Adaptive equalizer signal compensation method and system for high-speed serial interface

Abstract

The invention relates to the technical field of high-speed communication and signal processing, and discloses a self-adaptive equalizer signal compensation method and system for a high-speed serial interface, and the method comprises the steps: obtaining the real-time sampling data of the interface, determining an equalization coefficient through dynamic self-adaptive filtering, and extracting the features of a monitoring signal; calculating environmental interference intensity according to the characteristics, and if the environmental interference intensity exceeds the standard, triggering genetic evolution to obtain optimized parameters; constructing a constraint matrix to correct parameter deviation, generating balance configuration and mapping the balance configuration to a virtual link for stability simulation; if simulation jitter exceeds the standard, historical non-convergent individuals are called for backtracking and re-optimization; real-time error iteration and smoothing processing are carried out based on the environment reference, and a final compensation strategy is determined; and finally, driving a physical link to construct a verification digital eye pattern and counting error codes to obtain communication quality data. The method can solve the problem of low signal compensation precision in the prior art.

Description

Technical Field

[0001] This invention relates to the field of high-speed communication and signal processing technology, and in particular to an adaptive equalizer signal compensation method and system for high-speed serial interfaces. Background Technology

[0002] Currently, in complex wireless network base station interconnection and high-performance computing architectures, signal transmission rates are constantly increasing, leading to increasingly severe problems of channel loss, inter-symbol interference, and crosstalk. In order to recover high-quality digital signals in lossy channels, the receiver usually needs to deploy an adaptive equalizer to dynamically adjust compensation parameters in real time according to channel characteristics to ensure the signal integrity and communication stability of the link.

[0003] In existing technologies, the parameter adjustment of adaptive equalizers typically relies on gradient-based linear search algorithms (such as the Least Mean Square (LMS) algorithm). These schemes iteratively update filter coefficients using a fixed or simple variable step-size strategy by monitoring the gradient direction of the error signal in real time. For example, when the system detects an increase in the decision error, the algorithm successively fine-tunes the equalizer tap weights along the opposite direction of the error gradient, attempting to converge the mean square error to a minimum. In this process, the system assumes that the error surface is unimodal and that environmental disturbances (such as temperature changes or voltage fluctuations) are linearly stationary, relying solely on current local error feedback to drive the linear approximation of parameters. However, existing technologies, due to their over-reliance on local gradient information, have significant limitations when facing high-order nonlinear channels or complex environmental abrupt changes. Because gradient algorithms lack global search capabilities, when the channel is affected by temperature drift and strong crosstalk, the error surface of the objective function often exhibits a multimodal shape, making the algorithm prone to getting trapped in local extrema and unable to converge to the global optimum.

[0004] Therefore, existing technologies suffer from low signal compensation accuracy. Summary of the Invention

[0005] This invention provides an adaptive equalizer signal compensation method and system for high-speed serial interfaces to solve the problem of low signal compensation accuracy in existing technologies.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides an adaptive equalizer signal compensation method for high-speed serial interfaces, comprising:

[0007] Acquire real-time sampling data from the high-speed serial interface, calculate the equalization coefficient, and extract signal features based on the equalization coefficient;

[0008] Frequency domain analysis and monitoring are performed on the signal characteristics to obtain crosstalk components and temperature drift, and the environmental interference intensity is calculated. If the environmental interference intensity exceeds a preset interference trigger threshold, an initial genetic population is constructed and genetic evolution is performed to obtain optimized compensation parameters.

[0009] A loss function is constructed based on the optimized compensation parameters, an iterative calculation model is generated, and cyclic parameter correction is performed to obtain the parameter deviation change. When the parameter deviation change is less than the preset convergence threshold, the current parameter value is locked and solidified as the balanced parameter configuration.

[0010] The parameter configuration is mapped to a virtual transmission link to generate a simulated transmission signal stream, and the simulated jitter amplitude and simulated eye diagram opening are calculated to obtain the signal stability index.

[0011] If the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, then non-convergent individuals in the historical iteration record are retrieved to construct a new population for re-optimization, and the adjusted compensation parameters are obtained.

[0012] Environmental baseline mapping is performed based on the compensation parameters to obtain environmental update factors. Real-time error iteration is then performed based on the environmental update factors to determine the final compensation strategy.

[0013] The compensation strategy is mapped to the interface logic to drive the physical link to transmit test sequences. A verification digital eye diagram is constructed based on the collected test voltage waveform, and stability verification is performed. The improved communication quality data is obtained by combining the bit error distribution statistics.

[0014] Secondly, the present invention provides an adaptive equalizer signal compensation system for a high-speed serial interface, comprising:

[0015] The data acquisition and preliminary feature extraction module is used to acquire real-time sampling data from the high-speed serial interface, calculate the equalization coefficient, and extract signal features based on the equalization coefficient.

[0016] The environmental perception and genetic optimization module is used to perform frequency domain analysis and monitoring of the signal characteristics, obtain crosstalk components and temperature drift, and calculate the environmental interference intensity. If the environmental interference intensity exceeds the preset interference trigger threshold, an initial genetic population is constructed and genetic evolution is performed to obtain optimized compensation parameters.

[0017] The parameter coupling balancing module is used to construct a loss function based on the optimization compensation parameters, generate an iterative calculation model, and perform cyclic parameter correction to obtain the parameter deviation change. When the parameter deviation change is less than a preset convergence threshold, the current parameter value is locked and solidified as the balanced parameter configuration.

[0018] The virtual link simulation module is used to map the parameter configuration to a virtual transmission link, generate a simulated transmission signal stream, and calculate the simulated jitter amplitude and simulated eye diagram opening to obtain signal stability indicators.

[0019] The anomaly backtracking re-optimization module is used to retrieve non-convergent individuals from historical iteration records to construct a new population for re-optimization if the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, so as to obtain the adjusted compensation parameters.

[0020] The dynamic feedback strategy determination module is used to perform environmental baseline mapping based on the compensation parameters to obtain environmental update factors, perform real-time error iteration based on the environmental update factors, and determine the final compensation strategy.

[0021] The physical link verification and quality assessment module is used to map the compensation strategy to the interface logic, drive the physical link to transmit test sequences, construct a verification digital eye diagram based on the collected test voltage waveform, perform stability verification, and obtain improved communication quality data by combining bit error distribution statistics.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] (1) This invention extracts quality features by collecting real-time signals and monitors the “environmental interference intensity”, and triggers the global search of the genetic algorithm only when the interference exceeds the threshold. This mechanism effectively distinguishes between conventional linear loss and complex nonlinear interference, avoids the waste of computing power in low interference environment, and ensures the pertinence and robustness of parameter adjustment under high interference and harsh working conditions, thereby optimizing the system response efficiency while ensuring the compensation effect.

[0024] (2) The present invention constructs a "backtracking re-optimization" mechanism based on stability index. When the simulation verification fails to meet the standard, the algorithm backtracks and retrieves historical non-convergent individuals to reorganize the initial population. By introducing these historical non-convergent individuals with differentiated genes, the algorithm can effectively escape the local extreme value trap that caused the previous round of convergence failure, and use the search space that has not been fully explored to perform secondary evolution, which significantly improves the optimization ability and convergence success rate of the adaptive equalizer under extreme channel conditions.

[0025] (3) The present invention constructs a “parameter coupling degree model and constraint matrix” based on optimized parameters, and uses an iterative model containing a penalty function to correct parameter deviations and solidify the configuration. This processing method fully considers the mutual constraint relationship between different tap coefficients such as forward equalizer (FFE) and decision feedback equalizer (DFE), effectively suppresses the oscillation and divergence phenomena common in the process of multi-parameter concurrent adjustment, and ensures the coordination and steady-state maintenance capability of the final generated parameter configuration in electrical characteristics.

[0026] (4) The present invention performs “virtual link mapping simulation” and “environmental benchmark feedback update” operations. It predicts signal stability through convolution operation in virtual environment and performs dynamic weighted update in combination with real-time error vector. This process not only builds a security firewall before parameter distribution, reducing the risk of connection interruption that may be caused by direct trial and error of physical link, but also gives the system the ability to dynamically track and finely adjust time-varying environmental factors such as temperature drift and voltage fluctuation.

[0027] (5) The present invention applies the final strategy to the physical link and performs “multi-dimensional verification of communication quality”. Based on the digital eye diagram characterization, the vertical and horizontal opening is analyzed, the signal-to-noise ratio and inter-symbol interference are calculated and the error distribution is statistically analyzed. This multi-dimensional verification system can objectively and comprehensively quantify the actual physical gain of the compensation strategy, effectively improving the signal integrity, eye diagram quality and long-term reliability of the high-speed serial interface in actual high-throughput data transmission. Attached Figure Description

[0028] Figure 1 This is a schematic flowchart of the adaptive equalizer signal compensation method for high-speed serial interfaces provided in the first embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of the adaptive equalizer signal compensation system for high-speed serial interfaces provided in the second embodiment of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Reference Figure 1 The first embodiment of the present invention provides an adaptive equalizer signal compensation method for a high-speed serial interface, comprising the following steps:

[0032] S11, Data Acquisition and Preliminary Feature Extraction Module, is used to acquire real-time sampling data from the high-speed serial interface, calculate the equalization coefficient, and extract signal features based on the equalization coefficient;

[0033] S12, the environmental perception and genetic optimization module, is used to perform frequency domain analysis and monitoring on the signal characteristics to obtain crosstalk components and temperature drift, and calculate the environmental interference intensity. If the environmental interference intensity exceeds the preset interference trigger threshold, an initial genetic population is constructed and genetic evolution is performed to obtain optimized compensation parameters.

[0034] S13, parameter coupling balancing module, is used to construct a loss function based on the optimization compensation parameters, generate an iterative calculation model, and perform cyclic parameter correction to obtain the parameter deviation change. When the parameter deviation change is less than a preset convergence threshold, the current parameter value is locked and solidified as the balanced parameter configuration.

[0035] S14, Virtual link simulation module, is used to map the parameter configuration to a virtual transmission link, generate a simulated transmission signal stream, and calculate the simulated jitter amplitude and simulated eye diagram opening to obtain signal stability index;

[0036] S15, Dynamic Feedback Strategy Determination Module, is used by the Anomaly Backtracking Re-optimization Module to retrieve non-convergent individuals from historical iteration records to construct a new population for re-optimization if the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, thereby obtaining the adjusted compensation parameters.

[0037] S16, Perform environmental baseline mapping based on the compensation parameters to obtain environmental update factors, perform real-time error iteration based on the environmental update factors, and determine the final compensation strategy.

[0038] S17, Physical Link Verification and Quality Assessment Module, is used to map the compensation strategy to the interface logic, drive the physical link to transmit test sequences, construct a verification digital eye diagram based on the collected test voltage waveform, perform stability verification, and obtain improved communication quality data by combining bit error distribution statistics.

[0039] In step S11, real-time sampling data from the high-speed serial interface is acquired, and the equalization coefficient is calculated. Signal features are extracted based on the equalization coefficient, including:

[0040] The interface signal is continuously quantized and sampled using a high-speed analog-to-digital converter to obtain the real-time sampled data;

[0041] The variance of the real-time sampled data is calculated to obtain the interference intensity value. The cumulative slope deviation value in the non-jump region is calculated to obtain the error gradient value. The interference intensity value and the error gradient value are weighted and summed to obtain the distortion degree evaluation value.

[0042] The tap coefficients of the adaptive filter are adjusted using the minimum mean square error criterion, a positive correlation mapping relationship is established between the distortion evaluation value and the iteration step size, the iteration step size is dynamically adjusted based on the distortion evaluation value, and the optimal equalization coefficient is determined through online iteration.

[0043] The equalization coefficients are convolved with the real-time sampled data to obtain the corrected waveform sequence;

[0044] The modified waveform sequence is superimposed at unit intervals to construct a digital eye diagram, the eye diagram opening and decision threshold are calculated, and the modified waveform sequence, the eye diagram opening and the decision threshold are established as the signal features.

[0045] First, signal acquisition and digitization processing are performed using a high-speed serial interface. A high-speed analog-to-digital converter is used to continuously quantize and sample the differential analog signal in the interface, generating a continuously distributed digital voltage sequence over time. In this embodiment, the sampling process is performed according to a preset high-frequency clock rate (e.g., 10G times per second), and the analog levels are mapped to digital quantities with a fixed bit width (e.g., 8-bit precision). Finally, these discrete voltage sampling points are stored in a buffer queue according to the order of acquisition time to obtain real-time sampling data.

[0046] Secondly, a distortion assessment value is constructed based on real-time sampling data. This process involves calculating and fusing the differential variance and the cumulative slope deviation. For interference intensity, the differential variance of the real-time sampling data is calculated. Specifically, the statistical variance of the voltage jump amplitude between adjacent sampling times is calculated. This statistical variance directly reflects the severity of high-frequency noise and random jitter in the signal and is established as the interference intensity value. For error gradient, non-jumping regions (i.e., continuous logic 1 or logic 0 regions) in the logic level holding phase of the real-time sampling data are identified. The cumulative slope deviation of the voltage value at each sampling point within this region relative to the ideal standard level is calculated, and this cumulative result (characterizing the degree of waveform edge gradual change or drift) is normalized and established as the error gradient value. After obtaining the interference intensity value and the error gradient value, a weighted summation operation is performed on the two using a preset weighting ratio to obtain a distortion assessment value that quantitatively represents the current signal quality deterioration.

[0047] Then, adaptive filter coefficient updates based on distortion assessment values ​​are performed using an online iterative update mechanism. Specifically, the update logic involves configuring a forward equalizer as an adaptive filter and adjusting the filter's tap coefficients using the minimum mean square error criterion. During the adjustment process, a variable step-size control mechanism is introduced to establish a positive correlation between the distortion assessment value and the iteration step size. Specifically, when the calculated distortion assessment value is large, indicating severe signal distortion, the control logic automatically increases the iteration step size, enabling the filter's tap coefficients to respond quickly and adjust significantly, thereby enhancing the compensation capability for inter-symbol interference. When the distortion assessment value is small, the iteration step size is decreased to reduce steady-state error and prevent coefficient oscillations. Through this dynamic adjustment mechanism, the optimal equalization coefficients are updated and determined at each sampling time.

[0048] Furthermore, the equalization coefficients are convolved with the real-time sampled data. This process involves performing a time-domain discrete convolution between the updated equalization coefficient set and the real-time sampled data, essentially weighting and superimposing the historical sampled data. Through this operation, waveform tailing and distortion caused by channel transmission loss are canceled out in the time domain, resulting in a shaped corrected waveform sequence.

[0049] Finally, clock cycle-based feature extraction is performed on the corrected waveform sequence. For periodic overlay processing, a reference clock frequency is extracted from the corrected waveform sequence using clock recovery logic to determine the duration of a single bit (i.e., unit interval). Based on this unit interval, the corrected waveform sequence is divided into multiple data segments of equal length, and all data segments are aligned and overlaid on the time axis to construct a two-dimensional digital eye diagram. For eye diagram opening calculation, at the center sampling time of the digital eye diagram, the mean and standard deviation of the logic high-level distribution region and the mean and standard deviation of the logic low-level distribution region are statistically analyzed. The lower boundary of the high-level distribution is determined based on the mean and standard deviation of the logic low-level distribution, and the upper boundary of the low-level distribution is determined based on the mean and standard deviation of the logic low-level distribution. The difference between the lower boundary of the high-level distribution and the upper boundary of the low-level distribution is calculated to obtain the eye diagram opening. For decision threshold calculation, the arithmetic mean of the mean of the logic high-level distribution and the mean of the logic low-level distribution is calculated to obtain the decision threshold. The system ultimately establishes the corrected waveform sequence, eye diagram opening, and decision threshold as signal features, providing a data foundation for subsequent frequency domain analysis and monitoring.

[0050] In step S12, frequency domain analysis and monitoring are performed on the signal characteristics to obtain crosstalk components and temperature drift, and the environmental interference intensity is calculated. If the environmental interference intensity exceeds a preset interference trigger threshold, an initial genetic population is constructed, and genetic evolution is performed to obtain optimized compensation parameters, including:

[0051] Perform a fast Fourier transform on the corrected waveform sequence in the signal features, identify abnormal energy peaks at non-fundamental frequency positions in the spectrum and calculate their energy proportion to obtain the crosstalk component, and perform low-pass filtering on the corrected waveform sequence to extract the DC baseline drift amplitude to obtain the temperature drift amount.

[0052] The crosstalk component and the temperature drift are linearly weighted and summed according to preset weighting coefficients to obtain the environmental interference intensity;

[0053] If the intensity of the environmental interference exceeds the preset interference trigger threshold, then the current equilibrium coefficient is used as the seed individual, and random noise following a Gaussian distribution is superimposed on each dimension of the seed individual to generate multiple groups of mutated individuals, and the multiple groups of mutated individuals are established as the initial genetic population.

[0054] Using the eye diagram opening degree contained in the signal features as a fitness evaluation criterion, fitness calculation is performed on the initial genetic population. Based on the calculation results, genetic iterative operations including roulette wheel selection, multi-point crossover and random mutation mechanisms are performed to generate an evolutionary population.

[0055] The individual with the highest fitness in the evolutionary population is decoded into decimal filter coefficients to obtain the optimized compensation parameters.

[0056] First, frequency domain analysis and low-frequency component monitoring are performed on the corrected waveform sequence obtained after preliminary compensation to separate and quantify environmental interference sources. A Fast Fourier Transform (FFT) is performed on the corrected waveform sequence extracted in step S11 to convert the time-domain voltage signal into a frequency-domain spectrum. In the frequency domain, abnormal energy peaks located at non-signal fundamental frequencies and their integer multiples of harmonics are identified. The ratio of the sum of the energy in these abnormal frequency bands to the total signal energy across the entire frequency band is calculated, and this ratio is established as the crosstalk component. Simultaneously, a digital low-pass filter with an extremely low cutoff frequency (set to 10Hz in this embodiment) is applied to the corrected waveform sequence to filter out high-frequency signals and extract the DC baseline. The absolute value of the amplitude drift of this DC baseline within a unit time window is calculated, and this absolute value is established as the temperature drift.

[0057] Secondly, a weighted fusion calculation is performed based on the obtained crosstalk components and temperature drift to construct a unified environmental interference intensity index. In this embodiment, the weight coefficient for the crosstalk component is set to 0.65, and the weight coefficient for the temperature drift is set to 0.35. The weight coefficients are determined as follows: First, a controllable signal transmission test platform is constructed, and two sets of independent variable tests are conducted. In the first set of tests, the temperature is kept constant, and the signal coupling strength of adjacent channels is gradually increased. The signal-to-noise ratio (SNR) attenuation corresponding to each 0.1 unit increase in crosstalk component is recorded. In the second set of tests, the ambient temperature is gradually changed, and the SNR attenuation corresponding to each 0.1 unit increase in temperature drift is recorded. By comparing the two sets of test data, it is found that the SNR attenuation caused by a unit crosstalk component is approximately 1.86 times that caused by a unit temperature drift (i.e., 0.65 / 0.35). Based on this, the above weight allocation ratio is calculated by normalization. The real-time calculated crosstalk component value and temperature drift value are multiplied by their respective weight coefficients and then linearly summed to obtain the environmental interference intensity characterizing the overall degradation of the current link.

[0058] Then, the calculated environmental interference intensity is compared with a preset interference trigger threshold. In this embodiment, the interference trigger threshold is set to 0.55. This threshold is determined by applying mixed interference to the link in a laboratory environment, increasing the environmental interference intensity value in increments from 0.1 to 0.9. At each interference intensity node, continuous transmission... Each test bit was analyzed, and the bit error rate (BER) was calculated. Experimental data shows that when the interference intensity is below 0.55, the BER remains at [value missing]. Order of magnitude (effective region of linear compensation); when the interference intensity exceeds 0.55, the bit error rate increases exponentially. The above (linear compensation failure zone) is used. The inflection point value of 0.55, where the bit error rate deteriorates sharply, is selected as the interference trigger threshold. If the real-time calculated environmental interference intensity is lower than this threshold, the equilibrium coefficient determined in step S11 is maintained; if it exceeds this threshold, the current linear compensation is determined to be in failure, and the population construction and genetic evolution process is immediately initiated.

[0059] Next, an initial genetic population for re-optimization is constructed. Using the equilibrium coefficient determined in step S11 as the seed individual (baseline gene), random noise following a Gaussian distribution is superimposed on each dimension of the seed individual. The mean of the noise is set to 0, and the variance is set to 10% of the current coefficient value. This generates 30 groups of slightly different variant individuals, and this set of variant individuals is established as the initial genetic population.

[0060] Finally, using the eye diagram opening degree contained in the signal features as the fitness evaluation standard, fitness calculation is performed on the initial genetic population, and a genetic iterative operation including roulette wheel selection, multi-point crossover, and random mutation mechanisms is executed to generate an evolving population. Specifically, the eye diagram opening degree of the parameter configuration corresponding to each individual in the population under the current signal is calculated one by one; the larger the value, the higher the fitness. Based on the fitness calculation results, a roulette wheel strategy is used to select high-quality individuals. The selected individuals are paired up, and the crossover probability is set to 0.8. A cut point is randomly selected in the gene sequence of the paired individuals, and the filter coefficient fragments on the right are exchanged (multi-point crossover). Subsequently, a small random perturbation is superimposed on the coefficient bits of the offspring individuals, with a mutation probability set to 0.05 (random mutation). The above process is repeated until the evolutionary generation reaches the preset 120 generations, or the fitness score improvement is less than 0.01 for 10 consecutive generations. From the finally stopped evolving population, the individual with the highest fitness score is extracted, and its gene sequence is decoded into specific decimal filter tap coefficients to obtain the optimized compensation parameters after global optimization.

[0061] In step S13, a loss function is constructed based on the optimized compensation parameters, an iterative calculation model is generated, and cyclic parameter correction is performed to obtain the parameter deviation change. When the parameter deviation change is less than a preset convergence threshold, the current parameter value is locked and solidified as the balanced parameter configuration, including:

[0062] Call historical parameter tuning data, calculate the Pearson correlation coefficient between any two parameters in the optimization compensation parameters, and arrange the Pearson correlation coefficients according to their corresponding positions to generate a constraint matrix;

[0063] The parameter search neighborhood to be adjusted is determined based on the element values ​​in the constraint matrix. Within the parameter search neighborhood, a loss function containing a performance error term and a penalty term based on the constraint matrix is ​​constructed to generate the iterative calculation model.

[0064] The gradient descent algorithm is used to perform parameter updates based on the iterative calculation model. The Euclidean distance between the numerical sets before and after the parameter update is calculated to obtain the parameter deviation change. When the parameter deviation change is less than a preset convergence threshold, the current parameter value state is locked and solidified as the balanced parameter configuration.

[0065] First, historical parameter tuning data is retrieved to calculate the Pearson correlation coefficient between any two parameters in the optimization compensation parameters. These Pearson correlation coefficients are then arranged in their corresponding positions to generate a constraint matrix. Specifically, a pre-set historical parameter tuning database is accessed. This database contains tens of thousands of equalizer parameter combinations that have successfully established stable connections under different channel conditions, covering both forward equalizer coefficients and decision feedback equalizer coefficients. Pearson correlation analysis is performed on the data in this database to calculate the correlation coefficient between any two parameters. All the calculated correlation coefficients are then arranged in their corresponding positions to form a symmetric constraint matrix, with the matrix dimension matching the total number of parameters to be adjusted. The closer the absolute value of an element in the matrix is ​​to 1, the stronger the linkage and constraint relationship between the corresponding two parameters during the adjustment process.

[0066] Specifically, the historical parameter optimization data can be generated during the chip design phase by using simulation tools to scan, search, and record the equalizer parameter combinations that optimize eye diagram quality under various typical channel models, such as transmission lines, vias, and connectors of different lengths, forming a database; or during the product mass production testing phase, samples are extracted for link training under various temperature and voltage conditions, and the parameter combinations that successfully establish a stable connection are collected and stored in the database; the "call" refers to reading the previously stored parameter sets from the database.

[0067] Secondly, the search neighborhood for the parameter to be adjusted is determined based on the element values ​​in the constraint matrix. Within this neighborhood, a loss function containing a performance error term and a penalty term based on the constraint matrix is ​​constructed, generating an iterative calculation model. The parameter search neighborhood is determined by traversing the off-diagonal elements of the constraint matrix. If the absolute value of the correlation coefficient between two parameters is greater than a preset strong correlation threshold (set to 0.8 in this embodiment), the adjustment range of these two parameters is strictly limited to within 5% of the current value to prevent large changes in a single parameter from disrupting the coupling balance; if the absolute value of the correlation coefficient is less than the threshold, the adjustment range is widened to within 15% of the current value.

[0068] Here, the value of the strong correlation threshold (0.8) is determined from a statistical analysis experiment on the coupling effect of multi-channel parameters. In the experiment, a test set containing various typical channel characteristics (such as long-distance backplanes, via reflections, etc.) was constructed, and the strong correlation threshold was gradually increased from 0.5 to 0.95 in increments of 0.05. For each threshold setting, the "parameter oscillation rate" (defined as the probability that a parameter repeatedly jumps between two values ​​and cannot stabilize) during the statistical iteration process was statistically analyzed. Experimental statistics show that when the threshold is set below 0.75, the parameter oscillation rate exceeds 20% due to the overly loose constraint on highly coupled parameters, which seriously affects convergence; when the threshold is increased to 0.8, the parameter oscillation rate drops sharply to below 2%, and further increasing the threshold (e.g., to 0.9) improves stability by less than 0.5%. Therefore, 0.8 is selected as the optimal dividing point that can balance convergence stability and parameter search freedom.

[0069] The iterative computation model is constructed by establishing a target loss function, which consists of two parts: the first part is a performance error term, defined as the difference between the ideal eye opening (i.e., value 1) and the current actual eye opening, used to drive the parameters towards higher performance; the second part is a penalty term, calculated by multiplying the corresponding elements in the constraint matrix, the adjustment amounts of the two parameters, and a penalty weight coefficient. In this embodiment, the penalty weight coefficient is set to 10. This value was determined through comparative experiments. In the simulation environment, the penalty weight coefficient was gradually increased from 1 to 20, and the parameter convergence trajectory was observed. Experimental data shows that when the coefficient is less than 5, the parameters are prone to oscillations in the strongly coupled region; when the coefficient is greater than 15, the convergence speed slows down significantly. Finally, 10 was selected as the optimal balance point between stability and convergence speed.

[0070] Finally, the gradient descent algorithm is used to perform parameter updates based on the iterative computation model. The Euclidean distance between the numerical sets before and after the parameter update is calculated to obtain the parameter deviation change, and the balanced parameter configuration is locked. The gradient descent method is used to minimize the above-mentioned target loss function. In each iteration, the partial derivative of the loss function with respect to each parameter is calculated, and the parameter values ​​are updated along the negative gradient direction. After completing one parameter update, the Euclidean distance between the parameter sets before and after the update is calculated, and this Euclidean distance is established as the parameter deviation change. This parameter deviation change is compared with a preset convergence threshold. In this embodiment, the convergence threshold is set to the parameter deviation change being less than one ten-thousandth (0.0001) for five consecutive iterations. The convergence condition is set based on sensitivity analysis. In the test, it was found that when the parameter change is less than one ten-thousandth, the resulting change in the signal-to-noise ratio at the receiver is less than 0.01 dB, which falls within the scope of measurement noise, and further iteration has no actual physical gain. When the above convergence condition is met, the iteration loop is stopped, the current parameter value state is locked, and it is solidified as the balanced parameter configuration for subsequent virtual link verification.

[0071] In step S14, the parameter configuration is mapped to a virtual transmission link to generate a simulated transmission signal stream, and the simulated jitter amplitude and simulated eye diagram opening are calculated to obtain signal stability indices, including:

[0072] The balanced parameter configuration is loaded into a preset behavioral simulation model, a test code stream is generated using a pseudo-random binary sequence generator, and the test code stream is mapped to an analog voltage pulse sequence to obtain the analog transmission signal stream;

[0073] The S-parameter file of the physical channel is retrieved and converted into a channel impulse response sequence. The simulated transmitted signal stream is convolved with the channel impulse response sequence in the time domain, and Gaussian white noise and sinusoidal jitter determined based on the environmental interference intensity are superimposed to obtain the damaged received signal sequence.

[0074] A clock data recovery simulation is performed on the damaged received signal sequence to extract the recovered clock, calculate the peak-to-peak value of the time deviation between the signal zero crossing point and the recovered clock, obtain the simulation jitter amplitude, and calculate the vertical height of the inner eye contour at the center position of the unit interval to obtain the simulation eye diagram opening.

[0075] Using the simulated jitter amplitude and the simulated eye diagram opening as index keys, the corresponding error-free operation probability score is queried in a preset two-dimensional stability evaluation matrix to obtain the signal stability index.

[0076] First, the balanced parameter configuration is mapped to the virtual transmission link and the analog signal stream is generated. The balanced parameter configuration (including forward equalizer coefficients, decision feedback equalizer coefficients, and gain settings) fixed in step S13 is retrieved and loaded into a preset high-speed serial link behavioral simulation model (e.g., a software model based on the IBIS-AMI standard). At the transmitting end of this virtual link, a pseudo-random binary sequence generator is used to generate a sequence of lengths... The test bitstream (PRBS31) is generated and mapped to an ideal analog voltage pulse sequence, i.e., an analog transmission signal stream, according to the PAM4 or NRZ modulation format.

[0077] Secondly, channel simulation convolution operations are performed on the simulated transmitted signal stream to output a damaged received signal sequence superimposed with environmental characteristics. The specific process of the convolution operation involves retrieving the S-parameter file (Touchstone file) characterizing the transmission characteristics of a specific physical channel (such as a 20-inch backplane transmission line) and converting it into a time-domain channel impulse response sequence using an inverse fast Fourier transform. The simulated transmitted signal stream is then subjected to a time-domain discrete convolution with this channel impulse response sequence to simulate signal attenuation and dispersion during transmission. The superposition operation of environmental characteristics includes superimposing Gaussian white noise and sinusoidal jitter of a specific frequency onto the convolution output. The amplitude parameters of the noise and jitter are set based on the environmental interference intensity value calculated in step S12. For example, when the environmental interference intensity is 0.6, Gaussian noise with a variance of 5mV and jitter with an amplitude of 0.1UI are automatically set to be superimposed, thereby obtaining a damaged received signal sequence that truly reflects the impact of the current adverse environment.

[0078] Then, multidimensional analysis was performed on the damaged received signal sequence to extract the simulated jitter amplitude and simulated eye diagram opening. Clock data recovery (CDR) simulation was performed on the damaged received signal sequence to extract the recovered clock and align the data bits. In the time domain, the time deviation of the signal zero-crossing point relative to the edge of the recovered clock was statistically analyzed, and the peak-to-peak value of this deviation sequence was calculated and established as the simulated jitter amplitude. Simultaneously, the distribution histogram of the signal amplitude was measured at the center sampling position of the unit interval, and the vertical opening height of the inner eye contour was calculated and established as the simulated eye diagram opening.

[0079] Finally, the calculated simulated jitter amplitude and simulated eye opening are input into a preset stability evaluation matrix to query and obtain the signal stability index. The stability evaluation matrix is ​​a two-dimensional lookup table. The row index corresponds to the normalized simulated jitter amplitude range (e.g., 0.1UI to 0.5UI, step size 0.05UI), and the column index corresponds to the normalized simulated eye opening range (e.g., 50mV to 300mV, step size 25mV). Each element value in the matrix represents the "link error-free operation probability score" (range 0 to 1) under that jitter and eye height combination. This matrix is ​​constructed based on a large-scale offline Monte Carlo simulation experiment. In the experiment, hundreds of thousands of different jitter and eye height combinations were traversed, and long-term bit error tests were run under each combination. The frequency of uncorrectable bit errors under each combination was counted, and (1 minus the bit error frequency) was used as the score and filled into the corresponding cell of the matrix. Based on the currently calculated simulated jitter amplitude and simulated eye opening, the corresponding row and column in the matrix are located, and the value at the intersection is read as the signal stability index. For example, when the simulated jitter amplitude is 0.2UI and the simulated eye opening is 150mV, the value of the corresponding cell in the matrix is ​​read as 0.85 as the final signal stability index.

[0080] In step S15, if the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, then non-convergent individuals from the historical iteration records are retrieved to construct a new population for re-optimization, resulting in adjusted compensation parameters, including:

[0081] If the simulated jitter amplitude in the signal stability index is greater than the preset expected threshold, then the current parameter configuration is marked as abnormal and a re-optimization trigger signal is generated.

[0082] In response to the re-optimization trigger signal, historical iteration records are retrieved, and individuals whose fitness has not reached the optimal level and whose Hamming distance from the optimal individual is greater than a preset distance threshold are selected and established as the non-convergent individuals. The non-convergent individuals form the initial re-optimization population.

[0083] The fitness of the re-optimized initial population is calculated using a cost function that increases the weight of the jitter penalty. Based on the fitness, multi-point crossover and gene recombination are performed on individuals to generate offspring evolution sequences containing multiple sets of candidate spectral equilibrium coefficients.

[0084] Iterative calculations are performed on the offspring evolution sequence until the number of generations or the optimal individual index meets the preset convergence condition. The globally optimal individual is then selected for decimal decoding to obtain the adjusted compensation parameters.

[0085] First, threshold determination and anomaly triggering processing are performed on the signal stability index obtained in step S14. The calculated simulated jitter amplitude is compared with a preset expected threshold. In this embodiment, the preset expected threshold is set to 0.28 unit intervals (UI), which is determined according to the upper limit of the specification of high-speed serial interface protocols (such as PCIe 5.0 or 10G Ethernet). Typically, the protocol requires the receiver jitter tolerance to be better than 0.3UI. To leave a safety margin, the internal determination threshold is tightened to 0.28UI. If the current simulated jitter amplitude is lower than this threshold, the compensation is deemed effective, and the process ends; if it is higher than this threshold, it indicates that the current parameter configuration has a high risk in the simulation verification. Then, the current status bit is marked as an "abnormal state" and a re-optimization trigger signal in the form of a high-level pulse is generated to activate the subsequent backtracking correction process.

[0086] Secondly, in response to the re-optimization trigger signal, a backtracking to the genetic algorithm logic layer and initial population reconstruction are performed. The historical iteration records of step S12 stored in the storage unit are retrieved, and non-convergent individuals are selected from them. Here, the definition and selection criteria for non-convergent individuals refer to individuals whose fitness scores, although not optimal (i.e., not converged to the global maximum), are significantly different from the optimal individual in the last 5 generations of the previous genetic evolution. Specifically, the Hamming distance between the candidate individual and the optimal individual's gene encoding is calculated. If the Hamming distance is greater than a preset distance threshold (set to 5 in this embodiment), it is considered a non-convergent individual with effective differences. These individuals are selected to utilize their genetic diversity and prevent the algorithm from falling into the same local extremum trap that caused the current verification failure. The 50 selected non-convergent individuals are directly used to form the initial re-optimization population for this round of re-optimization.

[0087] Then, a cost function that increases the jitter penalty weight is used to calculate the fitness of the initial population after re-optimization, and offspring evolution sequences are generated. During the fitness calculation phase, the weight allocation in the cost function is adjusted, increasing the penalty weight for simulated jitter amplitude from the usual 0.4 to 0.8, thereby constructing a more stringent evaluation plane that forces the algorithm to prioritize searching low-jitter regions. Based on the fitness calculation results, multi-point crossover and gene recombination operations are performed on individuals within the population. Crossover points are set at the low-frequency gain segment and the high-frequency enhancement segment of the gene sequence, exchanging gene fragments of paired individuals. This operation generates offspring evolution sequences containing multiple sets of candidate spectral equalization coefficients.

[0088] Finally, iterative calculations and decoding are performed on the offspring evolution sequence to determine the final parameters. A genetic iterative loop is executed, repeating selection, crossover, and mutation steps in each generation. The convergence condition is set as a dual termination criterion: convergence is considered satisfied when the number of generations reaches 50, or when the jitter index of the best individual in the population is better than 0.25 UI for 10 consecutive generations. The decoding process involves selecting the globally optimal individual (usually a string of binary code) that meets the convergence condition and mapping it back to the decimal filter parameter domain. For example, the first 8 bits of the code are parsed into sign and value bits, mapped to the main tap coefficients of the forward equalizer (range -128 to +127); the subsequent coded segments are parsed to the first-order tap coefficients of the decision feedback equalizer. Through this specific mapping and decoding, adjusted compensation parameters that effectively suppress jitter are obtained.

[0089] In step S16, environmental baseline mapping is performed based on the compensation parameters to obtain environmental update factors. Real-time error iteration is then performed based on these environmental update factors to determine the final compensation strategy, including:

[0090] The low-frequency and high-frequency components are separated from the compensation parameters to form a spectrum gain vector. The spectrum gain vector is used as a query key to map to a preset environmental benchmark model, and the ideal spectrum response curve is read as the environmental update factor.

[0091] A feedback loop mechanism based on the aforementioned environmental update factors is constructed, a sliding window is used to capture real-time signals, and a short-time Fourier transform is performed on the real-time signals to extract instantaneous frequency response features.

[0092] The instantaneous frequency response characteristics and the environmental update factors are subjected to frequency-level difference operations to generate an error vector;

[0093] The dynamic weight allocation matrix is ​​calculated based on the magnitude of the deviation values ​​of each frequency band in the error vector. The compensation parameters are then updated using the dynamic weight allocation matrix with weighted gradients to obtain the updated parameter combination. These updated parameters are then stacked according to the time series to generate a correction matrix.

[0094] The correction matrix is ​​subjected to moving average filtering, the variance of the filtered parameter sequence is monitored, and the parameter values ​​are locked when the variance of the parameter sequence is less than a preset steady-state threshold, thereby determining the final compensation strategy.

[0095] First, the spectral gain component in the compensation parameters is extracted and mapped to a preset environmental benchmark model to obtain environmental update factors. From the adjusted compensation parameter set (i.e., filter coefficients) determined in step S15, the low-frequency coefficient component representing DC gain and the high-frequency coefficient component representing high-frequency peak gain are separated to form a spectral gain vector. This vector is used as a query key and input into the preset environmental benchmark model. This model is a multidimensional lookup table built based on laboratory measured data. During the construction phase, the device under test is placed in a temperature-controlled chamber, traversing the temperature range of -40°C to 85°C and the voltage fluctuation range of ±5%. At each temperature and pressure combination point, the standard spectral gain distribution data that can maintain the best signal quality (i.e., the lowest bit error rate) is measured and recorded. These discrete "optimal gain-environmental state" correspondences are interpolated through polynomial fitting to generate a continuous benchmark surface model. In this model, the benchmark point closest to the currently extracted spectral gain vector is found, and the ideal spectral response curve corresponding to this benchmark point is read. This ideal curve is established as the environmental update factor.

[0096] Secondly, a feedback loop mechanism is constructed based on environmental update factors, and an error vector is generated. A closed-loop feedback path based on real-time data stream is established. At the signal receiving end, a sliding window (with a window width set to 256 sampling points) is used to capture the real-time signal, and a Short-Time Fourier Transform (STFT) is performed on the real-time signal to extract the instantaneous frequency response characteristics at the current moment. This instantaneous frequency response characteristic (actual value) is then compared with the aforementioned environmental update factors (ideal reference value) at the frequency point level. Specifically, the amplitude difference at each frequency point is calculated, and the differences at all frequency points are arranged in order to generate a one-dimensional error vector. This vector intuitively reflects the deviation distribution between the current actual signal spectrum and the ideal environmental reference.

[0097] Then, a dynamic weight allocation matrix is ​​calculated based on the error vector, and a correction matrix is ​​generated. Correction weights are dynamically allocated according to the magnitude of the deviation values ​​for each frequency band in the error vector, thus constructing the dynamic weight allocation matrix. For frequency bands with an absolute deviation value exceeding 3dB, a larger weight coefficient (e.g., 0.8) is assigned to drive rapid parameter adjustment; for frequency bands with an absolute deviation value less than 0.5dB, a smaller weight coefficient (e.g., 0.1) is assigned to maintain stability. These weight coefficients are then arranged into a diagonal matrix, which is the dynamic weight allocation matrix.

[0098] Iterative Update and Correction Matrix Generation: The compensation parameters are updated using a weighted gradient using this dynamic weight allocation matrix. The iterative update of the parameters follows the mathematical model below:

[0099]

[0100] In the formula, Indicates the first The new compensation parameter vector after the next iteration; Indicates the first The old compensation parameter vector at the next iteration; This represents the preset learning rate coefficient (set to 0.01 in this embodiment, which can be adjusted according to the actual effect) used to control the update step size; This represents the dynamic weight allocation matrix (i.e., the diagonal matrix) obtained from the aforementioned calculation, which is used to weight and constrain the parameter adjustment amounts for different frequency bands. This represents the error gradient vector calculated based on the current error vector, guiding the direction of parameter optimization.

[0101] By execution to The process involves continuous small iterations, recording the parameter vector after each iteration. These 10 sets of parameter vectors are then stacked vertically according to the time series to generate a correction matrix in the following form. :

[0102]

[0103] The correction matrix The temporal evolution trajectory of the compensation parameters during the fine-tuning process was fully recorded and used for subsequent smoothing and filtering.

[0104] Finally, a smoothing filter is applied to the correction matrix, and the values ​​are locked to determine the final compensation strategy. A moving average filter is applied to each column of the correction matrix (i.e., the sequence of changes in each specific filter parameter over iterations). In this embodiment, the moving average window length is set to 5. The arithmetic mean of the 5 parameter values ​​within the window is calculated and used to replace the original parameter values ​​to filter out parameter spikes caused by transient noise. The variance of the filtered parameter sequence is monitored. When the variance of the parameter sequence within three consecutive time windows is less than... When the parameters have reached a steady state, the current filtered values ​​are read, fixed as the final filter tap coefficients and gain settings, established as the final compensation strategy, and sent to the hardware registers.

[0105] In step S17, the compensation strategy is mapped to the interface logic to drive the physical link to transmit test sequences. A verification digital eye diagram is constructed based on the collected test voltage waveform, and stability verification is performed. The improved communication quality data is obtained by combining the bit error distribution statistics, including:

[0106] The compensation strategy is written into the physical layer control register to drive the transmitter to send a pseudo-random binary test sequence and to acquire the test voltage waveform at the receiver pin at a preset oversampling rate.

[0107] By using a clock recovery unit to lock the reference phase, the test voltage waveform is periodically folded to construct a verification digital eye diagram, and the vertical eye opening amplitude at the optimal sampling phase point and the horizontal eye opening time span at the optimal decision voltage level are extracted.

[0108] The difference between the theoretical total swing of the signal and the vertical eye opening amplitude is calculated to obtain the inter-symbol interference intensity value. The logarithm of the ratio of the vertical eye opening amplitude to the standard deviation of the voltage noise at the sampling point is calculated to obtain the instantaneous signal-to-noise ratio value of the link. The instantaneous signal-to-noise ratio value of the link and the inter-symbol interference intensity value are compared with a preset stability threshold to generate a stable link state signal.

[0109] In response to the link stability signal, the received bit stream is compared with the pseudo-random binary test sequence, the total number of bit errors and the distribution interval of bit errors on the time axis are counted, and the improved communication quality data is generated by combining the vertical eye opening amplitude and the horizontal eye opening time span.

[0110] First, the hardware mapping and physical link driver for the final compensation strategy are executed. The final compensation strategy determined in step S16 (i.e., the register configuration word containing specific tap coefficients and gain values) is written into the physical layer control register of the high-speed serial interface. After the configuration takes effect, the transmitter driver of the interface is activated, and a high-speed pseudo-random binary test sequence is transmitted through the physical transmission medium (such as PCB backplane traces or copper cables). Simultaneously, the on-chip oscilloscope function integrated on the receiver or an external high-bandwidth probe is used to capture the test voltage waveform at the receiver pin after transmission through the physical link at a preset oversampling rate (e.g., 32 times the baud rate).

[0111] Secondly, a verification digital eye diagram is constructed for the test voltage waveform, and characteristic indicators are extracted. The reference phase of the data stream is locked using a clock recovery unit to determine the unit interval length. Based on this unit interval length, the test voltage waveform is periodically folded and statistically superimposed to construct a two-dimensional verification digital eye diagram. In the verification digital eye diagram, the optimal sampling phase point in the center of the eye diagram is identified, and the voltage difference between the lower edge of the logic high-level distribution and the upper edge of the logic low-level distribution at this phase point is extracted and established as the vertical eye opening amplitude. Simultaneously, at the optimal decision voltage level, the horizontal time width between the left and right intersection points of the eye diagram is measured and established as the horizontal eye opening time span.

[0112] Then, link performance indicators are calculated based on the vertical eye opening amplitude and the horizontal eye opening time span, and a stable link state signal is generated. The difference between the theoretical total swing of the signal and the measured vertical eye opening amplitude is calculated to obtain the inter-symbol interference (ISI) intensity value. Simultaneously, the ratio of the vertical eye opening amplitude to the standard deviation of the voltage noise at the sampling point is calculated, and the logarithm of this ratio is taken to obtain the instantaneous signal-to-noise ratio (SNR) value of the link. The calculated instantaneous SNR value and ISI intensity value are then compared with preset stability thresholds.

[0113] In this embodiment, the signal-to-noise ratio (SNR) stability threshold is set to 18 dB, and the inter-symbol interference (ISI) stability threshold is set to 0.15 unit intervals. These thresholds are based on failure analysis experiments of mass-produced chips at different temperature inflection points. Experimental data shows that when the SNR is below 18 dB or the ISI exceeds 0.15 unit intervals, the probability of sudden packet loss in the link increases significantly to more than one in a million, failing to meet telecom-grade stability requirements. If the calculated values ​​simultaneously satisfy the conditions of an SNR greater than 18 dB and ISI less than 0.15 unit intervals, the internal logic circuit is driven to generate a high-level active link stability signal.

[0114] Finally, in response to the link stabilization signal, the error distribution is statistically analyzed, and the final quality data is output. In response to the link stabilization signal, the error statistics logic within the physical layer is initiated. Within a set duration (e.g., 10 seconds), the received bit stream is continuously compared with a predicted pseudo-random binary test sequence, and the total number of errors and the distribution interval of errors on the time axis are calculated to distinguish between random and burst errors. The final error data, vertical eye opening amplitude, and horizontal eye opening time span are summarized to generate an improved communication quality data report, which serves as the final deliverable of this adaptive compensation process.

[0115] In summary, this invention discloses an adaptive equalizer signal compensation method for high-speed serial interfaces, comprising: acquiring real-time sampling data of the interface; determining the equalization coefficient and extracting monitoring signal features using dynamic adaptive filtering; calculating the environmental interference intensity based on the features; if the intensity exceeds the limit, triggering genetic evolution to obtain optimized parameters; constructing a constraint matrix to correct parameter deviations; generating a balanced configuration and mapping it to a virtual link for stability simulation; if the simulation jitter exceeds the limit, retrieving historical non-convergent individuals for backtracking and re-optimization; performing real-time error iteration and smoothing based on environmental benchmarks to determine the final compensation strategy; and finally driving the physical link to construct a verification digital eye diagram and statistically analyze bit errors to obtain communication quality data, thus solving the problem of low signal compensation accuracy in existing technologies.

[0116] Reference Figure 2 The second embodiment of the present invention provides an adaptive equalizer signal compensation system for a high-speed serial interface, comprising:

[0117] The data acquisition and preliminary feature extraction module is used to acquire real-time sampling data from the high-speed serial interface, calculate the equalization coefficient, and extract signal features based on the equalization coefficient.

[0118] The environmental perception and genetic optimization module is used to perform frequency domain analysis and monitoring of the signal characteristics, obtain crosstalk components and temperature drift, and calculate the environmental interference intensity. If the environmental interference intensity exceeds the preset interference trigger threshold, an initial genetic population is constructed and genetic evolution is performed to obtain optimized compensation parameters.

[0119] The parameter coupling balancing module is used to construct a loss function based on the optimization compensation parameters, generate an iterative calculation model, and perform cyclic parameter correction to obtain the parameter deviation change. When the parameter deviation change is less than a preset convergence threshold, the current parameter value is locked and solidified as the balanced parameter configuration.

[0120] The virtual link simulation module is used to map the parameter configuration to a virtual transmission link, generate a simulated transmission signal stream, and calculate the simulated jitter amplitude and simulated eye diagram opening to obtain signal stability indicators.

[0121] The anomaly backtracking re-optimization module is used to retrieve non-convergent individuals from historical iteration records to construct a new population for re-optimization if the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, so as to obtain the adjusted compensation parameters.

[0122] The dynamic feedback strategy determination module is used to perform environmental baseline mapping based on the compensation parameters to obtain environmental update factors, perform real-time error iteration based on the environmental update factors, and determine the final compensation strategy.

[0123] The physical link verification and quality assessment module is used to map the compensation strategy to the interface logic, drive the physical link to transmit test sequences, construct a verification digital eye diagram based on the collected test voltage waveform, perform stability verification, and obtain improved communication quality data by combining bit error distribution statistics.

[0124] It should be noted that the adaptive equalizer signal compensation system for high-speed serial interfaces provided in this embodiment of the invention is used to execute all the process steps of the adaptive equalizer signal compensation method for high-speed serial interfaces in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0125] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0126] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An adaptive equalizer signal compensation method for high-speed serial interfaces, characterized in that, include: Acquire real-time sampling data from the high-speed serial interface, calculate the equalization coefficient, and extract signal features based on the equalization coefficient; Frequency domain analysis and monitoring are performed on the signal characteristics to obtain crosstalk components and temperature drift, and the environmental interference intensity is calculated. If the environmental interference intensity exceeds a preset interference trigger threshold, an initial genetic population is constructed and genetic evolution is performed to obtain optimized compensation parameters. A loss function is constructed based on the optimized compensation parameters, an iterative calculation model is generated, and cyclic parameter correction is performed to obtain the parameter deviation change. When the parameter deviation change is less than the preset convergence threshold, the current parameter value is locked and solidified as the balanced parameter configuration. The parameter configuration is mapped to a virtual transmission link to generate a simulated transmission signal stream, and the simulated jitter amplitude and simulated eye diagram opening are calculated to obtain the signal stability index. If the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, then non-convergent individuals in the historical iteration record are retrieved to construct a new population for re-optimization, and the adjusted compensation parameters are obtained. Environmental baseline mapping is performed based on the compensation parameters to obtain environmental update factors. Real-time error iteration is then performed based on the environmental update factors to determine the final compensation strategy. The compensation strategy is mapped to the interface logic to drive the physical link to transmit test sequences. A verification digital eye diagram is constructed based on the collected test voltage waveform, and stability verification is performed. The improved communication quality data is obtained by combining the bit error distribution statistics.

2. The adaptive equalizer signal compensation method for high-speed serial interfaces according to claim 1, characterized in that, The process of acquiring real-time sampling data from the high-speed serial interface, calculating the equalization coefficient, and extracting signal features based on the equalization coefficient includes: The interface signal is continuously quantized and sampled using a high-speed analog-to-digital converter to obtain the real-time sampled data; The variance of the real-time sampled data is calculated to obtain the interference intensity value. The cumulative slope deviation value in the non-jump region is calculated to obtain the error gradient value. The interference intensity value and the error gradient value are weighted and summed to obtain the distortion degree evaluation value. The tap coefficients of the adaptive filter are adjusted using the minimum mean square error criterion, a positive correlation mapping relationship is established between the distortion evaluation value and the iteration step size, the iteration step size is dynamically adjusted based on the distortion evaluation value, and the optimal equalization coefficient is determined through online iteration. The equalization coefficient is convolved with the real-time sampled data to obtain the corrected waveform sequence. The modified waveform sequence is superimposed at unit intervals to construct a digital eye diagram, the eye diagram opening and decision threshold are calculated, and the modified waveform sequence, the eye diagram opening and the decision threshold are established as the signal features.

3. The adaptive equalizer signal compensation method for high-speed serial interfaces according to claim 2, characterized in that, The signal characteristics are analyzed and monitored in the frequency domain to obtain crosstalk components and temperature drift, and the environmental interference intensity is calculated. If the environmental interference intensity exceeds a preset interference trigger threshold, an initial genetic population is constructed, and genetic evolution is performed to obtain optimized compensation parameters, including: Perform a fast Fourier transform on the corrected waveform sequence in the signal features, identify abnormal energy peaks at non-fundamental frequency positions in the spectrum and calculate the energy ratio to obtain the crosstalk component, and perform low-pass filtering on the corrected waveform sequence to extract the DC baseline drift amplitude to obtain the temperature drift amount; The crosstalk component and the temperature drift are linearly weighted and summed according to preset weighting coefficients to obtain the environmental interference intensity; If the intensity of the environmental interference exceeds the preset interference trigger threshold, then the current equilibrium coefficient is used as the seed individual, and random noise following a Gaussian distribution is superimposed on each dimension of the seed individual to generate multiple groups of mutated individuals, and the multiple groups of mutated individuals are established as the initial genetic population. Using the eye diagram opening degree contained in the signal features as a fitness evaluation criterion, fitness calculation is performed on the initial genetic population. Based on the calculation results, genetic iterative operations including roulette wheel selection, multi-point crossover and random mutation mechanisms are performed to generate an evolutionary population. The individual with the highest fitness in the evolutionary population is decoded into decimal filter coefficients to obtain the optimized compensation parameters.

4. The adaptive equalizer signal compensation method for high-speed serial interfaces according to claim 1, characterized in that, The process of constructing a loss function based on the optimized compensation parameters, generating an iterative calculation model, and performing cyclic parameter correction to obtain the parameter deviation change, and locking the current parameter value to solidify it into the balanced parameter configuration when the parameter deviation change is less than a preset convergence threshold, includes: Call historical parameter tuning data, calculate the Pearson correlation coefficient between any two parameters in the optimization compensation parameters, and arrange the Pearson correlation coefficients according to their corresponding positions to generate a constraint matrix; The parameter search neighborhood to be adjusted is determined based on the element values ​​in the constraint matrix. Within the parameter search neighborhood, a loss function containing a performance error term and a penalty term based on the constraint matrix is ​​constructed to generate the iterative calculation model. The gradient descent algorithm is used to perform parameter updates based on the iterative calculation model. The Euclidean distance between the numerical sets before and after the parameter update is calculated to obtain the parameter deviation change. When the parameter deviation change is less than a preset convergence threshold, the current parameter value state is locked and solidified as the balanced parameter configuration.

5. The adaptive equalizer signal compensation method for high-speed serial interfaces according to claim 1, characterized in that, The process of mapping the parameter configuration to a virtual transmission link, generating a simulated transmission signal stream, and calculating the simulated jitter amplitude and simulated eye diagram opening to obtain signal stability indices includes: The balanced parameter configuration is loaded into a preset behavioral simulation model, a test code stream is generated using a pseudo-random binary sequence generator, and the test code stream is mapped to an analog voltage pulse sequence to obtain the analog transmission signal stream; The S-parameter file of the physical channel is retrieved and converted into a channel impulse response sequence. The simulated transmitted signal stream is convolved with the channel impulse response sequence in the time domain, and Gaussian white noise and sinusoidal jitter determined based on the environmental interference intensity are superimposed to obtain the damaged received signal sequence. A clock data recovery simulation is performed on the damaged received signal sequence to extract the recovered clock, calculate the peak-to-peak value of the time deviation between the signal zero crossing point and the recovered clock, obtain the simulation jitter amplitude, and calculate the vertical height of the inner eye contour at the center position of the unit interval to obtain the simulation eye diagram opening. Using the simulated jitter amplitude and the simulated eye diagram opening as index keys, the corresponding error-free operation probability score is queried in a preset two-dimensional stability evaluation matrix to obtain the signal stability index.

6. The adaptive equalizer signal compensation method for high-speed serial interfaces according to claim 1, characterized in that, If the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, then non-convergent individuals from the historical iteration records are retrieved to construct a new population for re-optimization, resulting in adjusted compensation parameters, including: If the simulated jitter amplitude in the signal stability index is greater than the preset expected threshold, then the current parameter configuration is marked as abnormal and a re-optimization trigger signal is generated. In response to the re-optimization trigger signal, historical iteration records are retrieved, and individuals whose fitness has not reached the optimal level and whose Hamming distance from the optimal individual is greater than a preset distance threshold are selected and established as the non-convergent individuals. The non-convergent individuals form the initial re-optimization population. The fitness of the re-optimized initial population is calculated using a cost function that increases the weight of the jitter penalty. Based on the fitness, multi-point crossover and gene recombination are performed on individuals to generate offspring evolution sequences containing multiple sets of candidate spectral equilibrium coefficients. Iterative calculations are performed on the offspring evolution sequence until the number of generations or the optimal individual index meets the preset convergence condition. The globally optimal individual is then selected for decimal decoding to obtain the adjusted compensation parameters.

7. The adaptive equalizer signal compensation method for high-speed serial interfaces according to claim 1, characterized in that, The step of mapping the environmental baseline based on the compensation parameters to obtain environmental update factors, and performing real-time error iteration based on the environmental update factors to determine the final compensation strategy includes: The low-frequency and high-frequency components are separated from the compensation parameters to form a spectrum gain vector. The spectrum gain vector is used as a query key to map to a preset environmental benchmark model, and the ideal spectrum response curve is read as the environmental update factor. A feedback loop mechanism based on the aforementioned environmental update factors is constructed, a sliding window is used to capture real-time signals, and a short-time Fourier transform is performed on the real-time signals to extract instantaneous frequency response features. The instantaneous frequency response characteristics and the environmental update factors are subjected to frequency-level difference operations to generate an error vector; The dynamic weight allocation matrix is ​​calculated based on the magnitude of the deviation values ​​of each frequency band in the error vector. The compensation parameters are then updated using the dynamic weight allocation matrix with weighted gradients to obtain the updated parameter combination. These updated parameters are then stacked according to the time series to generate a correction matrix. The correction matrix is ​​subjected to moving average filtering, the variance of the filtered parameter sequence is monitored, and the parameter values ​​are locked when the variance of the parameter sequence is less than a preset steady-state threshold, thereby determining the final compensation strategy.

8. The adaptive equalizer signal compensation method for high-speed serial interfaces according to claim 1, characterized in that, The process involves mapping the compensation strategy to interface logic, driving the physical link to transmit test sequences, constructing a verification digital eye diagram based on the collected test voltage waveforms, performing stability verification, and obtaining improved communication quality data by combining error distribution statistics, including: The compensation strategy is written into the physical layer control register to drive the transmitter to send a pseudo-random binary test sequence and to acquire the test voltage waveform at the receiver pin at a preset oversampling rate. By using a clock recovery unit to lock the reference phase, the test voltage waveform is periodically folded to construct a verification digital eye diagram, and the vertical eye opening amplitude at the optimal sampling phase point and the horizontal eye opening time span at the optimal decision voltage level are extracted. The difference between the theoretical total swing of the signal and the vertical eye opening amplitude is calculated to obtain the inter-symbol interference intensity value. The logarithm of the ratio of the vertical eye opening amplitude to the standard deviation of the voltage noise at the sampling point is calculated to obtain the instantaneous signal-to-noise ratio value of the link. The instantaneous signal-to-noise ratio value of the link and the inter-symbol interference intensity value are compared with a preset stability threshold to generate a stable link state signal. In response to the link stability signal, the received bit stream is compared with the pseudo-random binary test sequence, the total number of bit errors and the distribution interval of bit errors on the time axis are counted, and the improved communication quality data is generated by combining the vertical eye opening amplitude and the horizontal eye opening time span.

9. An adaptive equalizer signal compensation system for a high-speed serial interface, characterized in that, include: The data acquisition and preliminary feature extraction module is used to acquire real-time sampling data from the high-speed serial interface, calculate the equalization coefficient, and extract signal features based on the equalization coefficient. The environmental perception and genetic optimization module is used to perform frequency domain analysis and monitoring of the signal characteristics, obtain crosstalk components and temperature drift, and calculate the environmental interference intensity. If the environmental interference intensity exceeds the preset interference trigger threshold, an initial genetic population is constructed and genetic evolution is performed to obtain optimized compensation parameters. The parameter coupling balancing module is used to construct a loss function based on the optimization compensation parameters, generate an iterative calculation model, and perform cyclic parameter correction to obtain the parameter deviation change. When the parameter deviation change is less than a preset convergence threshold, the current parameter value is locked and solidified as the balanced parameter configuration. The virtual link simulation module is used to map the parameter configuration to a virtual transmission link, generate a simulated transmission signal stream, and calculate the simulated jitter amplitude and simulated eye diagram opening to obtain signal stability indicators. The anomaly backtracking re-optimization module is used to retrieve non-convergent individuals from historical iteration records to construct a new population for re-optimization if the simulated jitter amplitude in the signal stability index is higher than the preset expected threshold, so as to obtain the adjusted compensation parameters. The dynamic feedback strategy determination module is used to perform environmental baseline mapping based on the compensation parameters to obtain environmental update factors, perform real-time error iteration based on the environmental update factors, and determine the final compensation strategy. The physical link verification and quality assessment module is used to map the compensation strategy to the interface logic, drive the physical link to transmit test sequences, construct a verification digital eye diagram based on the collected test voltage waveform, perform stability verification, and obtain improved communication quality data by combining bit error distribution statistics.

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