A multi-source distortion ADC behavior model construction method, system, device and medium

Abstract

The application discloses a kind of multi-source distortion ADC behavior model construction method, system, equipment and medium, it is related to wideband microsystem modeling simulation technical field.The method is first to collect ADC measured data and construct training set, and the model for predicting ADC output signal pre-harmonic power and signal-to-noise ratio is obtained using BP neural network training;Second, based on the total signal-to-noise ratio predicted, the inherent quantization signal-to-noise ratio is eliminated, and the sampling clock deviation is calculated to represent aperture jitter distortion;Third, based on the pre-harmonic power predicted, polynomial coefficients are solved simultaneously to represent nonlinear distortion;Finally, the signal is quantized and encoded using the minimum quantization unit to calculate to represent quantization distortion.The application can accurately represent the multi-source distortion characteristics in the actual working process of ADC, and provide strong support for wideband microsystem radio frequency digital cross-domain simulation evaluation.

Description

Technical Field

[0001] This invention relates to the field of broadband microsystem modeling and simulation technology, specifically to a method, system, device, and medium for constructing a multi-source distortion ADC behavior model. Background Technology

[0002] The statements in this section are provided only as background information in connection with this disclosure and may not constitute prior art.

[0003] As the integration and complexity of broadband microsystems continue to increase, product forms are gradually evolving towards integrated RF and digital architectures, with multi-level and cross-domain coupling effects becoming increasingly significant. In the development of broadband microsystem products, system-level RF digital cross-domain simulation evaluation is increasingly becoming a crucial guarantee for product performance and design success rate. Currently, the industry has relatively mature solutions for multi-physics coupling and efficient, high-precision electromagnetic simulation across scales in single-discipline high-density integrated products. However, shortcomings remain in the field of efficient, high-precision system-level RF digital cross-domain simulation, especially in the construction of accurate cross-domain conversion models.

[0004] In the field of analog-to-digital converter (ADC) modeling and simulation, constructing accurate ADC behavior models has become crucial for simulation accuracy. However, many current industry methods either directly use ideal ADC models for simulation calculations or rely too heavily on simplification assumptions, resulting in an inability to accurately characterize the actual behavior of the ADC, especially the combined impact of non-ideal physical factors such as aperture jitter, nonlinearity, and quantization on the ADC's data acquisition and conversion process. This makes the constructed ADC behavior models perform poorly in broadband microsystem RF digital cross-domain simulation evaluation applications, hindering the efficient and accurate simulation design of broadband RF digital integrated microsystem products. Summary of the Invention

[0005] The purpose of this invention is to address the problem that existing ADC behavior models in broadband microsystem radio frequency digital cross-domain simulation are unable to accurately represent the combined effects of multiple non-ideal factors. This invention provides a method, system, device, and medium for constructing a multi-source distortion ADC behavior model. Based on the technique of BP neural network prediction combined with physical distortion inverse derivation, aperture jitter distortion, nonlinear distortion, and quantization distortion are applied sequentially to the input analog signal, thereby achieving a high-precision physical-level characterization of the combined effects of multiple sources of distortion in the actual operation of the ADC.

[0006] The technical solution of the present invention is as follows: A method for constructing a behavior model of a multi-source distortion ADC includes: Step S1: ADC modeling, data acquisition, and prediction model training; acquire the output codeword data of the ADC under analog signal inputs with different frequencies and powers, and obtain the measured time-domain sequence data through decoding and transformation; perform spectral analysis on the measured time-domain sequence data to obtain the preceding... A training set is constructed using first-order harmonic power and signal-to-noise ratio data; the training set is then trained using a BP neural network algorithm to obtain the first-order harmonic power and signal-to-noise ratio data used to predict the ADC output signal. A neural network prediction model for first harmonic power and signal-to-noise ratio; Step S2: Characterization of ADC aperture jitter distortion characteristics; For an input analog signal with a preset frequency and preset power, the total signal-to-noise ratio (SNR) of the input analog signal after passing through the ADC is predicted using the neural network prediction model; the corresponding quantization SNR is calculated based on the inherent quantization noise power of the ADC, and the quantization SNR is removed from the total SNR to extract the SNR introduced by aperture jitter, and the mean square error of the sampling clock deviation is calculated in combination with the preset frequency; the mean square error is used to generate ADC sampling time points with random deviation, and the input analog signal is sampled according to the ADC sampling time points to obtain a first discrete signal sequence containing the influence of aperture jitter distortion; Step S3: Characterize the nonlinear distortion characteristics of the ADC; use the neural network prediction model to predict the input analog signal after passing through the ADC. First harmonic power values; constructing a characterization of the nonlinear mapping relationship between ADC input and output. A polynomial model of order [order] is used to calculate the corresponding voltage amplitude based on the preset power, and based on the predicted [previous power]... The first harmonic power value and the The harmonic expressions of each order extracted after expanding the polynomial model are combined to construct a system of equations to obtain the desired result. The coefficients of the polynomial model of order 1; substituting the first discrete signal sequence into the solution of the coefficients... In the first-order polynomial model, a second discrete signal sequence containing the effects of nonlinear distortion is calculated; Step S4: Characterization of ADC quantization distortion characteristics; Calculate the minimum quantization unit based on the ADC's reference voltage and quantization bit depth; Quantize and encode the second discrete signal sequence using the minimum quantization unit to finally obtain the time-domain discrete signal output by the multi-source distortion ADC behavior model. The time-domain discrete signal characterizes the actual working characteristics of the ADC with aperture jitter distortion, nonlinear distortion, and quantization distortion.

[0007] Furthermore, the specific process of step S1 includes: Adjust the frequency and power of the analog signal input to the ADC according to the preset step, collect the actual codeword data output by the ADC, and collect the noise floor codeword data output by the ADC when there is no signal input in the analog signal off state; The actual codeword data and noise floor codeword data collected are decoded according to the corresponding data encoding format. The transformation calculation is performed by combining the quantization bits of the ADC and the reference voltage to obtain the measured time-domain sequence data and the noise floor time-domain sequence data, respectively. The measured time-domain sequence data is subjected to a Fast Fourier Transform to obtain spectral data, and the preceding data is extracted. The power value corresponding to the higher harmonic frequencies; where, when the higher harmonic frequencies exceed the first Nyquist zone... When folding to the first Nyquist zone, the folding frequency is calculated, and the power value corresponding to the folding frequency is extracted. The sampling rate of the ADC; Based on the measured time-domain sequence data and the noise floor time-domain sequence data, the signal-to-noise ratio (SNR) data is calculated, and then compared with the corresponding previous... The training set is constructed using the power values ​​of the first harmonics.

[0008] Further, in step S2, the process of extracting the signal-to-noise ratio introduced by aperture jitter and calculating the root mean square error of the sampling clock deviation specifically includes: According to the formula Calculate the quantization signal-to-noise ratio corresponding to the inherent quantization noise power. ,in The quantization bit depth of the ADC; According to the formula Extract the signal-to-noise ratio introduced by aperture jitter. ,in The total signal-to-noise ratio is the predicted value; According to the formula The mean square error of the sampling clock bias was calculated. ,in The preset frequency; A normal distribution random number generator is used to generate numbers with a mean of 0 and a variance of [value missing]. clock deviation and according to the formula The ADC sampling time point was calculated. ,in , For the sampling sequence index, The length of the discrete signal sequence output by the ADC.

[0009] Further, in step S3, the The polynomial model of order 1 is represented as:

[0010] in, For input signal, For output signal, denoted as the coefficients of the polynomial to be solved.

[0011] Furthermore, in step S3, a system of equations is constructed simultaneously to solve for the obtained equations. The process of determining the coefficients of each term in a polynomial model includes: The input analog signal is expressed as Substituting the form into the above The expansion is performed in a polynomial model of order X, and the coefficient expressions corresponding to each order harmonic in the expansion are extracted; where... The voltage amplitude is calculated from the preset power. For the preset frequency, It is a time variable; The predicted front The harmonic power values ​​are converted into voltage amplitude expressions for the corresponding harmonic orders, and equations are established with the corresponding harmonic coefficient expressions to form a system containing... The system of equations is solved to obtain the following equations. The specific value.

[0012] Furthermore, in step S4, the formula for calculating the smallest quantization unit is: ;in, The reference voltage for the ADC is... The quantization bit depth of the ADC is denoted as .

[0013] Further, in step S4, the formula for quantizing and encoding the second discrete signal sequence using the minimum quantization unit is as follows:

[0014] in, The second discrete signal sequence, The final time-domain discrete signal is obtained. This indicates a round-down operation.

[0015] This invention also proposes a system for constructing a multi-source distortion ADC behavior model, comprising: The data acquisition and prediction model training module is used to acquire the output codeword data of the ADC under analog signal inputs with different frequencies and powers, and obtain the measured time-domain sequence data through decoding and transformation; the measured time-domain sequence data is then subjected to spectral analysis to obtain the preceding... A training set is constructed using first-order harmonic power and signal-to-noise ratio data; the training set is then trained using a BP neural network algorithm to obtain the first-order harmonic power and signal-to-noise ratio data used to predict the ADC output signal. A neural network prediction model for first harmonic power and signal-to-noise ratio; An aperture jitter distortion characterization module is used to predict the total signal-to-noise ratio (SNR) of an input analog signal with a preset frequency and preset power after passing through an ADC using the neural network prediction model; calculate the corresponding quantization SNR based on the inherent quantization noise power of the ADC; remove the quantization SNR from the total SNR to extract the SNR introduced by aperture jitter; and calculate the mean square error of the sampling clock deviation based on the preset frequency. The module then uses the mean square error to generate ADC sampling time points with random bias, and samples the input analog signal according to the ADC sampling time points to obtain a first discrete signal sequence containing the effects of aperture jitter distortion. The nonlinear distortion characteristic characterization module is used to predict the input analog signal after passing through the ADC using the neural network prediction model. First harmonic power values; constructing a characterization of the nonlinear mapping relationship between ADC input and output. A polynomial model of order [order] is used to calculate the corresponding voltage amplitude based on the preset power, and based on the predicted [previous power]... The first harmonic power value and the The harmonic expressions of each order extracted after expanding the polynomial model are combined to construct a system of equations to obtain the desired result. The coefficients of the polynomial model of order 1; substituting the first discrete signal sequence into the solution of the coefficients... In the first-order polynomial model, a second discrete signal sequence containing the effects of nonlinear distortion is calculated; The quantization distortion characteristic characterization module is used to calculate the minimum quantization unit based on the reference voltage and quantization bit depth of the ADC; and to perform quantization encoding calculation on the second discrete signal sequence using the minimum quantization unit to finally obtain the time-domain discrete signal output by the multi-source distortion ADC behavior model. The time-domain discrete signal characterizes the actual working characteristics of the ADC with aperture jitter distortion, nonlinear distortion and quantization distortion.

[0016] The present invention also proposes an electronic device, comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor. By executing the instructions stored in the memory, the at least one processor performs the multi-source distortion ADC behavior model construction method described above.

[0017] The present invention also proposes a computer-readable storage medium for storing instructions that, when executed, enable the multi-source distortion ADC behavior model construction method described above to be implemented.

[0018] Compared with existing technologies, the advantages of this invention are: 1. This invention innovatively constructs a multi-source distortion behavior model framework that includes the characteristics of time axis (aperture jitter), mapping layer (nonlinear distortion) and amplitude axis (quantization distortion), breaking the limitations of traditional ideal ADC models or single distortion models, and can comprehensively and realistically reflect the comprehensive non-ideal effects of physical devices in actual operation.

[0019] 2. This invention trains a BP neural network using measured data, and cleverly decomposes the macroscopic indicators (total signal-to-noise ratio and harmonic power of each order) predicted by the black-box neural network in reverse and accurately projects them onto the microscopic parameters at the physical level (i.e., the root mean square error of the sampling clock deviation and the mapping coefficients of the nonlinear polynomial), thereby achieving a precise quantitative characterization of multi-source distortion characteristics.

[0020] 3. This invention significantly improves the representation accuracy of ADC behavior models (the simulation consistency of signal-to-noise ratio and spurious-free dynamic range can both reach over 95%), providing extremely strong data support for precise RF digital-to-analog conversion and cross-domain simulation evaluation of broadband microsystems, which helps in the optimized design and performance improvement of microsystem products. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0022] Figure 1 This is a framework diagram of the multi-source distortion ADC behavior model provided in the embodiments of the present invention; Figure 2 This is a spectrum diagram of the input analog signal in an embodiment of the present invention; Figure 3 This is the spectrum of the first discrete signal sequence after the input signal undergoes aperture jitter distortion in an embodiment of the present invention; Figure 4 This is the spectrum of the second discrete signal sequence after nonlinear distortion in this embodiment of the invention; Figure 5 This is the spectrum of the final time-domain discrete signal output after quantization distortion in this embodiment of the invention; Figure 6 This is a spectrum diagram of the measured output signal of the ADC in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0024] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0025] Example 1 While the industry has relatively mature solutions for multi-physics coupling and efficient, high-precision electromagnetic simulation across scales in single-discipline high-density integrated products, shortcomings remain in the field of efficient, high-precision cross-domain simulation of system-level RF digital systems, especially in the construction of accurate cross-domain conversion models. In the field of analog-to-digital converter (ADC) modeling and simulation, existing technologies often directly use ideal ADC models for simulation calculations or rely too heavily on simplified assumptions. This makes it difficult to accurately characterize the comprehensive impact of non-ideal physical factors such as aperture jitter, nonlinearity, and quantization on the ADC data acquisition and conversion process, and thus fails to support efficient and accurate simulation design of broadband RF digital integrated microsystem products.

[0026] To address the aforementioned technical issues, this embodiment provides a method for constructing a multi-source distortion ADC behavior model. Figure 1 This paper illustrates the framework of a multi-source distortion characteristic ADC behavior model constructed according to the present invention. Within this model framework, the input time-domain analog signals are sequentially processed... After applying aperture jitter distortion, nonlinear distortion, and quantization distortion processing, the time-domain discrete signal output by the ADC multi-source distortion behavior model is finally obtained. .

[0027] In this embodiment, specifically, a method for constructing a multi-source distortion ADC behavior model includes the following steps: Step S1: ADC modeling data acquisition and prediction model training.

[0028] The output codeword data of the ADC under analog signal inputs with different frequencies and powers is acquired, and then decoded and transformed to obtain measured time-domain sequence data; the measured time-domain sequence data is then subjected to spectral analysis to obtain the preceding data. A training set is constructed using first-order harmonic power and signal-to-noise ratio data; the training set is then trained using a BP neural network algorithm to obtain the first-order harmonic power and signal-to-noise ratio data used to predict the ADC output signal. A neural network prediction model for first harmonic power and signal-to-noise ratio.

[0029] Specifically, the modeling data used in this embodiment comes from actual ADC test data. The test environment is as follows: the ADC evaluation board under test is connected to the signal source and the reference clock respectively, and connected to the host computer via a serial port. The host computer software accompanying the ADC evaluation board is used to collect the ADC output data. The frequency and power of the analog signal input to the ADC are adjusted according to preset steps, and the actual codeword data output by the ADC are collected respectively. In addition, in order to evaluate the noise effect of the ADC itself, the noise floor codeword data output by the ADC when there is no signal input needs to be collected.

[0030] Depending on the encoding mechanism of the current / voltage signal on the ADC evaluation board, the obtained codeword data has different encoding formats (such as the commonly used Offset Binary and Two's Complement formats). The acquired data is decoded according to the corresponding data encoding format, and transformed and calculated in combination with the ADC's quantization bit depth and reference voltage to obtain the measured time-domain sequence data (actual current / voltage time-domain sequence data) and the noise floor time-domain sequence data, respectively.

[0031] The measured time-domain sequence data is subjected to Fast Fourier Transform (FFT) to obtain spectral data, and the preceding data is extracted. The power value corresponding to the first harmonic frequency. Since the FFT frequency range is the first Nyquist zone... Therefore, when the higher harmonic frequencies exceed the first Nyquist zone... When folding to the first Nyquist zone, the folding frequency is calculated, and the power value corresponding to the folding frequency is extracted. The sampling rate of the ADC.

[0032] Subsequently, the signal-to-noise ratio (SNR) data was calculated based on the measured time-domain sequence data and the noise floor time-domain sequence data. After preprocessing, a dataset for neural network training was obtained. Each data point in the dataset contains the input (frequency and power of the analog signal injected into the ADC) and the output (the front end of the digital signal output by the ADC). The data matrix structure of the first harmonic power value and signal-to-noise ratio (SNR) is shown in equation (1) below: Equation (1) By using the BP neural network algorithm to autonomously learn and train the dataset shown in equation (1), an ADC output signal that can accurately predict different input signal frequencies and powers is obtained. A neural network model for first harmonic power and signal-to-noise ratio.

[0033] Step S2: Characterization of ADC aperture jitter distortion characteristics.

[0034] For an input analog signal with a preset frequency and preset power, the total signal-to-noise ratio (SNR) of the input analog signal after passing through the ADC is predicted using the neural network prediction model; the corresponding quantization SNR is calculated based on the inherent quantization noise power of the ADC; the quantization SNR is removed from the total SNR to extract the SNR introduced by aperture jitter; and the mean square error of the sampling clock deviation is calculated in conjunction with the preset frequency; the mean square error is used to generate ADC sampling time points with random deviations; the input analog signal is sampled according to these sampling time points to obtain a first discrete signal sequence containing the influence of aperture jitter distortion.

[0035] In this embodiment, it should be noted that aperture jitter is a sampling clock deviation caused by the inherent physical randomness of the ADC device. For the analog signal injected into the ADC... Assuming its preset frequency is (Hz), preset power is (dBm), using the trained model to predict the output signal after passing through the ADC. First harmonic power And the total signal-to-noise ratio (SNR).

[0036] The total signal-to-noise ratio (SNR) value is the total SNR of the output signal, where the noise power mainly includes the noise power introduced by quantization. Noise power introduced by aperture jitter The following relationship exists: Equation (2) Among them, quantization signal-to-noise ratio It mainly depends on the quantization bit depth of the ADC. The relationship is as follows: Equation (3) The signal-to-noise ratio introduced by aperture jitter can be extracted from equations (2) and (3). As shown in the following formula: Equation (4) The sampling clock deviation caused by ADC aperture jitter is considered to have a mean of 0 and a variance of... Gaussian white noise, therefore the following relationship exists: Equation (5) The mean square error of the sampling clock bias is thus derived. for: Equation (6) The clock offset for each sampling time is generated using a normally distributed random number generator: Equation (7) Therefore, the ADC sampling time point with random bias obtained due to aperture jitter is: Equation (8) in , For the sampling sequence index, The length of the discrete signal sequence output by the ADC. This refers to the sampling rate. For the input analog signal... By sampling at the above sampling time points, the first discrete signal sequence considering aperture jitter distortion can be obtained. .

[0037] Step S3: Characterization of ADC nonlinear distortion characteristics.

[0038] Using the prediction model to obtain the previous First harmonic power values; construction The first discrete signal sequence is obtained by substituting the first discrete signal sequence into the solved model. The voltage amplitude is calculated based on the preset power, and the equations are constructed by combining the expanded harmonic expressions to solve for the polynomial coefficients.

[0039] In this embodiment, it should be noted that the nonlinear distortion of the ADC is caused by physical factors such as the non-ideality of the internal circuit and temperature drift. This embodiment adopts the equation shown in equation (9). A polynomial of order 1 represents a nonlinear mapping relationship: Equation (9) In equation (9), For input signal, For output signal, The coefficients to be solved are denoted as .

[0040] As a specific derivation method, we assume the order. , input signal (The voltage amplitude can be calculated from the input power, i.e.) , For the preset frequency, Substituting the variable (representing time) into the polynomial and expanding it, we obtain the following result: Equation (10) The output signal predicted based on the above neural network model is the front The power value of the first harmonic frequency is Then, by extracting the expressions for each harmonic from equation (10) and establishing equations, we can obtain the following set of equations: Equation (11) The coefficients of the polynomial can be obtained by solving the system of equations (11) simultaneously. The first discrete signal sequence obtained in step S2. Substituting into the nonlinear mapping relationship shown in equation (9), the second discrete signal sequence after nonlinear distortion can be calculated. .

[0041] Step S4: Characterization of ADC quantization distortion characteristics.

[0042] Because the register of an ADC has a finite number of bits, using a finite number of bits of digital signal to represent the input analog signal will introduce quantization error. For a reference voltage of... Quantization bits are The formula for calculating the minimum quantization unit (LSB) of an ADC is as follows: Equation (12) The second discrete signal sequence obtained in step S3 is processed using equation (12). Quantization encoding calculations are performed to obtain the actual time-domain discrete signal output by the ADC. The quantization process is shown in the following formula: Equation (13) in, This indicates a floor operation. The final discrete-time signal sequence is obtained. It comprehensively covers the combined effects of multiple sources of distortion in ADCs, including aperture jitter distortion, nonlinear distortion, and quantization distortion.

[0043] Example 2 This embodiment uses a specific ADC chip as an example to elaborate on and verify the accuracy of the above-mentioned method for constructing a multi-source distortion ADC behavior model. The sampling rate of this ADC chip is... Quantization bits are Bit, reference voltage The data encoding method is Two's Complement. The construction method described in this embodiment includes the following steps: Step S1: ADC modeling data acquisition and prediction model training.

[0044] The ADC evaluation board under test is connected to the signal source and the 2.4GHz reference clock respectively, and connected to the host computer via serial port. The host computer software that comes with the ADC evaluation board is used to acquire the ADC output data.

[0045] The frequency adjustment range of the signal source is 10~2300 MHz with a step of 20 MHz, and the power adjustment range is -60~10 dBm with a step of 2 dBm. Actual codeword data output by the ADC at different input frequencies and powers is sequentially acquired and stored according to the preset step size. Furthermore, the noise floor codeword data output by the ADC when there is no signal input is acquired when the analog signal is off.

[0046] The actual codeword data and noise floor codeword data are decoded according to the Two's Complement encoding method, and the transformation calculation is performed according to the quantization bit of the ADC and the reference voltage to obtain the measured time-domain sequence data (i.e. the actual voltage time-domain sequence data) and the noise floor time-domain sequence data, respectively.

[0047] Then, an FFT transform is performed on the measured time-domain sequence data to obtain the spectral data, and the preceding data is sequentially extracted from the spectral data. The power value of the first harmonic frequency is taken in this embodiment. For frequencies exceeding 1.2 GHz (i.e., the first Nyquist zone) The higher harmonic frequencies of the signal are calculated, and the folding frequency of the signal folding to the first Nyquist zone [0, 1.2GHz] is calculated. Then, the power value corresponding to the folding frequency is taken.

[0048] Finally, based on the measured time-domain sequence data at the current input frequency and power and the noise floor time-domain sequence data under no signal input, the signal-to-noise ratio (SNR) data is calculated.

[0049] After preprocessing, a dataset of 4140 data points was obtained for training. Each data point in the dataset contains input and output. The input is the frequency and power of the analog signal injected into the ADC, and the output is the output signal of the ADC. The power values ​​and signal-to-noise ratio (SNR) values ​​corresponding to the first harmonic are determined. A backpropagation (BP) neural network algorithm is used to autonomously learn and train on the obtained 4140 conditional data points, resulting in a neural network prediction model capable of accurate prediction.

[0050] Step S2: Characterization of ADC aperture jitter distortion characteristics.

[0051] In this embodiment, the preset frequency and preset power of the input analog signal are respectively and Its voltage amplitude can be calculated as follows: The spectrum of its input analog signal is as follows: Figure 2As shown, its fundamental frequency is folded to 1.1 GHz in the first Nyquist zone.

[0052] Using the neural network model trained in step S1, the total signal-to-noise ratio (SNR) of the above-mentioned input analog signal after passing through the ADC is predicted to be 45.69 dB.

[0053] The quantization signal-to-noise ratio corresponding to the inherent quantization noise power is calculated by combining the 12-bit quantization bit depth. The quantization signal-to-noise ratio is removed from the total signal-to-noise ratio to extract the signal-to-noise ratio introduced by aperture jitter. Furthermore, by combining this with the preset frequency, the mean square error of the sampling clock deviation is calculated as follows: .

[0054] Therefore, by obtaining the ADC sampling time points with random bias caused by aperture jitter distortion, and then sampling the input analog signal, the first discrete signal sequence considering the influence of aperture jitter distortion can be obtained. Its spectrum is as follows Figure 3 As shown.

[0055] Step S3: Characterization of ADC nonlinear distortion characteristics.

[0056] use The nonlinear distortion of the ADC is characterized by a polynomial of order 1, where denoted as the coefficients of the terms to be solved.

[0057] Using the neural network prediction model trained in step S1, predict the output of the above-mentioned input analog signal after passing through the ADC. The first harmonic power values ​​are as follows: , , , By combining the calculated voltage amplitude with a system of equations, the coefficients of the polynomial can be obtained. , , , , .

[0058] The first discrete signal sequence obtained in step S2 Substituting these values ​​into the nonlinear mapping relationship, the second discrete signal sequence, which includes the effects of nonlinear distortion, can be calculated. Its spectrum is as follows Figure 4 As shown.

[0059] Step S4: Characterization of ADC quantization distortion characteristics.

[0060] The minimum quantization unit is calculated based on a reference voltage of 1.2V and a quantization bit depth of 12 bits. The second discrete signal sequence obtained in step S3 is processed using the minimum quantization unit. Quantization encoding calculation (rounding down) is performed to obtain the final time-domain discrete signal output by the ADC. . The sequence contains multiple sources of distortion, including nonlinear distortion, aperture jitter distortion, and quantization distortion of the ADC, and its spectrum is shown in the figure. Figure 5 As shown.

[0061] The following is an accuracy evaluation of the ADC behavior model simulation: To evaluate the accuracy of the ADC behavior model constructed in this embodiment, the frequency of the ADC under test was collected in a test environment. and power The quantized codeword data actually output under the given conditions is then converted to obtain the measured time-domain sequence data. Its spectrum is as follows Figure 6 As shown.

[0062] The time-domain discrete signal output by the ADC model constructed based on this invention The signal-to-noise ratio (SNR) of the output signal can be calculated to be 43.788 dB and the spurious-free dynamic range (SFDR) to be 42.793 dB.

[0063] Based on hardware-measured time-domain sequence data The signal-to-noise ratio (SNR) of the measured output signal can be calculated to be 45.692 dB and the spurious-free dynamic range (SFDR) to be 43.332 dB.

[0064] The comparison shows that the signal-to-noise ratio (SNR) of the two is as consistent as 95.8%, and the spurious-free dynamic range (SFDR) is as consistent as 98.8%. The comparison results fully demonstrate that the multi-source distortion ADC behavior model construction method of the present invention can accurately characterize the multi-source distortion characteristics in the actual working process of ADC, and has extremely high engineering application and simulation evaluation value.

[0065] Example 3 Based on the same inventive concept as the above embodiments, this embodiment provides a multi-source distortion ADC behavior model construction system. Since the principle by which this system solves the problem is similar to the multi-source distortion ADC behavior model construction method described in Embodiment 1, the specific implementation process and technical principles of this system can be found in the detailed description of the foregoing method embodiments; repeated details will not be repeated here.

[0066] Specifically, the multi-source distortion ADC behavior model construction system provided in this embodiment includes: The data acquisition and prediction model training module is used to acquire the output codeword data of the ADC under analog signal inputs with different frequencies and powers, and obtain the measured time-domain sequence data through decoding and transformation; the measured time-domain sequence data is then subjected to spectral analysis to obtain the preceding... A training set is constructed using first-order harmonic power and signal-to-noise ratio data; the training set is then trained using a BP neural network algorithm to obtain the first-order harmonic power and signal-to-noise ratio data used to predict the ADC output signal. A neural network prediction model for first harmonic power and signal-to-noise ratio; An aperture jitter distortion characterization module is used to predict the total signal-to-noise ratio (SNR) of an input analog signal with a preset frequency and preset power after passing through an ADC using the neural network prediction model; calculate the corresponding quantization SNR based on the inherent quantization noise power of the ADC; remove the quantization SNR from the total SNR to extract the SNR introduced by aperture jitter; and calculate the mean square error of the sampling clock deviation based on the preset frequency. The module then uses the mean square error to generate ADC sampling time points with random bias, and samples the input analog signal according to the ADC sampling time points to obtain a first discrete signal sequence containing the effects of aperture jitter distortion. The nonlinear distortion characteristic characterization module is used to predict the input analog signal after passing through the ADC using the neural network prediction model. First harmonic power values; constructing a characterization of the nonlinear mapping relationship between ADC input and output. A polynomial model of order [order] is used to calculate the corresponding voltage amplitude based on the preset power, and based on the predicted [previous power]... The first harmonic power value and the The harmonic expressions of each order extracted after expanding the polynomial model are combined to construct a system of equations to obtain the desired result. The coefficients of the polynomial model of order 1; substituting the first discrete signal sequence into the solution of the coefficients... In the first-order polynomial model, a second discrete signal sequence containing the effects of nonlinear distortion is calculated; The quantization distortion characteristic characterization module is used to calculate the minimum quantization unit based on the reference voltage and quantization bit depth of the ADC; and to perform quantization encoding calculation on the second discrete signal sequence using the minimum quantization unit to finally obtain the time-domain discrete signal output by the multi-source distortion ADC behavior model. The time-domain discrete signal characterizes the actual working characteristics of the ADC with aperture jitter distortion, nonlinear distortion and quantization distortion.

[0067] It should be noted that the information interaction and execution process between the modules / units in the above system are based on the same concept as the method embodiments of the present invention. Their specific functions and technical effects can be found in the method embodiments section, and will not be repeated here. The above functional modules can be implemented in software, hardware, or a combination of both.

[0068] Based on the same technical concept, embodiments of the present invention also provide an electronic device that can implement the multi-source distortion ADC behavior model construction method provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic devices. Figure 7 As shown, the electronic device may include: At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 7 The example used is the connection between the processor and memory via a bus. The bus... Figure 7 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 7 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0069] In this embodiment of the invention, the memory stores instructions executable by at least one processor. By executing the instructions stored in the memory, the at least one processor can perform the multi-source distortion ADC behavior model construction method described above. The processor can implement... Figure 7 The functions of each module in the device shown.

[0070] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0071] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0072] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the multi-source distortion ADC behavior model construction method disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0073] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia cards, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), and electrically erasable programmable read-only memory (EPROM). Only memory (EEPROM), magnetic storage, magnetic disks, optical disks, etc. A memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. The memory in embodiments of this invention can also be a circuit or any other device capable of performing storage functions for storing program instructions and / or data.

[0074] By designing and programming the processor, the code corresponding to the multi-source distortion ADC behavior model construction method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during runtime. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0075] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a method for constructing a multi-source distortion ADC behavior model as described above.

[0076] In some alternative embodiments, the present invention also provides a method for constructing a multi-source distortion ADC behavior model that can also be implemented as a program product comprising program code that, when the program product is run on a device, causes the control device to perform the steps in the method for constructing a multi-source distortion ADC behavior model according to various exemplary embodiments of the present invention as described above.

[0077] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0078] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can be implemented in one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0079] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0080] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0081] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

[0085] This background section is provided to generally present the context of the invention. The work of the currently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither expressly nor impliedly acknowledged as prior art to the invention.

Claims

1. A method for constructing a behavior model of a multi-source distortion ADC, characterized in that, include: Step S1: ADC modeling, data acquisition, and prediction model training; acquire the output codeword data of the ADC under analog signal inputs with different frequencies and powers, and obtain the measured time-domain sequence data through decoding and transformation; perform spectral analysis on the measured time-domain sequence data to obtain the preceding... A training set is constructed using first-order harmonic power and signal-to-noise ratio data; the training set is then trained using a BP neural network algorithm to obtain the first-order harmonic power and signal-to-noise ratio data used to predict the ADC output signal. A neural network prediction model for first harmonic power and signal-to-noise ratio; Step S2: Characterization of ADC aperture jitter distortion characteristics; For an input analog signal with a preset frequency and preset power, the total signal-to-noise ratio (SNR) of the input analog signal after passing through the ADC is predicted using the neural network prediction model; the corresponding quantization SNR is calculated based on the inherent quantization noise power of the ADC, and the quantization SNR is removed from the total SNR to extract the SNR introduced by aperture jitter, and the mean square error of the sampling clock deviation is calculated in combination with the preset frequency; the mean square error is used to generate ADC sampling time points with random deviation, and the input analog signal is sampled according to the ADC sampling time points to obtain a first discrete signal sequence containing the influence of aperture jitter distortion; Step S3: Characterize the nonlinear distortion characteristics of the ADC; use the neural network prediction model to predict the input analog signal after passing through the ADC. First harmonic power values; constructing a characterization of the nonlinear mapping relationship between ADC input and output. A polynomial model of order [order] is used to calculate the corresponding voltage amplitude based on the preset power, and based on the predicted [previous power]... The first harmonic power value and the The harmonic expressions of each order extracted after expanding the polynomial model are combined to construct a system of equations to obtain the desired result. The coefficients of the polynomial model of order 1; substituting the first discrete signal sequence into the solution of the coefficients... In the first-order polynomial model, a second discrete signal sequence containing the effects of nonlinear distortion is calculated; Step S4: Characterization of ADC quantization distortion characteristics; Calculate the minimum quantization unit based on the ADC's reference voltage and quantization bit depth; Quantize and encode the second discrete signal sequence using the minimum quantization unit to finally obtain the time-domain discrete signal output by the multi-source distortion ADC behavior model. The time-domain discrete signal characterizes the actual working characteristics of the ADC with aperture jitter distortion, nonlinear distortion, and quantization distortion.

2. The method for constructing a multi-source distortion ADC behavior model according to claim 1, characterized in that, The specific process of step S1 includes: Adjust the frequency and power of the analog signal input to the ADC according to the preset step, collect the actual codeword data output by the ADC, and collect the noise floor codeword data output by the ADC when there is no signal input in the analog signal off state; The actual codeword data and noise floor codeword data collected are decoded according to the corresponding data encoding format. The transformation calculation is performed by combining the quantization bits of the ADC and the reference voltage to obtain the measured time-domain sequence data and the noise floor time-domain sequence data, respectively. The measured time-domain sequence data is subjected to a Fast Fourier Transform to obtain spectral data, and the preceding data is extracted. The power value corresponding to the higher harmonic frequencies; where, when the higher harmonic frequencies exceed the first Nyquist zone... When folding to the first Nyquist zone, the folding frequency is calculated, and the power value corresponding to the folding frequency is extracted. The sampling rate of the ADC; Based on the measured time-domain sequence data and the noise floor time-domain sequence data, the signal-to-noise ratio (SNR) data is calculated, and then compared with the corresponding previous... The training set is constructed using the power values ​​of the first harmonics.

3. The method for constructing a multi-source distortion ADC behavior model according to claim 1, characterized in that, In step S2, the process of extracting the signal-to-noise ratio introduced by aperture jitter and calculating the root mean square error of the sampling clock deviation specifically includes: According to the formula Calculate the quantization signal-to-noise ratio corresponding to the inherent quantization noise power. ,in The quantization bit depth of the ADC; According to the formula Extract the signal-to-noise ratio introduced by aperture jitter. ,in The total signal-to-noise ratio is the predicted value; According to the formula The mean square error of the sampling clock bias was calculated. ,in The preset frequency; A normal distribution random number generator is used to generate numbers with a mean of 0 and a variance of [value missing]. clock deviation and according to the formula The ADC sampling time point was calculated. ,in , For the sampling sequence index, The length of the discrete signal sequence output by the ADC.

4. The method for constructing a multi-source distortion ADC behavior model according to claim 1, characterized in that, In step S3, the The polynomial model of order 1 is represented as: in, For input signal, For output signal, denoted as the coefficients of the polynomial to be solved.

5. The method for constructing a multi-source distortion ADC behavior model according to claim 4, characterized in that, In step S3, a system of equations is constructed simultaneously to solve for the obtained equations. The process of determining the coefficients of each term in a polynomial model includes: The input analog signal is expressed as Substituting the form into the above The expansion is performed in a polynomial model of order X, and the coefficient expressions corresponding to each order harmonic in the expansion are extracted; where... The voltage amplitude is calculated from the preset power. For the preset frequency, It is a time variable; The predicted front The harmonic power values ​​are converted into voltage amplitude expressions for the corresponding harmonic orders, and equations are established with the corresponding harmonic coefficient expressions to form a system containing... The system of equations is solved to obtain the following equations. The specific value.

6. The method for constructing a multi-source distortion ADC behavior model according to claim 1, characterized in that, In step S4, the formula for calculating the minimum quantization unit is: ;in, The reference voltage for the ADC is... The quantization bit depth of the ADC is denoted as .

7. The method for constructing a multi-source distortion ADC behavior model according to claim 6, characterized in that, In step S4, the formula for quantizing and encoding the second discrete signal sequence using the minimum quantization unit is as follows: in, The second discrete signal sequence, The final time-domain discrete signal is obtained. This indicates a round-down operation.

8. A system for constructing a behavior model of a multi-source distortion ADC, characterized in that, include: The data acquisition and prediction model training module is used to acquire the output codeword data of the ADC under analog signal inputs with different frequencies and powers, and obtain the measured time-domain sequence data through decoding and transformation; the measured time-domain sequence data is then subjected to spectral analysis to obtain the preceding... A training set is constructed using first-order harmonic power and signal-to-noise ratio data; the training set is then trained using a BP neural network algorithm to obtain the first-order harmonic power and signal-to-noise ratio data used to predict the ADC output signal. A neural network prediction model for first harmonic power and signal-to-noise ratio; An aperture jitter distortion characterization module is used to predict the total signal-to-noise ratio (SNR) of an input analog signal with a preset frequency and preset power after passing through an ADC using the neural network prediction model; calculate the corresponding quantization SNR based on the inherent quantization noise power of the ADC; remove the quantization SNR from the total SNR to extract the SNR introduced by aperture jitter; and calculate the mean square error of the sampling clock deviation based on the preset frequency. The module then uses the mean square error to generate ADC sampling time points with random bias, and samples the input analog signal according to the ADC sampling time points to obtain a first discrete signal sequence containing the effects of aperture jitter distortion. The nonlinear distortion characteristic characterization module is used to predict the input analog signal after passing through the ADC using the neural network prediction model. First harmonic power values; constructing a characterization of the nonlinear mapping relationship between ADC input and output. A polynomial model of order [order] is used to calculate the corresponding voltage amplitude based on the preset power, and based on the predicted [previous power]... The first harmonic power value and the The harmonic expressions of each order extracted after expanding the polynomial model are combined to construct a system of equations to obtain the desired result. The coefficients of the polynomial model of order 1; substituting the first discrete signal sequence into the solution of the coefficients... In the first-order polynomial model, a second discrete signal sequence containing the effects of nonlinear distortion is calculated; The quantization distortion characteristic characterization module is used to calculate the minimum quantization unit based on the reference voltage and quantization bit depth of the ADC; and to perform quantization encoding calculation on the second discrete signal sequence using the minimum quantization unit to finally obtain the time-domain discrete signal output by the multi-source distortion ADC behavior model. The time-domain discrete signal characterizes the actual working characteristics of the ADC with aperture jitter distortion, nonlinear distortion and quantization distortion.

9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory to perform the multi-source distortion ADC behavior model construction method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, enable the method for constructing a multi-source distortion ADC behavior model as described in any one of claims 1-7 to be implemented.

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