Signal correction method and device, electronic equipment, chip and storage medium

Abstract

The invention provides a signal correction method and device, electronic equipment, a chip and a storage medium, and the method comprises the steps: determining a target DPD coefficient corresponding to an input signal from a plurality of DPD coefficients based on first model information, the first model information being used for identifying the type of a DPD model, and the plurality of DPD coefficients being output through a trained DPD model; based on the target DPD coefficient, a pre-distortion component of the input signal is determined, the pre-distortion component being used to correct an output signal of the input signal passing through the power amplifier. The output signal of the power amplifier can be corrected by adopting different types of DPD models, so that the output signal is a linear signal.

Description

Technical Field

[0001] The present disclosure relates to the field of communications, and in particular, to a signal correction method, apparatus, electronic device, chip, and storage medium. Background Art

[0002] In a communication system, a radio frequency power amplifier (PA) may be used to process a signal so that the signal can meet the requirements of the transmission power. When the power amplifier operates in a high-power range, it may cause the output signal of the power amplifier to be non-linear, affecting the communication quality. Summary of the Invention

[0003] The present disclosure provides a signal correction method, apparatus, electronic device, chip, and storage medium to solve the problems in the related art.

[0004] In a first aspect embodiment of the present disclosure, a signal correction method is proposed. The method includes: determining a target DPD coefficient corresponding to an input signal from a plurality of DPD coefficients based on first model information, where the first model information is used to identify the type of the DPD model, and the plurality of DPD coefficients are output by a trained DPD model; determining a pre-distortion component of the input signal based on the target DPD coefficient, where the pre-distortion component is used to correct the output signal of the input signal passing through a power amplifier.

[0005] In some embodiments of the present disclosure, the method further includes: training a digital pre-distortion (DPD) model based on training data, where the training data includes a plurality of wireless signals corresponding to the maximum bandwidths of a plurality of frequency points at a first baseband signal rate; outputting a plurality of DPD coefficients corresponding to each of the plurality of frequency points through the trained DPD model.

[0006] In some embodiments of the present disclosure, each wireless signal in the training data includes at least one of the following parameters: signal frequency; signal bandwidth; signal power.

[0007] In some embodiments of the present disclosure, when the first model information indicates a first value, the type of the DPD model is a polynomial model.

[0008] In some embodiments of the present disclosure, when the first model information indicates a second value, the type of the DPD model is a piecewise linear model.

[0009] In some embodiments of the present disclosure, the method further includes: determining the target DPD coefficient from the plurality of DPD coefficients based on the first model information includes: obtaining a first DPD coefficient corresponding to training data having the same first baseband signal rate, first model information, and frequency point information as the input signal; determining the first DPD coefficient as the target DPD coefficient.

[0010] In some embodiments of the present disclosure, determining the pre-distortion component of the input signal based on the target DPD coefficient includes: allocating the target DPD coefficient to the parameters of the DPD model corresponding to the first model information; performing modeling based on the parameters of the DPD model corresponding to the first model information to obtain a modeling result; and determining the pre-distortion component based on the modeling result.

[0011] An embodiment of the second aspect of the present disclosure provides a signal correction device, which includes: a first processing unit configured to determine a target DPD coefficient corresponding to an input signal from a plurality of DPD coefficients based on first model information, where the first model information is used to identify the type of the DPD model, and the plurality of DPD coefficients are output by a trained DPD model; and a second processing unit configured to determine a pre-distortion component of the input signal based on the target DPD coefficient, where the pre-distortion component is used to correct the output signal of the input signal passing through a power amplifier.

[0012] An embodiment of the third aspect of the present disclosure provides a digital pre-distorter, which is characterized by including an adder and at least one selector, where the adder is connected to the at least one selector, and the selector is configured to determine a target DPD coefficient corresponding to an input signal from a plurality of DPD coefficients based on first model information, where the first model information is used to identify the type of the DPD model; and the adder is configured to determine a pre-distortion component of the input signal based on the target DPD coefficient.

[0013] An embodiment of the fourth aspect of the present disclosure provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; where the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the embodiment of the first aspect of the present disclosure.

[0014] An embodiment of the fifth aspect of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to cause a computer to execute the method described in the embodiment of the first aspect of the present disclosure.

[0015] An embodiment of the sixth aspect of the present disclosure provides a chip, which is characterized by including at least one processor and a communication interface; the communication interface is configured to receive a signal input to the chip or a signal output from the chip, and the processor communicates with the communication interface and implements the method described in the embodiment of the first aspect of the present disclosure through a logic circuit or by executing code instructions.

[0016] In summary, for the signal correction method proposed in this disclosure, by training the DPD model for different scenarios respectively, obtaining the corresponding DPD coefficients, determining the target DPD coefficients corresponding to the input signal based on the obtained DPD coefficients, and determining the predistortion component of the input signal according to the DPD coefficients and compensating it, the output signal of the power amplifier can be corrected. This method can be compatible with multiple DPD models and has better adaptability.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit this disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with this disclosure, and are used together with the specification to explain the principles of this disclosure and do not constitute an improper limitation of this disclosure.

[0019] Figure 1 It is a schematic flowchart of a signal correction method provided by an embodiment of this disclosure;

[0020] Figure 2 It is a schematic flowchart of a signal correction method provided by an embodiment of this disclosure;

[0021] Figure 3 It is a schematic flowchart of a signal correction method provided by an embodiment of this disclosure;

[0022] Figure 4 It is a schematic diagram of a radio frequency forward link structure provided by an embodiment of this disclosure;

[0023] Figure 5 It is a schematic flowchart of a DPD model training process provided by an embodiment of this disclosure;

[0024] Figure 6 It is a schematic diagram of a digital predistortion circuit of a hybrid model provided by an embodiment of this disclosure;

[0025] Figure 7 It is a schematic diagram of the structure of a signal correction device provided by an embodiment of this disclosure;

[0026] Figure 8 It is a schematic diagram of the chip structure provided by an embodiment of this disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The embodiments of this disclosure will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain this disclosure and should not be construed as limiting this disclosure.

[0028] In a communication system, it is usually required to minimize the nonlinearity of the transmitted signal while ensuring efficient energy utilization. In an actual system, there is a contradiction between high efficiency and high linearity. For example, a radio frequency power amplifier is one of the most important and energy-consuming devices in a wireless communication system. Its design specifications are closely related to the performance and cost of the transmitter. As a typical nonlinear device, there is also a contradiction between the operating efficiency and linearity of the power amplifier.

[0029] Ideally, there should be a linear relationship between the input and output of a power amplifier. In an actual system, to improve the operating efficiency of the power amplifier, it is usually operated in a high-power range. When the input power of the amplifier reaches a certain threshold, it will cause the gain of the amplifier to decrease and the output power to increase nonlinearly, that is, it will cause gain compression and generate nonlinearity, thus affecting the communication quality of the system.

[0030] To correct the nonlinearity of the power amplifier and improve the quality of communication signals, digital predistortion technology can be used. Digital predistortion technology can configure predistortion devices in the digital domain, enabling it to achieve high-precision compensation for the nonlinear distortion of the power amplifier in a more flexible manner and at a more appropriate cost.

[0031] Digital predistortion technology can be implemented based on a look-up table and coefficient type. Its main principle is to obtain the DPD coefficients corresponding to the input signal, perform predistortion compensation on the input signal, and send the processed signal into the power amplifier. The part that compensates the signal can cancel out the part lost after the signal is amplified by the power amplifier, avoiding the occurrence of nonlinearity in the output signal.

[0032] In the application of existing technologies, the DPD coefficients of a signal can be obtained through a look-up table. The advantage of this method is that it is not limited by the specific DPD model selected. However, due to the limitations of the look-up table storage method, the storage table requires a large amount of memory, and the system performance is limited by the quantization accuracy of the look-up table. There is a contradiction between memory and performance. For this reason, the coefficient type implementation method can be used to obtain the DPD coefficients of the signal. This method only needs to store the DPD coefficients, greatly reducing the memory space. However, the model required to implement the predistorter may not be ideal when implementing an adaptive circuit. In the coefficient type implementation method, almost all circuits can only support the implementation of one model. Therefore, this method cannot flexibly configure different DPD models according to different application scenarios. However, when implementing a predistorter, there are indeed cases where some models may be better than others. This method is limited by the model selection and may not be able to obtain the best system performance.

[0033] To solve the problems existing in the related art, the present disclosure proposes a signal correction method, which can support obtaining DPD coefficients corresponding to signals under different models while reducing the system memory, implement pre-distortion processing of signals, ensure the linear characteristics of the output signals, and improve the system communication quality.

[0034] Figure 1 It is a schematic flowchart of a signal correction method provided by an embodiment of the present disclosure. As Figure 1 shown, this method can be executed by an electronic device. Optionally, this method can be executed by a network device. This method may include the following steps.

[0035] Step 101, determine the target DPD coefficient corresponding to the input signal from multiple DPD coefficients based on the first model information.

[0036] In some embodiments, the type of the digital pre-distortion (DPD) model can be a polynomial model or a piecewise linear model, and different types of DPD models can be trained to obtain the DPD coefficients corresponding to the training data.

[0037] In some embodiments, the training data may include multiple wireless signals corresponding to the maximum bandwidth of each of multiple frequency points at the first baseband signal rate. The trained DPD model is used to output multiple DPD coefficients corresponding to each of the multiple frequency points.

[0038] In some embodiments, the first baseband signal rate is the baseband signal rate generated according to the protocol.

[0039] In some embodiments, each wireless signal in the training data may include at least one of the following parameters: signal frequency; signal bandwidth; signal power, that is, the DPD model can determine the DPD coefficient corresponding to the signal according to at least one of the signal frequency, bandwidth, and power.

[0040] In some embodiments, since the prior data has not been stored yet, the first wireless signal in the training data may not pass through the digital pre-distortion (DPD) model first, but directly pass through a digital-to-analog converter to convert the digital signal into an analog signal, and then be frequency-converted to a specified frequency through quadrature modulation, and then input the signal into a power amplifier.

[0041] In some embodiments, in the power amplifier, the wireless signal will experience signal nonlinear distortion due to radio frequency (RF) PA damage and mixing. For example, let the signal with the maximum bandwidth at the first baseband rate be X cak (n), the signal bandwidth be BW xcal , when the frequency of the signal after modulation is f0, the input signal of the power amplifier is The distorted signal obtained after passing through the power amplifier is yPA(t) = ∫h(τ)*X cal∫(t - τ)h(τ)dτ, where h(τ) is the system response to be generated by the RF PA, including non - linear distortion in both amplitude and phase, and the symbol "*" represents convolution.

[0042] In some embodiments, the above - mentioned distorted signal can be transmitted to a feedback link to extract its corresponding DPD coefficients. The specific processing procedure of the feedback link is as follows: The signal distorted by the power amplifier is input into a quadrature demodulator to convert the frequency of the signal, and then through an analog - to - digital converter, the analog signal is converted into a digital signal. At this time, the signal is a digital signal containing non - linear components. Then, the signal is sent into a DPD coefficient extraction module to extract the DPD coefficients corresponding to the signal. Exemplarily, an equivalent complex baseband model can be used to extract the DPD coefficients of the signal. For example, when the distorted signal satisfies the bandwidth BW xcal << f0, the corresponding complex baseband equivalent model can be obtained based on the type of the DPD model. The complex baseband equivalent model can be expressed in matrix form. For example, yPA = φ(x)·a, where yPA represents the modeled PA output vector, x is the input signal vector composed of X cal (n), φ(x) is the basis function matrix constructed from the input signal vector, and a is the modeled DPD coefficient. Finally, calibration can be achieved by methods such as least squares, generating DPD calibration coefficients. Exemplarily, the coefficient obtained by using the least - squares method is:

[0043] a = (φ(x) H φ(x)) -1 ^(-1)φ(x) H yPA

[0044] In some embodiments, the coefficients obtained above can be used as prior data for subsequent data training and incorporated into the DPD model to pre - correct the non - linearity caused by the power amplifier on the digital side, thus ensuring the quality of the power amplifier output signal.

[0045] In some embodiments, based on the first model information, the target DPD coefficient corresponding to the input signal can be determined from multiple DPD coefficients.

[0046] In some embodiments, the first model information is used to identify the type of the DPD model. Exemplarily, the name of the first model information can be "model type", "model selection", "Model_sel", etc. The present disclosure does not limit the name of the first model.

[0047] In some embodiments, when the first model information indicates a first value, the type of the DPD model can be a polynomial model; when the first model information indicates a second value, the type of the DPD model can be a piece - wise linear model.

[0048] Exemplarily, the first value can be 0 or 1, and the second value can also be 0 or 1. The specific numerical values of the first value and the second value can be determined based on actual requirements, and the present disclosure does not limit this.

[0049] In some embodiments, the target DPD coefficient corresponding to the input signal can be determined according to the frequency, bandwidth, and frequency point of the current input signal.

[0050] Step 102: Determine the pre-distortion component of the input signal based on the target DPD coefficient.

[0051] In some embodiments, the pre-distortion component is used to correct the output signal of the input signal passing through the power amplifier.

[0052] In some embodiments, modeling can be performed according to the target DPD coefficient, and the pre-distortion component of the input signal can be obtained according to the modeling result.

[0053] In summary, in the above embodiments of the present disclosure, by training different DPD models, multiple DPD coefficients are obtained, and the target DPD coefficient corresponding to the input signal is obtained from the multiple DPD coefficients. The pre-distortion component of the signal is determined according to the DPD coefficient, which can facilitate compensating the signal according to the pre-distortion component, realize the linearization of the output signal of the power amplifier, can train the DPD model in different scenarios, and determine the coefficient, so as to determine the corresponding DPD coefficient for the power amplifier in different scenarios, and can support multiple DPD models, with strong adaptability and high flexibility in model switching.

[0054] Figure 2 It is a schematic flow chart of a signal correction method provided by an embodiment of the present disclosure. As Figure 2 shown, based on Figure 1 the embodiment shown, the method may further include the following steps.

[0055] Step 201: Obtain the first DPD coefficient corresponding to the training data with the same first baseband signal rate, first model information, and frequency point information as the input signal.

[0056] In some embodiments, the DPD coefficient corresponding to the training data with the same first baseband signal rate, first model information, and frequency point information as the input signal can be selected as the first DPD coefficient. At this time, the bandwidth of the input signal is different from that of the training data. Exemplarily, for all signals with the bandwidth under the first baseband signal rate, first model information, and frequency point information, the DPD coefficient corresponding to the training data can be used as the first DPD coefficient.

[0057] Step 202: Determine the first DPD coefficient as the target DPD coefficient.

[0058] In some embodiments, the above first DPD coefficient may be determined as a target DPD coefficient for determining the pre-distortion component of the input signal.

[0059] In summary, in the above embodiments of the present application, the target DPD coefficient of the input signal can be determined through the DPD model, which is convenient for subsequent determination of the pre-distortion component of the input signal. The target DPD coefficient of the input signal can be determined for different DPD models, and the adaptability to different models is good; by using the model to determine the target DPD coefficient, the storage space can be saved on the premise of the same modeling accuracy.

[0060] Figure 3 It is a schematic flowchart of a signal correction method provided by an embodiment of the present disclosure. As Figure 3 shown, based on Figure 1 the embodiment shown, the method may further include the following steps.

[0061] Step 301, allocate the target DPD coefficient to the parameters of the DPD model corresponding to the first model information.

[0062] In some embodiments, different types of DPD models may correspond to different parameters. For example, for a polynomial model, the target DPD coefficient can be allocated to Coef_x0, Coef_x1, Coef_x2, Coef_x3, Coef_x4, etc.; for another example, for a piecewise linear model, the target DPD coefficient can be allocated to Coef_10, Coef_11, …, Coeff_1K-1, etc.

[0063] Step 302, perform modeling based on the parameters of the DPD model corresponding to the first model information to obtain a modeling result.

[0064] In some embodiments, the signal can be modeled according to the above configured coefficients, where the modeling can be for the non-linear component of the signal or for the entire input signal.

[0065] In some embodiments, the corresponding modeling formula can be determined according to the first model information and the modeling method. By way of example, taking the modeling of the non-linear component as an example, when the DPD model type is a polynomial model, its modeling formula is:

[0066] f(|x(n)|)=x(n-D sig )*∑Coef-xi*|x(n-D abs )|

[0067] When the DPD model type is a piecewise linear model, its modeling formula is:

[0068] f(|x(n)1) = Coef_m + Coef_n * Spl_out

[0069] In some embodiments, when modeling a signal, the signal under different models can be instantiated item by item, and the multiple instantiated item models corresponding to each signal are accumulated to obtain a modeling result. For example, each segment of a piecewise linear model can be modeled to obtain the modeling of multiple parallel instantiated items, and the accumulation of all the instantiated item models corresponding to the signal can obtain the modeling result.

[0070] For another example, each term of a polynomial model can be modeled to obtain the modeling of multiple parallel instantiated items, and the accumulation of all the instantiated item models corresponding to the signal can obtain the modeling result.

[0071] In some embodiments, the modeling result can be a modeling component for the nonlinearity of a power amplifier, that is, the pre-distortion component corresponding to the input signal.

[0072] Step 303, determine the pre-distortion component based on the modeling result.

[0073] In some embodiments, the modeling result can be determined as the pre-distortion component of the input signal, and this pre-distortion component is added to the input signal. This pre-distortion component can cancel out the distorted component after the input signal passes through the power amplifier, making the output signal of the power amplifier linear.

[0074] In summary, in the above embodiments of the present application, according to the target DPD coefficient, corresponding instantiated item modeling is performed according to different models to obtain a modeling result, and the corresponding pre-distortion component is determined according to the modeling result, so that pre-distortion processing can be completed on the input signal to obtain a pre-distorted signal and correct the signal. This method can be compatible with polynomial models and piecewise linear models and can be flexibly adjusted according to different power amplifier behavior characteristics.

[0075] The technical solution of the present disclosure will be further described in detail below in conjunction with specific application embodiments.

[0076] The following is the specific content of a hybrid model digital pre-distortion implementation method provided by the embodiments of the present disclosure.

[0077] Such as Figure 4As shown, the digital predistortion module is a pre-stage module placed in the digital radio frequency front-end for calibrating the non-ideal characteristics of the PA. The PA calibration process mainly includes: sending the ideal digital signal into the DPD module of 401, introducing the components for pre-compensating the non-linear distortion of the PA. The signal is then converted into an analog signal through the digital-to-analog converter (DAC) of 402, modulated to the specified frequency through the quadrature modulator of 403, and finally passes through the RF PA of 404 to generate non-linearity. Ideally, the non-linearity generated by the PA and the pre-corrected non-linear components generated by the DPD can cancel each other out, making the signal at the output port of the PA a linear output signal, thus ensuring that the transmitter meets the corresponding RF specifications.

[0078] The digital predistortion module can determine the DPD coefficients corresponding to the signal according to the frequency, bandwidth, and input signal power of the signal, and determine the non-linear distortion components of the signal based on these DPD coefficients to perform pre-distortion compensation on the signal. Before determining the DPD coefficients corresponding to the signal, the DPD module needs to pre-train the DPD coefficients for compensating the non-linearity of the RF PA. The training process is as Figure 5 shown, and mainly includes: the ideal digital signal emitted by the signal source is first sent into the predistortion module of 501, converted into an analog signal through the digital-to-analog converter of 502, modulated to the specified frequency through the quadrature modulator of 503, and enters the RF power amplifier of 504 to generate non-linear distortion; in the feedback loop, the signal containing the PA non-linear distortion is sent into the feedback link through the coupler of 505, undergoes down-conversion of the analog signal through the quadrature demodulator of 506, and then the received feedback analog signal is converted into a digital signal through the analog-to-digital converter of 507 and sent into the DPD coefficient extraction module of 508 to extract the DPD coefficients for correcting the PA non-linearity in the current scenario.

[0079] In addition, in the first training, due to the lack of prior data, the digital predistortion module cannot determine the coefficients corresponding to the signal. Therefore, the digital predistortion module can be bypassed during the first training.

[0080] The digital predistortion module can use the signal with the maximum bandwidth at the current rate generated by the signal source as the training data. For example, let this signal be X cal (n), and the signal bandwidth is BW xcal . This training signal is frequency-converted to the specified frequency through the DAC and quadrature modulation. Without loss of generality, for example, let the frequency-conversion frequency be f0. Therefore, the RF signal sent into the PA is

[0081]

[0082] The above RF signal undergoes RF PA impairment and mixing to obtain the baseband distorted signal as:

[0083] yPA(t) = ∫h(τ)*X cal (t - τ)dτ

[0084] Among them, h(τ) is the system response to be generated by the RF PA, including non-linear distortion in amplitude and phase, and the symbol "*" represents convolution. The feedback link can obtain a digital signal containing PA non-linearity after quantizing and sampling the above baseband distortion signal through an ADC. Since the complex baseband model has high processing flexibility and less data volume, an equivalent complex baseband model can be used for processing. At this time, the input signal of the PA is a bandwidth-limited signal and satisfies the narrowband assumption BW xcal <<f0, based on the piecewise linear model or the polynomial model, the equivalent complex baseband models obtained can both be represented in the following matrix form:

[0085] yPA = φ(x) · a

[0086] Among them, yPA represents the modeled PA output vector, x is the input signal vector composed of X cal (n), φ(x) is the basis function matrix constructed from the input signal vector, and a is the DPD coefficient obtained by modeling. Calibration can be achieved by methods such as least squares, and finally DPD calibration coefficients are generated. For example, the coefficients obtained by using the least squares method are:

[0087] a = (φ(x) H φ(x)) -1 φ(x) H yPA

[0088] By configuring the updated coefficient a as the DPD calibration coefficient into the DPD module and pre-correcting the non-linearity of the PA on the digital side, the quality of the PA output signal can be guaranteed.

[0089] The embodiments of the present disclosure can achieve the fusion of the DPD model by adding two selectors and one adder. As Figure 6 shown, Figure 6 is an instantiated circuit structure for the fusion of a polynomial model and a piecewise linear model. In actual deployment, there may be multiple Figure 6 shown instantiated structures in a circuit.

[0090] The method proposed by the present disclosure will be specifically described below through two different model examples.

[0091] In some embodiments, the specific model type can be determined through Model_sel. For example, assuming Model_sel = 1, it can represent selecting a polynomial model for modeling; when Model_sel = 0, it can represent selecting a piecewise linear model. Specifically:

[0092] When Model_sel = 1 and a polynomial model is selected, the process of digital pre-distortion processing of the signal is as follows:

[0093] 1. Obtain the DPD coefficients corresponding to the input signal under the polynomial model.

[0094] 2. Assign the coefficients of the polynomial model to Coef_x0, Coef_x1, Coef_x2, Coef_x3, Coef_x4, …;

[0095] 3. The assigned coefficients are first multiplied by the envelope delay term |x(n - D abs ), and the sum of the products is multiplied by the corresponding signal envelope term x(n - D sig ), completing the modeling of the time-delay D sig signal envelope. Its operation formula is

[0096] f(|x(n)|) = x(n - D sig ) * ∑Coef_xi * |x(n - D abs )|

[0097] 4. Accumulate the results of modeling multiple parallel instantiation items to obtain the modeling component for PA nonlinearity;

[0098] 5. Finally, pre-compensate the modeled nonlinear component (i.e., the above-mentioned modeling component for PA nonlinearity) in the current signal, and output the pre-distorted signal.

[0099] When Model_sel = 0 and piecewise linear is selected, for example, without loss of generality, it can be assumed that the number of segments is K segments. Then the process of digital pre-distortion processing of the signal is as follows:

[0100] 1. Obtain the DPD coefficients corresponding to the input signal under the piecewise linear model.

[0101] 2. Assign the coefficients of the piecewise linear model to Coef_10, Coef_11, …, Coeff_1K - 1 in the Mixed Mul-Mux module;

[0102] 3. For the input signal at time n, select the two coefficients Coef_m and Coef_n corresponding to the segment where the signal amplitude is located according to the signal amplitude |x(n)|. Spl_out corresponds to the remaining signal amplitude after subtracting the current segment threshold. The output signal of the Mixed Mul-Mux module is f(|x(n)|) = Coef_m + Coef_n * Spl_out;

[0103] 4. Accumulate the results of modeling multiple parallel instantiation items to obtain the modeling component for PA nonlinearity;

[0104] 5. Finally, pre-compensate the modeled non-linear components (i.e., the modeled components for PA non-linearity) in the current signal, and output the pre-distorted signal.

[0105] After the above signal pre-distortion processing, the output signal after pre-distortion processing can be obtained. The signal can be converted into an analog signal by the DAC module of 202, and then modulated by the quadrature modulator module of 203, so as to complete the forward process of PA linearization.

[0106] In summary, for the above examples of the present disclosure, the model type can be determined, the DPD coefficients corresponding to the signal can be determined based on the model type, and the signal can be pre-distorted according to the DPD coefficients to achieve the linearization of the power amplifier. The above solution of the present disclosure can support multiple model structures through one circuit by integrating the polynomial model and the piecewise linear model, improving the adaptability of the model.

[0107] Figure 7 It is a schematic structural diagram of a signal correction device 700 provided by an embodiment of the present disclosure. As Figure 7 shown, the device includes:

[0108] A first processing unit 710, configured to determine a target DPD coefficient corresponding to an input signal from a plurality of DPD coefficients based on first model information, where the first model information is used to identify the type of the DPD model, and the plurality of DPD coefficients are output by a trained DPD model; a second processing unit 720, configured to determine a pre-distortion component of the input signal based on the target DPD coefficient, where the pre-distortion component is used to correct the output signal of the input signal passing through the power amplifier.

[0109] In some embodiments, the first processing unit 710 may further be configured to: obtain a first DPD coefficient corresponding to training data having the same first baseband signal rate, first model information, and frequency point information as the input signal; determine the first DPD coefficient as the target DPD coefficient.

[0110] In some embodiments, the second processing unit 720 may further be configured to: allocate the target DPD coefficient to the parameters of the DPD model corresponding to the first model information; perform modeling based on the parameters of the DPD model corresponding to the first model information to obtain a modeling result; determine the pre-distortion component based on the modeling result.

[0111] In the above embodiments provided by the present application, the methods and devices provided by the embodiments of the present application are introduced. To implement each function in the methods provided by the embodiments of the present application, the electronic device may include a hardware structure and software modules, and implement the above functions in the form of a hardware structure, a software module, or a combination of a hardware structure and a software module. A certain function among the above functions may be executed in the form of a hardware structure, a software module, or a combination of a hardware structure and a software module.

[0112] Embodiments of the present disclosure also propose a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the methods described in the above embodiments of the present disclosure.

[0113] Embodiments of the present disclosure also propose a computer program product, including a computer program, where the computer program, when executed by a processor, executes the methods described in the above embodiments of the present disclosure.

[0114] Figure 8 FIG. 800 is a schematic structural diagram of a chip 800 for implementing the above method according to an exemplary embodiment. Refer to Figure 8 , the chip 800 includes a communication interface 801 and at least one processor 802. The communication interface 801 is configured to receive signals input to the chip 800 or signals output from the chip 800, and the processor 802 communicates with the communication interface 801 and implements the methods described in the above embodiments of the present disclosure through logic circuits or by executing code instructions.

[0115] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such used data may be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0116] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in at least one embodiment or example.

[0117] Any process or method description represented in the flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations where functions may be executed not in the order shown or discussed, including in a substantially simultaneous manner according to the functions involved or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0118] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing logical functions and can be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processing module, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having at least one wiring (control method), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0119] It should be understood that various parts of the embodiments of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0120] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0121] In addition, each functional unit in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disc, etc.

[0122] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A signal correction method, characterized in that, The method includes: Based on first model information, determining a target DPD coefficient corresponding to an input signal from a plurality of DPD coefficients, where the first model information is used to identify the type of a DPD model, and the plurality of DPD coefficients are output by a trained DPD model; Based on the target DPD coefficient, determining a pre-distortion component of the input signal, where the pre-distortion component is used to correct an output signal of the input signal passing through a power amplifier.

2. The method according to claim 1, characterized in that The method further includes: Training a digital pre-distortion (DPD) model based on training data, where the training data includes a plurality of wireless signals corresponding to maximum bandwidths of respective multiple frequency points at a first baseband signal rate; Outputting, by the trained DPD model, a plurality of DPD coefficients corresponding to the respective multiple frequency points.

3. The method according to claim 2, wherein Each wireless signal in the training data includes at least one of the following parameters: Signal frequency; Signal bandwidth; Signal power.

4. The method according to claim 1, wherein when the first model information indicates a first value, the type of the DPD model is a polynomial model.

5. The method according to claim 1, wherein when the first model information indicates a second value, the type of the DPD model is a piecewise linear model.

6. The method according to claim 1, wherein The determining, based on the first model information, the target DPD coefficient from the plurality of DPD coefficients includes: Obtaining a first DPD coefficient corresponding to training data having the same first baseband signal rate, first model information, and frequency point information as the input signal; Determining the first DPD coefficient as the target DPD coefficient.

7. The method according to claim 1, wherein The determining, based on the target DPD coefficient, the pre-distortion component of the input signal includes: Assigning the target DPD coefficient to a parameter of the DPD model corresponding to the first model information; Performing modeling based on the parameter of the DPD model corresponding to the first model information to obtain a modeling result; Determining the pre-distortion component based on the modeling result.

8. A signal correction device, characterized in that, The apparatus includes: A first processing unit, configured to determine a target DPD coefficient corresponding to an input signal from a plurality of DPD coefficients based on first model information, where the first model information is used to identify the type of a DPD model, and the plurality of DPD coefficients are output by a trained DPD model; A second processing unit, configured to determine a pre-distortion component of the input signal based on the target DPD coefficient, where the pre-distortion component is used to correct an output signal of the input signal passing through a power amplifier.

9. A digital predistorter, characterized in that, Including an adder and at least one selector, where the adder is connected to the at least one selector, the selector is configured to determine a target DPD coefficient corresponding to an input signal from the plurality of DPD coefficients based on first model information, where the first model information is used to identify the type of a DPD model; the adder is configured to determine a pre-distortion component of the input signal based on the target DPD coefficient.

10. An electronic device, characterized in that, Including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-7.

11. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

12. A chip, characterized in that, Comprising at least one processor and a communication interface; the communication interface is used to receive signals input to the chip or signals output from the chip, and the processor communicates with the communication interface and implements the method according to any one of claims 1 to 7 through logic circuits or by executing code instructions.

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