A digital predistortion processing method and device

Abstract

An embodiment of the present invention provides a digital predistortion processing method and apparatus. The method comprises obtaining an input signal and an output signal of a power amplifier; sampling the input signal and the output signal respectively to obtain sample data; determining the coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions; and performing digital predistortion processing on the input signal of the power amplifier using the digital predistortion model with the determined coefficients. In particular, the embodiment of the present invention uses a basis function with a segmented characteristic, which is suitable for fitting the segmented characteristics of the power amplifier. Therefore, the embodiment of the present invention can more accurately fit a curve with a distinct segmented characteristic.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a digital predistortion processing method and device. Background Art

[0002] The rapid growth in the number of wireless communication users and rapidly increasing service requirements have led to an increasing shortage of spectrum resources. To improve data rates and spectrum efficiency, modern communication systems widely adopt increasingly complex digital modulation technologies, such as Orthogonal Frequency Division Multiplexing (OFDM) and Quadrature Amplitude Modulation (QAM). However, these efficient digital modulation technologies result in high peak-to-average signal ratios (PARs), placing significant pressure on the design of RF power amplifiers.

[0003] RF power amplifiers, as key modules in RF systems, are not only expensive but also the largest power consumers in the transmit chain. Furthermore, their nonlinearity can severely distort transmitted signals and cause out-of-band spectral spread, impacting adjacent and sub-adjacent channels. Furthermore, the high peak-to-average ratio (CPR) introduced by non-constant envelope modulation in modern communication systems makes relying solely on power back-off to maintain linearity impractical. Because PAs operate most efficiently in their compression regime, power back-off improves linearity at the expense of significant efficiency. Therefore, to address the conflict between efficiency and linearity in RF power amplifiers, power amplifier linearization technology has emerged.

[0004] At the system level, common linearization methods include feedback, feedforward, linear amplification of nonlinear components (LINC), envelope elimination and restoration (EE&R), and digital pre-distortion (DPD). Among these methods, DPD is typically implemented at baseband and intermediate frequencies. With the continuous advancements in manufacturing processes and processing speeds for digital devices like DSP and FPGA, DPD has become increasingly cost-effective, with increasing accuracy and high consistency in engineering applications. Consequently, DPD, with its low cost, high accuracy, and strong stability, has become the mainstream technology for RF power amplifier linearization.

[0005] like Figure 1The figure below illustrates the basic principle of digital predistortion (DPD). It can be seen that the DPD can be the inverse model of the power amplifier, thus reducing the linearization problem to a modeling problem. To facilitate engineering implementation, DPD model selection generally adheres to four application criteria: discrete characteristics, representation of nonlinearity and memory effects, applicability to complex signals, and linear coefficients. The Volterra series fully meets these four criteria and is complete. Therefore, existing solutions such as the memory polynomial (MP) and generalized memory polynomial (GMP) are often simplified versions of the Volterra series.

[0006] However, although the existing digital pre-distortion technology can describe nonlinearity and memory effects intuitively, the coefficients are easy to solve linearly, and the engineering implementation is simple, it cannot more accurately fit curves with obvious segmented characteristics. Summary of the Invention

[0007] The embodiments of the present invention provide a digital predistortion processing method and apparatus to solve the defects in the existing methods for solving abnormal startup of a base station.

[0008] In one aspect, an embodiment of the present invention provides a digital predistortion processing method, applied to a base station, the method comprising:

[0009] obtaining an input signal and an output signal of the power amplifier;

[0010] Sampling the input signal and the output signal respectively to obtain sample data;

[0011] Determining coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions, wherein an upper limit of a first value range is greater than or equal to a lower limit of a second value range, and an upper limit of the first value range is less than an upper limit of the second value range, the first value range is a value range of an independent variable of the i-th basis function when a value of a dependent variable of the i-th basis function is greater than a preset value, the second value range is a value range of an independent variable of the i+1-th basis function when a value of a dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0;

[0012] The digital predistortion model with the determined coefficients is used to perform digital predistortion processing on the input signal of the power amplifier.

[0013] On the other hand, an embodiment of the present invention further provides a digital predistortion processing device, the device comprising:

[0014] A signal acquisition module, used to obtain the input signal and output signal of the power amplifier;

[0015] A sample acquisition module, configured to sample the input signal and the output signal respectively to obtain sample data;

[0016] a coefficient determination module, configured to determine coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions, wherein an upper limit of a first value range is greater than or equal to a lower limit of a second value range, and an upper limit of the first value range is less than an upper limit of the second value range, the first value range is a value range of an independent variable of the i-th basis function when the value of the dependent variable of the i-th basis function is greater than a preset value, the second value range is a value range of an independent variable of the i+1-th basis function when the value of the dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0;

[0017] The processing module is configured to perform digital predistortion processing on the input signal of the power amplifier by using the digital predistortion model with the determined coefficients.

[0018] On the other hand, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the digital predistortion processing method described above when executing the computer program.

[0019] In another aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps in the digital predistortion processing method described above are implemented.

[0020] In an embodiment of the present invention, the input signal and output signal of a power amplifier can be obtained, and then the input signal and output signal are sampled respectively to obtain sample data. Based on the sample data and a plurality of predetermined basis functions, the coefficients of a pre-established digital predistortion model are determined, thereby facilitating the determination of the coefficients of the digital predistortion model and performing digital predistortion processing on the input signal of the power amplifier. The upper limit of the first value range is greater than or equal to the lower limit of the second value range, and the upper limit of the first value range is less than the upper limit of the second value range. The first value range is the value range of the independent variable of the i-th basis function when the value of the dependent variable of the i-th basis function is greater than a preset value. The second value range is the value range of the independent variable of the i+1-th basis function when the value of the dependent variable of the i+1-th basis function is greater than the preset value, where i is an integer greater than or equal to 0. Thus, the embodiment of the present invention uses basis functions with piecewise characteristics, which are suitable for fitting the piecewise characteristics of a power amplifier. Therefore, the embodiment of the present invention can more accurately fit curves with obvious piecewise characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0022] Figure 1 It is a schematic diagram of the principle of digital predistortion in the prior art;

[0023] Figure 2 is a normalized graph of a polynomial basis function used in the prior art;

[0024] Figure 3 A flowchart of the steps of a digital predistortion processing method provided by an embodiment of the present invention;

[0025] Figure 4 is a graph of basis functions used in an embodiment of the present invention;

[0026] Figure 5 A schematic diagram illustrating the principles of calculating and establishing coefficients of a digital predistortion model in an embodiment of the present invention;

[0027] Figure 6 Schematic diagram comparing frequency domain effects before and after digital predistortion processing in an embodiment of the present invention;

[0028] Figure 7 A structural block diagram of a digital predistortion processing device provided by an embodiment of the present invention;

[0029] Figure 8 This is a structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] It should be understood that references throughout this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic associated with the embodiment is included in at least one embodiment of the present invention. Therefore, the appearances of "in one embodiment" or "in an embodiment" throughout this specification do not necessarily refer to the same embodiment. Furthermore, these particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0032] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0033] In the embodiments provided herein, it should be understood that "B corresponding to A" means that B is associated with A and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B based solely on A; B can also be determined based on A and / or other information.

[0034] To facilitate understanding of the digital predistortion processing method according to the embodiment of the present invention, a digital predistortion processing method in the prior art is first introduced.

[0035] One digital pre-distortion processing method used in the prior art is a digital pre-distortion model established based on GMP. The model expression is as follows.

[0036] Wherein, j represents the serial number of the sample collected from the signal to be processed when the function is used to perform digital predistortion processing on the signal to be processed, and y(j) represents the value after processing the j-th sample.

[0037] It can be seen from the above expression that the model expression is essentially a linear weighting of a set of basis functions, where the set of basis functions F l For {1,x,x 2 ,…x L}, the normalized graph of the basis function is as follows Figure 2 As shown, Figure 2 Medium F l Represents the lth basis function in the group of basis functions, where l is an integer between 1 and L.

[0038] in, Figure 2 Each curve shown in is a basis function. It can be seen that when the order of this group of basis functions increases, F l-1 With F l The curves almost overlap and have extremely high correlation, resulting in a singular coefficient matrix. Furthermore, this basis function provides a global optimal fit within the 0-1 range, making it poorly adaptable to amplifiers with distinct segmented characteristics. Furthermore, after the coefficients are calculated, they need to be quantized into a lookup table (LUT), resulting in a loss of accuracy.

[0039] like Figure 3 As shown, an embodiment of the present invention provides a digital predistortion processing method, which may include the following steps:

[0040] Step 301: Acquire an input signal and an output signal of a power amplifier.

[0041] Among them, the main implementation method of digital pre-distortion technology is to sample the input and output signals of the power amplifier and perform an error algorithm, so as to add a signal in the opposite direction of the distortion of the power amplifier to the input port of the power amplifier to offset the distortion of the power amplifier device.

[0042] Step 302: Sample the input signal and the output signal respectively to obtain sample data.

[0043] The input signal and the output signal are sampled in step 302 to determine the amplitude of the signal input to the power amplifier at a certain moment and how much it becomes after being processed by the power amplifier.

[0044] Step 303: Determine coefficients of a pre-established digital pre-distortion model based on the sample data and a plurality of pre-determined basis functions.

[0045] Among them, the upper limit value of the first value range is greater than or equal to the lower limit value of the second value range, and the upper limit value of the first value range is less than the upper limit value of the second value range, the first value range is the value range of the independent variable of the i-th basis function when the value of the dependent variable of the i-th basis function is greater than the preset value, the second value range is the value range of the independent variable of the i+1-th basis function when the value of the dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0.

[0046] It can be seen from this that in the embodiment of the present invention, among the multiple basis functions sampled, for the value range of the independent variable when the dependent variable of each two adjacent basis functions is greater than the preset value, there is a non-overlapping part between the two, that is, the embodiment of the present invention uses a basis function with a segmented characteristic.

[0047] In addition, when determining the above basis functions, the signal amplitude of the signal to be processed may be divided into N segments, and the number of the above basis functions is N+1, where N is an integer greater than 1.

[0048] Step 304: Perform digital predistortion processing on the input signal of the power amplifier using the digital predistortion model with the determined coefficients.

[0049] After the coefficients of the digital pre-distortion model are determined, the digital pre-distortion model can be used to perform digital pre-distortion processing on the input signal of the power amplifier.

[0050] As can be seen from the above, in embodiments of the present invention, it is possible to obtain the input signal and output signal of a power amplifier, then sample the input signal and output signal respectively to obtain sample data. Based on the sample data and a plurality of predetermined basis functions, the coefficients of a pre-established digital predistortion model are determined, thereby facilitating the determination of the coefficients of the digital predistortion model and performing digital predistortion processing on the input signal of the power amplifier. The upper limit of the first value range is greater than or equal to the lower limit of the second value range, and the upper limit of the first value range is less than the upper limit of the second value range. The first value range is the value range of the independent variable of the i-th basis function when the value of the dependent variable of the i-th basis function is greater than a preset value. The second value range is the value range of the independent variable of the i+1-th basis function when the value of the dependent variable of the i+1-th basis function is greater than the preset value, where i is an integer greater than or equal to 0. Thus, embodiments of the present invention employ basis functions with piecewise characteristics, which are suitable for fitting the piecewise characteristics of a power amplifier. Therefore, embodiments of the present invention can more accurately fit curves with distinct piecewise characteristics.

[0051] Optionally, sampling the input signal and the output signal respectively to obtain sample data includes:

[0052] Sampling the input signal to obtain first data, and sampling the output signal to obtain second data, and using the first data and the second data as the sample data;

[0053] The first acquisition moment of the input signal corresponds to the second acquisition moment of the output signal in one-to-one correspondence, and the second acquisition moment is separated from the corresponding first acquisition moment by a preset time, and the preset time is the signal output delay of the power amplifier.

[0054] For example, if the input signal is sampled at t1, t2, t3, t4, and t5, respectively, the output signal needs to be sampled at t1+T, t2+T, t3+T, t4+T, and t5+T. This allows us to determine the amplitude of the signal input to the power amplifier at a certain moment and how much it becomes after being processed by the power amplifier, where T represents the signal output delay of the power amplifier.

[0055] Optionally, determining coefficients of a pre-established digital predistortion model based on the sample data and a plurality of pre-determined basis functions includes:

[0056] When j is an integer from 1 to n, and i is an integer from 0 to k, perform the following process:

[0057] Substituting the absolute value of the first data collected at the jth first collection moment as the value of the independent variable of the ith basis function into the ith basis function, and using the obtained value of the dependent variable of the ith basis function as the element in the jth row and i column of the first matrix, wherein the first matrix is a matrix with n rows and k+1 columns;

[0058] Using the second data collected at the second collection moment corresponding to the j-th first collection moment as the element in the j-th row and first column of a second matrix, where the second matrix is a matrix with n rows and one column;

[0059] A third matrix A is calculated according to a preset formula U*A=Y, where U represents the first matrix, Y represents the second matrix, and A represents a matrix with k+1 rows and one column composed of coefficients of the digital predistortion model;

[0060] Wherein, n represents the number of the first acquisition moments, k+1 represents the number of the basis functions, and the above-mentioned first matrix is a coefficient matrix.

[0061] For example, the first data includes x[1]~x[n], the second data includes y[1]~y[n], a0~a K is the coefficient of the pre-established digital predistortion model to be solved, then it can be calculated according to Figure 5 The principle shown is to calculate a0~a K The specific value of .

[0062] That is, substitute the absolute value of x[1] into P0 to P k In each basis function, then according to Figure 5 As shown in , multiply P0(|x[1]|) by a0, multiply P1(|x[1]|) by a1..., and then multiply P k (|x[1]|) multiplied by a K , and then sum these products to get an equation: P0(|x[1]|)*a0+P1(|x[1]|)*a1……+P k (|x[1]|)*a K =y[1].

[0063] Similarly, substitute the absolute value of each data in x[2]~x[n] into P0 to P k For each basis function in , we can get n-1 equations.

[0064] From the above, we can see that the n equations obtained through the above process can form a K The equations of a0~a can be solved by using this equations K The specific value of .

[0065] The above equations can also be expressed in the form of a matrix, that is: U*A=Y,

[0066]

[0067]

[0068] Among them, the coefficient of the digital predistortion model can be obtained by the least square method: A=(U H U) -1 U H Y.

[0069] In addition, in the embodiment of the present invention, the coefficients of the digital predistortion model obtained are LUTs, so there is no need for LUT quantization process, and its length is related to the number of segments of the independent variables of the basis function, which is much smaller than the traditional LUT length.

[0070] In addition, it should be noted that in an embodiment of the present invention, the first data collected from the input signal of the power amplifier is used as the independent variable of the basis function, and the second data collected from the output signal of the power amplifier is used as the dependent variable of the basis function. The coefficients in the above-mentioned third matrix obtained by calculation are the coefficients when the above-mentioned predetermined digital pre-distortion model is used as the error model.

[0071] Similarly, the second data collected from the output signal of the power amplifier can be used as the independent variable of the basis function, and the first data collected from the input signal of the power amplifier can be used as the dependent variable of the basis function. The coefficients in the above-mentioned third matrix obtained by calculation are the coefficients when the above-mentioned predetermined digital pre-distortion model is used as the inverse model.

[0072] Optionally, the i-th basis function is orthogonal to the other basis functions among the 1st to kth basis functions except the i-1th basis function and the i+1th basis function. That is, each basis function used in the embodiment of the present invention is not orthogonal to its adjacent basis functions, but is orthogonal to the other basis functions except the adjacent basis functions. In other words, the basis functions used in the embodiment of the present invention are approximately orthogonal. When the basis functions have the approximately orthogonal property, the first matrix (i.e., the coefficient matrix) has a good condition number, thereby making the digital predistortion processing method of the embodiment of the present invention more stable.

[0073] It's important to note that the condition number actually represents the sensitivity of matrix calculations to errors. For example, for the linear system Ax = b, if the condition number of A is large, a small change in b can cause a large change in the solution x, resulting in poor numerical stability. If the condition number of A is small, a small change in b will also cause a small change in x, resulting in good numerical stability. It can also indicate how x changes when b remains unchanged but A changes slightly.

[0074] Optionally, the digital predistortion model is expressed as:

[0075]

[0076] Wherein, M is a predetermined constant, k+1 represents the number of basis functions, and P i (|x(j-m2)|) represents the value of the dependent variable of the i-th basis function when the independent variable of the i-th basis function is |x(j-m2)|.

[0077] In addition, j represents the serial number of the sample collected from the signal to be processed when the expression is used to perform digital predistortion processing on the signal to be processed, x(j) represents the value of the j-th sample collected from the signal to be processed, and y(j) represents the value after the j-th sample is processed. That is, in the embodiment of the present invention, when using When performing digital predistortion processing on a signal to be processed, it is necessary to sample the signal to be processed, and then substitute the sampled data into the expression, so as to obtain the processed value of each sample value.

[0078] In addition, it should be noted that when the expression of the digital predistortion model in the embodiment of the present invention is When a 000 ~a MM0 are equal, we can set it to a0; a 001 ~a MM1 are equal, we can set them as a1, ...a 00K ~a MMK are equal, we can set it to a K , then a0~a K The matrix formed is the aforementioned matrix A.

[0079] Among them, it should be noted that a m1m2i In the subscript "m1m2i", m1, m2, and i are not in a multiplication relationship. The three are combined only to distinguish different coefficients.

[0080] Optionally, the expression of the i-th basis function is:

[0081]

[0082] Where Δ is a predetermined constant. For example, when Δ is 2000, the curve of the above basis function is as follows: Figure 4 shown.

[0083] In addition, when the expression of the i-th basis function adopts the above expression, the above-mentioned first matrix (i.e., the coefficient matrix) is a sparse matrix, that is, the basis function adopted in the embodiment of the present invention has a piecewise characteristic, so the coefficient matrix is a sparse matrix. Therefore, the embodiment of the present invention can reduce the amount of calculation in the process of solving the coefficients of the digital pre-distortion model.

[0084] Specifically, the expression of the i-th basis function used in the embodiment of the present invention is as follows:

[0085] And the expression of the predetermined digital predistortion model is as follows:

[0086]

[0087] The comparison diagram in the frequency domain of the input signal of the power amplifier before and after the digital predistortion processing is performed by the digital predistortion processing method of the embodiment of the present invention is shown as follows: Figure 6 That is, the output signal FB of the power amplifier is severely distorted without predistortion and cannot meet the 3GPP protocol requirements. However, after the input signal TR of the power amplifier is processed using the digital predistortion processing method of the embodiment of the present invention, the distortion of the output signal FB' of the power amplifier is significantly reduced and almost coincides with the input signal TR.

[0088] In summary, the embodiments of the present invention have the following advantages over the prior art:

[0089] First, it can fit the segmented characteristics of the power amplifier;

[0090] Secondly, the basis functions have approximately orthogonal characteristics, and the coefficient matrix has normalized characteristics, the matrix condition number is good, and it is relatively stable;

[0091] Third, the coefficient matrix is a sparse matrix, which can reduce calculations during the solution process.

[0092] Fourthly, the coefficients of the digital pre-distortion model are LUTs, and no LUT quantization process is required, so the LUT length is shorter, thereby saving storage space.

[0093] The above describes the digital predistortion processing method provided by the embodiment of the present invention. The following describes the digital predistortion processing device provided by the embodiment of the present invention with reference to the accompanying drawings.

[0094] See also Figure 7 The embodiment of the present invention further provides a digital predistortion processing device, the device comprising:

[0095] The signal acquisition module 701 is used to obtain the input signal and output signal of the power amplifier;

[0096] The sample acquisition module 702 is used to sample the input signal and the output signal respectively to obtain sample data;

[0097] a coefficient determination module 703, configured to determine coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions, wherein an upper limit of a first value range is greater than or equal to a lower limit of a second value range, and an upper limit of the first value range is less than an upper limit of the second value range, the first value range is a value range of an independent variable of the i-th basis function when the value of the dependent variable of the i-th basis function is greater than a preset value, the second value range is a value range of an independent variable of the i+1-th basis function when the value of the dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0;

[0098] The processing module 704 is configured to perform digital predistortion processing on the input signal of the power amplifier using the digital predistortion model with the determined coefficients.

[0099] Optionally, the sample collection module 702 is specifically configured to:

[0100] Sampling the input signal to obtain first data, and sampling the output signal to obtain second data, and using the first data and the second data as the sample data;

[0101] The first acquisition moment of the input signal corresponds to the second acquisition moment of the output signal in one-to-one correspondence, and the second acquisition moment is separated from the corresponding first acquisition moment by a preset time, and the preset time is the signal output delay of the power amplifier.

[0102] Optionally, the coefficient determination module 703 is specifically configured to:

[0103] When j is an integer from 1 to n, and i is an integer from 0 to k, perform the following process:

[0104] Substituting the absolute value of the first data collected at the jth first collection moment as the value of the independent variable of the ith basis function into the ith basis function, and using the obtained value of the dependent variable of the ith basis function as the element in the jth row and i column of the first matrix, wherein the first matrix is a matrix with n rows and k+1 columns;

[0105] Using the second data collected at the second collection moment corresponding to the j-th first collection moment as the element in the j-th row and first column of a second matrix, where the second matrix is a matrix with n rows and one column;

[0106] A third matrix A is calculated according to a preset formula U*A=Y, where U represents the first matrix, Y represents the second matrix, and A represents a matrix with k+1 rows and one column composed of coefficients of the digital predistortion model;

[0107] Here, n represents the number of the first acquisition moments, and k+1 represents the number of the basis functions.

[0108] Optionally, the i-th basis function is orthogonal to the other basis functions among the 1st to kth basis functions except the i-1th basis function and the i+1th basis function.

[0109] Optionally, the digital predistortion model is expressed as:

[0110]

[0111] Wherein, M is a predetermined constant, k+1 represents the number of basis functions, and P i (|x(j-m2)|) represents the value of the dependent variable of the i-th basis function when the independent variable of the i-th basis function is |x(j-m2)|.

[0112] Optionally, the expression of the i-th basis function is:

[0113]

[0114] Here, Δ is a predetermined constant.

[0115] The digital predistortion processing device provided by the embodiment of the present invention can realize Figure 3 To avoid repetition, the various processes implemented in the method embodiment will not be described again here.

[0116] As can be seen from the above, in embodiments of the present invention, it is possible to obtain the input signal and output signal of a power amplifier, then sample the input signal and output signal respectively to obtain sample data. Based on the sample data and a plurality of predetermined basis functions, the coefficients of a pre-established digital predistortion model are determined, thereby facilitating the determination of the coefficients of the digital predistortion model and performing digital predistortion processing on the input signal of the power amplifier. The upper limit of the first value range is greater than or equal to the lower limit of the second value range, and the upper limit of the first value range is less than the upper limit of the second value range. The first value range is the value range of the independent variable of the i-th basis function when the value of the dependent variable of the i-th basis function is greater than a preset value. The second value range is the value range of the independent variable of the i+1-th basis function when the value of the dependent variable of the i+1-th basis function is greater than the preset value, where i is an integer greater than or equal to 0. Thus, embodiments of the present invention employ basis functions with piecewise characteristics, which are suitable for fitting the piecewise characteristics of a power amplifier. Therefore, embodiments of the present invention can more accurately fit curves with distinct piecewise characteristics.

[0117] On the other hand, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned digital predistortion processing method when executing the program.

[0118] Here is an example: Figure 8 A schematic diagram of the physical structure of an electronic device is shown.

[0119] like Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the following method:

[0120] obtaining an input signal and an output signal of the power amplifier;

[0121] Sampling the input signal and the output signal respectively to obtain sample data;

[0122] Determining coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions, wherein an upper limit of a first value range is greater than or equal to a lower limit of a second value range, and an upper limit of the first value range is less than an upper limit of the second value range, the first value range is a value range of an independent variable of the i-th basis function when a value of a dependent variable of the i-th basis function is greater than a preset value, the second value range is a value range of an independent variable of the i+1-th basis function when a value of a dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0;

[0123] The digital predistortion model with the determined coefficients is used to perform digital predistortion processing on the input signal of the power amplifier.

[0124] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0125] In another aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the digital predistortion processing method provided in each of the above embodiments is implemented, for example, including:

[0126] obtaining an input signal and an output signal of the power amplifier;

[0127] Sampling the input signal and the output signal respectively to obtain sample data;

[0128] Determining coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions, wherein an upper limit of a first value range is greater than or equal to a lower limit of a second value range, and an upper limit of the first value range is less than an upper limit of the second value range, the first value range is a value range of an independent variable of the i-th basis function when a value of a dependent variable of the i-th basis function is greater than a preset value, the second value range is a value range of an independent variable of the i+1-th basis function when a value of a dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0;

[0129] The digital predistortion model with the determined coefficients is used to perform digital predistortion processing on the input signal of the power amplifier.

[0130] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0131] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A digital predistortion processing method, characterized in that: The method comprises: obtaining an input signal and an output signal of the power amplifier; Sampling the input signal and the output signal respectively to obtain sample data; Determining coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions, wherein an upper limit of a first value range is greater than or equal to a lower limit of a second value range, and an upper limit of the first value range is less than an upper limit of the second value range, the first value range is a value range of an independent variable of the i-th basis function when a value of a dependent variable of the i-th basis function is greater than a preset value, the second value range is a value range of an independent variable of the i+1-th basis function when a value of a dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0; Performing digital predistortion processing on an input signal of the power amplifier using the digital predistortion model with the determined coefficients; The expression of the digital predistortion model is: Wherein, M is a predetermined constant, k+1 represents the number of basis functions, k represents the number of segments of the signal amplitude of the signal to be processed, x represents the value of the sample collected from the signal to be processed, y represents the value after the sample is processed, j represents the sequence number of the sample collected from the signal to be processed, m1 represents the memory depth of the signal to be processed, m2 represents the memory depth of the power of the signal to be processed, and a m1m2i is the coefficient of the digital predistortion model, P i (|x(j-m2)|) represents the value of the dependent variable of the i-th basis function when the independent variable of the i-th basis function is |x(j-m2)|. The expression of the i-th basis function is: Here, Δ is a predetermined constant.

2. The digital predistortion processing method according to claim 1, wherein: The sampling of the input signal and the output signal to obtain sample data includes: Sampling the input signal to obtain first data, and sampling the output signal to obtain second data, and using the first data and the second data as the sample data; The first acquisition moment of the input signal corresponds to the second acquisition moment of the output signal in one-to-one correspondence, and the second acquisition moment is separated from the corresponding first acquisition moment by a preset time, and the preset time is the signal output delay of the power amplifier.

3. The digital predistortion processing method according to claim 2, wherein: The step of determining coefficients of a pre-established digital predistortion model based on the sample data and a plurality of pre-determined basis functions includes: When j is an integer from 1 to n, and i is an integer from 0 to k, perform the following process: Substituting the absolute value of the first data collected at the jth first collection moment as the value of the independent variable of the ith basis function into the ith basis function, and using the obtained value of the dependent variable of the ith basis function as the element in the jth row and i column of the first matrix, wherein the first matrix is a matrix with n rows and k+1 columns; Using the second data collected at the second collection moment corresponding to the j-th first collection moment as the element in the j-th row and first column of a second matrix, where the second matrix is a matrix with n rows and one column; A third matrix A is calculated according to a preset formula U*A=Y, where U represents the first matrix, Y represents the second matrix, and A represents a matrix with k+1 rows and one column composed of coefficients of the digital predistortion model; Here, n represents the number of the first acquisition moments, and k+1 represents the number of the basis functions.

4. The digital predistortion processing method according to claim 3, wherein: The i-th basis function is orthogonal to the other basis functions among the 1st to kth basis functions except the i-1th basis function and the i+1th basis function.

5. A digital predistortion processing device, characterized in that: The device comprises: A signal acquisition module, used to obtain the input signal and output signal of the power amplifier; A sample acquisition module, configured to sample the input signal and the output signal respectively to obtain sample data; a coefficient determination module, configured to determine coefficients of a pre-established digital predistortion model based on the sample data and a plurality of predetermined basis functions, wherein an upper limit of a first value range is greater than or equal to a lower limit of a second value range, and an upper limit of the first value range is less than an upper limit of the second value range, the first value range is a value range of an independent variable of the i-th basis function when the value of the dependent variable of the i-th basis function is greater than a preset value, the second value range is a value range of an independent variable of the i+1-th basis function when the value of the dependent variable of the i+1-th basis function is greater than the preset value, and i is an integer greater than or equal to 0; a processing module, configured to perform digital predistortion processing on an input signal of the power amplifier using the digital predistortion model with the determined coefficients; The expression of the digital predistortion model is: Wherein, M is a predetermined constant, k+1 represents the number of basis functions, k represents the number of segments of the signal amplitude of the signal to be processed, x represents the value of the sample collected from the signal to be processed, y represents the value after sample processing, j represents the sequence number of the sample collected from the signal to be processed, m1 represents the memory depth of the signal to be processed, m2 represents the memory depth of the power of the signal to be processed, am1m2i is the coefficient of the digital predistortion model, P i (|x(j-m2)|) represents the value of the dependent variable of the i-th basis function when the independent variable of the i-th basis function is |x(j-m2)|. The expression of the i-th basis function is: Here, Δ is a predetermined constant.

6. An electronic device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by the processor, the steps of the digital predistortion processing method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the digital predistortion processing method according to any one of claims 1 to 4 are implemented.

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