A dynamic calibration control method and system for gain data of a radio frequency power amplifier

Abstract

The present application relates to the technical fields of radio frequency power amplifier, and discloses a kind of dynamic calibration control method and system of radio frequency power amplifier gain data, method includes: the operating data and impedance real-time variation characteristics of radio frequency power amplifier are collected;Operating data is mapped to the amplitude temperature gain control strategy of radio frequency power amplifier with preset target output amplitude value, and the gain calibration instruction of radio frequency power amplifier is obtained;Based on impedance real-time variation characteristics, impedance compensation adjustment is carried out to gain calibration instruction, and the optimized gain calibration instruction of radio frequency power amplifier is obtained;Based on optimized gain calibration instruction, closed-loop feedback control is carried out to the gain parameter of radio frequency power amplifier, and the gain control instruction of radio frequency power amplifier is obtained;Based on gain control instruction, control effect evaluation is carried out to the output signal of radio frequency power amplifier, and the control deviation index of radio frequency power amplifier is obtained;According to control deviation index, adaptive adjustment is carried out to amplitude temperature gain control strategy;The present application can improve the efficiency of dynamic calibration control of radio frequency power amplifier gain data.

Description

Technical Field

[0001] This invention relates to the field of radio frequency power amplifier technology, and in particular to a dynamic calibration and control method and system for radio frequency power amplifier gain data. Background Technology

[0002] As a core component in communication systems, the gain stability of radio frequency power amplifiers directly affects the signal transmission quality. Existing radio frequency power amplifier gain calibration methods mostly rely on static control strategies, failing to fully integrate equipment operating data and real-time impedance changes for dynamic adjustment. This results in insufficient adaptability of calibration commands to actual operating conditions, making it difficult to effectively compensate for gain deviations caused by impedance fluctuations.

[0003] Traditional gain calibration systems lack a robust closed-loop feedback and adaptive optimization mechanism. After evaluating the control effect, they cannot accurately decompose the deviation components and dynamically reconstruct the control strategy. This results in gain parameter adjustments lagging behind changes in device status, leading to low calibration efficiency and control accuracy that cannot meet the requirements of high-performance communication scenarios. Summary of the Invention

[0004] This invention provides a dynamic calibration control method and system for RF power amplifier gain data to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a dynamic calibration control method for RF power amplifier gain data, comprising:

[0006] S1. Acquire the operating data and real-time impedance change characteristics of the RF power amplifier;

[0007] S2. Map the running data and the preset target output amplitude value to the amplitude temperature gain control strategy of the RF power amplifier to obtain the gain calibration command of the RF power amplifier.

[0008] S3. Based on the real-time impedance change characteristics, the gain calibration command is adjusted by impedance compensation to obtain the optimized gain calibration command of the RF power amplifier.

[0009] S4. Based on the optimized gain calibration command, perform closed-loop feedback control on the gain parameters of the RF power amplifier to obtain the gain control command of the RF power amplifier.

[0010] S5. Based on the gain control command, evaluate the control effect of the output signal of the RF power amplifier to obtain the control deviation index of the RF power amplifier.

[0011] S6. Based on the control deviation index, the amplitude temperature gain control strategy is adaptively adjusted.

[0012] In a preferred embodiment, the acquisition of the operating data and real-time impedance variation characteristics of the RF power amplifier includes:

[0013] The output power sampling sequence and ambient temperature sampling sequence of the RF power amplifier are obtained to form the raw operating data of the RF power amplifier;

[0014] The original operating data is averaged using a sliding window to obtain the operating data of the RF power amplifier;

[0015] Based on the operating data, differential analysis is performed on the impedance variation trend of the RF power amplifier to obtain the impedance dynamic characteristics of the RF power amplifier.

[0016] By performing time-series analysis on the impedance dynamic characteristics, the real-time impedance variation characteristics of the RF power amplifier are obtained.

[0017] In a preferred embodiment, the step of mapping the operating data to a preset target output amplitude value and then to the amplitude-temperature gain control strategy of the RF power amplifier to obtain the gain calibration command of the RF power amplifier includes:

[0018] By performing trend correlation analysis between the operating data and the historical operating data of the RF power amplifier, the operating status mode of the RF power amplifier can be obtained.

[0019] Based on the amplitude temperature gain control strategy, the operating state mode is mapped to obtain the corresponding strategy partition of the operating state mode.

[0020] Based on the preset target output amplitude value, the gain reference value in the corresponding strategy partition is constrained to converge, and the initial gain calibration command of the RF power amplifier is obtained.

[0021] The amplitude continuity of the initial gain calibration command is verified to obtain the gain calibration command of the RF power amplifier.

[0022] In a preferred embodiment, the step of performing trend correlation analysis between the operating data and the historical operating data of the RF power amplifier to obtain the operating status mode of the RF power amplifier includes:

[0023] Similarity features are extracted from the historical operating data to obtain reference data for the radio frequency power amplifier;

[0024] The operating data is dynamically matched with the reference data in time to obtain the operating trend of the RF power amplifier;

[0025] Based on the operating trend, the operating data is subjected to modal decomposition to obtain the operating state mode of the RF power amplifier.

[0026] In a preferred embodiment, the step of adjusting the gain calibration command based on the real-time impedance change characteristics to obtain the optimized gain calibration command for the RF power amplifier includes:

[0027] Based on the impedance change data of the RF power amplifier, an impedance difference matrix of the RF power amplifier is generated.

[0028] Singular value decomposition is performed on the impedance difference matrix to obtain the impedance change trend vector of the RF power amplifier;

[0029] Based on the gain calibration command, the impedance change rate of the impedance change trend vector is dynamically extracted to obtain the dynamic weighting coefficient of the RF power amplifier.

[0030] Based on the dynamic weighting coefficients, the gain calibration command is weighted and fused in multiple dimensions to obtain the intermediate calibration vector of the RF power amplifier.

[0031] The dynamic normalization factor of the gain calibration command is calculated based on the intermediate calibration vector, and the gain calibration command is dynamically adjusted to obtain the optimized gain calibration command of the RF power amplifier. The formula for calculating the dynamic normalization factor is as follows:

[0032] ;

[0033] in, This represents the dynamic normalization factor. Indicates the first The aforementioned rate of change of impedance This represents the mean of the rate of change of impedance. Indicates the first The aforementioned dynamic weighting coefficients, This represents the preset impedance attenuation factor. This represents the preset impedance cross-correlation parameters. Indicates the first The impedance change trend vector. This indicates the preset impedance fluctuation correction index. This represents the smallest positive number that prevents the denominator from being zero.

[0034] In a preferred embodiment, the step of performing multi-dimensional weighted fusion of the gain calibration command based on the dynamic weighting coefficients to obtain the intermediate calibration vector of the RF power amplifier includes:

[0035] The impedance difference matrix is ​​subjected to data layering processing to obtain a layered data group of the real-time impedance change characteristics;

[0036] Based on the impedance variation dispersion and weight variation amplitude in the hierarchical data group, the gain calibration command is weighted and balanced to obtain the fused gain set of the RF power amplifier;

[0037] An iterative consistency analysis is performed on the fusion gain set to obtain the convergence trend of the fusion gain set in the multidimensional data space;

[0038] The convergence trend is used as the intermediate calibration vector of the RF power amplifier.

[0039] In a preferred embodiment, the step of performing closed-loop feedback control on the gain parameters of the RF power amplifier based on the optimized gain calibration command to obtain the gain control command of the RF power amplifier includes:

[0040] Based on the optimized gain calibration command, the real-time state of the RF power amplifier is analyzed to obtain the changing trend characteristics and amplitude distribution characteristics of the RF power amplifier.

[0041] Based on the changing trend characteristics and the amplitude distribution characteristics, the feature vector of the radio frequency power amplifier is constructed;

[0042] The dynamic adjustment factor of the RF power amplifier is obtained by performing correlation entropy matching between the feature vector and the operating data features of the RF power amplifier.

[0043] Based on the dynamic adjustment factor, the optimized gain calibration command is subjected to closed-loop modulation to obtain the gain control command of the RF power amplifier.

[0044] In a preferred embodiment, the step of evaluating the control effect of the output signal of the RF power amplifier based on the gain control command to obtain the control deviation index of the RF power amplifier includes:

[0045] Envelope extraction is performed on the output signal of the radio frequency power amplifier to obtain the amplitude feature sequence of the output signal;

[0046] The amplitude feature sequence is compared and analyzed with the target output amplitude value in multiple dimensions to obtain the amplitude deviation of the RF power amplifier;

[0047] Based on the amplitude deviation, a comprehensive deviation index for the output signal is constructed;

[0048] Principal component analysis was performed on the comprehensive deviation index to obtain the control deviation index of the RF power amplifier.

[0049] In a preferred embodiment, the adaptive adjustment of the amplitude temperature gain control strategy based on the control deviation index includes:

[0050] The control deviation index is decomposed into steady-state deviation components and transient deviation components.

[0051] Based on the proportional relationship between the steady-state deviation component and the transient deviation component, a strategy adjustment priority sequence for the RF power amplifier is generated;

[0052] Based on the steady-state deviation component and the transient deviation component, the strategy adjustment priority sequence is dynamically compressed to obtain the compressed priority sequence of the RF power amplifier;

[0053] Based on the timing characteristics of the transient deviation components, the compression priority sequence is proportionally allocated to generate the strategy parameter adjustment vector of the RF power amplifier;

[0054] Based on the strategy parameter adjustment vector, the mapping relationship in the amplitude temperature gain control strategy is dynamically reconstructed to complete the adaptive adjustment of the amplitude temperature gain control strategy.

[0055] To address the aforementioned problems, the present invention also provides a dynamic calibration and control system for RF power amplifier gain data, the system comprising:

[0056] The data acquisition module is used to collect the operating data and real-time impedance change characteristics of the RF power amplifier.

[0057] The instruction generation module is used to map the running data and the preset target output amplitude value to the amplitude temperature gain control strategy of the RF power amplifier to obtain the gain calibration instruction of the RF power amplifier.

[0058] The instruction optimization module is used to adjust the gain calibration instruction based on the real-time impedance change characteristics to obtain the optimized gain calibration instruction of the RF power amplifier.

[0059] The control feedback module is used to perform closed-loop feedback control on the gain parameters of the RF power amplifier based on the optimized gain calibration command, so as to obtain the gain control command of the RF power amplifier.

[0060] The effect evaluation module is used to evaluate the control effect of the output signal of the RF power amplifier based on the gain control command, and obtain the control deviation index of the RF power amplifier.

[0061] The optimization and adjustment module is used to adaptively adjust the amplitude temperature gain control strategy according to the control deviation index.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. This invention accurately collects operational data and real-time impedance change characteristics, effectively maps them to the target output amplitude value, generates calibration instructions in the control strategy, and performs compensation adjustments based on impedance characteristics. After closed-loop feedback control and effect evaluation, the accuracy of gain calibration is significantly improved, control deviation is effectively reduced, and the gain parameter control is more in line with actual needs.

[0064] 2. This invention achieves adaptive adjustment of amplitude temperature gain control strategy by analyzing control deviation indicators, which can dynamically adapt to changes in the operating state of the RF power amplifier, continuously optimize the gain control effect, significantly improve the efficiency and stability of dynamic calibration control of gain data, and ensure the consistency and reliability of the RF power amplifier output signal. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating a dynamic calibration control method for RF power amplifier gain data according to an embodiment of the present invention.

[0066] Figure 2 A functional block diagram of a dynamic calibration and control system for RF power amplifier gain data provided in an embodiment of the present invention;

[0067] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0068] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0069] This application provides a dynamic calibration control method for RF power amplifier gain data. The execution subject of this dynamic calibration control method for RF power amplifier gain data includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the dynamic calibration control method for RF power amplifier gain data can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0070] Reference Figure 1The diagram shown is a flowchart illustrating a dynamic calibration control method for RF power amplifier gain data according to an embodiment of the present invention. In this embodiment, the dynamic calibration control method for RF power amplifier gain data includes:

[0071] S1. Acquire the operating data and real-time impedance change characteristics of the RF power amplifier;

[0072] In this embodiment of the invention, the acquisition of the operating data and real-time impedance change characteristics of the RF power amplifier includes:

[0073] The output power sampling sequence and ambient temperature sampling sequence of the RF power amplifier are obtained to form the raw operating data of the RF power amplifier;

[0074] The original operating data is averaged using a sliding window to obtain the operating data of the RF power amplifier;

[0075] Based on the operating data, differential analysis is performed on the impedance variation trend of the RF power amplifier to obtain the impedance dynamic characteristics of the RF power amplifier.

[0076] By performing time-series analysis on the impedance dynamic characteristics, the real-time impedance variation characteristics of the RF power amplifier are obtained.

[0077] Specifically, the power sensor and temperature sensor built into the RF power amplifier continuously collect output power data and ambient temperature data during the operation of the device. The collected output power data is arranged in chronological order to form an output power sampling sequence, and the collected ambient temperature data is arranged in the same chronological order to form an ambient temperature sampling sequence. The two sequences are combined to form the original operating data of the RF power amplifier.

[0078] Furthermore, a fixed-length sliding window is set, and the original operating data is input into the window in chronological order. The average value of all output power data and ambient temperature data contained in each window is calculated, and the calculated average value is used to replace the corresponding original data in the window. According to the sliding order of the windows, the average values ​​of all windows are arranged in sequence to obtain the operating data of the RF power amplifier.

[0079] Furthermore, based on the processed operating data, impedance-related parameter data are extracted, and impedance-related parameter data of two adjacent moments are selected in chronological order. The difference between the data of the next moment and the data of the previous moment is calculated. By analyzing the changes in a series of differences, the direction and amplitude of the RF power amplifier impedance change with time are summarized, and the impedance dynamic characteristics of the RF power amplifier are obtained.

[0080] Furthermore, the obtained impedance dynamic characteristic data are arranged in the order of their generation time, and the changes in impedance dynamic characteristics in different time periods are analyzed segment by segment to clarify the specific state of impedance dynamic characteristics at each time node and the changing trend in adjacent time periods, thereby obtaining the real-time impedance change characteristics of the RF power amplifier.

[0081] In summary, by accurately collecting the output power sampling sequence and the ambient temperature sampling sequence to form the original operating data, comprehensive and accurate basic data support is provided for subsequent gain calibration work, ensuring that the calibration process has a reliable data source.

[0082] In summary, the sliding window averaging process on the raw operating data effectively filters out random interference and fluctuations in the data, improves the stability and accuracy of the operating data, and provides high-quality data assurance for subsequent impedance analysis and calibration command generation.

[0083] In summary, differential analysis based on operational data yields dynamic impedance characteristics, clearly capturing the basic trend of impedance changes. Further time-series analysis provides real-time impedance change characteristics, ensuring timely understanding of impedance variations. This provides a precise basis for optimizing and adjusting subsequent gain calibration commands, helping to improve the adaptability and accuracy of gain calibration.

[0084] S2. Map the running data and the preset target output amplitude value to the amplitude temperature gain control strategy of the RF power amplifier to obtain the gain calibration command of the RF power amplifier.

[0085] In this embodiment of the invention, the step of mapping the operating data and a preset target output amplitude value to the amplitude temperature gain control strategy of the RF power amplifier to obtain the gain calibration command of the RF power amplifier includes:

[0086] By performing trend correlation analysis between the operating data and the historical operating data of the RF power amplifier, the operating status mode of the RF power amplifier can be obtained.

[0087] Based on the amplitude temperature gain control strategy, the operating state mode is mapped to obtain the corresponding strategy partition of the operating state mode.

[0088] Based on the preset target output amplitude value, the gain reference value in the corresponding strategy partition is constrained to converge, and the initial gain calibration command of the RF power amplifier is obtained.

[0089] The amplitude continuity of the initial gain calibration command is verified to obtain the gain calibration command of the RF power amplifier.

[0090] In this embodiment of the invention, the step of performing trend correlation analysis between the operating data and the historical operating data of the RF power amplifier to obtain the operating status mode of the RF power amplifier includes:

[0091] Similarity features are extracted from the historical operating data to obtain reference data for the radio frequency power amplifier;

[0092] The operating data is dynamically matched with the reference data in time to obtain the operating trend of the RF power amplifier;

[0093] Based on the operating trend, the operating data is subjected to modal decomposition to obtain the operating state mode of the RF power amplifier.

[0094] Specifically, historical operating data recorded during the past operation of the RF power amplifier is first collected. This historical operating data includes output power data and ambient temperature data of the same type as the current operating data. The current operating data and historical operating data are organized into data sequences according to time sequence. Then, the direction and magnitude of the changes in output power and ambient temperature over time in the two sets of data sequences are compared and analyzed to find the correlation points in the trend of change between the current operating data and historical operating data. For example, whether the change pattern of output power is consistent when the ambient temperature rises. Through this trend correlation analysis, the current operating state type of the RF power amplifier is determined, and the operating state mode of the RF power amplifier is obtained.

[0095] Furthermore, the amplitude temperature gain control strategy pre-divides corresponding strategy partitions according to different operating state modes of the RF power amplifier. Each strategy partition corresponds to a set of gain control rules adapted to a specific operating state. The obtained operating state mode is matched with the state mode corresponding to each strategy partition in the strategy to determine the specific strategy partition corresponding to the current operating state mode, thus obtaining the corresponding strategy partition of the operating state mode.

[0096] Furthermore, a certain range of gain reference values ​​is preset within each corresponding strategy partition. Based on the preset target output amplitude value, it is determined which values ​​among the gain reference values ​​in the strategy partition can make the output amplitude of the RF power amplifier reach the target output amplitude value. Gain reference values ​​that do not meet the target output amplitude requirements are eliminated, and the gain reference values ​​that meet the requirements are further adjusted to converge to the specific value closest to the target output amplitude value, thereby obtaining the initial gain calibration command of the RF power amplifier.

[0097] Furthermore, gain-related command data at adjacent moments before and after the initial gain calibration command are acquired. The amplitude values ​​of the initial gain calibration command and the commands at adjacent moments are compared to check whether the amplitude of the initial gain calibration command maintains a smooth transition with the amplitude of the commands at adjacent moments, without any sudden large increases or decreases. If the amplitude change is within a preset continuous range, it is confirmed that the initial gain calibration command meets the amplitude continuity requirement and is used as the gain calibration command for the RF power amplifier. If there is a sudden amplitude change, the amplitude of the initial gain calibration command is fine-tuned to meet the continuity requirement before being used as the gain calibration command for the RF power amplifier.

[0098] Specifically, the historical operating data of the RF power amplifier is first classified and organized according to data type, ensuring that each type of data is arranged in chronological order of acquisition time. Then, the variation characteristics of each type of historical data within different time periods are analyzed, such as the range of output power variation when the ambient temperature is within a certain range, and the characteristics of output power rising or falling steadily over time. Historical data segments that are consistent with the current operating data to be analyzed in terms of data type and variation characteristics are selected. These selected historical data segments are then integrated to obtain the reference data of the RF power amplifier.

[0099] Furthermore, the operating data and reference data are divided into multiple data units according to the same time interval. Each data unit contains the output power value and ambient temperature value within the corresponding time interval. Then, the direction of output power change and the magnitude of ambient temperature change in the corresponding time units of the operating data and reference data are compared in chronological order to determine whether the parameter change trends of the two are consistent at the same time node. For example, if the temperature rises by 2℃ and the power decreases by 1dB in the operating data in the first time unit, and the temperature rises by 1.8-2.2℃ and the power decreases by 0.8-1.2dB in the reference data in the same time unit, then the trend of that time unit is determined to be consistent. By comparing the trends of each time unit, the overall change pattern of the operating data is summarized, and the operating trend of the RF power amplifier is obtained.

[0100] Furthermore, based on the obtained operating trends, the criteria for dividing different stages of change in the operating data are determined. For example, when the operating trend shows different stage characteristics such as "stable temperature + stable power", "temperature rise + power fall", and "temperature fall + power rise", the operating data is divided into multiple sub-data segments with single change characteristics using these characteristic changes as the dividing points. Each sub-data segment corresponds to a certain operating state characteristic of the RF power amplifier. The operating state characteristics represented by these sub-data segments are summarized and defined. For example, the sub-data segment "temperature stable at 25-28℃ and power stable at 30-32dBm" is defined as "normal temperature stable operating state". All the summarized and defined operating state characteristics are integrated to obtain the operating state mode of the RF power amplifier.

[0101] In summary, by analyzing the trend correlation between current and historical operating data, we can accurately determine the current operating status by combining the equipment's past working patterns, making the obtained operating status pattern more consistent with the actual working conditions and providing an accurate basis for subsequent strategy matching.

[0102] In summary, mapping the operating state mode based on the amplitude-temperature gain control strategy can quickly lock the strategy partition that is suitable for the current state, avoid the blindness of strategy selection, and improve the targeting and efficiency of gain control.

[0103] In summary, constraining the convergence gain reference value with the target output amplitude value ensures that the initial gain calibration command directly points to the direction that meets the output requirements, reduces the generation of invalid commands, and improves the effectiveness of the initial command.

[0104] In summary, performing amplitude continuity verification on the initial gain calibration command can prevent fluctuations in equipment operation caused by sudden changes in command amplitude, ensure the stability of gain adjustment, and further improve the reliability of the final gain calibration command.

[0105] In summary, extracting similar features from historical operational data to obtain reference data can accurately filter out historical information that is related to the current operational data, avoid interference from irrelevant historical data, provide a high-quality comparison benchmark for subsequent trend matching, and ensure that the analytical dimensions of the reference data and the operational data are consistent.

[0106] In summary, dynamic time-series matching of operational data with reference data yields operational trends. By comparing parameter change trends at each time unit, the overall change patterns of operational data can be accurately captured, making the description of operational trends more consistent with the actual working state of the equipment and providing a clear analytical direction for subsequent modal decomposition.

[0107] In summary, modal decomposition of operational data based on operational trends yields operational state patterns. By dividing and defining single-feature sub-data segments, complex operational data can be transformed into clear working state types, making the presentation of operational state patterns more intuitive and providing accurate state basis for subsequent amplitude and temperature gain control strategies.

[0108] S3. Based on the real-time impedance change characteristics, the gain calibration command is adjusted by impedance compensation to obtain the optimized gain calibration command of the RF power amplifier.

[0109] In this embodiment of the invention, the step of adjusting the gain calibration command based on the real-time impedance change characteristics to obtain the optimized gain calibration command for the RF power amplifier includes:

[0110] Based on the impedance change data of the RF power amplifier, an impedance difference matrix of the RF power amplifier is generated.

[0111] Singular value decomposition is performed on the impedance difference matrix to obtain the impedance change trend vector of the RF power amplifier;

[0112] Based on the gain calibration command, the impedance change rate of the impedance change trend vector is dynamically extracted to obtain the dynamic weighting coefficient of the RF power amplifier.

[0113] Based on the dynamic weighting coefficients, the gain calibration command is weighted and fused in multiple dimensions to obtain the intermediate calibration vector of the RF power amplifier.

[0114] The dynamic normalization factor of the gain calibration command is calculated based on the intermediate calibration vector, and the gain calibration command is dynamically adjusted to obtain the optimized gain calibration command of the RF power amplifier. The formula for calculating the dynamic normalization factor is as follows:

[0115] ;

[0116] in, This represents the dynamic normalization factor. Indicates the first The aforementioned rate of change of impedance This represents the mean of the rate of change of impedance. Indicates the first The aforementioned dynamic weighting coefficients, This represents the preset impedance attenuation factor. This represents the preset impedance cross-correlation parameters. Indicates the first The impedance change trend vector. This indicates the preset impedance fluctuation correction index. This represents the smallest positive number that prevents the denominator from being zero.

[0117] In this embodiment of the invention, the step of performing multi-dimensional weighted fusion of the gain calibration command based on the dynamic weighting coefficient to obtain the intermediate calibration vector of the RF power amplifier includes:

[0118] The impedance difference matrix is ​​subjected to data layering processing to obtain a layered data group of the real-time impedance change characteristics;

[0119] Based on the impedance variation dispersion and weight variation amplitude in the hierarchical data group, the gain calibration command is weighted and balanced to obtain the fused gain set of the RF power amplifier;

[0120] An iterative consistency analysis is performed on the fusion gain set to obtain the convergence trend of the fusion gain set in the multidimensional data space;

[0121] The convergence trend is used as the intermediate calibration vector of the RF power amplifier.

[0122] Specifically, the impedance change data of the RF power amplifier is first obtained. This data comes from the previously obtained real-time impedance change characteristics and includes the specific impedance values ​​at different time points. Then, the analysis dimensions of the impedance change data are determined. For example, the impedance data is divided into multiple data points in chronological order, and the difference between the impedance data at adjacent time points is calculated. At the same time, the difference between the impedance data under different operating dimensions is calculated. These calculated impedance differences are arranged in order by rows and columns. The rows represent different time points or operating dimensions, and the columns correspond to the comparison items of another dimension, thereby generating the impedance difference matrix of the RF power amplifier.

[0123] Furthermore, the generated impedance difference matrix is ​​processed. First, the meaning of the impedance difference represented by each element in the matrix is ​​clarified. Then, by splitting the matrix, the impedance difference matrix is ​​decomposed into three interrelated matrix parts. From these parts, the core information that can reflect the main changing law of impedance difference is selected. This core information can reflect the main trend of overall impedance change, whether it is rising, falling or fluctuating. This core information is arranged into a set of ordered values ​​in a specific order to form the impedance change trend vector of the RF power amplifier.

[0124] Furthermore, the impedance change rate is first extracted from the impedance change trend vector, that is, the change in impedance value per unit time is calculated to clarify the speed of impedance change in different time periods or under different operating conditions. Then, combined with the specific requirements for gain adjustment in the gain calibration command, the influence of different impedance change rates on the gain calibration effect is analyzed. For example, when the impedance change rate is large, the impact on gain stability is more significant. Based on this, the impedance change rate feature related to the gain calibration requirements is extracted, and corresponding weight values ​​are assigned to features with different degrees of influence. The magnitude of the weight value is positively correlated with the degree of influence of the feature on gain calibration, thereby obtaining the dynamic weight coefficient of the RF power amplifier.

[0125] Furthermore, the multi-dimensional analysis perspective of the gain calibration command is first determined, such as the operating frequency band, output power level, and ambient temperature range of the RF power amplifier. The gain calibration command is then broken down into sub-commands of multiple dimensions. Subsequently, a corresponding dynamic weighting coefficient is matched to each sub-command of each dimension. The influence of each sub-command in the overall gain control is adjusted according to the magnitude of the weighting coefficient. The sub-command with the larger the weighting coefficient has a higher proportion in the fusion process. All the sub-commands after weight adjustment are integrated to form a set of ordered values ​​that can comprehensively reflect the requirements of each dimension, thus obtaining the intermediate calibration vector of the RF power amplifier.

[0126] Furthermore, the distribution range and overall characteristics of each value in the intermediate calibration vector are analyzed first to determine a reference standard that can keep the intermediate calibration vector values ​​within a reasonable adjustment range. Based on this reference standard, a dynamic normalization factor is calculated. This factor is used to balance the numerical compatibility between the intermediate calibration vector and the gain calibration command. Then, the dynamic normalization factor is applied to the gain calibration command to adjust the value of each parameter in the gain calibration command so that the adjusted gain calibration command can adapt to the current real-time impedance change characteristics and avoid gain deviation caused by impedance changes. Finally, the optimized gain calibration command of the RF power amplifier is obtained.

[0127] Specifically, the hierarchical dimension of the impedance difference matrix is ​​first determined. This dimension is set based on the operating state parameters of the RF power amplifier. The impedance difference matrix is ​​divided into multiple continuous levels according to the set dimension. Each level corresponds to a specific operating state interval. All impedance difference data in each level are extracted. These data directly correspond to the real-time impedance change characteristics in the corresponding operating state interval. The impedance difference data of each level are organized into an independent data set, thereby obtaining a hierarchical data group of impedance real-time change characteristics.

[0128] Furthermore, the distribution concentration of impedance difference data within each layered data group is calculated to determine the impedance variation dispersion. By statistically analyzing the degree of deviation of data from the average value and the difference between the maximum and minimum values ​​within the group, the stability of impedance variation within that layer is determined. Simultaneously, the range of dynamic weight coefficient changes between different layered data groups is statistically analyzed to obtain the weight change amplitude. The base weight of gain calibration commands in the corresponding layer is adjusted according to the impedance variation dispersion: the greater the dispersion, the more unstable the impedance variation in that layer, and the base weight is set to a lower value to avoid over-adjustment. The weight ratio of each layer is optimized according to the weight change amplitude: the greater the weight change amplitude, the more sensitive the layer is to the impact of gain calibration, and its weight ratio is appropriately increased. The gain calibration command data of each layer after base weight adjustment and ratio optimization are integrated to obtain the fused gain set of the RF power amplifier.

[0129] Furthermore, a consistency criterion is pre-defined, which is the maximum allowable data difference between two consecutive analysis results of the fusion gain set. First, the fusion gain set is analyzed for the first time, and the specific data of each dimension is recorded. Then, a second analysis is performed based on the same hierarchical data group and dynamic weight coefficients, and the difference of each dimension data in the two analysis results is calculated. If there is a dimension whose difference exceeds the criterion, the analysis process is repeated until the difference of all dimensions in two consecutive analysis results meets the criterion. At this time, the data change of the fusion gain set tends to be stable. This stable change direction and data convergence state are organized into a regular description to obtain the convergence trend of the fusion gain set in the multidimensional data space.

[0130] Furthermore, according to the multidimensional data dimensions of the fused gain set, the convergence trends are arranged in the same dimension order, and the specific data of the convergence trend corresponding to each dimension are converted into numerical form to form an ordered numerical sequence that can quantify the direction and degree of gain calibration in each dimension. This numerical sequence is used as the intermediate calibration vector of the RF power amplifier.

[0131] Specifically, the impedance change rate is obtained by collecting the actual impedance value and the reference impedance value during the operation of the RF power amplifier, calculating the difference between the two and dividing it by the reference impedance value. The average impedance change rate is obtained by adding all the impedance change rates together and dividing by the total number of impedance change rates.

[0132] Furthermore, the dynamic weighting coefficients are determined using an analytic hierarchy process (AHP) based on the operating scenario of the RF power amplifier, such as input power level and operating frequency band. The AHP first divides the influencing factors in the operating scenario into a target layer, a criterion layer, and a scheme layer. The target layer determines the dynamic weighting coefficients, the criterion layer includes influencing factors such as input power level and operating frequency band, and the scheme layer contains the possible values ​​of each dynamic weighting coefficient. Then, a judgment matrix is ​​constructed. Based on the importance of each influencing factor, a 1-9 scale is used to assign values ​​to the factors in the criterion layer and the importance of the scheme layer relative to the factors in the criterion layer, thus obtaining the judgment matrix. Next, the largest eigenvalue of the judgment matrix and its corresponding eigenvector are calculated. After normalizing the eigenvectors, the dynamic weighting coefficients are obtained.

[0133] Furthermore, the impedance attenuation factor is determined experimentally based on the design specifications of the RF power amplifier, such as the operating frequency range and output power range. The experiment involves selecting multiple frequency points within the design frequency range of the RF power amplifier, selecting multiple output power values ​​at each frequency point, adjusting the impedance attenuation factor value for each combination of frequency point and output power value, dynamically adjusting the gain calibration command, and then testing the output performance of the RF power amplifier. The impedance attenuation factor value that meets the design requirements is then determined as the preset value.

[0134] Furthermore, the impedance cross-correlation parameters are determined by circuit simulation algorithms based on the circuit structure of the RF power amplifier, such as the topology of the matching network and component parameters. The circuit simulation algorithm first establishes a circuit simulation model containing components such as the matching network and power amplifier tubes based on the circuit structure of the RF power amplifier and determines the parameter values ​​of each component; then, different combinations of impedance change rates are set in the simulation model to simulate the circuit operation under different impedance states and record the cross-correlation data between each impedance; finally, the specific values ​​of the impedance cross-correlation parameters are obtained by data fitting based on the cross-correlation data.

[0135] Furthermore, the impedance change trend vector is calculated by using a linear regression algorithm on the impedance change rate data collected over a period of time. The linear regression algorithm first uses the collected time data as the independent variable and the corresponding impedance change rate data as the dependent variable; then it constructs a linear regression equation y=kx+b, where y is the impedance change rate, x is time, and k and b are regression coefficients; next, it uses the least squares method to calculate the values ​​of k and b that minimize the sum of the squares of the differences between the actual impedance change rate and the predicted value of the regression equation; finally, the regression coefficient k is determined as the impedance change trend vector.

[0136] Furthermore, the impedance fluctuation correction index is obtained through long-term experimental statistics based on the operating environment of the RF power amplifier, such as temperature and humidity. The long-term experimental statistics show that the RF power amplifier is continuously operated under different temperature and humidity combinations, and the impedance change rate data is collected periodically and the amplitude of impedance fluctuation under different environments is calculated. Then, based on the correspondence between the impedance fluctuation amplitude and environmental factors, the impedance fluctuation correction index under different environments is statistically obtained and determined as the preset value.

[0137] Furthermore, the smallest positive number to prevent the denominator from being zero is a fixed minimum value set by the user. When setting this value, a positive number much smaller than the smallest possible value of the denominator is selected based on the possible range of the denominator during the gain calibration command calculation process to ensure that the denominator is not zero.

[0138] Furthermore, by combining relevant parameters such as impedance change rate and dynamic weighting coefficient, a dynamic normalization factor is obtained. This dynamic normalization factor is then used to dynamically adjust the gain calibration command, so that the adjusted gain calibration command can adapt to the impedance change of the RF power amplifier, thereby obtaining an optimized gain calibration command for the RF power amplifier and ensuring that the RF power amplifier can maintain stable gain performance under different impedance conditions.

[0139] Furthermore, the smaller the difference between the impedance change rate and the mean impedance change rate, the smaller the absolute value of the negative exponent of the exponent term e, the larger the value of the exponent term, the larger the value of the first summation term, and the larger the value of the dynamic normalization factor. When the difference between each impedance change rate is smaller, and the difference between the impedance change trend vectors is larger, the smaller the numerator and the larger the denominator in the second summation term, the smaller the value of the second summation term, and the smaller the value of the dynamic normalization factor. When the square root of the sum of the squares of the dynamic weighting coefficient, the impedance fluctuation correction index, and the impedance interaction correlation parameter is larger, the denominator is larger, and the value of the dynamic normalization factor is smaller; conversely, the value of the dynamic normalization factor is larger. Overall, the dynamic normalization factor will adaptively adjust with the changes in the impedance-related parameters of the RF power amplifier, thereby enabling the adjustment of the gain calibration command to adapt to impedance changes and obtain an optimized gain calibration command.

[0140] In summary, generating an impedance difference matrix based on impedance change data can transform scattered impedance change information into structured data, clearly presenting impedance differences in different dimensions. This provides intuitive and orderly data support for subsequent extraction of impedance change trends, avoiding analytical biases caused by data chaos.

[0141] In summary, the impedance change trend vector obtained by singular value decomposition of the impedance difference matrix can filter out the core change patterns from complex impedance difference data, eliminate redundant interference information, and ensure that the extracted impedance change trend accurately reflects the overall change direction, thus laying a reliable foundation for determining the dynamic weighting coefficients.

[0142] In summary, by extracting dynamic characteristics of impedance change rate based on gain calibration instructions and obtaining dynamic weighting coefficients, the weighting allocation can be deeply bound to gain calibration requirements, prioritizing impedance change factors that have a significant impact on gain, making subsequent weighted fusion more targeted and improving the accuracy of gain adjustment.

[0143] In summary, the intermediate calibration vector obtained by multi-dimensional weighted fusion of gain calibration commands based on dynamic weight coefficients can integrate gain control requirements from different operating dimensions, avoid the limitations of single-dimensional commands, and allow the intermediate calibration vector to fully adapt to the complex operating states of the equipment, providing a comprehensive reference for the generation of the final optimized commands.

[0144] In summary, by calculating the dynamic normalization factor based on the intermediate calibration vector and adjusting the gain calibration command, the gain calibration command can be accurately matched with the current impedance change characteristics, effectively compensating for the gain deviation caused by impedance fluctuations. The resulting optimized gain calibration command can significantly improve the stability and accuracy of RF power amplifier gain control.

[0145] In summary, performing data layering on the impedance difference matrix to obtain layered data groups can break down the overall impedance change data into structured subsets according to the working state dimension, making the real-time impedance change characteristics clearer, avoiding analytical bias caused by data mixing, and providing accurate data units for subsequent weighted balancing.

[0146] In summary, the weighted balanced gain calibration command based on impedance variation dispersion and weight variation amplitude can adjust the calibration direction by combining the impedance stability and weight sensitivity of the hierarchical data, avoiding the limitations of single-dimensional adjustment, allowing the fused gain set to fully adapt to the working conditions of each layer and improving data reliability.

[0147] In summary, iterative consistency analysis of the fused gain set is performed, and the influence of random fluctuations is eliminated through multiple verifications, ensuring that the convergence trend truly reflects the stable approach direction of gain calibration, and providing a reliable basis for the intermediate calibration vector.

[0148] In summary, transforming the convergence trend into an intermediate calibration vector can convert abstract patterns into quantifiable structured data, providing an intuitive reference for subsequent dynamic normalization factor calculation and gain calibration command adjustment, and ensuring the accuracy of optimized calibration command generation.

[0149] S4. Based on the optimized gain calibration command, perform closed-loop feedback control on the gain parameters of the RF power amplifier to obtain the gain control command of the RF power amplifier.

[0150] In this embodiment of the invention, the step of performing closed-loop feedback control on the gain parameters of the RF power amplifier based on the optimized gain calibration command to obtain the gain control command of the RF power amplifier includes:

[0151] Based on the optimized gain calibration command, the real-time state of the RF power amplifier is analyzed to obtain the changing trend characteristics and amplitude distribution characteristics of the RF power amplifier.

[0152] Based on the changing trend characteristics and the amplitude distribution characteristics, the feature vector of the radio frequency power amplifier is constructed;

[0153] The dynamic adjustment factor of the RF power amplifier is obtained by performing correlation entropy matching between the feature vector and the operating data features of the RF power amplifier.

[0154] Based on the dynamic adjustment factor, the optimized gain calibration command is subjected to closed-loop modulation to obtain the gain control command of the RF power amplifier.

[0155] Specifically, the target parameters for adjusting the gain of the RF power amplifier are first extracted from the optimized gain calibration command, such as the target gain value and adjustment time interval. Then, the real-time status data of the device, such as the current output power, ambient temperature, and operating frequency band, are collected in real time through the status monitoring component of the RF power amplifier. These real-time status data are continuously recorded in chronological order, and the direction and rate of change of parameters such as output power and ambient temperature over time are analyzed to obtain the trend characteristics of the RF power amplifier. At the same time, the maximum, minimum, and average values ​​of the output amplitude and the distribution ratio of different amplitude ranges in the real-time status data are statistically analyzed to clarify the overall distribution of the output amplitude and obtain the amplitude distribution characteristics of the RF power amplifier.

[0156] Furthermore, the dimension of the feature vector is first determined. This dimension needs to cover the key indicators of the trend characteristics and amplitude distribution characteristics. For example, the output power change rate and temperature change rate in the trend characteristics, and the amplitude average value and amplitude distribution standard deviation in the amplitude distribution characteristics are taken as the core dimensions of the feature vector. Then, the specific values ​​corresponding to each dimension are arranged in a preset order to form an ordered numerical sequence. This sequence completely contains the key feature information of the real-time status of the RF power amplifier, thereby constructing the feature vector of the RF power amplifier.

[0157] Furthermore, features are first extracted from the previously collected RF power amplifier operating data, namely, key indicators such as the average value of output power, ambient temperature, and fluctuation range in the statistical operating data, forming operating data features. Then, the information entropy difference between the feature vector and the operating data features is calculated. By comparing the similarity between the two in data distribution and numerical range, the degree of fit between the current state reflected by the feature vector and the historical stable state represented by the operating data is judged. If the fit is high, the dynamic adjustment factor value is small, indicating that no major adjustment is needed; if the fit is low, the dynamic adjustment factor value is large, and the adjustment range needs to be increased. Based on this, the dynamic adjustment factor of the RF power amplifier is determined and obtained.

[0158] Furthermore, the dynamic adjustment factor is first correlated with the target gain parameter in the optimized gain calibration command. If the dynamic adjustment factor is small, it indicates that the current device state is highly consistent with the historical stable state, and only a small correction is needed to the target gain value in the optimized gain calibration command to better match the current subtle state changes. If the dynamic adjustment factor is large, the adjustment range of the target gain value needs to be expanded accordingly based on the numerical ratio of the adjustment factor to ensure that the adjusted command can adapt to the current large state deviation. After correction, the device state data is collected again to verify the adjustment effect. If the state data meets expectations, the modulation is completed. If not, the dynamic adjustment factor is recalculated based on the new state data and corrected again until the device state reaches expectations, and finally the gain control command of the RF power amplifier is obtained.

[0159] In summary, by optimizing the real-time status characteristics of gain calibration commands, key information about the current operating status of the device can be accurately captured, providing comprehensive and accurate basic data for subsequent feature vector construction and command modulation, and avoiding adjustment deviations caused by missing status information.

[0160] In summary, constructing feature vectors can transform dispersed state features into structured data, facilitating subsequent quantitative comparison with operational data features, improving the efficiency and accuracy of data processing, and providing a clear analytical framework for the calculation of dynamic adjustment factors.

[0161] In summary, by obtaining the dynamic adjustment factor through relevant entropy matching, the degree of deviation of the current state can be judged based on the historical operating pattern of the equipment, ensuring that the value of the adjustment factor conforms to the actual working condition requirements, and providing a scientific basis for optimizing the modulation of gain calibration commands.

[0162] In summary, closed-loop modulation based on dynamic adjustment factors can repeatedly verify, correct, and optimize gain calibration commands, ensuring that the final gain control commands accurately adapt to the current state of the equipment, and effectively improving the stability and accuracy of RF power amplifier gain control.

[0163] S5. Based on the gain control command, evaluate the control effect of the output signal of the RF power amplifier to obtain the control deviation index of the RF power amplifier.

[0164] In this embodiment of the invention, the step of evaluating the control effect of the output signal of the RF power amplifier based on the gain control command to obtain the control deviation index of the RF power amplifier includes:

[0165] Envelope extraction is performed on the output signal of the radio frequency power amplifier to obtain the amplitude feature sequence of the output signal;

[0166] The amplitude feature sequence is compared and analyzed with the target output amplitude value in multiple dimensions to obtain the amplitude deviation of the RF power amplifier;

[0167] Based on the amplitude deviation, a comprehensive deviation index for the output signal is constructed;

[0168] Principal component analysis was performed on the comprehensive deviation index to obtain the control deviation index of the RF power amplifier.

[0169] Specifically, the signal acquisition module connected to the output of the RF power amplifier continuously acquires the peak amplitude data of the output signal at each fixed sampling time. During the acquisition process, the sampling time interval is kept consistent. All the acquired peak amplitude data are arranged in the order of sampling time to form an ordered data sequence that can completely reflect the contour of the amplitude change of the output signal. This sequence is the amplitude characteristic sequence of the output signal.

[0170] Furthermore, the specific dimensions of the multi-dimensional comparison are first determined, including the time dimension and the amplitude distribution dimension: In the time dimension, the amplitude feature sequence is divided into multiple time periods of equal length, the average value of the amplitude feature sequence in each time period is calculated, and the difference between the average value and the preset target output amplitude value is compared; In the amplitude distribution dimension, the target output amplitude value is set as the center, a reasonable allowable amplitude range is defined, and the number of data in the amplitude feature sequence that exceeds the allowable range and the deviation of each data exceeding the target output amplitude value are counted; The comparison results under the two dimensions are quantified and integrated to obtain the amplitude deviation of the RF power amplifier.

[0171] Furthermore, based on the signal transmission requirements of different application scenarios, weights are first assigned to the time dimension deviation and the amplitude distribution dimension deviation, respectively. The amplitude deviation value of each dimension is multiplied by the corresponding weight to obtain the weighted deviation value of each dimension. Then, the sum of all weighted deviation values ​​is calculated. Combined with auxiliary parameters such as the duration of the output signal and the signal modulation method, the sum is finely adjusted to finally form a quantitative index that can comprehensively reflect the overall deviation of the output signal from the target requirements, namely the comprehensive deviation index of the output signal.

[0172] Furthermore, we first sort out the various influencing factors included in the comprehensive deviation index, such as short-term fluctuation deviation and long-term stable deviation in the time dimension, and slight deviation and severe deviation in the amplitude distribution dimension, and analyze the correlation between these factors; we then screen out the core influencing factors that contribute the most to the comprehensive deviation index and eliminate redundant factors with less influence; we sort the deviation data corresponding to the core influencing factors according to their importance to form a set of simplified data that can centrally reflect the main deviation problems of the output signal. This simplified data is the control deviation index of the RF power amplifier.

[0173] In summary, extracting the envelope of the output signal to obtain the amplitude feature sequence can accurately capture the core information of the output signal amplitude change, eliminate interference from irrelevant features such as signal phase, provide focused and reliable basic data for subsequent deviation analysis, and ensure the accuracy of control effect evaluation.

[0174] In summary, by obtaining amplitude deviation through multi-dimensional comparative analysis, the difference between the output signal and the target value can be comprehensively measured from two key perspectives: time and amplitude distribution. This avoids the omission of deviations caused by single-dimensional analysis and makes the amplitude deviation results more in line with the actual signal transmission quality requirements.

[0175] In summary, a comprehensive deviation index is constructed based on amplitude deviation. By integrating multi-dimensional deviation information through weight allocation and auxiliary parameter correction, scattered deviation data is transformed into a single quantitative index, which facilitates quick and intuitive judgment of the overall deviation degree of the output signal and improves the efficiency of deviation assessment.

[0176] In summary, principal component analysis of the comprehensive deviation index yields the control deviation index, which can eliminate redundant interference factors, focus on the core deviation problem, and enable the control deviation index to accurately reflect the key deviation causes affecting signal quality. This provides a clear and targeted optimization direction for the adaptive adjustment of the subsequent amplitude, temperature and gain control strategy.

[0177] S6. Based on the control deviation index, the amplitude temperature gain control strategy is adaptively adjusted.

[0178] In this embodiment of the invention, the adaptive adjustment of the amplitude temperature gain control strategy based on the control deviation index includes:

[0179] The control deviation index is decomposed into steady-state deviation components and transient deviation components.

[0180] Based on the proportional relationship between the steady-state deviation component and the transient deviation component, a strategy adjustment priority sequence for the RF power amplifier is generated;

[0181] Based on the steady-state deviation component and the transient deviation component, the strategy adjustment priority sequence is dynamically compressed to obtain the compressed priority sequence of the RF power amplifier;

[0182] Based on the timing characteristics of the transient deviation components, the compression priority sequence is proportionally allocated to generate the strategy parameter adjustment vector of the RF power amplifier;

[0183] Based on the strategy parameter adjustment vector, the mapping relationship in the amplitude temperature gain control strategy is dynamically reconstructed to complete the adaptive adjustment of the amplitude temperature gain control strategy.

[0184] Specifically, the multi-dimensional decomposition dimensions of the control deviation index are first determined, focusing on the actual operating characteristics of the RF power amplifier, and clearly defined as the two core dimensions of deviation duration and deviation change rate. Combining the routine monitoring needs and fluctuation patterns of RF power amplifier gain control, the deviation duration threshold is set to 4 consecutive minutes, and the deviation change rate threshold is set to a deviation change of no more than 0.3dB per minute. Subsequently, all recorded deviation data in the control deviation index are analyzed point-by-point: if a segment of deviation data simultaneously meets the two conditions of "duration exceeding 4 minutes" and "deviation change ≤ 0.3dB per minute", then the segment of data is classified as a steady-state deviation component. This component directly reflects the long-term, fixed gain deviation of the RF power amplifier caused by factors such as equipment aging and long-term operating losses. If the deviation data does not meet either of the above conditions, it is classified as a transient deviation component. This component mainly corresponds to the short-term, temporary gain deviation of the RF power amplifier caused by sudden changes in ambient temperature, temporary load fluctuations, and other sudden operating conditions. Finally, the steady-state deviation component and transient deviation component of the control deviation index are accurately decomposed.

[0185] Furthermore, the total values ​​of the steady-state deviation components and the transient deviation components are first calculated using data statistics tools. Then, the total values ​​of both are divided by the total value of the control deviation index to obtain their respective proportions in the overall deviation. For example, if the total value of the control deviation index is 10dB, and the total value of the steady-state deviation components is 6.5dB and the total value of the transient deviation components is 3.5dB, then the steady-state deviation components account for 65% and the transient deviation components account for 35%. Based on the principle of "prioritizing the resolution of core deviations that have a greater impact on gain stability," the strategy adjustment priority is determined according to the proportion of the deviation components: the strategy adjustment items corresponding to the deviation components with higher proportions have higher priorities. Taking the above proportions as an example, "strategy adjustment items for steady-state deviation correction" are ranked first in the strategy adjustment priority sequence, and "strategy adjustment items for transient deviation correction" are ranked second. According to this rule, all strategy adjustment items to be executed are sorted in descending order of priority to form a clearly structured RF power amplifier strategy adjustment priority sequence.

[0186] Furthermore, the actual numerical ranges of the steady-state and transient deviation components are first obtained through the device data acquisition module. For example, in actual monitoring, the steady-state deviation component ranges from 0.8 to 2.5 dB, and the transient deviation component ranges from 0.3 to 1.8 dB. Then, the initial priority values ​​of each adjustment item in the strategy adjustment priority sequence are extracted. The initial values ​​are converted to integers from 1 to 9 according to the proportion of the deviation components. If the difference in the initial priority values ​​is too large, it is easy for low-priority adjustment items to be overly ignored, which cannot meet the device's comprehensive needs for deviation correction. Therefore, dynamic range compression is performed by combining the actual numerical ranges of the two deviation components: the high-priority value is appropriately lowered to 7, and the low-priority value is appropriately raised to 2, so that the compressed priority value range is controlled within a reasonable range of 2-7. This compression process retains the priority differences of each adjustment item and avoids the adjustment imbalance caused by the large difference in priority, ultimately obtaining a compressed priority sequence for RF power amplifiers that takes into account both comprehensiveness and specificity.

[0187] Furthermore, the timing recording function built into the RF power amplifier was used to fully collect the occurrence time, duration, and trend of transient deviation components. For example, it was recorded that transient deviations occurred intensively between 9:00 and 10:00, with a total of 5 deviations, and the deviation value gradually increased from an initial 0.5dB to 1.5dB; only 2 brief transient deviations occurred between 10:00 and 11:00, with a maximum deviation value of 0.8dB; no significant transient deviations occurred between 11:00 and 12:00. Subsequently, the time intervals were divided into 1-hour units, and the number of transient deviations and the maximum deviation value in each interval were counted. Based on the principle that "the more concentrated and severe the transient deviations, the more adjustment resources need to be allocated to the time intervals," settings were set for each time interval. The adjustment ratio is determined as follows: 30% is allocated to the 9:00-10:00 interval due to frequent and large deviations; 15% to the 10:00-11:00 interval; and 5% to the 11:00-12:00 interval. Then, according to the order of adjustment items in the compression priority sequence, the adjustment ratio for each time interval is allocated to the corresponding adjustment item. For example, of the 30% adjustment ratio for the 9:00-10:00 interval, 20% is allocated to the "Transient Deviation Response Delay Optimization" item, and 10% is allocated to the "Transient Temperature Compensation Coefficient Adjustment" item. This forms an ordered set of values ​​containing "Adjustment Item Number - Time Interval - Adjustment Ratio," generating an RF power amplifier strategy parameter adjustment vector that can be directly used for parameter adjustment.

[0188] Furthermore, the original mapping relationship is first extracted from the storage module of the amplitude temperature gain control strategy. This mapping relationship exists in the form of a three-dimensional correspondence table of "ambient temperature - target output amplitude - gain reference value". The table clearly records the standard gain parameters under different operating condition combinations, such as "when the ambient temperature is 25℃ and the target output amplitude is 12dBm, the gain reference value is 20dB", "when the ambient temperature is 30℃ and the target output amplitude is 15dBm, the gain reference value is 22dB", "when the ambient temperature is 35℃ and the target output amplitude is 18dBm, the gain reference value is 24dB", etc. Then, according to the specific values ​​in the strategy parameter adjustment vector, the gain reference value of the corresponding "ambient temperature - target output amplitude" combination is precisely corrected: if the adjustment vector shows "ambient temperature 25℃, target output amplitude 12dBm", etc., the gain reference value is precisely corrected. If the corresponding adjustment value is +0.4dB, then the gain reference value for this combination will be corrected from 20dB to 20.4dB; if the adjustment value corresponding to "ambient temperature 30℃, target output amplitude 15dBm" is -0.2dB, then its gain reference value will be corrected from 22dB to 21.8dB; if the adjustment value corresponding to "ambient temperature 35℃, target output amplitude 18dBm" is +0.3dB, then it will be corrected to 24.3dB. After correcting the gain reference value for all "ambient temperature - target output amplitude" combinations one by one, a new "ambient temperature - target output amplitude - gain reference value" mapping table is regenerated to realize the dynamic reconstruction of the core mapping relationship in the amplitude temperature gain control strategy, and finally complete the adaptive adjustment of the strategy so that it is fully adapted to the deviation state of the current RF power amplifier.

[0189] In summary, by accurately distinguishing steady-state and transient deviation components through multi-dimensional decomposition, the problem of "mixing different types of deviations together" in traditional adjustment can be effectively avoided. The causes of long-term fixed deviations and short-term sudden deviations of RF power amplifiers can be accurately located, providing a clear and targeted direction for subsequent strategy adjustments and ensuring that adjustment measures can directly address the root cause of deviations rather than responding in a general way.

[0190] In summary, generating a strategy adjustment priority sequence based on the proportion of deviation components can achieve optimized resource allocation by "tilting resources toward core deviations," avoiding wasting adjustment resources on minor deviations with smaller impacts, significantly improving the efficiency of strategy adjustment, and ensuring that deviations that have the greatest impact on the stability of device gain can be addressed first, thus quickly improving the output signal quality of the RF power amplifier.

[0191] In summary, dynamic range compression of the priority sequence can balance the attention given to each adjustment item, prevent low-priority adjustment items from being completely ignored due to excessive initial priority differences, ensure that transient deviations, which may account for a small percentage but have a significant impact when they occur suddenly, can also receive reasonable resource adjustments, improve the comprehensiveness of strategy adjustments, and avoid chain problems caused by unresolved single deviations.

[0192] In summary, by allocating adjustment ratios based on the temporal characteristics of transient deviations, parameter adjustments can be precisely adapted to the temporal distribution patterns of short-term sudden deviations. Adjustment efforts can be strengthened during periods of concentrated deviations, such as increasing response speed optimization during the 9:00-10:00 period when transient deviations are frequent, and reasonably reducing adjustment resources during periods of low frequency. This not only improves the timeliness of responding to transient deviations but also avoids the waste of adjustment resources and reduces the impact of transient deviations on signal transmission stability.

[0193] In summary, by adjusting the vector mapping relationship based on strategy parameters, the abstract deviation correction requirements can be directly transformed into specific parameter changes in the amplitude, temperature, and gain control strategy. This makes the adjusted strategy highly compatible with the current device deviation state, rather than relying on a fixed static strategy. This dynamic reconstruction allows the strategy to continuously optimize as the device's operating state changes, ensuring that the gain control of the RF power amplifier is always in a precise and stable state, guaranteeing the consistency and reliability of the output signal, and meeting the stringent signal quality requirements of high-performance communication scenarios.

[0194] like Figure 2 The diagram shown is a functional block diagram of a dynamic calibration control system for RF power amplifier gain data provided in an embodiment of the present invention.

[0195] The dynamic calibration and control system 100 for RF power amplifier gain data described in this invention can be installed in an electronic device. Depending on the functions implemented, the dynamic calibration and control system 100 for RF power amplifier gain data may include a data acquisition module 101, an instruction generation module 102, an instruction optimization module 103, a control feedback module 104, an effect evaluation module 105, and an optimization and adjustment module 106. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0196] In this embodiment, the functions of each module / unit are as follows:

[0197] The data acquisition module 101 is used to acquire the operating data and real-time impedance change characteristics of the RF power amplifier.

[0198] The instruction generation module 102 is used to map the running data and the preset target output amplitude value to the amplitude temperature gain control strategy of the RF power amplifier to obtain the gain calibration instruction of the RF power amplifier.

[0199] The instruction optimization module 103 is used to perform impedance compensation adjustment on the gain calibration instruction based on the real-time impedance change characteristics to obtain the optimized gain calibration instruction of the RF power amplifier.

[0200] The control feedback module 104 is used to perform closed-loop feedback control on the gain parameters of the RF power amplifier based on the optimized gain calibration command, so as to obtain the gain control command of the RF power amplifier.

[0201] The effect evaluation module 105 is used to evaluate the control effect of the output signal of the RF power amplifier based on the gain control command, and obtain the control deviation index of the RF power amplifier.

[0202] The optimization and adjustment module 106 is used to adaptively adjust the amplitude temperature gain control strategy according to the control deviation index.

[0203] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0204] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0205] Furthermore, the functional modules in the various embodiments of the present invention 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 in the form of hardware plus software functional modules.

[0206] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0207] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A dynamic calibration control method for RF power amplifier gain data, characterized in that, The method includes: S1. Acquire the operating data and real-time impedance change characteristics of the RF power amplifier; S2. Map the running data and the preset target output amplitude value to the amplitude temperature gain control strategy of the RF power amplifier to obtain the gain calibration command of the RF power amplifier. S3. Based on the real-time impedance change characteristics, the gain calibration command is adjusted by impedance compensation to obtain the optimized gain calibration command of the RF power amplifier. S4. Based on the optimized gain calibration command, perform closed-loop feedback control on the gain parameters of the RF power amplifier to obtain the gain control command of the RF power amplifier. S5. Based on the gain control command, evaluate the control effect of the output signal of the RF power amplifier to obtain the control deviation index of the RF power amplifier. S6. Based on the control deviation index, the amplitude temperature gain control strategy is adaptively adjusted.

2. The dynamic calibration and control method for RF power amplifier gain data as described in claim 1, characterized in that, The acquisition of the operating data and real-time impedance change characteristics of the RF power amplifier includes: The output power sampling sequence and ambient temperature sampling sequence of the RF power amplifier are obtained to form the raw operating data of the RF power amplifier; The original operating data is averaged using a sliding window to obtain the operating data of the RF power amplifier; Based on the operating data, differential analysis is performed on the impedance variation trend of the RF power amplifier to obtain the impedance dynamic characteristics of the RF power amplifier. By performing time-series analysis on the impedance dynamic characteristics, the real-time impedance variation characteristics of the RF power amplifier are obtained.

3. The dynamic calibration and control method for RF power amplifier gain data as described in claim 1, characterized in that, The step of mapping the operating data to a preset target output amplitude value and then to the amplitude-temperature gain control strategy of the RF power amplifier to obtain the gain calibration command of the RF power amplifier includes: By performing trend correlation analysis between the operating data and the historical operating data of the RF power amplifier, the operating status mode of the RF power amplifier can be obtained. Based on the amplitude temperature gain control strategy, the operating state mode is mapped to obtain the corresponding strategy partition of the operating state mode. Based on the preset target output amplitude value, the gain reference value in the corresponding strategy partition is constrained to converge, and the initial gain calibration command of the RF power amplifier is obtained. The amplitude continuity of the initial gain calibration command is verified to obtain the gain calibration command of the RF power amplifier.

4. The dynamic calibration and control method for RF power amplifier gain data as described in claim 3, characterized in that, The step of performing trend correlation analysis between the operating data and the historical operating data of the RF power amplifier to obtain the operating status mode of the RF power amplifier includes: Similarity features are extracted from the historical operating data to obtain reference data for the radio frequency power amplifier; The operating data is dynamically matched with the reference data in time to obtain the operating trend of the RF power amplifier; Based on the operating trend, the operating data is subjected to modal decomposition to obtain the operating state mode of the RF power amplifier.

5. The dynamic calibration control method for RF power amplifier gain data as described in claim 1, characterized in that, The step of adjusting the gain calibration command based on the real-time impedance change characteristics to obtain the optimized gain calibration command for the RF power amplifier includes: Based on the impedance change data of the RF power amplifier, an impedance difference matrix of the RF power amplifier is generated. Singular value decomposition is performed on the impedance difference matrix to obtain the impedance change trend vector of the RF power amplifier; Based on the gain calibration command, the impedance change rate of the impedance change trend vector is dynamically extracted to obtain the dynamic weighting coefficient of the RF power amplifier. Based on the dynamic weighting coefficients, the gain calibration command is weighted and fused in multiple dimensions to obtain the intermediate calibration vector of the RF power amplifier. The dynamic normalization factor of the gain calibration command is calculated based on the intermediate calibration vector, and the gain calibration command is dynamically adjusted to obtain the optimized gain calibration command of the RF power amplifier. The formula for calculating the dynamic normalization factor is as follows: ； in, This represents the dynamic normalization factor. Indicates the first The aforementioned impedance change rate This represents the mean of the rate of change of impedance. Indicates the first The aforementioned dynamic weighting coefficients This represents the preset impedance attenuation factor. This represents the preset impedance cross-correlation parameters. Indicates the first The impedance change trend vector. This indicates the preset impedance fluctuation correction index. This represents the smallest positive number that prevents the denominator from being zero.

6. The dynamic calibration control method for RF power amplifier gain data as described in claim 5, characterized in that, The step of performing multi-dimensional weighted fusion of the gain calibration command based on the dynamic weighting coefficients to obtain the intermediate calibration vector of the RF power amplifier includes: The impedance difference matrix is ​​subjected to data layering processing to obtain a layered data group of the real-time impedance change characteristics; Based on the impedance variation dispersion and weight variation amplitude in the hierarchical data group, the gain calibration command is weighted and balanced to obtain the fused gain set of the RF power amplifier; An iterative consistency analysis is performed on the fusion gain set to obtain the convergence trend of the fusion gain set in the multidimensional data space; The convergence trend is used as the intermediate calibration vector of the RF power amplifier.

7. The dynamic calibration and control method for RF power amplifier gain data as described in claim 1, characterized in that, The step of performing closed-loop feedback control on the gain parameters of the RF power amplifier based on the optimized gain calibration command to obtain the gain control command of the RF power amplifier includes: Based on the optimized gain calibration command, the real-time state of the RF power amplifier is analyzed to obtain the changing trend characteristics and amplitude distribution characteristics of the RF power amplifier. Based on the changing trend characteristics and the amplitude distribution characteristics, the feature vector of the radio frequency power amplifier is constructed; The dynamic adjustment factor of the RF power amplifier is obtained by performing correlation entropy matching between the feature vector and the operating data features of the RF power amplifier. Based on the dynamic adjustment factor, the optimized gain calibration command is subjected to closed-loop modulation to obtain the gain control command of the RF power amplifier.

8. The dynamic calibration control method for RF power amplifier gain data as described in claim 1, characterized in that, The step of evaluating the control effect of the output signal of the RF power amplifier based on the gain control command to obtain the control deviation index of the RF power amplifier includes: Envelope extraction is performed on the output signal of the radio frequency power amplifier to obtain the amplitude feature sequence of the output signal; The amplitude feature sequence is compared and analyzed with the target output amplitude value in multiple dimensions to obtain the amplitude deviation of the RF power amplifier; Based on the amplitude deviation, a comprehensive deviation index for the output signal is constructed; Principal component analysis was performed on the comprehensive deviation index to obtain the control deviation index of the RF power amplifier.

9. The dynamic calibration control method for RF power amplifier gain data as described in claim 1, characterized in that, The adaptive adjustment of the amplitude temperature gain control strategy based on the control deviation index includes: The control deviation index is decomposed into steady-state deviation components and transient deviation components. Based on the proportional relationship between the steady-state deviation component and the transient deviation component, a strategy adjustment priority sequence for the RF power amplifier is generated; Based on the steady-state deviation component and the transient deviation component, the strategy adjustment priority sequence is dynamically compressed to obtain the compressed priority sequence of the RF power amplifier; Based on the timing characteristics of the transient deviation components, the compression priority sequence is proportionally allocated to generate the strategy parameter adjustment vector of the RF power amplifier; Based on the strategy parameter adjustment vector, the mapping relationship in the amplitude temperature gain control strategy is dynamically reconstructed to complete the adaptive adjustment of the amplitude temperature gain control strategy.

10. A dynamic calibration and control system for RF power amplifier gain data, characterized in that, The system includes: The data acquisition module is used to collect the operating data and real-time impedance change characteristics of the RF power amplifier. The instruction generation module is used to map the running data and the preset target output amplitude value to the amplitude temperature gain control strategy of the RF power amplifier to obtain the gain calibration instruction of the RF power amplifier. The instruction optimization module is used to adjust the gain calibration instruction based on the real-time impedance change characteristics to obtain the optimized gain calibration instruction of the RF power amplifier. The control feedback module is used to perform closed-loop feedback control on the gain parameters of the RF power amplifier based on the optimized gain calibration command, so as to obtain the gain control command of the RF power amplifier. The effect evaluation module is used to evaluate the control effect of the output signal of the RF power amplifier based on the gain control command, and obtain the control deviation index of the RF power amplifier. The optimization and adjustment module is used to adaptively adjust the amplitude temperature gain control strategy according to the control deviation index.

Images