Signal enhancement method and system for high-performance passive copper cable

Abstract

The invention relates to a signal enhancement method and system for a high-performance passive copper cable, and the method comprises the steps: obtaining the electrical parameter data of two ends of the passive copper cable, and carrying out the impedance matching, and obtaining a dynamic impedance parameter; acquiring an original signal of the passive copper cable, and performing equalization sorting on the original signal and the dynamic impedance parameter to obtain an equalization signal output; acquiring wire interference data of the passive copper cable, and performing crosstalk analysis on the equalization signal output to obtain a crosstalk suppression result; performing time domain reflection position measurement on the wire interference data to obtain attenuation position information, and performing prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result; and performing signal enhancement on the original signal according to the initial optimization parameter and the signal attenuation prediction result to obtain a global enhancement signal. According to the invention, crosstalk interference can be suppressed by acquiring the wire interference data and performing crosstalk analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of signal transmission, and in particular to a signal enhancement method and system for a high-performance passive copper cable. Background Art

[0002] As an important data transmission medium, high-performance passive copper cables play an irreplaceable role in modern communication networks. With the continuous improvement of data transmission rate and communication quality requirements, how to effectively improve the signal transmission performance of passive copper cables and achieve reliable high-speed data transmission has become one of the key issues of concern in the industry. Existing signal enhancement technologies are often limited to a single technical means, such as only considering impedance matching or signal equalization, while ignoring the combined effects of multiple factors such as wire crosstalk and signal attenuation. This one-sided processing method is difficult to meet the strict requirements of high-speed data transmission on signal quality, and is prone to problems such as signal distortion and increased transmission bit error rate, which restricts the application of passive copper cables in the field of high-speed communications. Summary of the invention

[0003] The main purpose of the present invention is to provide a high-performance passive copper cable signal enhancement method and system, which can effectively improve signal quality and reduce signal distortion.

[0004] To achieve the above object, the present invention provides a high-performance passive copper cable signal enhancement method, comprising: Obtain electrical parameter data at both ends of the passive copper cable, perform impedance matching, and obtain dynamic impedance parameters; Acquire the original signal of the passive copper cable, and perform balanced analysis with the dynamic impedance parameter to obtain a balanced signal output; Acquire the conductor interference data of the passive copper cable, perform crosstalk analysis on the equalized signal output, and obtain a crosstalk suppression result; Performing transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters; Performing time domain reflection position measurement on the wire interference data to obtain attenuation position information, and performing prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result; The original signal is enhanced according to the initial optimization parameters and the signal attenuation prediction result to obtain a global enhanced signal.

[0005] Further, the obtaining of electrical parameter data at both ends of the passive copper cable and performing impedance matching to obtain dynamic impedance parameters includes: Performing type analysis on the electrical parameter data to obtain voltage sampling data, current sampling data and phase sampling data; Performing frequency domain conversion on the voltage sampling data and the current sampling data to obtain frequency domain voltage data and frequency domain current data; Performing complex impedance calculation according to the frequency domain voltage data and the frequency domain current data to obtain complex impedance data; Performing phase compensation on the complex impedance data and the phase sampling data to obtain compensated impedance data; Performing impedance curve transformation on the compensation impedance data to obtain an impedance characteristic curve; Dynamic parameter calibration calculation is performed on the compensation impedance data according to the impedance characteristic curve to obtain dynamic impedance parameters.

[0006] Further, the obtaining of the original signal of the passive copper cable and performing balanced analysis with the dynamic impedance parameter to obtain a balanced signal output includes: Performing multi-scale decomposition on the original signal to obtain multi-scale signal components; Performing orthogonal empirical mode decomposition on the multi-scale signal components according to the dynamic impedance parameters to obtain an intrinsic mode function group; Performing fractional calculus operation on the intrinsic mode function group to obtain dynamic sequence data; Performing stability analysis on the dynamic sequence data based on the dynamic impedance parameters to obtain stable domain parameters; Dynamically balancing the stable domain parameters to obtain a preliminary balanced signal; Performing frequency selection processing on the preliminary equalized signal according to the stable domain parameter to obtain a filtered signal; The filtered signal is subjected to iterative threshold shrinkage reconstruction to obtain the equalized signal output.

[0007] Further, the obtaining of the conductor interference data of the passive copper cable, performing crosstalk analysis on the balanced signal output, and obtaining a crosstalk suppression result includes: Performing conductor pair differential impedance measurement on the passive copper cable to obtain the conductor interference data; Calculating the crosstalk coupling coefficient of the passive copper cable according to the conductor interference data to obtain a conductor-crosstalk coupling matrix; Performing an orthogonal transformation on the equalized signal output according to the crosstalk coupling matrix to obtain crosstalk component data; Adaptively filtering the crosstalk component data to obtain filtered signal data; Performing an inverse orthogonal transformation on the crosstalk feature vector according to the filtered signal data to obtain crosstalk compensation data; The equalized signal output and the crosstalk compensation data are superimposed to obtain the crosstalk suppression result.

[0008] Further, the performing transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters includes: Extracting signal quality parameters from the equalized signal output to obtain signal amplitude parameters, signal phase parameters and signal jitter parameters; Performing amplitude statistical analysis on the signal amplitude parameter to obtain an amplitude evaluation coefficient; Performing phase offset calculation on the signal phase parameter to obtain a phase compensation coefficient; Performing jitter accumulation analysis on the signal jitter parameters to obtain a jitter correction coefficient; Performing signal integrity calculation on the amplitude evaluation coefficient, the phase compensation coefficient and the jitter correction coefficient to obtain a signal integrity evaluation value; Performing double-ended crosstalk coefficient calculation on the crosstalk suppression result to obtain a crosstalk suppression evaluation value; Performing transmission quality calculation on the signal integrity evaluation value and the crosstalk suppression evaluation value to obtain a signal quality parameter; The signal quality parameters are iteratively optimized to obtain the initial optimization parameters.

[0009] Further, the performing time domain reflection position measurement on the wire interference data to obtain attenuation position information, and performing prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result, includes: Performing time domain pulse injection on the wire interference data to obtain an impulse response sequence; Perform peak detection on the wire interference data according to the pulse response sequence to obtain reflection point position data; Performing time domain window analysis on the wire interference data according to the reflection point position data to obtain time domain attenuation characteristics; Performing cluster analysis on the time domain attenuation characteristics to obtain attenuation position information; Performing preliminary signal prediction on the attenuation position information to obtain initial attenuation prediction data; The initial attenuation prediction data is iteratively predicted according to the crosstalk suppression result to obtain the signal attenuation prediction result.

[0010] Further, performing peak detection on the wire interference data according to the impulse response sequence to obtain reflection point position data includes: Performing Bezier curve fitting on the impulse response sequence to obtain smooth curve data; Performing curvature calculation on the wire interference data according to the smooth curve data to obtain a curvature change sequence; Extracting extreme points of the curvature change sequence to obtain target inflection point data; Performing spline curve segmentation on the smooth curve data according to the target inflection point data to obtain a segmented curve set; Performing curve extraction on the segmented curve set to obtain a curve feature vector; Perform asymptotic line tracing on the segmented curve set according to the curve feature vector to obtain a peak contour line; The peak contour line is geometrically located to obtain reflection point position data.

[0011] Furthermore, the signal enhancement is performed on the original signal according to the initial optimization parameter and the signal attenuation prediction result to obtain a global enhanced signal, including: Adaptively equalizing the initial optimization parameters and the signal attenuation prediction result to obtain frequency domain compensation parameters; Performing forward equalization on the original signal based on the frequency domain compensation parameter to obtain a preliminary enhanced signal; Performing time domain reflection analysis on the preliminary enhanced signal and identifying signal distortion points to obtain a reflection characteristic signal; Performing crosstalk elimination on the reflection characteristic signal to obtain an anti-crosstalk signal; Performing adaptive threshold processing on the anti-crosstalk signal to obtain a stable signal; Performing eye diagram analysis based on the stable signal to obtain a quality assessment result; Performing nonlinear compensation on the stable signal according to the quality evaluation result to obtain a compensated enhanced signal; The compensated enhanced signal is subjected to global signal-to-noise ratio optimization to obtain a global enhanced signal.

[0012] The present invention also provides a high-performance passive copper cable signal enhancement system, which is applied to any one of the high-performance passive copper cable signal enhancement methods described above, comprising: An acquisition module, which is used to acquire electrical parameter data at both ends of the passive copper cable and perform impedance matching to obtain dynamic impedance parameters; An analysis module, the analysis module is used to obtain the original signal of the passive copper cable, and perform balanced analysis and sorting with the dynamic impedance parameter to obtain a balanced signal output; An association module, the association module is used to obtain the wire interference data of the passive copper cable, perform crosstalk analysis on the balanced signal output, and obtain a crosstalk suppression result; A processing module, the processing module is used to perform transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters; A control module, the control module is used to perform time domain reflection position measurement on the wire interference data to obtain attenuation position information, and perform prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result; An execution module is used to perform signal enhancement on the original signal according to the initial optimization parameters and the signal attenuation prediction result to obtain a global enhanced signal.

[0013] The present invention provides a high-performance passive copper cable signal enhancement method and system, which has the following beneficial effects: By obtaining the electrical parameter data at both ends of the passive copper cable and performing impedance matching, the impedance parameters can be dynamically adjusted to ensure the optimal impedance matching state during signal transmission. By balancing and sorting the original signal and the dynamic impedance parameters, the signal quality can be effectively improved and the signal distortion can be reduced. In addition, by obtaining the wire interference data and performing crosstalk analysis, the crosstalk interference can be accurately identified and suppressed, thereby improving the integrity of signal transmission. By performing transmission quality analysis on the balanced signal output and the crosstalk suppression results, the initial optimization parameters can be obtained, providing a reliable basis for subsequent signal enhancement. By performing time domain reflection position measurement on the wire interference data, the position of signal attenuation can be accurately located, and a predictive analysis can be performed based on the crosstalk suppression results to obtain the signal attenuation prediction results. Based on the initial optimization parameters and the signal attenuation prediction results, the original signal is enhanced to achieve global signal enhancement and ensure the high quality of the signal on the entire transmission path. In summary, through the comprehensive analysis and optimization of multiple factors, the efficient enhancement of passive copper cable signal transmission is achieved, and the problems of signal distortion and increased transmission bit error rate in the prior art are effectively solved, thereby significantly improving the application performance of passive copper cables in the field of high-speed communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a signal enhancement method for a high-performance passive copper cable provided by the present invention; Figure 2 The present invention provides a high-performance passive copper cable signal enhancement system structure diagram.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides a signal enhancement method for a high-performance passive copper cable, comprising: Step S1: obtaining electrical parameter data at both ends of the passive copper cable, and performing impedance matching to obtain dynamic impedance parameters; Step S2: obtaining the original signal of the passive copper cable, performing balanced analysis with the dynamic impedance parameter, and obtaining a balanced signal output; Step S3: Obtaining conductor interference data of the passive copper cable, performing crosstalk analysis on the balanced signal output, and obtaining a crosstalk suppression result; Step S4: Perform transmission quality analysis on the equalized signal output and crosstalk suppression results to obtain initial optimization parameters; Step S5: performing time domain reflection position measurement on the wire interference data to obtain attenuation position information, and performing prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result; Step S6: Perform signal enhancement on the original signal according to the initial optimization parameters and the signal attenuation prediction result to obtain a global enhanced signal.

[0019] Based on the above steps, the detailed process is as follows: Step S1: In the process of obtaining the electrical parameter data at both ends of the passive copper cable and performing impedance matching, the key electrical parameters such as voltage, current, impedance, etc. at both ends of the copper cable are collected through high-precision measuring equipment. The measuring equipment converts the collected data into digital signals, and performs preliminary filtering and noise reduction processing through the data processing module. The processed data is input into the impedance matching algorithm module, which calculates the relationship between the characteristic impedance of the copper cable and the impedance of the source and load ends based on the transmission line theory. The impedance matching algorithm adjusts the matching network parameters through iterative optimization to minimize the reflection loss of the signal during transmission. The acquisition of dynamic impedance parameters takes into account the influence of environmental factors such as temperature and frequency on impedance, and adjusts the matching parameters in real time to adapt to changes in working conditions. The dynamic impedance parameters finally output contain complete information such as impedance amplitude, phase, and frequency response characteristics, providing an important reference for subsequent signal equalization processing.

[0020] Step S2: During the process of obtaining the original signal of the passive copper cable and performing equalization and sorting, a high-speed sampling technique is used to collect the original signal transmitted in the copper cable. After the collected original signal undergoes analog-to-digital conversion, it is data-fused with the step dynamic impedance parameters. The equalization processing algorithm adaptively equalizes the signal according to the dynamic impedance parameters to compensate for the signal distortion caused by transmission line losses. The equalization algorithm combines feed-forward equalization and feedback equalization to independently compensate for signals in different frequency bands. During the signal equalization process, factors such as frequency-selective attenuation and dispersion effects are fully considered, and the amplitude-frequency characteristics of the signal are precisely controlled by adjusting the tap coefficients of the equalizer. The output result of the equalized signal includes the compensated signal time-domain waveform and frequency-domain characteristics, laying a foundation for subsequent crosstalk analysis.

[0021] Step S3: During the process of obtaining the wire interference data of the passive copper cable and performing crosstalk analysis, a high-precision interference measurement instrument is used to comprehensively measure the near-end crosstalk and far-end crosstalk in the copper cable. After the measurement data is preprocessed, it is combined with the output of the equalized signal for crosstalk characteristic analysis. The crosstalk analysis algorithm is based on the electromagnetic field theory, establishes a coupling model between wires, and calculates the transfer function between the crosstalk signal and the original signal. During the analysis process, factors such as the cable geometry structure and insulation material characteristics affecting crosstalk are considered, and the crosstalk suppression coefficient is solved through numerical calculation methods. The crosstalk suppression result includes key parameters such as the crosstalk attenuation amount and phase delay in each frequency band, providing a basis for subsequent signal quality optimization.

[0022] Step S4: During the process of performing transmission quality analysis on the output of the equalized signal and the crosstalk suppression result, a multi-dimensional evaluation method is used to comprehensively detect the signal transmission performance. The evaluation system uses the output of the equalized signal in Step S2 and the crosstalk suppression result as input data, and calculates key indicators such as eye diagram, jitter, and signal-to-noise ratio through signal integrity analysis tools. The transmission quality analysis process uses the Monte Carlo simulation method to simulate the signal transmission characteristics under different working conditions. The analysis algorithm jointly optimizes the time-domain and frequency-domain characteristics of the signal and constructs a signal quality evaluation model. The generation process of the initial optimization parameters comprehensively considers multiple performance indicators such as signal bandwidth, transmission distance, and bit error rate, and solves the optimal parameter combination through mathematical optimization methods. The obtained initial optimization parameters include key information such as equalizer coefficients and filter parameters, providing an optimization benchmark for global signal enhancement.

[0023] Step S5: During the process of measuring the time-domain reflection position of the wire interference data, a time-domain reflectometer is used to send a detection signal to the cable and receive the reflected waveform. The measurement system obtains the time-domain characteristics of the reflected signal through high-speed sampling, and calculates the signal attenuation position in combination with the propagation speed of electromagnetic waves in the cable. The position measurement algorithm is based on the reflection coefficient analysis method to identify the impedance discontinuity points and damage positions in the cable. The attenuation position information is fused with the crosstalk suppression result to establish a signal attenuation prediction model. The prediction analysis process uses a machine learning algorithm to fit the variation law of signal attenuation with the transmission distance through training data. The signal attenuation prediction result includes the attenuation coefficient and frequency response characteristics of each position point, providing a position-related compensation basis for subsequent signal enhancement processing.

[0024] Step S6: During the process of signal enhancement based on the initial optimization parameters and the signal attenuation prediction result, the initial optimization parameters and the attenuation prediction result are input into the signal enhancement processing module. The signal enhancement algorithm uses an adaptive compensation method to dynamically adjust the gain parameters according to the attenuation characteristics of the signal at different positions. The compensation process combines frequency-domain equalization and time-domain filtering techniques to precisely control the amplitude and phase characteristics of the signal. The generation process of the global enhanced signal fully considers the balance of multiple performance indicators such as signal integrity, crosstalk suppression, and power consumption. The enhanced processing result undergoes a performance verification test to ensure that the signal quality meets the requirements of high-speed transmission. The finally output global enhanced signal has a higher signal-to-noise ratio, lower distortion, and better timing characteristics.

[0025] A signal enhancement method for a high-performance passive copper cable provided by the present invention can dynamically adjust the impedance parameters by obtaining the electrical parameter data at both ends of the passive copper cable and performing impedance matching, ensuring the optimal matching state of the impedance during the signal transmission process. By performing equalization and sorting on the original signal and the dynamic impedance parameters, the signal quality can be effectively improved and signal distortion can be reduced. In addition, by obtaining the wire interference data and performing crosstalk analysis, the crosstalk interference can be accurately identified and suppressed, thereby improving the integrity of signal transmission. By performing transmission quality analysis on the equalized signal output and the crosstalk suppression result, the initial optimization parameters can be obtained, providing a reliable basis for subsequent signal enhancement. By measuring the time-domain reflection position of the wire interference data, the position of signal attenuation can be accurately located, and prediction analysis can be performed based on the crosstalk suppression result to obtain the signal attenuation prediction result. Based on the initial optimization parameters and the signal attenuation prediction result, the original signal is enhanced, enabling the enhancement of the global signal and ensuring the high quality of the signal throughout the transmission path. Through the above steps, through the comprehensive analysis and optimization of multiple factors, the efficient enhancement of passive copper cable signal transmission is achieved, effectively solving problems such as signal distortion and increased transmission error rate in the prior art, thereby significantly improving the application performance of passive copper cables in the field of high-speed communication.

[0026] In one embodiment, electrical parameter data at both ends of a passive copper cable are acquired and impedance matching is performed to obtain dynamic impedance parameters, including: In the stage of type analysis of the electrical parameter data, classification processing is performed on the original electrical parameter data collected at both ends of the passive copper cable. The voltage sampling data is obtained through a high-precision voltage sampling circuit, the sampling frequency is set to 1 MHz, and the sampling accuracy is 16 bits; the current sampling data is collected through a Hall current sensor, and the sampling frequency is synchronized with the voltage sampling; the phase sampling data is obtained through a phase detection circuit, and the sampling period is 1 μs. Through type analysis, voltage sampling data, current sampling data, and phase sampling data are obtained, and all three types of sampling data are stored in the data buffer area to provide a data basis for subsequent processing.

[0027] In the stage of frequency-domain conversion of the voltage sampling data and the current sampling data, fast Fourier transform (FFT) processing is adopted. The FFT transform uses 1024-point operations, and preprocessing is performed through a Hanning window function to reduce spectral leakage. Floating-point operations are used during the transformation process to ensure accuracy, and the processing result is represented in complex form. After conversion, frequency-domain voltage data and frequency-domain current data are obtained, which contain amplitude spectrum and phase spectrum information, and the data resolution reaches 0.1 Hz.

[0028] In the stage of complex impedance calculation based on the frequency-domain voltage data and the frequency-domain current data, the complex impedance value at each frequency point is calculated based on the frequency-domain voltage data divided by the frequency-domain current data. Complex impedance data is calculated, which contains impedance amplitude and phase angle information, the amplitude range is from 0.1 Ω to 1000 Ω, and the phase angle range is from -90° to +90°. The complex impedance data reflects the impedance characteristics of the passive copper cable at different frequencies.

[0029] In the stage of phase compensation for the complex impedance data and the phase sampling data, the compensation algorithm is based on the phase detection result to correct the phase component of the complex impedance. The linear interpolation method is used in the compensation process, and the compensation coefficient is determined according to the temperature and frequency relationship. Compensated impedance data is obtained, which eliminates the phase error caused by sampling delay and device nonlinearity, and the compensation accuracy is better than ±0.1°.

[0030] In the stage of impedance curve transformation for the compensated impedance data, a polynomial model is used for curve fitting, the order is 6th order, and the fitting accuracy is controlled within ±1%. The polynomial coefficients are determined through the least squares method to obtain the impedance characteristic curve. The curve reflects the variation law of impedance with frequency, and the covered frequency range is 1 kHz to 100 MHz.

[0031] In the stage of dynamic parameter calibration calculation of the compensation impedance data according to the impedance characteristic curve, the curve is divided into low frequency band, medium frequency band and high frequency band, and multiple characteristic points are selected in each frequency band. The characteristic point impedance values ​​are weighted averaged, and the weight coefficient is determined by the frequency response characteristics. The calibration calculation takes into account the influence of temperature drift and introduces a temperature compensation factor. The dynamic impedance parameters are calculated, including the characteristic impedance value, frequency response coefficient and temperature correction term.

[0032] This embodiment achieves high-precision impedance matching and dynamic parameter acquisition by accurately collecting and processing the electrical parameter data at both ends of the passive copper cable. The use of high-precision voltage sampling and Hall current sensors for data acquisition, combined with frequency domain analysis of fast Fourier transform, significantly improves the accuracy of impedance measurement, and makes the measurement accuracy of dynamic impedance parameters reach ±0.5%. By introducing phase compensation algorithm and temperature correction mechanism, the influence of sampling delay and temperature drift is effectively overcome, and the phase error is controlled within ±1°. The polynomial fitting and segmented feature extraction methods are used to achieve accurate characterization of impedance characteristics.

[0033] In one embodiment, the original signal of the passive copper cable is obtained, and balanced and sorted with the dynamic impedance parameter to obtain a balanced signal output, including: In the multi-scale decomposition stage, the acquired passive copper cable original signal is processed by wavelet transform. The db4 wavelet basis function is used, and the number of decomposition layers is set to 5 to perform multi-scale analysis on the signal. During the decomposition process, the signal boundary is processed by periodic extension to ensure the continuity of the decomposition result. Signal components of different frequency bands are obtained through multi-scale decomposition, including high-frequency detail components and low-frequency approximate components. The multi-scale signal components reflect the characteristic information of the original signal at different scales.

[0034] In the orthogonal empirical mode decomposition stage, the multi-scale signal components are decomposed by EMD combined with dynamic impedance parameters. The decomposition process uses cubic spline interpolation to construct the envelope, and the endpoint effect is eliminated by the mirror extension method. The termination condition of the screening process is set to the residual component energy is less than 0.1% of the original signal energy. The intrinsic mode function group obtained by decomposition contains 8-12 IMF components, each of which represents the different frequency characteristics of the signal. The intrinsic mode function group realizes the adaptive decomposition of the signal and provides a basis for subsequent processing.

[0035] In the fractional calculus operation stage, the Riemann-Liouville fractional calculus calculation is performed on the intrinsic mode function group. The fractional order α is set in the range of 0.1-0.9, and the calculation adopts the improved GL definition. The sliding window mechanism is introduced into the operation process, and the window length is 256 points. Through the fractional calculus operation, the dynamic sequence data is obtained, which retains the long-range correlation characteristics of the signal. The dynamic sequence data reflects the nonlinear dynamic characteristics of the signal.

[0036] In the stability analysis stage, the Lyapunov exponent is calculated for the dynamic sequence data based on the dynamic impedance parameters. The embedding dimension is selected as 4, and the time delay is taken as 10 sampling points. The small data volume method is adopted in the analysis process to calculate the maximum Lyapunov exponent. The stable domain parameters are obtained through stability analysis, including the boundaries of the stable interval and the stability index. The stable domain parameters reflect the dynamic characteristics of the signal system.

[0037] In the dynamic equalization stage, the stable domain parameters are adaptively equalized. The equalization algorithm adopts the LMS architecture, and the step size factor μ is adaptively adjusted according to the signal energy. The order of the equalizer is set to 32, and a complex structure is used for implementation. The equalization process obtains a preliminary equalized signal, and the frequency response of the signal is optimized. The group delay fluctuation of the preliminary equalized signal is reduced to less than 50% of the original.

[0038] In the frequency selection processing stage, the preliminary equalized signal is band-pass filtered according to the stable domain parameters. The filter is designed using the Parks-McClellan algorithm, with the passband ripple controlled within ±0.1 dB and the stopband attenuation greater than 60 dB. The filtering bandwidth is dynamically adjusted according to the signal spectrum characteristics. After the frequency selection processing, a filtered signal is obtained, and the out-of-band noise of the signal is effectively suppressed. The signal-to-noise ratio of the filtered signal is increased by more than 10 dB.

[0039] In the iterative threshold shrinkage reconstruction stage, the filtered signal is subjected to wavelet domain soft threshold processing. The SURE threshold criterion is adopted, and the threshold size is adaptively adjusted according to the decomposition scale. The number of iterations is set to 5, and the convergence criterion is that the relative error between two adjacent reconstruction results is less than 0.1%. The final equalized signal output is obtained through iterative threshold shrinkage reconstruction.

[0040] In this embodiment, by accurately detecting and analyzing the electrical parameter data at both ends of the passive copper cable, the dynamic matching of impedance characteristics is achieved, and the signal transmission quality is significantly improved. The method combining multi-scale decomposition and orthogonal empirical mode decomposition is adopted to realize the adaptive separation of signals, effectively overcoming the limitations of traditional methods in processing non-linear signals. The introduction of fractional calculus operations and stability analysis accurately captures the long-range correlation characteristics of the signal, providing a reliable theoretical basis for dynamic equalization.

[0041] In one embodiment, the crosstalk interference data of the passive copper cable is obtained, and crosstalk analysis is performed on the equalized signal output to obtain the crosstalk suppression results, including: The differential impedance of the passive copper cable is measured by sweeping the differential impedance using a network analyzer. The measurement frequency range is 1MHz to 40GHz, and the sampling interval is 0.1MHz. The near-end crosstalk coefficient and far-end crosstalk coefficient between the wire pairs, as well as the differential impedance value of the wire pairs, are obtained by measuring to form wire interference data. The wire interference data includes the electromagnetic coupling effect between the wire pairs and its characteristics that vary with frequency.

[0042] After obtaining the conductor interference data, the crosstalk coupling coefficient of the passive copper cable is calculated. The crosstalk coupling coefficient calculation process is based on the transmission line theory, and the conductor pair is modeled as a multi-conductor transmission line system. The characteristic impedance matrix method is used in the calculation to convert the conductor interference data into a four-port S parameter matrix, and then the crosstalk coupling coefficient is solved through the S parameter matrix. The calculation results form a 4×4-dimensional conductor-crosstalk coupling matrix, which describes the electromagnetic coupling strength and phase relationship between the conductor pairs. The diagonal elements of the conductor-crosstalk coupling matrix represent the self-coupling coefficient of each conductor pair, and the non-diagonal elements represent the mutual coupling coefficient between the conductor pairs.

[0043] According to the obtained guided-crosstalk coupling matrix, the equalized signal output is orthogonally transformed. The orthogonal transformation uses the singular value decomposition method to decompose the guided-crosstalk coupling matrix into the product of three matrices: the left singular matrix, the singular value diagonal matrix, and the transpose of the right singular matrix. The left singular matrix is ​​used to transform the equalized signal output and decompose the signal into unrelated orthogonal components. The crosstalk component data obtained after the transformation contains the main signal component and the crosstalk interference component, and the components are orthogonal and statistically independent. The crosstalk component data retains the crosstalk characteristics in the original signal, which is convenient for subsequent filtering processing.

[0044] When adaptively filtering the crosstalk component data, an adaptive filtering algorithm based on the minimum mean square error criterion is used. The algorithm continuously adjusts the filter coefficients in an iterative manner to minimize the mean square error between the filter output and the desired signal. The order of the adaptive filter is 64, the step factor is set to 0.01, and the convergence threshold is 10-6. During the filtering process, each orthogonal component is processed separately to suppress the crosstalk interference component and retain the main signal component. In the filtered signal data obtained after filtering, the crosstalk interference is effectively suppressed.

[0045] The crosstalk feature vector is inversely orthogonally transformed according to the filtered signal data. The inverse orthogonal transformation uses the transpose of the right singular matrix to restore the filtered orthogonal component signal to the original signal space. During the transformation process, the signal amplitude is scaled using the singular value diagonal matrix to ensure signal energy conservation. The result of the inverse transformation forms the crosstalk compensation data, which contains the compensation information required for crosstalk suppression.

[0046] Finally, perform a superposition operation on the equalized signal output and the crosstalk compensation data. The superposition operation uses complex addition to process both the amplitude and phase of the signal simultaneously. Through the superposition operation, the crosstalk compensation effect is applied to the original equalized signal to obtain the crosstalk suppression result. This result shows that the signal transmission performance after crosstalk suppression processing is significantly better than that before processing, and the signal quality is improved. In high-speed data transmission application scenarios, the processed signal has better integrity and reliability.

[0047] In this embodiment, by accurately measuring the differential impedance of wire pairs and calculating the crosstalk coupling coefficient of the passive copper cable, accurate data on the electromagnetic coupling characteristics between wires are obtained, laying a reliable foundation for subsequent signal enhancement processing. The singular value decomposition method is used for orthogonal transformation to effectively separate signal components, clearly distinguishing crosstalk interference from the main signal components. Based on the least mean square error criterion adaptive filtering algorithm, by precisely controlling the filter parameters, efficient suppression of crosstalk interference is achieved. During the inverse orthogonal transformation process, through the precise scaling of the singular value matrix, the maximum degree of signal energy retention is ensured.

[0048] In one embodiment, perform a transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters, including: In the signal enhancement method for high-performance passive copper cables, the transmission quality analysis of the equalized signal output and the crosstalk suppression result specifically includes the following multiple processing steps. During the process of extracting signal quality parameters from the equalized signal output, use a high-speed oscilloscope to collect the waveform of the equalized output signal, perform digital processing on the collected waveform, and extract signal amplitude parameters, signal phase parameters, and signal jitter parameters from it. The signal amplitude parameters include the signal peak-to-peak value, signal rise time, and signal fall time; the signal phase parameters include the signal phase offset and phase jitter; the signal jitter parameters include deterministic jitter and random jitter.

[0049] When performing amplitude statistical analysis on the signal amplitude parameters, perform probability density distribution statistics on the amplitude values within multiple signal periods collected, establish an amplitude distribution histogram, and obtain an amplitude distribution curve through Gaussian fitting. Calculate the amplitude standard deviation based on the amplitude distribution curve, and combine the ratio of the signal peak-to-peak value to the reference threshold to obtain an amplitude evaluation coefficient. This coefficient reflects the stability and consistency of the signal amplitude.

[0050] When calculating the phase offset of the signal phase parameters, compare the actual signal phase with the ideal signal phase to calculate the phase offset. Analyze the signal frequency spectrum characteristics through Fourier transform, determine the phase difference of the main frequency components, and establish a phase compensation model. Based on this model, calculate the compensation values of each frequency component, and comprehensively obtain a phase compensation coefficient. This coefficient is used to correct the phase distortion during signal transmission.

[0051] When performing jitter accumulation analysis on signal jitter parameters, statistical modeling is performed on deterministic jitter and random jitter respectively. Periodic analysis is performed on deterministic jitter to identify data-related jitter and duty cycle-related jitter; Gaussian distribution fitting is performed on random jitter to calculate the jitter probability density function. Through jitter decomposition and synthesis algorithms, the total jitter distribution characteristics are obtained, the jitter peak-to-peak value and RMS value are calculated, and the jitter correction coefficient is generated. This coefficient is used to evaluate and control the timing characteristics of the signal.

[0052] When calculating the signal integrity of the amplitude evaluation coefficient, phase compensation coefficient, and jitter correction coefficient, a signal quality evaluation model is established. The model comprehensively considers the weight distribution of the three coefficients and obtains the signal integrity evaluation value through weighted summation. The closer the evaluation value is to 1, the better the signal quality is. When the evaluation value is lower than 0.8, it means that the signal quality does not meet the transmission requirements.

[0053] When calculating the double-ended crosstalk coefficient for the crosstalk suppression results, measure the near-end crosstalk and far-end crosstalk separately. The near-end crosstalk measurement is performed at the signal transmitting end, and the far-end crosstalk measurement is performed at the signal receiving end. The ratio of the measured crosstalk value to the signal amplitude is used as the crosstalk coefficient to calculate the crosstalk suppression evaluation value. This evaluation value reflects the degree of electromagnetic interference between signal lines.

[0054] When calculating the transmission quality of the signal integrity evaluation value and the crosstalk suppression evaluation value, a transmission quality scoring model is established. The model combines the two evaluation values ​​according to the preset weights to generate a comprehensive score as the signal quality parameter. A signal quality parameter greater than 0.9 indicates excellent transmission quality, between 0.8 and 0.9 indicates good transmission quality, and less than 0.8 indicates unqualified transmission quality.

[0055] When iteratively optimizing the signal quality parameters, the gradient descent algorithm is used. Set the initial optimization parameters, including the equalizer parameters and the crosstalk suppression parameters. Through multiple iterative calculations, the parameter values ​​are continuously adjusted to gradually improve the signal quality parameters. When the improvement of the signal quality parameters is less than the preset threshold or reaches the maximum number of iterations, the current parameter values ​​are output as the initial optimization parameters. These parameters will be used for subsequent signal enhancement processing.

[0056] This embodiment realizes a comprehensive quality assessment of signal transmission by extracting signal quality parameters from the output of the balanced signal, combining amplitude statistical analysis, phase offset calculation and jitter accumulation analysis. This method establishes a complete signal quality assessment system, and accurately reflects the actual quality status of signal transmission through the comprehensive calculation of amplitude assessment coefficient, phase compensation coefficient and jitter correction coefficient. In terms of crosstalk suppression, the double-ended crosstalk coefficient calculation method is adopted to effectively evaluate the degree of electromagnetic interference between signal lines, providing a reliable basis for improving the quality of signal transmission. Through transmission quality calculation and iterative optimization, a scientific parameter optimization mechanism is established, which significantly improves the signal enhancement effect. This systematic signal enhancement method not only improves the transmission performance of passive copper cables, but also realizes accurate evaluation and continuous optimization of signal quality, providing reliable technical support for high-speed data transmission.

[0057] In one embodiment, time domain reflection position measurement is performed on the wire interference data to obtain attenuation position information, and the attenuation position information is predicted and analyzed based on the crosstalk suppression result to obtain a signal attenuation prediction result, including: A standardized time domain pulse signal is injected into the conductor. The pulse signal adopts a Gaussian pulse waveform, a pulse width of 1ns, and an amplitude of 1V. The pulse injection adopts a differential method, and pulse signals with opposite phases are injected simultaneously at the positive and negative ends of the differential conductor pair. The pulse response sequence is collected by a sampling device, the sampling rate is set to 20GS / s, the sampling time is 100ns, and 2000 points of discrete response sequence data are obtained. The response sequence contains the reflection signal information generated by each reflection point in the conductor.

[0058] The peak detection link is processed based on the acquired pulse response sequence data. The dual threshold detection method is used, and the amplitude threshold is set to ±0.1V and the time interval threshold is set to 5ns. When the signal amplitude in the response sequence is detected to exceed the amplitude threshold, and the time interval with the adjacent peak point is greater than the time interval threshold, the point is marked as a valid reflection point. The reflection point position data records the time position and amplitude information of each valid reflection point.

[0059] The time domain window analysis uses a sliding window method to process the reflection point position data. The window length is set to 10ns and the window overlap rate is 50%. The time domain characteristic parameters such as the root mean square value, peak-to-average ratio, and zero-crossing rate of the signal are calculated in each window. The obtained time domain attenuation feature contains the statistical characteristic information of the signal attenuation in each time window. This feature reflects the attenuation change law of the signal during the transmission process.

[0060] The cluster analysis uses the K-means algorithm to process the time domain attenuation features, and the number of cluster centers K is set to 3. The cluster feature dimensions include three parameters: root mean square value, peak-to-average ratio, and zero-crossing rate. The feature data is iteratively clustered by minimizing the intra-class sum of squares criterion until the class center position is stable. The attenuation position information contains the time position corresponding to each cluster center and the mean value of its characteristic parameters.

[0061] The initial signal prediction models the attenuation position information based on the linear regression model. The prediction features include four dimensions: time position, root mean square value, peak-to-average ratio, and zero-crossing rate. The least squares method is used to estimate the regression coefficient and establish a mapping relationship between attenuation characteristics and transmission distance. The initial attenuation prediction data contains the predicted signal attenuation parameters at each time position.

[0062] The iterative prediction process optimizes the initial prediction in combination with the crosstalk suppression results. The Gauss-Newton algorithm is used for iteration, and the measured data after crosstalk suppression is used as a reference to iteratively update the prediction model parameters. The iterative termination condition is that the prediction error is less than 1% or the maximum number of iterations is 50. The final signal attenuation prediction result contains the optimized signal attenuation prediction value at each time position.

[0063] This embodiment can accurately locate the reflection point in the wire through the combination of time domain pulse injection and peak detection, thereby improving the measurement accuracy of the signal attenuation position. Using time domain window analysis and cluster analysis, the time domain attenuation characteristics in the wire interference data are successfully extracted and identified, revealing the main mode of signal attenuation, and providing a reliable data basis for subsequent predictions. The preliminary signal prediction uses a linear regression model to establish a mapping relationship between attenuation characteristics and transmission distance, so that the initial attenuation prediction data has a good reference value. By combining the crosstalk suppression results for iterative prediction, the model parameters are optimized, and the accuracy of signal attenuation prediction is significantly improved.

[0064] In one embodiment, peak detection is performed on the wire interference data according to the impulse response sequence to obtain reflection point position data, including: When fitting the impulse response sequence with a Bezier curve, the third-order Bezier curve is selected as the fitting basis function, and the discrete impulse response data points are fitted based on the least squares method. The fitting process adopts a segmented fitting strategy, and four adjacent data points are selected in each segment to construct a control polygon. The fitting accuracy is optimized by adjusting the position of the control points. The control point position adjustment adopts an iterative optimization method, and the error threshold is set to 0.001. The iteration is stopped when the fitting error is less than the threshold. For abnormal data points, the weight coefficient is set to reduce their influence. After the Bezier curve fitting is completed, the smooth curve data is obtained, which contains the smooth contour information of the impulse response, and the data point density is 4 times that of the original data.

[0065] According to the obtained smooth curve data, the three-point method is used to calculate the curvature value of each point on the curve. In the curvature calculation, each point on the curve and one adjacent point on its left and right are selected, and an arc is constructed based on these three points. The curvature of the arc is the curvature value of the point. The selection interval of adjacent points is 1 / 100 of the total length of the curve to ensure the balance between calculation accuracy and efficiency. The curvature calculation adopts an analytical method to calculate the center position of the circle through the coordinates of three points, and then solve the curvature value. The curvature calculation is completed by traversing all points on the curve to form a curvature change sequence. This sequence reflects the change in the degree of curvature of the curve at each point, and the sequence length is the same as the number of smooth curve data points.

[0066] The curvature calculation includes: for any point P(x, y) in the smooth curve data, select its left and right adjacent points P1( , ) and P2( , ) form a three-point group. The curvature calculation uses the vector method based on the following calculation steps and formula: Calculating vectors =P1P and vector =PP2: Compute the vector cross product: Calculating vectors and Mould length: Distance between three points Calculation: ; The curvature k calculation formula is: Point P (x, y): represents the target point on the curve whose curvature is to be calculated. Its coordinate value (x, y) reflects the exact position of the point in the two-dimensional plane.

[0067] Adjacent point P1( , ) and P2( , ): They are the left and right adjacent sampling points of the target point P. These two points together with point P represent the shape of the local curve. The intervals between the three points are usually kept equal to ensure the consistency of the calculation results.

[0068] vector and : represents the vector pointing from P1 to P, represents the vector pointing from P to P2. The direction and magnitude of these two vectors together determine the curvature of the curve at point P. The components of the vectors express the change in the direction of the tangent of the curve at that point.

[0069] Vector cross product K: represents the vector and The area of ​​the parallelogram formed reflects the degree of change in the angle between the two vectors. The larger the K value, the greater the curvature of the curve at that point. The calculation of the cross product is carried out in the determinant way.

[0070] Vector modulus and : Represents vectors and The length of reflects the actual distance between adjacent sampling points. The modulus length is used as a normalization factor to eliminate the influence of uneven sampling intervals on the calculation results.

[0071] Distance between three points : Represents the straight-line distance between P1 and P2, which is used as the scaling factor for curvature calculation to standardize the curvature value. The introduction of makes the calculation results scale invariant.

[0072] Curvature k: describes the degree to which the curve deviates from the straight line at point P. Its physical meaning is the degree of curvature of the curve at this point. The larger the k value, the more severe the curve is at this point. The calculation formula of curvature takes into account the vector cross product and various distance parameters.

[0073] When extracting extreme points from a curvature change sequence, the curvature threshold is set to 1.5 times the sequence mean. When the curvature value of a point is greater than the threshold and greater than the curvature values ​​of its left and right adjacent points, the point is marked as an extreme point. In the process of extracting extreme points, the search window size is set to 1 / 20 of the sequence length, and the local maximum is found within the window. To prevent noise interference, the candidate extreme points are screened twice, requiring that the spacing between adjacent extreme points is not less than 1 / 2 of the window size. Extreme point detection is completed by traversing the curvature sequence to obtain the target inflection point data. This data set contains key position points where the shape of the curve changes significantly, and each inflection point records the position coordinates and curvature value.

[0074] When the smooth curve data is segmented into spline curves according to the target inflection point data, the two adjacent inflection points are used as endpoints, and the cubic spline interpolation method is used to fit each segment. The endpoint derivative continuity constraint is imposed during the interpolation process to ensure that the segmented curve has a smooth transition at the connection. The endpoint derivative value is approximately calculated by the difference of adjacent data points. For the second-order derivative at the endpoint, the natural boundary condition is used. The interpolation node is selected based on the equidistant principle, and the node spacing is 1 / 50 of the curve segment length. After the segmentation is completed, a segmented curve set is obtained, which contains multiple local curve segments, and each curve segment meets the endpoint continuity requirements.

[0075] When extracting curve feature parameters for a set of segmented curves, the length, average curvature, curvature change rate and other geometric feature parameters of each segment of the curve are calculated. The curve length is obtained by segmented cumulative summation, the average curvature is the arithmetic mean of the curvature values ​​of all points on the curve, and the curvature change rate is calculated by the first-order difference of the curvature sequence. At the same time, the starting and end position coordinates of the curve and the tangent direction angle information are extracted. The tangent direction is approximately calculated by the difference at the endpoints. These parameters are organized into feature vectors to characterize the shape characteristics of each segment of the curve. The feature vector dimension is 8, including geometric features and position information.

[0076] When tracing the asymptotic line of a segmented curve set based on the curve feature vector, the asymptotic point is searched inside the curve based on the tangent direction of each segment of the curve. The search step is set to 1 / 100 of the curve length, and the search direction is perpendicular to the tangent. The search is stopped when the distance between the search path and the original curve is greater than the preset threshold, and the threshold value is 1 / 10 of the curve length. During the search process, the distance change on the search path is recorded, and when the distance reaches a minimum value, it is marked as an asymptotic point. The peak contour line is obtained by connecting the asymptotic points of each segment of the curve. The contour line reflects the main shape characteristics of the entire curve and has good geometric continuity.

[0077] When locating the geometric center of the peak contour line, calculate the distance from each point on the contour line to the coordinate origin. Perform a weighted average on the distance sequence, with the weight being proportional to the curvature value of the point. The weight calculation is normalized so that the sum of the weights is 1. The point corresponding to the weighted average is the geometric center position, and this position data is output as the final reflection point position data. The reflection point position data identifies the specific location where the reflection occurs during signal transmission, and includes two coordinate components: horizontal and vertical.

[0078] This embodiment uses Bezier curve fitting pulse response sequence, combined with least squares method and segmented fitting strategy, to achieve high-precision smoothing of raw data and effectively eliminate the influence of measurement noise. The three-point method is used to calculate curvature and the window search mechanism is introduced to extract extreme points, accurately capturing the key change points of signal characteristics. Based on the method combining spline curve segmentation and feature parameter extraction, the local characteristics of the curve are accurately characterized, providing a reliable data basis for subsequent processing. Through asymptotic line tracking and geometric center positioning technology, a complete set of reflection point positioning methods is established, which significantly improves the accuracy of position detection. This method can not only adapt to different types of signal interference characteristics, but also has strong anti-noise ability, which is of great significance to improving the signal quality of high-performance passive copper cables. The entire processing process has a high degree of automation, the processing efficiency is significantly improved, and the processing results of each link are traceable, which is convenient for system optimization and performance improvement.

[0079] In one embodiment, the original signal is enhanced according to the initial optimization parameters and the signal attenuation prediction result to obtain a global enhanced signal, including: In the signal enhancement process of high-performance passive copper cables, in order to enhance the original signal, this method processes the initial optimization parameters and the signal attenuation prediction results, and finally obtains the global enhanced signal through multiple steps.

[0080] The adaptive equalization stage uses a dynamic adjustment mechanism to convert the initial optimization parameters and signal attenuation prediction results into the frequency domain space. This process uses fast Fourier transform to map the signal to the frequency domain and calculate the amplitude and phase characteristics of the signal in the frequency domain. Based on the preset frequency response template, the system optimizes the frequency domain parameters using the minimum mean square error criterion to obtain the frequency domain compensation parameters. The frequency domain compensation parameters include the gain and phase adjustment values ​​of each frequency point, which are used for subsequent signal compensation.

[0081] In the forward equalization stage, the original signal is pre-compensated before the signal is transmitted using the obtained frequency domain compensation parameters. This process uses a finite impulse response filter and designs the filter coefficients based on the frequency domain compensation parameters. The filter performs a time domain convolution operation on the signal to compensate for the frequency selective fading during the transmission process, thereby obtaining a preliminary enhanced signal. The preliminary enhanced signal has been improved in frequency domain characteristics, but further optimization is still required.

[0082] The time domain reflection analysis stage performs time domain characteristic analysis on the preliminary enhanced signal. This analysis uses time domain reflection measurement technology to locate the impedance discontinuity point on the signal transmission path by sending a detection signal and analyzing its reflected waveform. The system sets a reflection coefficient threshold and marks the position where the reflection amplitude exceeds the threshold as the signal distortion point. Based on the distortion point location information and the reflection waveform characteristics, a reflection characteristic signal is generated for subsequent signal quality improvement.

[0083] The crosstalk elimination stage processes the crosstalk interference in the reflected characteristic signal. This process uses an adaptive crosstalk elimination algorithm to establish a coupling model between multi-channel signals. The system estimates and eliminates the crosstalk component by minimizing the correlation between the crosstalk signal and the target signal. The influence of near-end crosstalk and far-end crosstalk is considered during the processing, and the anti-crosstalk signal is finally obtained. The anti-crosstalk signal significantly reduces the interference between adjacent channels.

[0084] The adaptive threshold processing stage performs threshold judgment on the crosstalk-resistant signal. This processing is based on the statistical characteristics of the signal and dynamically calculates the optimal judgment threshold. The system uses a histogram analysis method to determine the initial threshold according to the signal amplitude distribution characteristics, and realizes adaptive adjustment of the threshold through iterative optimization. This process effectively suppresses the random fluctuation of the signal and outputs a stable signal. The stable signal has good time domain stability, which lays the foundation for subsequent analysis.

[0085] The eye diagram analysis stage evaluates the quality of stable signals. This analysis constructs a signal eye diagram and extracts key parameters such as eye opening, jitter, and crossover points. The system sets a quality evaluation index system, including requirements such as vertical eye opening not less than 70% of the rated value, horizontal eye opening not less than 60% of the unit interval, and jitter peak-to-peak value not exceeding 15% of the cycle. Based on these indicators, a comprehensive quality score is calculated to generate a quality evaluation result.

[0086] In the nonlinear compensation stage, the stable signal is compensated according to the quality assessment results. The compensation adopts a nonlinear model based on the volt-ampere characteristic to correct the amplitude distortion of the signal. The system realizes fast mapping through a lookup table and adjusts the compensation intensity in combination with a dynamic feedback mechanism. During the compensation process, the signal distortion is ensured to be reduced below the index requirements and a compensation enhancement signal is output. The compensation enhancement signal overcomes the nonlinear distortion introduced by the channel.

[0087] The global signal-to-noise ratio optimization stage performs the final optimization on the compensation enhancement signal. This optimization combines the signal power spectrum analysis and noise level estimation to calculate the signal-to-noise ratio distribution of each frequency band. The system uses the Wiener filtering technology to optimally filter the signal to achieve a balance between noise suppression and signal enhancement. The optimization process ensures that the signal-to-noise ratio of the output signal is not less than 35dB, and finally obtains the global enhancement signal. The global enhancement signal has excellent transmission performance and meets the technical requirements of high-performance passive copper cable systems.

[0088] This embodiment achieves a comprehensive improvement in the signal quality of high-performance passive copper cables by adopting a multi-stage signal processing method. In the adaptive equalization stage, the system dynamically adjusts the frequency domain compensation parameters to effectively overcome the problem of frequency selective fading during transmission. By combining forward equalization and time domain reflection analysis, the signal distortion points are accurately identified and processed, significantly improving the transmission quality of the signal. The dual optimization mechanism of crosstalk elimination and adaptive threshold processing not only effectively suppresses the interference between adjacent channels, but also enhances the time domain stability of the signal. The quality assessment system based on eye diagram analysis, combined with nonlinear compensation technology, reduces the signal distortion to below the index requirements. The final global signal-to-noise ratio optimization ensures that the output signal has a signal-to-noise ratio of more than 35dB, greatly improving the overall performance of the system. This multi-level signal enhancement method not only improves the transmission reliability of passive copper cables, but also provides a stable guarantee for high-speed data transmission.

[0089] Reference Figure 2 As shown, the present invention also provides a high-performance passive copper cable signal enhancement system, which is applied to any of the above high-performance passive copper cable signal enhancement methods, comprising: The acquisition module is used to obtain the electrical parameter data of both ends of the passive copper cable and perform impedance matching to obtain dynamic impedance parameters; Analysis module: The analysis module is used to obtain the original signal of the passive copper cable, and perform balanced analysis and sorting with the dynamic impedance parameters to obtain a balanced signal output; A correlation module, which is used to obtain the conductor interference data of the passive copper cable, perform crosstalk analysis on the balanced signal output, and obtain a crosstalk suppression result; A processing module, the processing module is used to perform transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters; A control module, the control module is used to perform time domain reflection position measurement on the wire interference data to obtain attenuation position information, and perform prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result; The execution module is used to enhance the original signal according to the initial optimization parameters and the signal attenuation prediction result to obtain a global enhanced signal.

[0090] The present invention provides a high-performance passive copper cable signal enhancement system, which can dynamically adjust the impedance parameters by acquiring the electrical parameter data at both ends of the passive copper cable and performing impedance matching, so as to ensure the optimal impedance matching state during signal transmission. By balancing and sorting the original signal and the dynamic impedance parameters, the signal quality can be effectively improved and the signal distortion can be reduced. In addition, by acquiring the wire interference data and performing crosstalk analysis, the crosstalk interference can be accurately identified and suppressed, thereby improving the integrity of the signal transmission. By performing transmission quality analysis on the balanced signal output and the crosstalk suppression results, the initial optimization parameters can be obtained, providing a reliable basis for subsequent signal enhancement. By performing time domain reflection position measurement on the wire interference data, the position of the signal attenuation can be accurately located, and a predictive analysis is performed based on the crosstalk suppression results to obtain the signal attenuation prediction results. Based on the initial optimization parameters and the signal attenuation prediction results, the original signal is enhanced, and the global signal can be enhanced to ensure the high quality of the signal on the entire transmission path. Combining the above steps, through comprehensive analysis and optimization of multiple factors, the efficient enhancement of passive copper cable signal transmission is achieved, and the problems of signal distortion and increased transmission bit error rate in the existing technology are effectively solved, thereby significantly improving the application performance of passive copper cables in the field of high-speed communications.

[0091] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0092] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for signal enhancement of a high-performance passive copper cable, characterized in that: include: Obtain electrical parameter data at both ends of the passive copper cable, perform impedance matching, and obtain dynamic impedance parameters; Acquire the original signal of the passive copper cable, and perform balanced analysis with the dynamic impedance parameter to obtain a balanced signal output; Acquire the conductor interference data of the passive copper cable, perform crosstalk analysis on the equalized signal output, and obtain a crosstalk suppression result; Performing transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters; Performing time domain reflection position measurement on the wire interference data to obtain attenuation position information, and performing prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result; The original signal is enhanced according to the initial optimization parameters and the signal attenuation prediction result to obtain a global enhanced signal.

2. The signal enhancement method of high-performance passive copper cable according to claim 1, characterized in that: The step of obtaining electrical parameter data at both ends of the passive copper cable and performing impedance matching to obtain dynamic impedance parameters includes: Performing type analysis on the electrical parameter data to obtain voltage sampling data, current sampling data and phase sampling data; Performing frequency domain conversion on the voltage sampling data and the current sampling data to obtain frequency domain voltage data and frequency domain current data; Performing complex impedance calculation according to the frequency domain voltage data and the frequency domain current data to obtain complex impedance data; Performing phase compensation on the complex impedance data and the phase sampling data to obtain compensated impedance data; Performing impedance curve transformation on the compensation impedance data to obtain an impedance characteristic curve; Dynamic parameter calibration calculation is performed on the compensation impedance data according to the impedance characteristic curve to obtain dynamic impedance parameters.

3. The signal enhancement method of high-performance passive copper cable according to claim 1, characterized in that: The obtaining of the original signal of the passive copper cable and performing balanced analysis and sorting with the dynamic impedance parameter to obtain a balanced signal output includes: Performing multi-scale decomposition on the original signal to obtain multi-scale signal components; Performing orthogonal empirical mode decomposition on the multi-scale signal components according to the dynamic impedance parameters to obtain an intrinsic mode function group; Performing fractional calculus operation on the intrinsic mode function group to obtain dynamic sequence data; Performing stability analysis on the dynamic sequence data based on the dynamic impedance parameters to obtain stable domain parameters; Dynamically balancing the stable domain parameters to obtain a preliminary balanced signal; Performing frequency selection processing on the preliminary equalized signal according to the stable domain parameter to obtain a filtered signal; The filtered signal is subjected to iterative threshold shrinkage reconstruction to obtain the equalized signal output.

4. The signal enhancement method for high-performance passive copper cable according to claim 1, characterized in that: The obtaining of the conductor interference data of the passive copper cable, performing crosstalk analysis on the balanced signal output, and obtaining a crosstalk suppression result includes: Performing conductor pair differential impedance measurement on the passive copper cable to obtain the conductor interference data; Calculating the crosstalk coupling coefficient of the passive copper cable according to the conductor interference data to obtain a conductor-crosstalk coupling matrix; Performing an orthogonal transformation on the equalized signal output according to the crosstalk coupling matrix to obtain crosstalk component data; Adaptively filtering the crosstalk component data to obtain filtered signal data; Performing an inverse orthogonal transformation on the crosstalk feature vector according to the filtered signal data to obtain crosstalk compensation data; The equalized signal output and the crosstalk compensation data are superimposed to obtain the crosstalk suppression result.

5. The signal enhancement method of high-performance passive copper cable according to claim 1, characterized in that: The performing transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters includes: Extracting signal quality parameters from the equalized signal output to obtain signal amplitude parameters, signal phase parameters and signal jitter parameters; Performing amplitude statistical analysis on the signal amplitude parameter to obtain an amplitude evaluation coefficient; Performing phase offset calculation on the signal phase parameter to obtain a phase compensation coefficient; Performing jitter accumulation analysis on the signal jitter parameters to obtain a jitter correction coefficient; Performing signal integrity calculation on the amplitude evaluation coefficient, the phase compensation coefficient and the jitter correction coefficient to obtain a signal integrity evaluation value; Performing double-ended crosstalk coefficient calculation on the crosstalk suppression result to obtain a crosstalk suppression evaluation value; Performing transmission quality calculation on the signal integrity evaluation value and the crosstalk suppression evaluation value to obtain a signal quality parameter; The signal quality parameters are iteratively optimized to obtain the initial optimization parameters.

6. The signal enhancement method of high-performance passive copper cable according to claim 1, characterized in that: The performing time domain reflection position measurement on the wire interference data to obtain attenuation position information, and performing prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result, includes: Performing time domain pulse injection on the wire interference data to obtain an impulse response sequence; Perform peak detection on the wire interference data according to the pulse response sequence to obtain reflection point position data; Performing time domain window analysis on the wire interference data according to the reflection point position data to obtain time domain attenuation characteristics; Performing cluster analysis on the time domain attenuation characteristics to obtain attenuation position information; Performing preliminary signal prediction on the attenuation position information to obtain initial attenuation prediction data; The initial attenuation prediction data is iteratively predicted according to the crosstalk suppression result to obtain the signal attenuation prediction result.

7. The signal enhancement method of high-performance passive copper cable according to claim 6, characterized in that: The step of performing peak detection on the wire interference data according to the pulse response sequence to obtain reflection point position data includes: Performing Bezier curve fitting on the impulse response sequence to obtain smooth curve data; Performing curvature calculation on the wire interference data according to the smooth curve data to obtain a curvature change sequence; Extracting extreme points of the curvature change sequence to obtain target inflection point data; Performing spline curve segmentation on the smooth curve data according to the target inflection point data to obtain a segmented curve set; Performing curve extraction on the segmented curve set to obtain a curve feature vector; Perform asymptotic line tracing on the segmented curve set according to the curve feature vector to obtain a peak contour line; The peak contour line is geometrically located to obtain reflection point position data.

8. The signal enhancement method of high-performance passive copper cable according to claim 1, characterized in that: The performing signal enhancement on the original signal according to the initial optimization parameter and the signal attenuation prediction result to obtain a global enhanced signal includes: Adaptively equalizing the initial optimization parameters and the signal attenuation prediction result to obtain frequency domain compensation parameters; Performing forward equalization on the original signal based on the frequency domain compensation parameter to obtain a preliminary enhanced signal; Performing time domain reflection analysis on the preliminary enhanced signal and identifying signal distortion points to obtain a reflection characteristic signal; Performing crosstalk elimination on the reflection characteristic signal to obtain an anti-crosstalk signal; Performing adaptive threshold processing on the anti-crosstalk signal to obtain a stable signal; Performing eye diagram analysis based on the stable signal to obtain a quality assessment result; Performing nonlinear compensation on the stable signal according to the quality evaluation result to obtain a compensated enhanced signal; The compensated enhanced signal is subjected to global signal-to-noise ratio optimization to obtain a global enhanced signal.

9. A high-performance passive copper cable signal enhancement system, characterized in that: The signal enhancement method for a high-performance passive copper cable according to any one of claims 1 to 8 comprises: An acquisition module, which is used to acquire electrical parameter data at both ends of the passive copper cable and perform impedance matching to obtain dynamic impedance parameters; An analysis module, the analysis module is used to obtain the original signal of the passive copper cable, and perform balanced analysis and sorting with the dynamic impedance parameter to obtain a balanced signal output; An association module, the association module is used to obtain the wire interference data of the passive copper cable, perform crosstalk analysis on the balanced signal output, and obtain a crosstalk suppression result; A processing module, the processing module is used to perform transmission quality analysis on the equalized signal output and the crosstalk suppression result to obtain initial optimization parameters; A control module, the control module is used to perform time domain reflection position measurement on the wire interference data to obtain attenuation position information, and perform prediction analysis on the attenuation position information based on the crosstalk suppression result to obtain a signal attenuation prediction result; An execution module is used to perform signal enhancement on the original signal according to the initial optimization parameters and the signal attenuation prediction result to obtain a global enhanced signal.

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