Signal processing method and device, electronic equipment and storage medium

Abstract

The invention provides a signal processing method and device, electronic equipment and a storage medium, and relates to the technical field of communication. The method comprises the following steps: acquiring an input signal of a signal amplifier; the input signal comprises a non-divergent signal segment and a divergent signal segment; processing the input signal by using a digital pre-distortion model to obtain an output signal; the output signal comprises a first nonlinear signal segment obtained by performing nonlinear processing on the non-divergent signal segment and the divergent signal segment directly output, and the digital pre-distortion model is obtained based on non-divergent signal segment sample training. According to the invention, the method achieves the training of the digital pre-distortion model only through the non-divergent signal segment, avoids the interference of the divergent signal segment on the model training, improves the training effect of the digital pre-distortion model, and improves the signal processing effect of the non-divergent signal segment in the input signal.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a signal processing method, apparatus, electronic device and storage medium. Background Technology

[0002] In wireless communication, signal amplifiers are prone to signal distortion due to nonlinearity. Digital predistortion technology adds a predistortion module before the signal amplifier to generate a signal with the opposite nonlinear characteristics to the signal amplifier, thus canceling out the distortion and improving the efficiency of the signal amplifier and the signal transmission performance. Summary of the Invention

[0003] This disclosure provides a signal processing method, apparatus, electronic device, and storage medium to solve problems in the related art.

[0004] A first aspect of this disclosure provides a signal processing method, the method comprising: Acquire the input signal of the signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; The input signal is processed using a digital predistortion model to obtain an output signal. The output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment. The digital predistortion model is trained based on samples of the non-divergent signal segment.

[0005] In some embodiments, the method further includes: Obtain the amplitude of the input signal; Based on the amplitude, the non-divergent signal segment and the divergent signal segment in the input signal are determined.

[0006] In some embodiments, determining the non-divergent signal segment and the divergent signal segment in the input signal based on the amplitude includes: The input signal segment corresponding to the first amplitude value that is less than a preset threshold is determined as the non-divergent signal segment; The input signal segment corresponding to the second amplitude that is greater than or equal to the preset threshold is determined as the divergent signal segment.

[0007] In some embodiments, the method further includes: Obtain the model parameters of the initial digital predistortion model, and obtain the first nonlinear characteristic of the signal amplifier; Based on the non-divergent signal segment samples and the first nonlinear feature, the model parameters are adjusted to obtain the digital predistortion model.

[0008] In some embodiments, adjusting the model parameters based on the non-divergent signal segment samples and the first nonlinear feature to obtain the digital predistortion model includes: Using the initial digital predistortion model, the non-divergent signal segment sample is subjected to nonlinear processing to obtain a second nonlinear signal segment. Based on the non-divergent signal segment sample and the second nonlinear signal segment, determine the second nonlinear feature of the initial digital predistortion model; The model parameters are adjusted based on the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model.

[0009] In some embodiments, adjusting the model parameters based on the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model includes: The model parameters are adjusted according to the trend of making the first nonlinear feature negatively correlated with the second nonlinear feature to obtain the digital predistortion model.

[0010] A second aspect of this disclosure provides a signal processing apparatus, comprising: An acquisition unit is used to acquire the input signal of the signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; The processing unit is used to process the input signal using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment, wherein the digital predistortion model is trained based on samples of the non-divergent signal segment.

[0011] In some embodiments, the apparatus further includes: The acquisition unit is further configured to acquire the amplitude of the input signal; A determining unit is configured to determine the non-divergent signal segment and the divergent signal segment in the input signal based on the amplitude.

[0012] In some embodiments, the determining unit is further configured to: The input signal segment corresponding to the first amplitude value that is less than a preset threshold is determined as the non-divergent signal segment; The input signal segment corresponding to the second amplitude that is greater than or equal to the preset threshold is determined as the divergent signal segment.

[0013] In some embodiments, the apparatus further includes: The acquisition unit is further configured to acquire the model parameters of the initial digital predistortion model and acquire the first nonlinear characteristic of the signal amplifier; The adjustment unit is used to adjust the model parameters according to the non-divergent signal segment sample and the first nonlinear feature to obtain the digital predistortion model.

[0014] In some embodiments, the adjustment unit includes: The processing module is used to perform nonlinear processing on the non-divergent signal segment sample using the initial digital predistortion model to obtain a second nonlinear signal segment; The determining module is used to determine the second nonlinear feature of the initial digital predistortion model based on the non-divergent signal segment sample and the second nonlinear signal segment; The adjustment module is used to adjust the model parameters according to the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model.

[0015] In some embodiments, the adjustment module is further configured to: The model parameters are adjusted according to the trend of making the first nonlinear feature negatively correlated with the second nonlinear feature to obtain the digital predistortion model.

[0016] A third aspect of this disclosure provides an electronic device comprising: 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 to enable the at least one processor to perform the methods described in the first aspect of this disclosure.

[0017] A fourth aspect of this disclosure provides a non-transitory computer-readable storage medium that, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to perform the methods described in the first aspect of this disclosure.

[0018] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the methods described in the embodiments of the first aspect of this disclosure.

[0019] A sixth aspect of this disclosure provides a chip including one or more interfaces and one or more processors; the interfaces are configured to receive signals from the memory of an electronic device and send signals to the processors, the signals including computer instructions stored in the memory, which, when executed by the processors, cause the electronic device to perform the methods described in the first aspect of this disclosure.

[0020] In summary, the signal processing method proposed in this disclosure includes acquiring an input signal of a signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; processing the input signal using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment, wherein the digital predistortion model is trained based on samples of the non-divergent signal segment. This method achieves training of the digital predistortion model using only the non-divergent signal segment, avoiding interference from the divergent signal segment on model training, thereby improving the training effect of the digital predistortion model and enhancing the signal processing effect of the non-divergent signal segment in the input signal.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0023] Figure 1 A flowchart of a signal processing method provided in an embodiment of this disclosure; Figure 2 A flowchart illustrating another signal processing method provided in an embodiment of this disclosure; Figure 3 This diagram illustrates an application scenario of signal processing provided by an embodiment of the present disclosure. Figure 4 A flowchart illustrating a training method for a digital predistortion model provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the structure of a signal processing device provided in an embodiment of the present disclosure; Figure 6 This is a schematic diagram of another signal processing device provided in an embodiment of the present disclosure; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure; Figure 8 This is a schematic diagram of the structure of a chip provided in an embodiment of the present disclosure. Detailed Implementation

[0024] Some embodiments of this disclosure will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0025] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0026] In wireless communication, signal amplifiers are prone to signal distortion due to nonlinearity. Digital predistortion technology adds a predistortion module before the signal amplifier to generate a signal with the opposite nonlinear characteristics to the signal amplifier, thus canceling out the distortion and improving the efficiency of the signal amplifier and the signal transmission performance.

[0027] In signal processing techniques, the input signal of a signal amplifier is typically processed nonlinearly using a digital predistortion model. However, since the digital predistortion model needs to be trained using input signal samples, and these samples contain divergent signal segments, the training effect of the digital predistortion model is poor, which in turn leads to poor signal processing of the non-divergent signal segments in the input signal.

[0028] Therefore, to address the problems existing in related technologies, this disclosure proposes a signal processing method. The method includes acquiring an input signal of a signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; processing the input signal using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment, wherein the digital predistortion model is trained based on samples of the non-divergent signal segment. This method achieves training of the digital predistortion model using only the non-divergent signal segment, avoiding interference from the divergent signal segment on model training, thereby improving the training effect of the digital predistortion model and enhancing the signal processing effect of the non-divergent signal segment in the input signal.

[0029] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments. In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships. The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure. In this disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular or a plural expression. In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably. In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”. The prefixes such as "first" and "second" in the embodiments of this disclosure are only for distinguishing different descriptive objects and do not constitute restrictions on the position, order, priority, number or content of the descriptive objects. For the description of the descriptive objects, please refer to the description in the claims or the context of the embodiments. The use of prefixes should not constitute unnecessary restrictions.

[0030] In the embodiments disclosed herein, "multiple" refers to two or more. In the embodiments disclosed herein, terms such as “import”, “input”, and “read in” can be used interchangeably.

[0031] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0032] Figure 1 This is a flowchart illustrating a signal processing method provided in an embodiment of this disclosure. This method can be applied to application scenarios such as smart terminals, and can be executed by a terminal with integrated signal processing functions or a signal processor within a terminal, or by other devices suitable for signal processing; this disclosure does not limit its application. Figure 1 As shown, the signal processing method includes steps S101-S102.

[0033] Step S101: Obtain the input signal of the signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment.

[0034] In the embodiments of this disclosure, a signal amplifier refers to a hardware device used to enhance signal power, including but not limited to a radio frequency power amplifier (PA) and an intermediate frequency amplifier. The signal amplifier amplifies the received signal before transmission, but it is prone to signal distortion due to its own nonlinear characteristics (such as transistor saturation effect). The input signal refers to the original signal to be input to the signal amplifier, and its sources include but are not limited to baseband signals in wireless communication systems (such as modulation signals sent by base stations) and transmitted signals generated by terminal devices (such as mobile phones and IoT modules). The signal form is a continuous time-domain signal.

[0035] In the embodiments of this disclosure, a divergent signal segment refers to a segment of the input signal whose instantaneous amplitude or power exceeds a preset threshold. The preset threshold is associated with the upper limit of the linear operating range of the signal amplifier. When a divergent signal segment is input into a digital predistortion model for training, because it has penetrated deep into the nonlinear saturation region of the signal amplifier, the corresponding model output becomes extremely unstable or loses its physical meaning, leading to drastic and erroneous updates to the model parameters; hence, it is called a divergent signal segment. In contrast to the divergent signal segment, a non-divergent signal segment refers to a segment of the input signal whose instantaneous amplitude or power is below the preset threshold. The non-divergent signal segment is mainly located within the linear or slightly nonlinear operating range of the signal amplifier, providing stable and effective feature information for the training of the digital predistortion model, and is the core data source for the model to learn its inverse characteristics.

[0036] In embodiments of this disclosure, the amplitude of the input signal is calculated to obtain an instantaneous amplitude sequence synchronized with the input signal. A judgment criterion is established to distinguish between non-divergent and divergent signal segments. One implementation involves setting a fixed threshold. The determination of the fixed threshold is based on prior knowledge of the characteristics of the signal amplifier, for example, obtaining the input power level corresponding to its 1dB compression point from its datasheet and converting it into an equivalent digital amplitude threshold. Another implementation involves using an adaptive dynamic threshold. The system can monitor the average power or peak statistical characteristics of the input signal in real time and dynamically adjust the amplitude threshold based on these statistics. For example, the dynamic threshold can be set as a fixed multiple of the long-term average amplitude of the signal, or finely adjusted according to the real-time changes in the signal peak-to-average power ratio (PAPR). This approach better adapts to changes in signal modulation and power levels.

[0037] In embodiments of this disclosure, the entire instantaneous amplitude sequence is traversed. The instantaneous amplitude of each sampling point is compared with a threshold: if it is less than the threshold, the sampling point is determined to belong to a non-divergent signal segment. If it is greater than or equal to the threshold, the sampling point is determined to belong to a divergent signal segment. Typically, consecutive sampling points with the same properties constitute a signal segment. Therefore, the system performs segment marking, marking consecutive non-divergent sampling points as a non-divergent signal segment and consecutive divergent sampling points as a divergent signal segment.

[0038] By distinguishing between non-divergent and divergent signal segments, customized processing can be performed based on the characteristics of different signal segments, thus optimizing the application scope and effectiveness of the digital predistortion model.

[0039] Step S102: The input signal is processed using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment; the digital predistortion model is trained based on samples of the non-divergent signal segment.

[0040] In the embodiments of this disclosure, the digital predistortion model refers to an algorithmic model used for nonlinear compensation processing of the input signal. The core function of the digital predistortion model is to generate a nonlinear signal that is opposite to the nonlinear characteristics of the signal amplifier, thereby canceling the distortion generated by the amplifier. The digital predistortion model is constructed through machine learning or numerical fitting methods and is trained only using samples of non-divergent signal segments (i.e., the model parameters only adapt to the characteristics of non-divergent signals) and does not include adaptation logic for divergent signal segments.

[0041] In the embodiments of this disclosure, the output signal refers to the final signal to be input to the signal amplifier after processing by the digital predistortion model. It consists of two parts: a first nonlinear signal segment after processing the non-divergent signal segment, and a divergent signal segment that is directly retained without model processing. Its function is to provide the signal amplifier with a pre-compensated signal, thereby reducing distortion after amplification.

[0042] In the embodiments of this disclosure, the first nonlinear signal segment refers to the signal segment obtained after the digital predistortion model performs nonlinear processing on the non-divergent signal segment in the input signal. Its nonlinear characteristics are inversely complementary to the nonlinear characteristics of the signal amplifier (e.g., if the amplification device causes gain overshoot in a certain range of the signal amplitude, the first nonlinear signal segment will pre-attenuate the gain), thereby canceling the original distortion after processing by the amplification device. The directly output divergent signal segment refers to the portion of the input signal where the divergent signal segment has not undergone nonlinear processing by the digital predistortion model and is directly retained and included in the output signal. This is because the amplitude or fluctuation characteristics of the divergent signal segment exceed the training range of the model (the model is only trained based on non-divergent samples), and forcibly processing it would introduce new distortion; therefore, direct output is chosen.

[0043] In the embodiments of this disclosure, the non-divergent signal segment sample refers to the reference signal segment used to train the digital predistortion model. It is derived from the part of the historical input signal that has been identified as non-divergent (such as a signal segment with stable amplitude and no abnormal fluctuations). Its characteristics are consistent with the non-divergent signal segment in the input signal to be processed, and it is used to enable the digital predistortion model to learn and adapt the nonlinear compensation law of the non-divergent signal.

[0044] In the embodiments of this disclosure, the input signal is first segmented and labeled: a "to be processed" label is added to non-divergent signal segments, and a "direct output" label is added to divergent signal segments. The labeling information includes the start time, end time, and type (non-divergent / divergent) of the signal segment, ensuring that the model can quickly identify and distinguish the processing objects.

[0045] In the embodiments of this disclosure, by reading the labeling information of the input signal in real time, only the non-divergent signal segments with the labels to be processed are processed: the digital predistortion model adjusts the amplitude, phase, and other characteristics of the non-divergent signal segments according to the model parameters obtained from its own training (i.e., the nonlinear compensation law learned based on samples of the non-divergent signal segments). (For example, pre-amplification is performed on sub-segments with higher amplitudes, and the phase shift trend is reversed) to generate a first nonlinear signal segment that complements the nonlinear characteristics of the signal amplifier. During the processing, the digital predistortion model only calls the algorithm logic adapted to the non-divergent signal (because it is not trained with divergent samples, there are no parameters for processing divergent signals), ensuring processing accuracy and efficiency.

[0046] In the embodiments of this disclosure, for divergent signal segments with direct output markers, the digital predistortion model directly preserves and transmits their original signal characteristics (amplitude, phase, timing, etc.) to the output of the digital predistortion model. The output signal integration involves splicing and integrating the first nonlinear signal segment processed by the digital predistortion model and the directly output divergent signal segment according to the original timing of the input signals: ensuring that the two signal segments are continuous on the time axis (without overlap or gaps) and that the signal interface parameters (such as impedance and sampling rate) are consistent, ultimately forming an output signal that can be directly input to a signal amplifier. During the integration process, a synchronization verification mechanism (such as timing alignment based on the signal frame header) is used to avoid timing distortion caused by splicing.

[0047] By directly outputting the divergent signal segment, the new distortions introduced by forcibly processing the divergent signal (such as abnormal amplitude amplification and phase disorder) using a model adapted to non-divergent signals are avoided, ensuring the consistency of the divergent signal segment characteristics before and after amplification and reducing interference with the overall signal transmission quality.

[0048] According to the signal processing method proposed in this disclosure, the method includes acquiring an input signal of a signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; processing the input signal using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment, wherein the digital predistortion model is trained based on samples of the non-divergent signal segment. This method achieves training of the digital predistortion model using only the non-divergent signal segment, avoiding interference from the divergent signal segment on model training, thereby improving the training effect of the digital predistortion model and enhancing the signal processing effect of the non-divergent signal segment in the input signal.

[0049] In practical applications, the determination of non-divergent signal segments and divergent signal segments can be achieved in the following ways, but are not limited to: obtaining the amplitude of the input signal; and determining the non-divergent signal segment and the divergent signal segment in the input signal based on the amplitude.

[0050] In embodiments of this disclosure, amplitude refers to the instantaneous strength or magnitude of the input signal at any given moment. For a baseband signal represented in complex form in the digital domain (typically containing in-phase and quadrature components), its amplitude refers to the modulus of the complex signal, i.e., the instantaneous level of its envelope. Amplitude is a scalar quantity that reflects the magnitude of the signal power at that moment. Amplitude is a key parameter for identifying the dynamic range and behavioral characteristics of a signal.

[0051] In the embodiments of this disclosure, preset threshold settings include an upper amplitude threshold and a fluctuation threshold. The upper amplitude threshold is determined based on the linear operating range of the signal amplifier (e.g., if the maximum input amplitude of the linear amplifier is 20 dBm, the threshold is set to 20 dBm); the fluctuation threshold is determined based on the statistical characteristics of historical non-divergent signals (e.g., if the maximum fluctuation of the non-divergent signal in the past 24 hours is 3 dBm, the fluctuation threshold is set to 3 dBm). The thresholds can be manually adjusted through the system configuration interface or automatically updated by the device based on real-time operating status (e.g., when changes in ambient temperature cause a shift in the device's linear range, the threshold is corrected accordingly). In the embodiments of this disclosure, amplitude comparison is performed by continuously comparing the smoothed real-time amplitude with a preset threshold. If the amplitude of all sampling points of a certain signal segment is less than the upper limit threshold and the amplitude difference between any two adjacent sampling points is less than the fluctuation threshold, it is initially determined to be a non-divergent signal segment. If there is at least one sampling point in a certain signal segment with an amplitude greater than or equal to the upper limit threshold, or the amplitude difference between adjacent sampling points is greater than or equal to the fluctuation threshold, it is initially determined to be a divergent signal segment. In the embodiments of this disclosure, to avoid misjudging brief normal fluctuations as divergent signal segments (such as instantaneous amplitude jumps caused by signal modulation, with a duration of <100μs), the duration of the initially determined signal segments is verified: the signal segment type is finally confirmed only when the duration of the initially determined non-divergent signal segment is ≥ the minimum effective frame length (e.g., 500μs) and the duration of the divergent signal segment is ≥ the minimum interference duration (e.g., 100μs); for segments with insufficient duration, they are classified as adjacent main signal segments (e.g., if a brief fluctuation is sandwiched in the middle of a non-divergent segment, it is merged into a non-divergent signal segment).

[0052] By accurately identifying divergent signal segments, it is ensured that the subsequent digital predistortion model is trained and processed only with non-divergent signal segments. This avoids the problem of model parameter deviation caused by the mixing of divergent signal segments in the background technology, indirectly improving the compensation accuracy of the digital predistortion model for non-divergent signal segments, and ultimately reducing the output distortion of the signal amplifier.

[0053] As a refinement of the above embodiments, when performing the step of determining the non-divergent signal segment and the divergent signal segment in the input signal based on the amplitude, it can be implemented in the following ways, but is not limited to: determining the input signal segment corresponding to the first amplitude that is less than a preset threshold as the non-divergent signal segment; and determining the input signal segment corresponding to the second amplitude that is greater than or equal to the preset threshold as the divergent signal segment.

[0054] In the embodiments of this disclosure, the first amplitude refers to the portion of the input signal amplitude that is less than a preset threshold. It is a quantitative indicator reflecting that the signal strength is within a stable range. Its characteristics are: the value is continuously distributed below the preset threshold, and the amplitude of its change over time conforms to the normal fluctuation pattern within the linear operating range of the signal amplifier (such as small amplitude changes due to modulation methods), corresponding to a stable signal state. The second amplitude refers to the portion of the input signal amplitude that is greater than or equal to a preset threshold. It is a quantitative indicator reflecting that the signal strength exceeds a stable range. Its characteristics are: the value reaches or exceeds the preset threshold, manifesting as an instantaneous peak (such as a sudden increase in amplitude caused by sudden interference) or a sustained high value (such as a high amplitude maintained due to signal source overload), corresponding to an abnormal signal state.

[0055] In the embodiments of this disclosure, the preset threshold refers to the critical value used to distinguish between stable and abnormal signals. It is an amplitude critical index determined based on the linear operating characteristics of the signal amplifier. Its value is usually equal to the minimum input amplitude at which the signal amplifier enters a nonlinear operating state (for example, if a power amplifier begins to show obvious nonlinear distortion when the input amplitude is ≥30dBm, then the preset threshold is set to 30dBm). It can be dynamically adjusted according to the device model and the working environment (such as temperature and humidity).

[0056] In the embodiments of this disclosure, the preset threshold needs to be pre-calibrated according to the hardware characteristics of the signal amplifier: Calibration stage: Test signals of different amplitudes are input to the signal amplifier through test equipment (such as signal generator, spectrum analyzer), and the distortion of the output signal of the device (such as intermodulation distortion, adjacent channel leakage ratio) is recorded. When the distortion exceeds the maximum value allowed by the communication standard (such as 30dBc), the corresponding input signal amplitude is the threshold reference; Threshold solidification: The threshold reference obtained by calibration is written into the configuration file of the signal processing system (such as the firmware parameters of the baseband processor) and supports dynamic fine-tuning - for example, when the device operating temperature rises and the linear range shrinks (such as the threshold needs to be reduced by 2dBm), the system can automatically correct the threshold through the feedback of the temperature sensor to ensure that the threshold always matches the real-time linear range of the device.

[0057] In the embodiments of this disclosure, the real-time comparison of amplitude with a preset threshold is based on the input signal amplitude (smoothed real-time amplitude) obtained in the preceding steps. This comparison is performed in real-time through hardware logic circuits or a software judgment module: for the amplitude of each sampling point, if its value is less than the preset threshold, it is marked as the first amplitude, and the timestamp of that sampling point is recorded; if its value is greater than or equal to the preset threshold, it is marked as the second amplitude, and the timestamp is also recorded. The comparison process must be synchronized with signal sampling (e.g., when the sampling rate is 100 MSps, the comparison frequency is also 100 MSps) to ensure that no instantaneous amplitude changes are missed. Signal segment determination based on continuous amplitude is based on either consecutive first amplitudes or consecutive second amplitudes to divide the signal into segments: In the embodiments of this disclosure, the non-divergent signal segment is determined as follows: when the amplitude of N consecutive sampling points (N≥ the number of points corresponding to the signal sampling period, such as 10 sampling points, corresponding to 100ns) are all the first amplitude, these sampling points are determined to constitute a continuous non-divergent signal segment, with the start time being the timestamp of the first first amplitude sampling point and the end time being the timestamp of the last first amplitude sampling point. In the embodiments of this disclosure, the divergent signal segment is determined as follows: when at least one second amplitude exists in the amplitudes of M consecutive sampling points (M≥1, since a single second amplitude can reflect an anomaly), these sampling points are determined to constitute a continuous divergent signal segment. If a small number (e.g., <3) of first amplitude sampling points (due to transient drops caused by noise) are interspersed between adjacent second amplitude sampling points, they are still merged into the same divergent signal segment to avoid misjudging transient drops as non-divergent signal segments.

[0058] In the embodiments of this disclosure, to avoid errors in segmentation due to amplitude fluctuations at the signal segment boundaries, the precise confirmation of signal segment boundaries involves secondary verification of the start and end boundaries of the signal segments: For the start boundary of a non-divergent signal segment, it is necessary to confirm that the amplitude of the previous sampling point is the second amplitude (i.e., transitioning from a divergent state to a non-divergent state); for the end boundary, it is necessary to confirm that the amplitude of the next sampling point is the second amplitude (i.e., transitioning from a non-divergent state to a divergent state). For the start boundary of a divergent signal segment, it is necessary to confirm that the amplitude of the previous sampling point is the first amplitude; for the end boundary, it is necessary to confirm that the amplitude of the next sampling point is the first amplitude. After the boundary verification is passed, information such as the signal segment type (non-divergent / divergent), start time, and end time is stored in the signal segment information table for subsequent use by the digital predistortion model.

[0059] By monitoring the amplitude of the input signal in real time and dividing the signal into segments based on a preset threshold, it is possible to effectively distinguish between the stable region and the region of violent fluctuations in the signal, thereby enabling targeted selection of algorithms in subsequent processing and improving the stability of the signal output.

[0060] In practical applications, digital predistortion models need to be trained before they can be used. The training method for digital predistortion models can be implemented in, but is not limited to, the following ways: obtaining the model parameters of the initial digital predistortion model and obtaining the first nonlinear characteristic of the signal amplifier; adjusting the model parameters according to the non-divergent signal segment sample and the first nonlinear characteristic to obtain the digital predistortion model.

[0061] In embodiments of this disclosure, the initial digital predistortion model is a mathematical model framework that has not been trained or has only undergone basic initialization. It possesses the ability to simulate nonlinear characteristics but has not yet learned the precise inverse characteristics to match the target signal amplifier. The structure of the initial digital predistortion model can be a polynomial model, a lookup table model, a neural network model, or a combination thereof. Its initial state means that its model parameters are unverified; for example, the coefficients of the polynomial are set to zero, random decimals, or a simple linear unity gain, and the entries in the lookup table are flat or have simple gradients.

[0062] In the embodiments of this disclosure, model parameters refer to variable variables that define the behavioral characteristics of a digital predistortion model. The specific form of model parameters differs in different model structures: in a multinomial model, model parameters are the coefficients of each term; in a lookup table model, model parameters are the output value corresponding to each index address; in a neural network model, model parameters are the connection weights and biases between neurons. The training process is essentially the process of adjusting model parameters.

[0063] In embodiments of this disclosure, the first nonlinear characteristic refers to the inherent, uncorrected, raw nonlinearity of the signal amplifier. The first nonlinear characteristic is a descriptive set of features that characterizes the degree of distortion of the signal amplifier's output signal relative to its input signal.

[0064] In the embodiments of this disclosure, a preset initial digital predistortion model (a general model designed for similar signal amplifiers (such as power amplifiers in the same frequency band)) is called from the model library of the signal processing system. Its parameters include default values ​​such as basic gain coefficient and phase offset (e.g., the gain coefficient is initially set to 1.2 and the phase offset is initially set to -5°). The initial parameters are initially adapted according to the basic parameters of the current signal amplifier (such as the operating frequency band and rated power). For example, if the device operates in a high-frequency band (such as 28GHz), the bandwidth parameter is adjusted to match the frequency range of the high-frequency signal; if the rated power of the device is low, the initial gain coefficient is reduced to avoid overcompensation.

[0065] In the embodiments of this disclosure, a test signal with known characteristics (such as a sinusoidal signal with gradually changing amplitude or a multi-carrier combined signal) is input to the signal amplifier. The amplitude range of the test signal covers the linear and nonlinear operating ranges of the device (e.g., gradually increasing from 5dBm to 30dBm). The output signal of the device is acquired using devices such as a spectrum analyzer and a vector signal analyzer, and the relationship between the amplitude and phase of the output signal and the input signal is recorded. The differences between the input and output signals are compared to extract the specific manifestation of the first nonlinear characteristic: for example, by analyzing the ratio of the output amplitude to the input amplitude, the saturation threshold of amplitude nonlinearity is determined (e.g., the output gain decreases significantly when the input is ≥25dBm). By comparing the phase difference between the input and output signals, a phase nonlinearity curve is plotted (e.g., for every 5dBm increase in input amplitude, the phase offset increases by an additional 3°). By analyzing the influence of the input signal on the output at different times, the time constant of the memory effect is determined (e.g., the signal in the first 5μs has the greatest impact on the current output).

[0066] In the embodiments of this disclosure, the model parameters are adjusted based on non-divergent signal segment samples and the first nonlinear characteristic to make the nonlinear characteristics generated by the model complementary to the first nonlinear characteristic. This is achieved through multiple iterations: non-divergent signal segment samples are input into the initial digital predistortion model to obtain the predistortion signal output by the model, and the nonlinear characteristics of the signal (such as the pre-adjusted amplitude and phase change patterns) are recorded; the nonlinear characteristics of the model output signal are compared with the first nonlinear characteristics of the signal amplifier to determine the direction of parameter adjustment—for example, if the first nonlinear characteristic of the device is that the output gain decreases by 10% when the input amplitude is ≥20dBm, then the model needs to adjust the gain coefficient to pre-increase the gain of the predistortion signal in this amplitude range by 10% to offset the gain decrease of the device; if the device has a phase lead of 5°, then the model needs to adjust the phase offset to a phase lag of 5°.

[0067] In the embodiments of this disclosure, the process of repeated sample input → feature comparison → parameter fine-tuning is carried out. After each adjustment, the processing effect of the model on non-divergent signal segment samples is retested until the nonlinear characteristics of the model output signal and the first nonlinear characteristics of the device form a stable inverse complementary relationship (e.g., when the amplitude distortion rate of the device is ≤2%, the pre-compensation error of the model is ≤0.5%). The finally adjusted parameters (e.g., the optimized gain coefficient and phase offset) are written into the model to form a digital predistortion model adapted to the current device and non-divergent signals.

[0068] By combining non-divergent signal segment samples with the nonlinear characteristics of the signal amplifier, the parameters of the digital predistortion model can be precisely adjusted, thereby providing more effective distortion compensation and reducing distortion during signal amplification.

[0069] Figure 2A flowchart of a signal processing method proposed in this disclosure is further shown. Based on Figure 1 The embodiments shown further explain the above embodiments. Figure 2 This may include the following steps: Step S201: Using the initial digital predistortion model, perform nonlinear processing on the non-divergent signal segment sample to obtain the second nonlinear signal segment.

[0070] In the embodiments of this disclosure, nonlinear processing refers to the targeted adjustment of the amplitude, phase, and other characteristics of the non-divergent signal segment samples by the initial digital predistortion model or the digital predistortion model according to its own preset parameters, in order to simulate the process of canceling the nonlinear distortion of the signal amplifier. Specifically, this includes: pre-attenuating the amplitude segment in the sample that is over-gained by the signal amplifier, and pre-reverse-shifting the signal segment whose phase is shifted by the device, so that the processed signal has a tendency to complement the nonlinear characteristics of the signal amplifier (although the complementary effect has not yet reached its optimum due to the unoptimized initial parameters).

[0071] In the embodiments of this disclosure, the second nonlinear signal segment refers to the signal segment obtained after the non-divergent signal segment sample has undergone nonlinear processing by the initial digital predistortion model. Its core feature is that it carries traces of nonlinear compensation from the initial predistortion model (such as pre-adjustment of specific amplitude segments and pre-correction of specific phases). However, because the model parameters are not adapted to the current equipment, the complementarity between its nonlinear characteristics and the first nonlinear characteristics of the signal amplifier is weak (such as insufficient or excessive compensation). The second nonlinear signal segment is a key benchmark for subsequently comparing the model compensation effect with the actual needs of the equipment, and is used to guide the adjustment direction of the model parameters.

[0072] In the embodiments of this disclosure, to ensure the accuracy of the processing effect, the non-divergent signal segment samples need to undergo standardization processing before being input into the initial model: Format unification: Non-divergent signal segment samples from different sources (such as samples collected at different times) are converted into a unified digital signal format (such as the same sampling rate and quantization bit depth) to avoid processing deviations caused by format differences; Noise filtering: High-frequency noise mixed in the samples (such as electromagnetic interference introduced during sampling) is removed by a low-pass filter, while retaining the core signal characteristics of the samples (such as modulation information and amplitude change trends); Segmentation and marking: The samples are segmented and marked in time sequence (such as each 1ms segment) to facilitate subsequent tracking of the processing effect of each segment and to provide a refined basis for parameter adjustment.

[0073] In the embodiments of this disclosure, the algorithm logic (including preset parameters) of the initial model is loaded into the signal processing unit (such as a dedicated digital signal processor) through a hardware interface to ensure that the model can respond to sample input in real time; parameter initialization: based on the basic characteristics of the non-divergent signal segment sample (such as average amplitude and center frequency), the core parameters of the model are initially set - for example, if the average amplitude of the sample is 15dBm, the initial gain coefficient of the model is set to the default value adapted to the amplitude (such as 1.1); if the sample is a modulated signal (multi-carrier characteristics), the phase compensation logic for multi-carrier signals in the model is enabled.

[0074] In the embodiments of this disclosure, the initial digital predistortion model makes differentiated adjustments to sub-segments of different amplitudes in the non-divergent signal segment sample based on the initial gain coefficient. For example, for high-amplitude sub-segments close to a preset threshold (e.g., 20dBm), a moderate attenuation (e.g., 10% attenuation) is applied according to the initial parameters to simulate pre-compensation for device amplitude saturation. For low-amplitude sub-segments (e.g., 5dBm), a small gain (e.g., 5% gain) is maintained to avoid overcompensation leading to insufficient signal strength. For phase changes in the sample caused by the modulation method, the model makes a reverse correction based on the initial phase offset parameter. For example, if the phase of a certain sub-segment in the sample tends to lead by 3° over time, the model applies a pre-correction of lag of 3° to it to offset the additional phase offset generated by the device. In the embodiments of this disclosure, the processing procedure is strictly synchronized with the input timing of the non-divergent signal segment samples (e.g., when the sampling rate is 100MSps, the processing rate is also 100MSps), ensuring that the adjustment of each sampling point corresponds to the instantaneous characteristics of the sample, and avoiding processing distortion caused by timing misalignment.

[0075] The second nonlinear signal segment is a direct result of the initial model processing non-divergent samples. Its nonlinear characteristics (such as amplitude adjustment rules and phase correction trends) can intuitively reflect the compensation capability of the initial model. By comparing this signal segment with the first nonlinear characteristic of the signal amplifier, the deviation of the initial parameters (such as insufficient or excessive compensation) can be clearly located, providing a clear direction for subsequent parameter adjustments (such as increasing the gain coefficient of a certain interval or correcting the phase shift).

[0076] Step S202: Determine the second nonlinear feature of the initial digital predistortion model based on the non-divergent signal segment sample and the second nonlinear signal segment.

[0077] In the embodiments of this disclosure, the second nonlinear characteristic refers to the nonlinear processing behavior exhibited by the initial model when processing samples in the non-divergent signal range. This includes, but is not limited to, the model's modification pattern of the amplitude, phase, and other characteristics of the input signal. Specifically, it includes, but is not limited to: the model's gain adjustment behavior for signals in different amplitude ranges (e.g., +8% gain for signals of 10-15dBm and -5% gain for signals of 15-20dBm), the trend of phase correction (e.g., the phase correction increases linearly with the increase of the input signal frequency), and the response characteristics to signal timing (e.g., whether there is processing delay and the duration of the delay). This characteristic is determined by the preset parameters of the initial model and is the core basis for subsequent comparison with the first nonlinear characteristic of the signal amplifier.

[0078] In the embodiments of this disclosure, to ensure the accuracy of feature analysis, the two signal segments must first be strictly aligned in the time dimension: based on the timestamp information of the signal segments (such as the absolute time stamp of the sampling points), each sampling point of the second nonlinear signal segment is matched one by one with the corresponding original sampling point in the sample of the non-divergent signal segment (such as the nth sampling point of the second nonlinear signal segment corresponding to the nth sampling point of the sample); if there is a processing delay (such as signal output lag caused by model processing), the timing deviation is eliminated by time offset calibration (such as shifting the second nonlinear signal segment as a whole to the corresponding delay time) to ensure that the original signal and the processed signal at the same time are compared.

[0079] In the embodiments of this disclosure, the feature dimension extraction extracts core dimensions for analyzing nonlinear features from the two aligned signal segments, including but not limited to: amplitude variation dimension: calculating the ratio of the processed amplitude to the original amplitude (i.e., gain factor) for each corresponding sampling point, and statistically analyzing the distribution of this ratio according to the original amplitude range (e.g., 5-10dBm, 10-15dBm), reflecting the amplitude adjustment characteristics of the model for signals of different intensities; phase difference dimension: calculating the difference between the processed phase and the original phase (i.e., phase correction amount) for each corresponding sampling point, and analyzing the trend of this difference with the frequency and amplitude of the original signal (e.g., whether the phase correction amount changes linearly or nonlinearly when the frequency of the original signal increases), reflecting the phase processing law of the model; time response dimension: analyzing the processing effect of abrupt changes in the signal segment (e.g., the moment when the amplitude of the original signal suddenly increases by 5dBm), and statistically analyzing the delay time from receiving the abrupt signal to outputting a stable processing result, reflecting the response speed of the model to dynamic signals.

[0080] In the embodiments of this disclosure, the quantification of differences and the extraction of characteristic patterns involve quantifying and analyzing the differences in each dimension to extract the nonlinear processing patterns of the model: For the amplitude variation dimension: by statistically analyzing the average gain multiple within different original amplitude ranges, an amplitude-gain curve is formed—for example, if the average gain multiple is 1.08 (i.e., 8% gain) when the original amplitude is 10-15dBm; and the average gain multiple is 0.95 (i.e., 5% attenuation) when the original amplitude is 15-20dBm, then this curve directly reflects the amplitude nonlinearity characteristics of the model; For the phase difference dimension: by fitting the functional relationship between the phase correction amount and the original signal frequency and amplitude (such as linear fitting, piecewise fitting), a frequency / amplitude-phase correction curve is formed—for example, if the fitting shows that for every 1GHz increase in the original signal frequency, the phase correction amount increases by an average of 2°, then this pattern represents the phase nonlinearity characteristics of the model; For the time response dimension: by statistically analyzing the delay time after multiple signal abrupt changes, the average value is taken as the response delay characteristic of the model (e.g., an average delay of 3 sampling periods, corresponding to 30ns).

[0081] In the embodiments of this disclosure, the integration of the second nonlinear feature integrates the rules extracted from each dimension into a complete description of the second nonlinear feature, forming structured data (such as feature tables and feature curve sets), including: amplitude nonlinear parameters: the gain multiple range of each amplitude interval, the inflection point of the gain curve (such as the critical amplitude at which the gain turns from positive to negative); phase nonlinear parameters: the correlation coefficient between the phase correction amount and the frequency / amplitude, the maximum phase correction amount; and time response parameters: the average response delay, the maximum delay fluctuation range. The integrated feature must be able to comprehensively reflect the nonlinear processing capability of the initial model for non-divergent signal segment samples, providing a clear benchmark for subsequent comparison with the first nonlinear feature of the signal amplifier.

[0082] By comparing the original samples with the processed signal segments, the processing rules of the model are quantified from multiple dimensions such as amplitude, phase, and time response. This avoids the one-sidedness of inferring features solely from model parameters (e.g., the parameter values ​​are the same, but the actual processing effect varies due to different signal types), and enables the second nonlinear feature to truly reflect the actual performance of the model.

[0083] Step S203: Adjust the model parameters according to the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model.

[0084] In the embodiments of this disclosure, based on the first and second nonlinear characteristics, the differences between the two can be quantitatively analyzed from three core dimensions: amplitude, phase, and memory effect, to clarify the target parameters that need to be adjusted: Amplitude dimension difference: Compare the gain performance of the device and the initial model in the same amplitude range—for example, the average gain deviation of the device in the 15-20dBm amplitude range is +12% (i.e., the output is 12% higher than the linear amplification, which is distortion), while the average gain compensation of the initial model in this range is only -8% (i.e., it can only pre-attenuate 8%, which is insufficient compensation), the difference between the two is 4%, and the gain coefficient of this range needs to be adjusted accordingly; Phase dimension Phase difference: Compare the phase performance of the device and the initial model at the same frequency—for example, the device leads the phase by 8° (distortion) at 28GHz, while the initial model can only provide a -5° phase correction at the same frequency (i.e., 5° pre-lag, insufficient correction), the difference is 3°, and the frequency-phase correction coefficient needs to be adjusted; Memory effect difference: Compare the response of the device and the initial model to historical signals—for example, the device is affected by a strong signal in the previous 5μs, and the current signal gain deviation is +5% (memory distortion), while the memory effect compensation window of the initial model is only 3μs (cannot cover the 5μs influence duration, compensation is missing), the memory effect compensation window duration needs to be adjusted.

[0085] In embodiments of this disclosure, after difference analysis, a structured list of dimensions-parameters-difference values ​​(e.g., amplitude dimension-15-20dBm gain coefficient-difference 4%) is formed as a clear target for parameter adjustment. Based on the principle of reverse complementarity (model characteristics must be opposite to device characteristics to offset distortion), and combined with the above difference values, determine the adjustment direction and specific adjustment amount for each parameter: Adjustment direction: If the device exhibits gain overshoot (+deviation), the model needs to enhance attenuation compensation (reduce the gain coefficient, i.e., adjust in the negative direction); if the device exhibits phase lead (+deviation), the model needs to enhance lag correction (reduce the phase correction coefficient, i.e., adjust in the negative direction); if the device has a long memory effect window, the model needs to extend the compensation window (increase the window duration parameter); Adjustment amount calculation: Determine the adjustment range based on the difference value, and prioritize step-by-step adjustment (avoid over-adjustment in a single step) – for example, if the amplitude dimension difference is 4%, adjust the gain coefficient in the 15-20dBm range from the current -8% (compensation amount) to -10% (first compensate 2%, reserving 2% iteration space); if the phase dimension difference is 3°, adjust the phase correction coefficient corresponding to 28GHz from -5° to -7° (first compensate 2°); adjust the memory effect window from 3μs to 4μs (first extend 1μs).

[0086] In the embodiments of this disclosure, after parameter adjustment, the model features and device features are optimally complementary through an iterative process of test-verification-readjustment: Iterative test: Input non-divergent signal segment samples into the model after parameter adjustment to obtain a new second nonlinear feature (i.e., the adjusted model processing characteristics). In the embodiments of this disclosure, the matching degree between the new second nonlinear feature and the first nonlinear feature is compared. If the amplitude dimension difference decreases from 4% to 1% (less than the preset threshold of 2%), the phase difference decreases from 3° to 0.5% (less than the preset threshold of 1°), and the memory effect compensation window covers the device requirements (the device memory distortion decreases to less than 1% at 4μs), then the current adjustment is determined to be up to standard. If it is not up to standard (e.g., the amplitude difference is still 3%), then the difference analysis-adjustment parameter steps are repeated (e.g., the gain coefficient is further adjusted from -10% to -11%). In the embodiments of this disclosure, iteration stops when the feature differences in all dimensions are less than preset thresholds (e.g., amplitude difference ≤ 2%, phase difference ≤ 1%, memory distortion ≤ 1%), and the results of two consecutive iterations are stable (difference fluctuation ≤ 0.5%). The iteratively optimized parameters (e.g., 15-20dBm gain coefficient -11%, 28GHz phase correction coefficient -7°, memory compensation window 4μs) are written into the model configuration file and solidified into the signal processing unit to form the final digital predistortion model. Simultaneously, a parameter adjustment log is recorded (including the difference value, adjustment amount, and verification results for each adjustment) to facilitate parameter updates during subsequent digital predistortion model maintenance (e.g., re-optimization after equipment aging).

[0087] The parameters are adjusted based on the characteristics of the non-divergent signal segment samples, without introducing interference from divergent signals. This ensures that the adjusted model is optimized only for valid signals (non-divergent segments), avoiding misprocessing of divergent signals and maintaining the stability of subsequent real-time signal processing.

[0088] As a refinement of step S203, when performing the step of adjusting the model parameters according to the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model, it can be implemented in the following ways, but not limited to: adjusting the model parameters according to the trend of making the first nonlinear feature and the second nonlinear feature negatively correlated to obtain the digital predistortion model.

[0089] In the embodiments of this disclosure, a negative correlation trend refers to the opposite trends of the first nonlinear characteristic and the second nonlinear characteristic. That is, when the distortion of the signal amplifier intensifies as a certain signal characteristic increases, the pre-compensation capability of the model increases synchronously with the increase of that characteristic (in opposite directions). For example, if the amplitude-gain deviation of the signal amplifier is positively correlated (the larger the amplitude, the larger the deviation), then the amplitude-pre-attenuation of the model must also be positively correlated (the larger the amplitude, the more pre-attenuation), forming a negative correlation where signal amplifier deviation + model compensation = approaching zero; if the frequency-phase shift of the signal amplifier is positively correlated (the higher the frequency, the larger the shift), then the frequency-phase lag of the model must also be positively correlated (the higher the frequency, the more lag), forming a reverse cancellation.

[0090] In the embodiments of this disclosure, the changing trends of the first and second nonlinear characteristics are first converted into comparable quantitative indicators to clarify whether the current trend deviates from a negative correlation: Trend dimension selection: Focusing on core characteristic dimensions strongly correlated with signal amplifier distortion, such as input amplitude - device gain deviation, input frequency - device phase offset, historical signal strength - current distortion residue (memory effect); Trend quantification method: For each dimension, by fitting the relationship curve between signal characteristic parameters (such as amplitude) and nonlinear characteristic parameters (such as device gain deviation, model pre-compensation amount), the trend slope is extracted—for example, the slope of the device amplitude-gain deviation curve. A positive gain rate (gain deviation increases by 3% for every 5dBm increase in amplitude) indicates a positive correlation trend. The initial model's amplitude-pre-compensation curve has a positive slope but a small absolute value (pre-compensation increases by only 1% for every 5dBm increase in amplitude), indicating that the trend direction is correct but the strength is insufficient, and no effective negative correlation has been formed. Trend difference assessment: Calculate the matching degree between the device trend slope and the model trend slope—if the device slope is +3% / 5dBm, the model slope should be -3% / 5dBm (equal absolute values, opposite directions), at which point the matching degree is 100%; if the model slope is -1% / 5dBm, the matching degree is only 33%, and the trend strength needs to be enhanced.

[0091] In the embodiments of this disclosure, based on the results of quantitative analysis, a clear negative correlation target is set for each dimension: Directional Objective: The model trend and the device trend must be opposite—if the device trend is signal feature enhancement → increased distortion (positive correlation), then the model trend must be signal feature enhancement → pre-compensation enhancement (positively correlated compensation direction, i.e., opposite to the distortion direction); if the device shows a negative correlation trend in a certain dimension (e.g., signal frequency increase → phase shift decrease), then the model must show a negative correlation trend of signal frequency increase → phase lag correction decrease; Strength Objective: The absolute value of the model trend slope must match the absolute value of the device trend slope (±10% allowed). Error) - For example, if the device amplitude-gain deviation slope is +3% / 5dBm, then the model amplitude-pre-compensation slope needs to be set to -3% / 5dBm (error tolerance -0.3%~+0.3%); Coverage target: The negative correlation trend needs to cover the entire characteristic range of the non-divergent signal segment - For example, if the amplitude range of the non-divergent signal is 5-20dBm, then the negative correlation trend of the model needs to be stable within 5-20dBm to avoid local trend reversal (such as suddenly becoming positively correlated above 15dBm).

[0092] In the embodiments of this disclosure, model parameters are adjusted to correct the trend slope for each dimension of the target. The core is to strengthen the trend strength and calibrate the trend direction: Direction calibration: If the model trend direction does not match the target (e.g., the device is positively correlated, but the model is negatively correlated), adjustment is achieved by reversing the direction of the parameters—for example, changing the amplitude-pre-compensation coefficient from a positive value (gain) to a negative value (attenuation), so that the model trend changes from amplitude increase → pre-gain increase (exacerbating distortion) to amplitude increase → pre-attenuation increase (compensating for distortion); Strength enhancement: If the model trend strength is insufficient (the absolute value of the slope is less than the target), it is achieved by amplifying the influence weight of the parameters. Adjustments—For example, if the device slope is +3% / 5dBm and the current model slope is -1% / 5dBm, the amplitude segment compensation weight parameter needs to be adjusted from 0.5 to 1.5, so that the rate at which the pre-compensation amount increases with the amplitude increases to the target slope of -3% / 5dBm; Range expansion: If the model trend fails in some feature ranges (such as a sudden drop in slope in the 18-20dBm interval), full coverage can be achieved by refining the parameter segments—for example, the original single compensation parameter of 15-20dBm can be split into two segment parameters of 15-18dBm and 18-20dBm, and the slopes of each segment can be adjusted to the target value to ensure the stability of the trend across the entire range.

[0093] In the embodiments of this disclosure, the adjusted model is verified to meet the target through simulation testing and trend reproduction: Simulation testing: A test signal covering the characteristic range of the non-divergent signal segment (e.g., an amplitude of 5-20dBm and a frequency of 20-30GHz) is generated and input into the signal amplifier and the adjusted model respectively, and the first nonlinear characteristic of the device and the second nonlinear characteristic of the model are collected; Trend reproduction check: The trend curves of the two are refitted to verify whether they meet the requirements of opposite direction, intensity matching, and full range coverage—for example, if the slope of the device amplitude-gain deviation curve is +3% / 5dBm and the slope of the model amplitude-pre-compensation curve is -2.8% / 5dBm (within the error range), and the trend is maintained in the 5-20dBm range, then the negative correlation trend is determined to be valid; Iterative optimization: If a certain dimension does not meet the target (e.g., the slope of the model in the 18-20dBm range is -1.5% / 5dBm), the parameter adjustment-test process is repeated until all dimensions meet the negative correlation target. Finally, the qualified parameters are solidified to obtain the digital predistortion model.

[0094] By adjusting the trend of negative correlation between the first nonlinear characteristic and the second nonlinear characteristic, it is possible to accurately compensate for the nonlinear distortion of the device under different power conditions, especially in the saturation region or high power region of the device, and significantly reduce the signal distortion rate.

[0095] In the embodiments of this disclosure, in order to facilitate a better understanding of the application scenarios of signal processing, such as Figure 3 As shown, Figure 3 This diagram illustrates an application scenario of signal processing provided by an embodiment of the present disclosure. The signal processing method in this embodiment is applied to a digital pre-distortor (DPD). The digital pre-distortor is a nonlinear module placed in the digital radio frequency front end for calibrating the radio frequency power amplifier (PA). The radio frequency power amplifier is the signal amplifier in this embodiment.

[0096] In the embodiments of this disclosure, the digital signal transmitted from the baseband (i.e., the input signal) is first processed by the pre-trained DPD coefficients (i.e., model parameters in the digital predistortion model) in the DPD module, generating a pre-calibrated signal that is the inverse of the PA's nonlinear output signal. Then, it is converted from a digital signal to an analog signal by a digital-to-analog converter (DAC). Finally, the converted analog signal is up-converted to a specified center frequency by a quadrature modulator (QM) and then sent to the PA. Under ideal DPD modeling conditions, the nonlinearity generated by the PA and the pre-calibrated nonlinearity generated by the DPD cancel each other out, and the signal at the PA's output port is a linear amplification of the DPD's input signal, thereby ensuring the linearity of the entire RF system.

[0097] In the embodiments of this disclosure, obtaining the calibration coefficients (i.e., model parameters in the digital predistortion model) in the DPD requires first sampling and feeding back the analog output signal of the PA containing the PA's nonlinear component. This signal is then down-converted by an orthogonal demodulator to a center frequency consistent with the ideal input signal corresponding to the DPD, and further processed by an analog-to-digital converter while maintaining consistency between the sampled signal and the ideal input signal. During this process, direct learning structures or indirect learning structures can be used to update the module coefficients (i.e., model parameters in the digital predistortion model) in the DPD.

[0098] In embodiments of this disclosure, the nonlinear modeling of PA based on a linear model can be represented in the following matrix form:

[0099] in, This indicates the output signal of PA. The coefficient vector represents the linear digital predistortion model that models the output signal of PA. This represents the basis function matrix formed by the ideal input signal passing through a digital predistortion model.

[0100] This can be further expressed as:

[0101] in, To take a value for a basis function, For the input signal, the subscripts M and K typically represent the memory depth and the nonlinear order, respectively. The specific form of its manifestation depends on the actual model used.

[0102] The model used can be transformed into a form capable of processing complex baseband signals based on the location of the predistorter, as shown in the following equation:

[0103] in, It refers to time n. The output value of the model used , , , These represent different memory depth values. , For natural numbers, , These represent different nonlinear orders. , , For different model coefficients, , For different input signals, This represents the position of the k-th segment node.

[0104] Further organizing the above, we can obtain the following expression:

[0105] in, , , For different memory depth values, It is a nonlinear order. , For different model coefficients, , , , , For natural numbers, , , For different input signals, It refers to a nonlinear mapping function. The expression for the nonlinear mapping function is as follows:

[0106] in, It is a nonlinear mapping function. For input signal, This represents the segment node position.

[0107] For scenarios that are not the highest score range, This can be expanded as follows:

[0108] in, It is a nonlinear mapping function. For input signal, , , For different segment node positions, Otherwise.

[0109] The highest score segment position needs to be processed into the following form:

[0110] in, It is a nonlinear mapping function. For input signal, , For different segment node positions, Otherwise.

[0111] In the embodiments of this disclosure, the processing method for the highest segment can ensure that the high-power input in the highest segment of the segment passes through the model in a direct manner. Mathematically, this part of the data is not considered to participate in the modeling, thereby avoiding high-power divergence that leads to signal divergence.

[0112] In the embodiments of this disclosure, it is assumed that the starting position of the divergence point of PA is P, which can be obtained by averaging after batch measurements in a laboratory environment. Furthermore, it is assumed that the length of the training data for updating the DPD coefficients in small batches is N. At this point, it is first necessary to calculate the amplitude of the sampled signal, and then obtain a calibration signal that meets the constraints using a sliding judgment method. Alternatively, depending on the complexity of the algorithm implementation, a simpler slicing method can be used to obtain the optimal training data. The ultimate goal of the optimal signal gate is to find a training signal set of length N that meets the following constraints to ensure the robustness of the algorithm: max(|x(n)|,…,|x(n+N-1)|) <P Where |x(n)…x(n+N-1) are different input signals, and P is the position of the input signal.

[0113] In the embodiments disclosed herein, special training data can also be generated in a laboratory environment as a method for obtaining coefficients during actual implementation. However, in actual business scenarios, it is still necessary to select the optimal signal set in a sliding or slicing manner.

[0114] In the embodiments of this disclosure, in order to facilitate a better understanding of the training process of the digital predistortion model, such as Figure 4 As shown, Figure 4 This is a flowchart illustrating a training method for a digital predistortion model provided in an embodiment of this disclosure. After modifying the above formula and configuring the hyperparameters, it can be used... Figure 4Under the indirect learning structure shown, the update expression formula for the digital predistortion coefficients, obtained using the least squares method, is as follows:

[0115] in, This represents the currently calculated digital predistortion coefficients, where x represents the input signal of the digital predistortion module. This represents the basis function matrix according to the input signal x. This represents the Hermitian transpose. Finally, the calculated digital predistortion coefficients are configured into the digital predistorter to achieve the goal of calibrating the nonlinearity of the RF power amplifier and improving signal quality. The training method can also be implemented under other learning structures, without constraints on the specific implementation architecture.

[0116] Corresponding to the signal processing method described above, the present invention also proposes a signal processing apparatus. Since the apparatus embodiments of the present invention correspond to the method embodiments described above, details not disclosed in the apparatus embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0117] Figure 5 This is a schematic diagram of the structure of a signal processing device 400 provided in an embodiment of the present disclosure. The signal processing device includes: Acquisition unit 41 is used to acquire the input signal of the signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; The processing unit 42 is used to process the input signal using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment, and the digital predistortion model is trained based on samples of the non-divergent signal segment.

[0118] According to the signal processing apparatus disclosed herein, the apparatus includes acquiring an input signal of a signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; processing the input signal using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment, wherein the digital predistortion model is trained based on samples of the non-divergent signal segment. This achieves training of the digital predistortion model using only the non-divergent signal segment, avoiding interference from the divergent signal segment on model training, thereby improving the training effect of the digital predistortion model and enhancing the signal processing effect of the non-divergent signal segment in the input signal.

[0119] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 6 As shown, the device further includes: The acquisition unit 41 is further configured to acquire the amplitude of the input signal; The determining unit 43 is used to determine the non-divergent signal segment and the divergent signal segment in the input signal based on the amplitude.

[0120] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 6 As shown, the determining unit 43 is further configured to: The input signal segment corresponding to the first amplitude value that is less than a preset threshold is determined as the non-divergent signal segment; The input signal segment corresponding to the second amplitude that is greater than or equal to the preset threshold is determined as the divergent signal segment.

[0121] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 6 As shown, the device further includes: The acquisition unit 41 is further configured to acquire the model parameters of the initial digital predistortion model and acquire the first nonlinear characteristic of the signal amplifier; The adjustment unit 44 is used to adjust the model parameters according to the non-divergent signal segment sample and the first nonlinear feature to obtain the digital predistortion model.

[0122] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 6 As shown, the adjustment unit 44 includes: Processing module 441 is used to perform nonlinear processing on the non-divergent signal segment sample using the initial digital predistortion model to obtain a second nonlinear signal segment; The determining module 442 is used to determine the second nonlinear feature of the initial digital predistortion model based on the non-divergent signal segment sample and the second nonlinear signal segment; The adjustment module 443 is used to adjust the model parameters according to the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model.

[0123] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 6 As shown, the adjustment module 443 is further used for: The model parameters are adjusted according to the trend of making the first nonlinear feature negatively correlated with the second nonlinear feature to obtain the digital predistortion model.

[0124] Since the apparatus provided in this embodiment corresponds to the methods provided in the above embodiments, the implementation of the methods is also applicable to the apparatus provided in this embodiment, and will not be described in detail in this embodiment.

[0125] The methods and apparatus provided in the embodiments of this application have been described above. To implement the functions of the methods provided in the embodiments of this application, the electronic device may include a hardware structure and software modules, and may implement the above functions in the form of a hardware structure, software modules, or a hardware structure plus software modules. One of the above functions may be executed in the form of a hardware structure, software modules, or a hardware structure plus software modules.

[0126] Figure 7 This is a block diagram illustrating an electronic device 500 for implementing the above-described signal processing method according to an exemplary embodiment. For example, the electronic device 500 may be applied to servers, cloud environments, operational service platforms, various computer platforms, terminal systems, and web page systems.

[0127] Reference Figure 7 The electronic device 500 may include one or more of the following components: processing component 502, memory 504, power supply component 506, multimedia component 508, audio component 510, input / output (I / O) interface 512, sensor component 514, and communication component 516.

[0128] Processing component 502 typically controls the overall operation of electronic device 500, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 502 may include one or more processors 520 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 502 may include one or more modules to facilitate interaction between processing component 502 and other components. For example, processing component 502 may include a multimedia module to facilitate interaction between multimedia component 508 and processing component 502.

[0129] Memory 504 is configured to store various types of data to support the operation of electronic device 500. Examples of this data include instructions for any application or method operating on electronic device 500, contact data, phonebook data, messages, pictures, videos, etc. Memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0130] Power supply component 506 provides power to various components of electronic device 500. Power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 500.

[0131] Multimedia component 508 includes a screen that provides an output interface between electronic device 500 and user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 508 includes a front-facing camera and / or a rear-facing camera. When electronic device 500 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0132] Audio component 510 is configured to output and / or input audio signals. For example, audio component 510 includes a microphone (MIC) configured to receive external audio signals when electronic device 500 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 504 or transmitted via communication component 516. In some embodiments, audio component 510 also includes a speaker for outputting audio signals.

[0133] I / O interface 512 provides an interface between processing component 502 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0134] Sensor assembly 514 includes one or more sensors for providing state assessments of various aspects of electronic device 500. For example, sensor assembly 514 may detect the on / off state of electronic device 500, the relative positioning of components such as the display and keypad of electronic device 500, changes in position of electronic device 500 or a component of electronic device 500, the presence or absence of user contact with electronic device 500, orientation or acceleration / deceleration of electronic device 500, and temperature changes of electronic device 500. Sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 514 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 514 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0135] Communication component 516 is configured to facilitate wired or wireless communication between electronic device 500 and other devices. Electronic device 500 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G LTE, 5G NR (NewRadio), or combinations thereof. In one exemplary embodiment, communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 516 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0136] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0137] Embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods described in the above embodiments of this disclosure.

[0138] Embodiments of this disclosure also provide a computer program product comprising a computer program executable by a programmable device, the computer program having, when executed by the programmable device, the method described in the above embodiments of this disclosure.

[0139] For cases where electronic devices can be chips or chip systems, see [link to relevant documentation]. Figure 8 The diagram shows the structure of the chip. Figure 8 The chip shown includes a processor 601 and an interface 602. There can be one or more processors 601, and multiple interfaces 602.

[0140] Optionally, the chip also includes a memory 603 for storing necessary computer programs and data.

[0141] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this application.

[0142] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0143] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0144] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0145] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (control method), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic device, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0146] It should be understood that various parts of the embodiments of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0147] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0148] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc.

[0149] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled 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 processing method, characterized in that, The method includes: Acquire the input signal of the signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; The input signal is processed using a digital predistortion model to obtain an output signal. The output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment. The digital predistortion model is trained based on samples of the non-divergent signal segment.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the amplitude of the input signal; Based on the amplitude, the non-divergent signal segment and the divergent signal segment in the input signal are determined.

3. The method according to claim 2, characterized in that, The step of determining the non-divergent signal segment and the divergent signal segment in the input signal based on the amplitude includes: The input signal segment corresponding to the first amplitude value that is less than a preset threshold is determined as the non-divergent signal segment; The input signal segment corresponding to the second amplitude that is greater than or equal to the preset threshold is determined as the divergent signal segment.

4. The method according to claim 1, characterized in that, The method further includes: Obtain the model parameters of the initial digital predistortion model, and obtain the first nonlinear characteristic of the signal amplifier; Based on the non-divergent signal segment samples and the first nonlinear feature, the model parameters are adjusted to obtain the digital predistortion model.

5. The method according to claim 4, characterized in that, The step of adjusting the model parameters based on the non-divergent signal segment samples and the first nonlinear feature to obtain the digital predistortion model includes: Using the initial digital predistortion model, the non-divergent signal segment sample is subjected to nonlinear processing to obtain a second nonlinear signal segment. Based on the non-divergent signal segment sample and the second nonlinear signal segment, determine the second nonlinear feature of the initial digital predistortion model; The model parameters are adjusted based on the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model.

6. The method according to claim 5, characterized in that, The step of adjusting the model parameters based on the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model includes: The model parameters are adjusted according to the trend of making the first nonlinear feature negatively correlated with the second nonlinear feature to obtain the digital predistortion model.

7. A signal processing apparatus, characterized in that, The device includes: An acquisition unit is used to acquire the input signal of the signal amplifier; the input signal includes a non-divergent signal segment and a divergent signal segment; The processing unit is used to process the input signal using a digital predistortion model to obtain an output signal; the output signal includes a first nonlinear signal segment obtained by nonlinear processing of the non-divergent signal segment and the directly output divergent signal segment, wherein the digital predistortion model is trained based on samples of the non-divergent signal segment.

8. The apparatus according to claim 7, characterized in that, The device further includes: The acquisition unit is further configured to acquire the model parameters of the initial digital predistortion model and acquire the first nonlinear characteristic of the signal amplifier; The adjustment unit is used to adjust the model parameters according to the non-divergent signal segment sample and the first nonlinear feature to obtain the digital predistortion model.

9. The apparatus according to claim 8, characterized in that, The adjustment unit includes: The processing module is used to perform nonlinear processing on the non-divergent signal segment sample using the initial digital predistortion model to obtain a second nonlinear signal segment; The determining module is used to determine the second nonlinear feature of the initial digital predistortion model based on the non-divergent signal segment sample and the second nonlinear signal segment; The adjustment module is used to adjust the model parameters according to the first nonlinear feature and the second nonlinear feature to obtain the digital predistortion model.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

11. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

12. A program product, characterized in that, Includes computer instructions for causing a computer to perform the method of any one of claims 1-6.

13. A chip, characterized in that, The chip includes a processing circuit and an interface circuit; wherein the interface circuit is used to read instructions and send the instructions to the processing circuit so that the processing circuit executes the method as described in any one of claims 1-6.

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