Power amplifier calibration methods, devices, storage media, and electronic equipment

Abstract

The application discloses a correction method and device of a power amplifier, a storage medium and an electronic device, and relates to the technical field of wireless communication and terminals. The method comprises the following steps: acquiring temperature information and output power of the power amplifier in a current running state; determining a target predistortion parameter matched with the temperature information and the output power, wherein the target predistortion parameter is used for representing the degree of non-linear distortion of the power amplifier; and correcting the current non-linear distortion result of the power amplifier based on the target predistortion parameter. The application solves the technical problem that the complexity of correcting the non-linear distortion of the power amplifier is high.

Description

Technical Field

[0001] This application relates to the fields of wireless communication and terminal technology, and more specifically, to a power amplifier calibration method, apparatus, storage medium, and electronic device. Background Technology

[0002] Currently, Digital Pre-Distortion (DPD) is the most widely used power amplifier linearization technology, offering advantages such as simple implementation, significant effects, and low cost. Common DPD solutions simultaneously perform both pre-distortion parameter calculation and update, and digital pre-distortion operations in hardware. For example, DPD can be implemented using Digital Signal Processing (DSP) and Field Programmable Gate Arrays (FPGAs). Pre-distortion parameter calculation and update are performed in the DSP, while digital pre-distortion operations are completed in the FPGA, achieving high computational accuracy. However, this approach using two types of chips presents challenges such as complex circuit design and high resource consumption, resulting in a high level of technical complexity in correcting the nonlinear distortion of the power amplifier.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a power amplifier calibration method, apparatus, storage medium, and electronic device to at least solve the technical problem of high complexity in correcting the nonlinear distortion of power amplifiers.

[0005] According to one aspect of the embodiments of this application, a correction method for a power amplifier is provided. The method may include: acquiring temperature information and output power of the power amplifier in its current operating state; determining a target predistortion parameter that matches the temperature information and output power, wherein the target predistortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier; and correcting the current nonlinear distortion result of the power amplifier based on the target predistortion parameter.

[0006] Optionally, before acquiring the temperature information and output power of the power amplifier under the current operating state, the method further includes: acquiring the input signal and output signal of the power amplifier under different historical temperature information and historical output power; modeling the input signal and output signal to obtain multiple sets of predistortion parameters, and storing the multiple sets of predistortion parameters in the parameter buffer.

[0007] Optionally, after acquiring the temperature information and output power of the power amplifier in its current operating state, the method further includes: transmitting the temperature information and output power to the microcontroller unit; and acquiring the numbering information of the target predistortion parameter that matches the temperature information and output power sent by the microcontroller unit.

[0008] Optionally, determining a target predistortion parameter that matches the temperature information and output power includes: determining a target predistortion parameter that matches the temperature information and output power from multiple sets of predistortion parameters stored in a parameter buffer based on the numbering information.

[0009] Optionally, before correcting the current nonlinear distortion result of the power amplifier based on the target predistortion parameter, the method further includes: acquiring the feedback signal of the power amplifier; and determining the target predistortion parameter based on the feedback signal.

[0010] Optionally, the target predistortion parameters are determined based on the feedback signal, including: determining the adjacent channel leakage ratio of multiple sets of predistortion parameters based on the feedback signal; and determining the target predistortion parameters based on the adjacent channel leakage ratio.

[0011] Optionally, the target predistortion parameter is determined based on the adjacent channel leakage ratio, including: determining the predistortion parameter with the minimum adjacent channel leakage ratio as the target predistortion parameter.

[0012] According to another aspect of the embodiments of this application, a power amplifier calibration apparatus is also provided. The apparatus may include: an acquisition unit for acquiring temperature information and output power of the power amplifier in its current operating state; a first determination unit for determining a target predistortion parameter that matches the temperature information and output power, wherein the target predistortion parameter characterizes the degree of nonlinear distortion of the power amplifier; and a calibration unit for correcting the current nonlinear distortion result of the power amplifier based on the target predistortion parameter.

[0013] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, comprising: the storage medium including a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute any power amplifier calibration method.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement any power amplifier calibration method.

[0015] According to another aspect of the embodiments of this application, a computer program product is also provided. This computer program product may include a computer program configured to execute any of the above-described power amplifier calibration methods when running.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided. This computer program product may include a non-volatile computer-readable storage medium storing a computer program configured to execute any of the power amplifier calibration methods described above when run.

[0017] According to another aspect of the embodiments of this application, a computer program is also provided. This computer program is configured to execute any of the above-described power amplifier calibration methods when run.

[0018] In this embodiment, the temperature information and output power of the power amplifier under its current operating state are obtained; a target predistortion parameter matching the temperature information and output power is determined, wherein the target predistortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier; and the current nonlinear distortion result of the power amplifier is corrected based on the target predistortion parameter. In other words, this application obtains the temperature information and output power of the power amplifier under its current operating state, determines a target predistortion parameter matching the temperature information and output power from pre-calculated predistortion parameters, and further corrects the current nonlinear distortion result of the power amplifier based on the determined target predistortion parameter. This achieves the technical effect of reducing the complexity of correcting the nonlinear distortion of the power amplifier, thereby solving the technical problem of high complexity in correcting the nonlinear distortion of the power amplifier. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 This is a schematic flowchart of a power amplifier calibration method according to an embodiment of this application;

[0021] Figure 2 This is a flowchart illustrating a low-resource digital predistortion method according to an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of a pre-distortion coefficient calculation process according to an embodiment of this application;

[0023] Figure 4 This is a schematic flowchart of a digital predistortion operation according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the predistortion coefficient at different power and temperature according to an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of a low-resource digital predistortion device according to an embodiment of this application;

[0026] Figure 7 This is a schematic diagram of the structure of a power amplifier calibration device according to an embodiment of this application;

[0027] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of this application is shown. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application 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 application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] To facilitate a better understanding by those skilled in the art, the technical terms or some nouns that may be involved in this application are explained below in the relevant embodiments:

[0031] DPD: Digital Pre-Distortion, is a linearization technique for power amplifiers (PA).

[0032] ACLR: Adjacent Channel Leakage Ratio, is a metric used to measure the performance of radio frequency (RF).

[0033] FPGA: Field Programmable Gate Array, a programmable logic device, is a further development based on programmable array logic (PAL) and generic array logic (GAL).

[0034] DSP: Digital Signal Processor, is a microprocessor that is particularly suitable for performing digital signal processing operations. Its main application is to implement various digital signal processing algorithms in real time and at high speed.

[0035] According to an embodiment of this application, a method embodiment for calibrating a power amplifier is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] Figure 1 This is a schematic flowchart of a power amplifier calibration method according to an embodiment of this application, as shown below. Figure 1 As shown, the method may include the following steps:

[0037] Step S102: Obtain the temperature information and output power of the power amplifier under the current operating state.

[0038] In step S102 above, the temperature information and output power of the power amplifier under its current operating state can be obtained. The temperature information can be the temperature of the power amplifier obtained through a temperature sensor. The output power can be the output power of the input signal after amplification by the power amplifier.

[0039] Step S104: Determine the target predistortion parameter that matches the temperature information and output power, wherein the target predistortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier.

[0040] In step S104 above, after acquiring the temperature information and output power of the power amplifier in its current operating state, a target predistortion parameter that matches the temperature information and output power can be determined. This target predistortion parameter can characterize the degree of nonlinear distortion of the power amplifier. It can be the predistortion parameter selected from multiple sets of predistortion parameters that best matches the acquired temperature information and output power of the current power amplifier; it can also be called the target predistortion coefficient.

[0041] Step S106: Correct the current nonlinear distortion result of the power amplifier based on the target predistortion parameters.

[0042] In step S106 above, after determining the target predistortion parameter that matches the temperature information and output power, the current nonlinear distortion result of the power amplifier can be corrected based on the determined target predistortion parameter.

[0043] In this embodiment, the temperature information and output power of the power amplifier under its current operating state are obtained; a target predistortion parameter matching the temperature information and output power is determined, wherein the target predistortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier; and the current nonlinear distortion result of the power amplifier is corrected based on the target predistortion parameter. In other words, this application obtains the temperature information and output power of the power amplifier under its current operating state, determines a target predistortion parameter matching the temperature information and output power from pre-calculated predistortion parameters, and further corrects the current nonlinear distortion result of the power amplifier based on the determined target predistortion parameter. This achieves the technical effect of reducing the complexity of correcting the nonlinear distortion of the power amplifier, thereby solving the technical problem of high complexity in correcting the nonlinear distortion of the power amplifier.

[0044] In some embodiments of this application, before obtaining the temperature information and output power of the power amplifier under the current operating state, the method further includes: obtaining the input signal and output signal of the power amplifier under different historical temperature information and historical output power; modeling the input signal and output signal to obtain multiple sets of predistortion parameters, and storing the multiple sets of predistortion parameters in the parameter cache area.

[0045] Understandably, it's possible to acquire the input and output signals of the power amplifier under different historical temperature and output power conditions. After acquiring these signals, they can be modeled to obtain multiple sets of predistortion parameters, which are then stored in a parameter buffer. This parameter buffer, also known as a coefficient buffer, can be used to store pre-calculated predistortion parameters.

[0046] For example, the maximum and minimum power of the power amplifier in actual operation can be statistically analyzed, and the power range can be segmented at fixed power intervals, resulting in a total of M power sampling points and M-1 power intervals. Similarly, the operating temperature range of the power amplifier in actual operation can be statistically analyzed, and the temperature range can be segmented at fixed temperature intervals, resulting in a total of N temperature sampling points and N-1 temperature intervals. Combining the N temperature sampling points with the M power sampling points yields M*N possible scenarios. Furthermore, the input and output signals of the power amplifier (PA) at different output powers and temperatures can be collected, and modeling can be performed using tools (such as Matlab) to calculate M*N sets of predistortion coefficients. The calculated M*N sets of predistortion coefficients are saved and exported, and these exported M*N sets of predistortion coefficients are loaded into the coefficient buffer when the FPGA is powered on.

[0047] This embodiment collects the input and output signals of a power amplifier (hereinafter referred to as a power amplifier) ​​at different output powers and power amplifier temperatures, and calculates the DPD predistortion coefficients for different conditions in advance in Matlab.

[0048] In some embodiments of this application, after obtaining the temperature information and output power of the power amplifier in its current operating state, the method further includes: transmitting the temperature information and output power to a microcontroller unit; and obtaining the numbering information of the target predistortion parameter that matches the temperature information and output power sent by the microcontroller unit.

[0049] Understandably, after acquiring the temperature and output power information of the power amplifier in its current operating state, this information can be transmitted to the microcontroller unit (MCU). The MCU then sends the target predistortion parameter number information that matches the temperature and output power, allowing the user to obtain this target predistortion parameter number information. The MCU can be used to implement various control and processing tasks.

[0050] Optionally, the M*N predistortion coefficients are loaded into the coefficient buffer when the FPGA is powered on. After obtaining the output power of the PA and the temperature of the PA through the temperature sensor, the obtained output power and temperature of the PA can be reported to the MCU. The MCU sends the number of the predistortion coefficient to be loaded under the current output power and temperature, as well as the number of the predistortion coefficients of the adjacent power and temperature.

[0051] This embodiment loads the corresponding predistortion coefficients in the FPGA to perform digital predistortion operation based on the output power of the power amplifier under the current operating state and the collected temperature, and fine-tunes the relevant parameters of the power amplifier.

[0052] In some embodiments of this application, step S104, determining the target predistortion parameter that matches the temperature information and output power, includes: determining the target predistortion parameter that matches the temperature information and output power from multiple sets of predistortion parameters stored in the parameter buffer based on the numbering information.

[0053] It is understandable that after obtaining the numbering information of the target predistortion parameter that matches the temperature information and output power sent by the microcontroller unit, the target predistortion parameter that matches the temperature information and output power can be determined from multiple sets of predistortion parameters stored in the parameter buffer based on the obtained numbering information.

[0054] Optionally, after obtaining the numbering information of the predistortion coefficients, the predistortion coefficients with the corresponding numbers can be loaded from the coefficient buffer into the predistortion model (Adapt Filter) to obtain the target predistortion parameters for nonlinear correction of the current input signal.

[0055] In some embodiments of this application, before correcting the current nonlinear distortion result of the power amplifier based on the target predistortion parameter, the method further includes: acquiring the feedback signal of the power amplifier; and determining the target predistortion parameter based on the feedback signal.

[0056] Understandably, the feedback signal from the power amplifier can be obtained, and based on the obtained feedback signal, the target predistortion parameters can be determined.

[0057] In some embodiments of this application, determining the target predistortion parameter based on the feedback signal includes: determining the adjacent channel leakage ratio of multiple sets of predistortion parameters based on the feedback signal; and determining the target predistortion parameter based on the adjacent channel leakage ratio.

[0058] Understandably, after acquiring the feedback signal from the power amplifier, the adjacent channel leakage ratio (ACLR) of multiple sets of predistortion parameters can be determined based on the acquired feedback signal. Furthermore, based on the determined adjacent channel leakage ratio, the target predistortion parameter can be determined.

[0059] In this embodiment, based on the actual operation of the system, the optimal predistortion coefficient is loaded and predistortion calculation is performed in the FPGA by monitoring the ACLR index of the feedback signal. At the same time, the relevant parameters of the power amplifier are fine-tuned. This saves a lot of computing resources while achieving high calculation accuracy.

[0060] In some embodiments of this application, determining a target predistortion parameter based on the adjacent channel leakage ratio includes: determining the predistortion parameter with the minimum adjacent channel leakage ratio as the target predistortion parameter.

[0061] Understandably, after determining the adjacent channel leakage ratios of multiple sets of predistortion parameters, the predistortion parameter with the smallest adjacent channel leakage ratio can be determined as the target predistortion parameter.

[0062] Optionally, after loading the corresponding numbered predistortion coefficients from the coefficient buffer into the predistortion model, nonlinear correction can be performed on the current input signal, and the ACLR of different predistortion coefficients can be statistically analyzed through the feedback channel signal. The set of predistortion coefficients with the best performance can then be selected. The power amplifier-related parameters can be fine-tuned based on the ACLR performance of the feedback signal.

[0063] The technical solutions of this application obtain the temperature information and output power of a power amplifier in its current operating state; determine a target pre-distortion parameter that matches the temperature information and output power, wherein the target pre-distortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier; and correct the current nonlinear distortion result of the power amplifier based on the target pre-distortion parameter. In other words, this application obtains the temperature information and output power of a power amplifier in its current operating state, determines a target pre-distortion parameter that matches the temperature information and output power from pre-calculated pre-distortion parameters, and further corrects the current nonlinear distortion result of the power amplifier based on the determined target pre-distortion parameter. This achieves the technical effect of reducing the complexity of correcting the nonlinear distortion of the power amplifier, thereby solving the technical problem of high complexity in correcting the nonlinear distortion of the power amplifier.

[0064] To facilitate a better understanding of the technical solutions of this application by those skilled in the art, a specific embodiment will now be described.

[0065] Power amplifiers amplify modulated signals to their rated power for transmission via antennas, making them a crucial component in wireless communication systems. However, the inherent nonlinear characteristics of power amplifiers can lead to in-band signal distortion and out-of-band spectral spread, severely impacting the quality of transmitted signals and reducing the transmitter's bit error rate (EVM) and ACLR. Therefore, correcting the nonlinear distortion of power amplifiers is essential.

[0066] Digital predistortion (DPD) is currently the most widely used power amplifier linearization technology, offering advantages such as simple implementation, significant effects, and low cost. Common DPD solutions currently perform both predistortion parameter calculation and update, and digital predistortion, simultaneously in hardware, mostly implemented using Application-Specific Integrated Circuits (ASICs), DSP+FPGA, and FPGA platforms. ASIC implementations are costly and inflexible in terms of updates. DSP+FPGA implementations, where predistortion parameter calculation and update are performed in the DSP and digital predistortion is handled in the FPGA, achieve high computational accuracy; however, using two different chip types leads to complex circuit design and high resource consumption. FPGA implementations can handle both parameter iteration and digital predistortion within the FPGA; however, FPGA word length is limited, requiring data truncation during parameter iteration, reducing accuracy, and predistortion parameter updates consume additional FPGA resources. Therefore, it is necessary to consider minimizing FPGA resource usage while maintaining high computational accuracy.

[0067] To address the aforementioned issues, this embodiment proposes a low-resource digital DPD predistortion method. This method separates the iteration of predistortion parameters from digital predistortion. The predistortion coefficients under different output power and power amplifier temperature conditions can be pre-calculated in Matlab. In the FPGA, based on the actual system operation, the optimal coefficients are loaded for predistortion calculation by monitoring the ACLR index of the feedback signal. At the same time, the relevant parameters of the power amplifier are fine-tuned. This method saves a significant amount of computing resources while achieving high computational accuracy.

[0068] This embodiment can acquire the input and output signals of the power amplifier at different output powers and temperatures, and pre-calculate the DPD predistortion coefficients for different scenarios in Matlab. It can statistically analyze the maximum and minimum power of the power amplifier in actual operation, segmenting the power range at fixed power intervals, resulting in a total of M power acquisition points and M-1 power ranges. It can also statistically analyze the operating temperature range of the power amplifier in actual operation, segmenting the temperature range at fixed temperature intervals, resulting in a total of N temperature acquisition points and N-1 temperature ranges. Combining the N temperature acquisition points with the M power acquisition points yields M*N possible scenarios. Furthermore, it can acquire the input and output signals of the power amplifier (PA) at different output powers and temperatures, model them in a tool (e.g., Matlab), and calculate the M*N sets of predistortion coefficients. The calculated M*N sets of predistortion coefficients are saved and exported, and loaded into the coefficient buffer when the FPGA is powered on.

[0069] Furthermore, based on the current power amplifier output power and the acquired power amplifier temperature, corresponding predistortion coefficients can be loaded into the FPGA for digital predistortion operation, and the relevant parameters of the power amplifier can be fine-tuned. M*N sets of predistortion coefficients are loaded into the coefficient buffer when the FPGA is powered on. The power amplifier's output power and temperature can be obtained via a temperature sensor. The power amplifier's output power and temperature are reported to the MCU, which sends the predistortion coefficient numbers to be loaded under the current output power and temperature conditions, as well as the predistortion coefficient numbers of neighboring power and temperature conditions. The corresponding numbered predistortion coefficients are loaded from the coefficient buffer into the predistortion model, performing nonlinear correction on the current input signal. The ACLR of different predistortion coefficients is statistically analyzed through the feedback channel signal, and the set of predistortion coefficients with the best performance is selected. The relevant parameters of the power amplifier are fine-tuned based on the ACLR performance of the feedback signal.

[0070] Compared to ASIC implementations, this embodiment offers advantages such as flexibility and a shorter development cycle. Compared to DSP+FPGA implementations, the circuit design of this embodiment is simpler and easier to implement. Compared to implementations that perform coefficient calculations and digital predistortion operations in an FPGA, this embodiment offers higher accuracy and saves on FPGA resources. This embodiment achieves better DPD performance through the comparison and selection of called predistortion coefficients and the fine-tuning of relevant power amplifier parameters.

[0071] Figure 2 This is a flowchart illustrating a low-resource digital predistortion method according to an embodiment of this application, as shown below. Figure 2 As shown, the method may include the following steps:

[0072] Step S201: Calculate the predistortion coefficient based on the input and output signals of the power amplifier at different output powers and temperatures.

[0073] In step S201 above, the input and output signals of the power amplifier at different output powers and power amplifier temperatures are collected, and the DPD predistortion coefficients for different conditions are calculated in advance in Matlab.

[0074] Step S202: Based on the current output power and temperature of the power amplifier, apply the calculated predistortion coefficients to perform digital predistortion operation.

[0075] In step S202 above, based on the power amplifier output power and the collected power amplifier temperature under the current operating state, the corresponding predistortion coefficients are loaded into the FPGA to perform digital predistortion operation.

[0076] This embodiment collects the input and output signals of the power amplifier at different output powers and temperatures, and calculates the predistortion coefficients under different conditions in Matlab. Further, during actual DPD operation, based on the current operating state, corresponding coefficients are loaded into the predistortion coefficient set for predistortion calculation. Specifically, step S201 is completed before the DPD actually operates; during actual operation, only step S202 is performed, i.e., the power amplifier output power and temperature are collected periodically, and corresponding coefficients are loaded for predistortion operation.

[0077] Figure 3 This is a schematic diagram of a predistortion coefficient calculation process according to an embodiment of this application, such as... Figure 3 As shown, the method may include the following steps:

[0078] Step S301: Obtain M power acquisition points.

[0079] In step S301 above, the maximum and minimum power of the power amplifier in actual operation are statistically analyzed, and the power range is segmented with fixed power intervals, resulting in a total of M power sampling points and M-1 power range segments.

[0080] Step S302: Obtain N temperature acquisition points.

[0081] In step S302 above, the actual operating temperature range of the power amplifier is statistically analyzed. This temperature range is then segmented at fixed temperature intervals, resulting in a total of N temperature sampling points and N-1 temperature intervals. Further combining the N temperature sampling points with the M power sampling points yields M*N possible scenarios.

[0082] Step S303: Calculate multiple sets of predistortion coefficients.

[0083] In step S303 above, the input and output signals of PA at different output powers and temperatures are collected, and modeling is performed in Matlab to calculate M*N predistortion coefficients.

[0084] Step S304: Output the predistortion coefficients.

[0085] In step S304 above, the M*N predistortion coefficients calculated in Matlab are saved and exported.

[0086] This embodiment calculates the predistortion coefficients of a power amplifier under different output power and temperature conditions using Matlab. First, the output power range and temperature range of the power amplifier are statistically analyzed and segmented at fixed intervals. Then, the input and output signals of the power amplifier under different conditions are collected to calculate and save the predistortion coefficients. For example, when the power output range can be from 20 dBm to 30 dBm, the temperature range can be from -40 °C to 55 °C, the power interval can be 1 dBm, and the temperature interval can be 5 °C, it is necessary to collect input and output signals at 11 power points and 20 temperature points. Combining these results in a total of 220 sets of input and output signals, which are then calculated in Matlab to obtain 220 sets of predistortion coefficients.

[0087] Figure 4 This is a flowchart illustrating a digital predistortion operation according to an embodiment of this application, such as... Figure 4 As shown, the method may include the following steps:

[0088] Step S401: Load the predistortion coefficients.

[0089] In step S401 above, the M*N predistortion coefficients can be loaded into the coefficient buffer when the FPGA is powered on.

[0090] Step S402: Obtain the output power of the power amplifier.

[0091] In step S402 above, the output power of PA can be obtained.

[0092] Step S403: Obtain the temperature of the power amplifier.

[0093] In step S403 above, the temperature of PA can be obtained through a temperature sensor.

[0094] Step S404: Determine the predistortion coefficient number based on the output power and temperature.

[0095] In step S404 above, the output power and temperature of PA can be reported to MCU. MCU sends the predistortion coefficient number to be applied under the current output power and temperature conditions, as well as the predistortion coefficient numbers of neighboring power and temperature.

[0096] Step S405: Obtain the predistortion coefficients based on the predistortion coefficient numbers.

[0097] In step S405 above, the corresponding numbered predistortion coefficients can be loaded from the coefficient buffer into the predistortion model to perform nonlinear correction on the current input signal, and the ACLR of different predistortion coefficients can be statistically analyzed through the feedback channel signal to select the set of coefficients with the best performance.

[0098] Step S406: Fine-tune the relevant parameters of the power amplifier based on the predistortion coefficient.

[0099] In step S406 above, the relevant parameters of the power amplifier can be fine-tuned by using the feedback signal ACLR performance.

[0100] Figure 5 This is a schematic diagram of the predistortion coefficient at different power and temperature according to an embodiment of this application, as shown below. Figure 5 As shown, power points 1 to M are the M output power points used to calculate the predistortion coefficient, and temperature points 1 to N are the N temperature points used to calculate the predistortion coefficient. Power point 1 corresponds to the minimum output power of the amplifier, power point M corresponds to the maximum output power of the amplifier, temperature point 1 corresponds to the lowest temperature of the amplifier, and temperature point N corresponds to the highest temperature of the amplifier. Coefficient 1 is the predistortion coefficient calculated when the amplifier output power is at power point 1 and the amplifier temperature is at temperature point 1; coefficient 2 is the predistortion coefficient calculated when the amplifier output power is at power point 2 and the amplifier temperature is at temperature point 1, and so on, with coefficient M*N being the predistortion coefficient calculated when the amplifier output power is at power point M and the amplifier temperature is at temperature point N.

[0101] When DPD is actually running, if the power amplifier's output power is at power point 1 and the temperature is at temperature point 1, then DPD calculation is performed with coefficient 1 applied. If the power is between power point 1 and power point 2 (inclusive), then coefficients are applied according to the power point 2 scenario. In other words, when the signal output power is within a certain power range, the predistortion coefficients for the maximum power condition within that range are applied for DPD calculation. The operation for temperature variables is similar; when the power amplifier temperature is within a certain temperature range, the predistortion coefficients for the maximum temperature condition within that range are applied for calculation.

[0102] Figure 6 This is a schematic diagram of a low-resource digital predistortion device according to an embodiment of this application, such as... Figure 6 As shown, the low-resource digital predistortion device may include a temperature acquisition module 601, a power acquisition module 602, a microcontroller unit 603, a predistortion coefficient buffer 604, and a predistortion module 605. The temperature acquisition module 601 acquires the temperature of the power amplifier. The power acquisition module 602 acquires the output power of the power amplifier. The microcontroller unit 603 receives the temperature and output power of the power amplifier and, based on... Figure 5 The system establishes the correspondence between the power amplifier's operation under different conditions and the predistortion coefficients, outputs the predistortion coefficients to be applied, and monitors the ACLR performance of the feedback channel signal. The predistortion coefficient buffer 604 stores M*N sets of predistortion coefficients calculated in Matlab. The predistortion module 605 is used to perform predistortion calculations on the signal.

[0103] The microcontroller unit (MCU) receives the temperature and output power of the power amplifier from the temperature acquisition module and the power acquisition module, and outputs the number of the predistortion coefficient to be loaded to the FPGA. The FPGA loads the corresponding number of the predistortion coefficient from the predistortion coefficient buffer into the predistortion module Adapt Filter. The input signal completes digital predistortion after passing through the Adapt Filter.

[0104] This embodiment proposes separating the iterative calculation of predistortion parameters from the digital predistortion operation. The predistortion coefficients of the power amplifier at different temperatures and output powers are pre-calculated in Matlab. During actual DPD operation, only the corresponding predistortion coefficients need to be loaded based on the current system operating state to complete the calculation. In actual hardware deployment, only the digital predistortion part needs to be deployed, eliminating the need for the iterative calculation part of the predistortion coefficients, simplifying hardware design and saving hardware resources. The iterative calculation of predistortion parameters is completed in Matlab, eliminating the need for data truncation during parameter iteration, resulting in high calculation accuracy. While maintaining high calculation accuracy, hardware resources are saved. The predistortion coefficients corresponding to the current power and temperature, as well as those of neighboring power and temperature values, are loaded, and the ACLR performance of the feedback channel is detected. The set of predistortion coefficients with the best performance is selected to achieve superior DPD performance. The relevant parameters of the power amplifier are fine-tuned, and the effect of the fine-tuning is monitored through the ACLR performance of the feedback channel to improve DPD performance.

[0105] The technical solutions of this application obtain the temperature information and output power of a power amplifier in its current operating state; determine a target pre-distortion parameter that matches the temperature information and output power, wherein the target pre-distortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier; and correct the current nonlinear distortion result of the power amplifier based on the target pre-distortion parameter. In other words, this application obtains the temperature information and output power of a power amplifier in its current operating state, determines a target pre-distortion parameter that matches the temperature information and output power from pre-calculated pre-distortion parameters, and further corrects the current nonlinear distortion result of the power amplifier based on the determined target pre-distortion parameter. This achieves the technical effect of reducing the complexity of correcting the nonlinear distortion of the power amplifier, thereby solving the technical problem of high complexity in correcting the nonlinear distortion of the power amplifier.

[0106] Figure 7 This is a schematic diagram of the structure of a power amplifier calibration device according to an embodiment of this application, as shown below. Figure 7 As shown, the limiting device 700 of the power amplifier may include: a first acquisition unit 702, a first determination unit 704, and a correction unit 706.

[0107] The first acquisition unit 702 is used to acquire the temperature information and output power of the power amplifier in its current operating state.

[0108] The first determining unit 704 is used to determine a target predistortion parameter that matches the temperature information and output power, wherein the target predistortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier.

[0109] The correction unit 706 is used to correct the current nonlinear distortion result of the power amplifier based on the target predistortion parameters.

[0110] Optionally, before the first acquisition unit 702 acquires the temperature information and output power of the power amplifier in the current operating state, the device further includes: a second acquisition unit, for acquiring the input signal and output signal of the power amplifier under different historical temperature information and historical output power; and a modeling unit, for modeling the input signal and output signal to obtain multiple sets of predistortion parameters, and storing the multiple sets of predistortion parameters in the parameter buffer area.

[0111] Optionally, after the first acquisition unit 702 acquires the temperature information and output power of the power amplifier in its current operating state, the device further includes: a transmission unit for transmitting the temperature information and output power to the microcontroller unit; and a third acquisition unit for acquiring the numbering information of the target predistortion parameter that matches the temperature information and output power sent by the microcontroller unit.

[0112] Optionally, the first determining unit 704 includes: a first determining module, used to determine, based on the numbering information, a target predistortion parameter that matches the temperature information and the output power from multiple sets of predistortion parameters stored in the parameter buffer.

[0113] Optionally, before the correction unit 706 corrects the current nonlinear distortion result of the power amplifier based on the target predistortion parameter, the device further includes: a fourth acquisition unit for acquiring the feedback signal of the power amplifier; and a second determination unit for determining the target predistortion parameter based on the feedback signal.

[0114] Optionally, the second determining unit includes: a second determining module, used to determine the adjacent channel leakage ratio of multiple sets of predistortion parameters based on the feedback signal; and a third determining module, used to determine the target predistortion parameter based on the adjacent channel leakage ratio.

[0115] Optionally, the third determining module includes: a determining submodule, used to determine the predistortion parameter with the minimum adjacent channel leakage ratio as the target predistortion parameter.

[0116] In this device, the temperature information and output power of the power amplifier under its current operating state are acquired by the first acquisition unit 702. The first determination unit 704 determines a target pre-distortion parameter that matches the temperature information and output power, wherein the target pre-distortion parameter characterizes the degree of nonlinear distortion of the power amplifier. The correction unit 706 corrects the current nonlinear distortion result of the power amplifier based on the target pre-distortion parameter. In other words, this application acquires the temperature information and output power of the power amplifier under its current operating state, determines a target pre-distortion parameter that matches the temperature information and output power from pre-calculated pre-distortion parameters, and further corrects the current nonlinear distortion result of the power amplifier based on the determined target pre-distortion parameter. This achieves the technical effect of reducing the complexity of correcting the nonlinear distortion of the power amplifier, thereby solving the technical problem of high complexity in correcting the nonlinear distortion of the power amplifier.

[0117] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the device where the non-volatile storage medium is located to execute any power amplifier calibration method.

[0118] Specifically, the aforementioned storage medium is used to store program instructions for the following functions, thereby implementing the following functions:

[0119] Acquire the temperature information and output power of the power amplifier under the current operating state; determine the target predistortion parameter that matches the temperature information and output power, wherein the target predistortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier; and correct the current nonlinear distortion result of the power amplifier based on the target predistortion parameter.

[0120] Optionally, in this embodiment, the storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of the storage medium include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0121] In an exemplary embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described power amplifier calibration methods.

[0122] Optionally, when executed by a processor, the computer program may perform the following steps:

[0123] Acquire the temperature information and output power of the power amplifier under the current operating state; determine the target predistortion parameter that matches the temperature information and output power, wherein the target predistortion parameter is used to characterize the degree of nonlinear distortion of the power amplifier; and correct the current nonlinear distortion result of the power amplifier based on the target predistortion parameter.

[0124] An electronic device is provided according to an embodiment of this application, the 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, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-described power amplifier calibration methods.

[0125] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0126] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0127] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0128] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0129] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as a power amplifier calibration method. For example, in some embodiments, the power amplifier calibration method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the power amplifier calibration method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the power amplifier calibration method by any other suitable means (e.g., by means of firmware).

[0130] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0134] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0135] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0136] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0137] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0141] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0142] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of correction of a power amplifier, characterized in that, The method comprises the following steps: obtaining temperature information and output power of a power amplifier in a current operating state; determining a target predistortion parameter matched with the temperature information and the output power from a parameter cache area, wherein the target predistortion parameter is used to represent a degree of nonlinear distortion of the power amplifier, and the parameter cache area is used to store a plurality of groups of predistortion parameters calculated in advance; correcting a current nonlinear distortion result of the power amplifier based on the target predistortion parameter; before the step of correcting the current nonlinear distortion result of the power amplifier based on the target predistortion parameter, the method further comprises the following steps: obtaining a feedback signal of the power amplifier based on a feedback channel; and statistically analyzing adjacent channel leakage ratios of the plurality of groups of predistortion parameters based on the feedback signal; and 2. The method of claim 1, wherein, determining the predistortion parameter with the minimum adjacent channel leakage ratio as the target predistortion parameter. The method further comprises the following steps: when the temperature is in any temperature interval, determining the maximum temperature in the temperature interval as the temperature information; and 3. A correction device for a power amplifier, characterized in that when the power is in any power interval, determining the maximum power in the power interval as the output power. After the step of obtaining the temperature information and the output power of the power amplifier in the current operating state, the method further comprises the following steps: transmitting the temperature information and the output power to a microcontroller unit; obtaining number information of the target predistortion parameter matched with the temperature information and the output power sent by the microcontroller unit; and determining the target predistortion parameter matched with the temperature information and the output power from the plurality of groups of predistortion parameters stored in the parameter cache area based on the number information. Before the step of obtaining the temperature information and the output power of the power amplifier in the current operating state, the method further comprises the following steps: obtaining input signals and output signals of the power amplifier under different historical temperature information and historical output power; and modeling the input signals and the output signals to obtain a plurality of groups of predistortion parameters, and storing the plurality of groups of predistortion parameters in the parameter cache area. The device comprises: a first obtaining unit configured to obtain temperature information and output power of a power amplifier in a current operating state; a first determining unit configured to determine a target predistortion parameter matched with the temperature information and the output power from a parameter cache area, wherein the target predistortion parameter is used to represent a degree of nonlinear distortion of the power amplifier, and the parameter cache area is used to store a plurality of groups of predistortion parameters calculated in advance; a correcting unit configured to correct a current nonlinear distortion result of the power amplifier based on the target predistortion parameter; before the correcting unit is used to correct the current nonlinear distortion result of the power amplifier based on the target predistortion parameter, the device is further configured to: obtain a feedback signal of the power amplifier based on a feedback channel; statistically analyze adjacent channel leakage ratios of the plurality of groups of predistortion parameters based on the feedback signal; and determine the predistortion parameter with the minimum adjacent channel leakage ratio as the target predistortion parameter. The device is also used for determining the maximum temperature in the temperature interval as the temperature information when the temperature is located in any temperature interval; determining the maximum power in the power interval as the output power when the power is located in any power interval; After obtaining the temperature information and the output power of the power amplifier in the current operating state, the device is also used for transmitting the temperature information and the output power to a microcontroller unit; obtaining the number information of the target pre-distortion parameter sent by the microcontroller unit and matched with the temperature information and the output power; based on the number information, determining the target pre-distortion parameter matched with the temperature information and the output power from the multiple groups of pre-distortion parameters stored in the parameter cache area.

4. A non-volatile storage medium, characterized by, The storage medium includes a stored program, wherein the device where the storage medium is located executes the method in any one of claims 1-2 when the program runs.

5. An electronic device, comprising: Comprise: A processor; A memory for storing instructions executable by the processor; Wherein the processor is configured to execute the instructions to implement the method of any one of claims 1-2.

6. A computer program product, characterised in that, The computer program product comprises a computer program, wherein the computer program is configured to execute the method in any one of claims 1-2 when the processor runs. The computer program product comprises a computer program, wherein the computer program is configured to execute the method in any one of claims 1-2 when the processor runs.

Images