error between the k+1 th predicted output signal and the actual output signal; the k+1th segment set comprises sampling points from a first sampling point to a point to be selected of the k+1th segment point in the input signal;
And determining a sampling point with the minimum corresponding error from the first sampling points as the (k+1) th segmentation point.
5. The apparatus of claim 4, wherein the processing unit is configured to, when adjusting the linearization model of the power amplifier according to the first segment set corresponding to the 1 st segment point candidate point and the preset memory depth of the first segment set to obtain a first linearization model, specifically:
establishing a piecewise function model corresponding to the first piecewise set according to the 1 st piecewise point to be selected, the first piecewise set and a preset memory depth of the first piecewise set; the piecewise function model corresponding to the first piecewise set is used for representing the corresponding relation among the 1 st piecewise point to be selected, the first piecewise set and the preset memory depth of the first piecewise set;
and adjusting the linearization model of the power amplifier according to the established piecewise function model to obtain a first linearization model.
6. The apparatus of claim 4, wherein a linearization model of the power amplifier satisfies the following equation:
Wherein x is n Represents the input signal, y' (n) represents the first predicted output signal, K represents the kth segmentation point, K is the total number of segmentation points, M represents the value of the memory depth, M is the maximum value of the memory depth, beta k Representing a segmented set corresponding to a k segmented point to be selected, wherein the symbol "| … |" is an absolute value symbol, a m And c km Is a parameter of the model.
7. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when run on an indicator monitoring device, causes the indicator monitoring device to perform the method according to any of claims 1-3.
8. A chip for reading a computer program stored in a memory, performing the method of any of claims 1-3.
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