Multi-channel signal integrity optimization method and system for passive copper cable

Abstract

The invention relates to a multi-channel signal integrity optimization method and system for a passive copper cable, and the method comprises the steps: obtaining channel characteristic parameters and signal transmission requirements of the passive copper cable, and carrying out the impedance matching, and obtaining an optimized transmission channel; acquiring real-time transmission data of the passive copper cable, and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; acquiring crosstalk interference data acquired by the passive copper cable, and carrying out shielding analysis on the crosstalk interference data and the optimized transmission channel to obtain a crosstalk suppression measure; performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain a frequency compensation parameter; and performing multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimization signal. According to the invention, complex and changeable transmission environments can be coped, and stable transmission of signals is ensured.

Description

Technical Field

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

[0002] With the rapid development of electronic communication technology, passive copper cables play a vital role in the field of data transmission and are widely used in various electronic devices and communication systems. In today's context of increasing requirements for data transmission speed and quality, ensuring the signal integrity of passive copper cables has become the key to achieving efficient and reliable communication. Currently, there are many problems in the process of passive copper cable signal transmission. The existing signal transmission methods often fail to fully consider the channel characteristic parameters of passive copper cables, resulting in unreasonable impedance matching, making it easy for signals to be reflected and attenuated during transmission, seriously affecting the transmission quality of the signal. When processing real-time transmission data, there is a lack of effective equalization processing methods, which cannot cope with complex and changeable transmission environments and is difficult to ensure stable signal transmission. Furthermore, for the crosstalk interference data collected by passive copper cables, there is a lack of in-depth shielding analysis and effective crosstalk suppression measures. Crosstalk interference will seriously affect the purity of the signal and introduce a lot of noise. Summary of the invention

[0003] The main purpose of the present invention is to provide a multi-channel signal integrity optimization method and system for a passive copper cable, which can cope with complex and changeable transmission environments and ensure stable signal transmission.

[0004] To achieve the above object, the present invention provides a multi-channel signal integrity optimization method for a passive copper cable, comprising: Obtain the channel characteristic parameters and signal transmission requirements of the passive copper cable, and perform impedance matching to obtain the optimized transmission channel; Acquiring real-time transmission data of the passive copper cable, and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; Obtaining crosstalk interference data collected by the passive copper cable, performing shielding analysis with the optimized transmission channel, and obtaining crosstalk suppression measures; Performing noise optimization on the stable transmission signal according to the crosstalk suppression measure to obtain an initial signal optimization result; Performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain frequency compensation parameters; The initial signal optimization result is subjected to multi-channel optimization according to the frequency compensation parameter to obtain a multi-channel optimized signal.

[0005] Further, the obtaining of channel characteristic parameters and signal transmission requirements of the passive copper cable and performing impedance matching to obtain an optimized transmission channel includes: Collecting channel physical parameters of the passive copper cable to obtain the channel characteristic parameters; Measuring the signal transmission rate and bandwidth of the passive copper cable to obtain the signal transmission requirement; Performing a multi-point impedance test on the passive copper cable according to the channel characteristic parameters to obtain impedance distribution data; Performing discrete spectrum analysis on the impedance distribution data to obtain a frequency domain characteristic curve; Perform impedance segmentation compensation calculation according to the frequency domain characteristic curve and the signal transmission requirement to obtain an impedance compensation parameter group; Performing impedance optimization configuration on the passive copper cable according to the impedance compensation parameter group to obtain impedance matching information; Channel matching is performed on the passive copper cable based on the impedance matching information to obtain an optimized transmission channel.

[0006] Furthermore, the acquiring of the real-time transmission data of the passive copper cable and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal includes: Performing real-time data sampling on the input end and the output end of the passive copper cable to obtain the real-time transmission data; Performing time-frequency domain conversion on the real-time transmission data to obtain a transmission parameter group; Performing linear regression analysis of signal amplitude and phase based on the transmission parameter group to obtain an equalization coefficient; Performing forward equalization and backward equalization calculation on the real-time transmission data according to the equalization coefficient to obtain an equalization compensation parameter; Constructing a compensation algorithm according to the equalization compensation parameters, and iteratively optimizing the real-time transmission data to obtain an optimized parameter group; Signal compensation is performed on the real-time transmission data according to the optimization parameter group to obtain the stable transmission signal.

[0007] Furthermore, the crosstalk interference data collected by the passive copper cable is obtained, and shielding analysis is performed on the optimized transmission channel to obtain crosstalk suppression measures, including: Collecting the spacing and material parameters of the passive copper cable to obtain channel spacing data and conductor material parameters, and performing crosstalk attenuation calculation to obtain a crosstalk attenuation coefficient; Performing crosstalk interference sampling and signal decomposition on the passive copper cable to obtain crosstalk spectrum data; Performing energy distribution analysis on the crosstalk spectrum data according to the crosstalk attenuation coefficient to obtain a crosstalk energy distribution diagram; Perform multi-channel crosstalk path identification according to the crosstalk energy distribution diagram to obtain a crosstalk propagation path; Performing electromagnetic simulation on the optimized transmission channel according to the crosstalk propagation path to obtain electromagnetic field distribution data; A suppression analysis is performed on the crosstalk propagation path and the electromagnetic field distribution data to obtain crosstalk suppression measures.

[0008] Further, the noise optimization of the stable transmission signal according to the crosstalk suppression measure to obtain an initial signal optimization result includes: Performing waveform analysis on the stable transmission signal to obtain signal waveform characteristics; Calculate the noise spectrum density of the stable transmission signal according to the signal waveform characteristics to obtain noise distribution parameters; Performing a filtering operation on the stable transmission signal based on the noise distribution parameter to obtain a filtered signal; Performing signal compensation calculation on the filtered signal to obtain a compensated signal; Performing a multi-channel gain equalization operation on the compensation signal to obtain an equalized signal; The equalized signal is subjected to waveform shaping optimization to obtain an initial signal optimization result.

[0009] Further, the calculating of the noise spectrum density of the stable transmission signal according to the signal waveform characteristics to obtain the noise distribution parameters includes: Performing time domain sampling segmentation processing on the signal waveform feature to obtain multiple signal sampling segments; Performing autocorrelation function calculation on the stable transmission signal according to the multiple signal sampling segments to obtain an initial noise power spectrum; Performing multi-window spectrum analysis on the stable transmission signal according to the initial noise power spectrum to obtain an improved noise spectrum; Based on the improved noise spectrum, the stable transmission signal is divided into frequency bands to obtain a multi-band noise distribution map; A noise source identification calculation is performed on the multi-band noise distribution mapping according to preset transmission medium characteristic parameters to obtain noise distribution parameters.

[0010] Further, the performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain frequency compensation parameters, includes: Performing wavelet decomposition on the crosstalk interference data to obtain crosstalk spectrum characteristics; Dividing the crosstalk spectrum characteristics into frequency component levels to obtain frequency characteristic groups; Performing frequency band analysis on the frequency feature groups to obtain frequency band loss characteristics; Fitting the frequency band loss characteristics with a signal attenuation law to obtain a frequency compensation equation; Numerically solving the frequency compensation equation to obtain the frequency compensation information; quantifying the transmission error of the frequency compensation information according to the crosstalk suppression measure to obtain a channel error parameter; Performing compensation prediction on the channel error parameter to obtain a compensation prediction result; The frequency compensation information is adaptively updated according to the compensation prediction result to obtain the frequency compensation parameter.

[0011] Furthermore, according to the multi-channel signal integrity optimization method of the passive copper cable according to claim 1, the multi-channel optimization of the initial signal optimization result according to the frequency compensation parameter to obtain the multi-channel optimized signal includes: Performing frequency segmentation on the frequency compensation parameter to obtain frequency segmentation coefficients; Perform multi-channel decomposition on the initial signal optimization result according to the frequency segmentation coefficient to obtain channel decomposition data; Performing frequency response calculation on the channel decomposition data to obtain frequency response parameters; Performing frequency compensation on the channel decomposition data according to the frequency response parameter to obtain frequency compensation data; Performing channel coupling on the frequency compensation data to obtain a channel coupling coefficient; Performing channel isolation on the frequency compensation data according to the channel coupling coefficient to obtain channel isolation data; Performing signal reorganization on the channel isolation data to obtain reorganized signal data; The recombined signal data is compensated and optimized according to the frequency segmentation coefficients to obtain a multi-channel optimized signal.

[0012] The present invention also provides a multi-channel signal integrity optimization system for a passive copper cable, which is applied to any one of the multi-channel signal integrity optimization methods for a passive copper cable described above, comprising: An acquisition module, which is used to obtain channel characteristic parameters and signal transmission requirements of the passive copper cable, and perform impedance matching to obtain an optimized transmission channel; An analysis module, the analysis module is used to obtain real-time transmission data of the passive copper cable, and perform equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; An association module, the association module is used to obtain the crosstalk interference data collected by the passive copper cable, perform shielding analysis with the optimized transmission channel, and obtain crosstalk suppression measures; A processing module, the processing module is used to perform noise optimization on the stable transmission signal according to the crosstalk suppression measure to obtain an initial signal optimization result; A control module, the control module is used to perform frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and perform transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain frequency compensation parameters; An execution module is used to perform multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimized signal.

[0013] The multi-channel signal integrity optimization method and system of the passive copper cable provided by the present invention have the following beneficial effects: By obtaining the channel characteristic parameters and signal transmission requirements of the passive copper cable, performing impedance matching, and optimizing the transmission channel, the problem of unreasonable impedance matching in the existing signal transmission mode is effectively solved, the reflection and attenuation of the signal during the transmission process are reduced, and the transmission quality of the signal is improved. The real-time transmission data through the passive copper cable is obtained, and balanced processing is performed in the optimized transmission channel, which can cope with complex and changeable transmission environments, ensure the stable transmission of the signal, and solve the problem of lack of effective balanced processing means for real-time transmission data. At the same time, the present invention also performs shielding analysis on the crosstalk interference data collected by the passive copper cable, obtains crosstalk suppression measures, performs noise optimization on the stable transmission signal, improves the purity of the signal, reduces the noise introduced by the crosstalk interference, and solves the problem of lack of effective suppression measures for the crosstalk interference data. By performing frequency characteristic analysis on the crosstalk interference data, frequency compensation information is obtained, and transmission prediction of the frequency compensation information is performed based on the crosstalk suppression measures to obtain frequency compensation parameters, and further multi-channel optimization is performed on the initial signal optimization result, realizing multi-channel optimization processing of the signal, and improving the efficiency and quality of signal transmission. BRIEF DESCRIPTION OF THE DRAWINGS

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

[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 multi-channel signal integrity optimization method for a passive copper cable, comprising: Step S1: Obtain channel characteristic parameters and signal transmission requirements of the passive copper cable, and perform impedance matching to obtain an optimized transmission channel; Step S2: acquiring real-time transmission data of the passive copper cable, performing equalization processing on the real-time transmission data by optimizing the transmission channel, and obtaining a stable transmission signal; Step S3: Obtain crosstalk interference data collected by the passive copper cable, perform shielding analysis on the optimized transmission channel, and obtain crosstalk suppression measures; Step S4: performing noise optimization on the stable transmission signal according to the crosstalk suppression measure to obtain an initial signal optimization result; Step S5: Analyze the frequency characteristics of the crosstalk interference data to obtain frequency compensation information, and perform transmission prediction on the frequency compensation information based on the crosstalk suppression measures to obtain frequency compensation parameters; Step S6: Perform multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimized signal.

[0019] Based on the above steps, the detailed steps are as follows: Step S1: The transmission performance of passive copper cables is determined by distributed parameters such as conductor resistance, capacitance, and inductance, which are significantly affected by the cable material, structure (such as wire diameter, insulation thickness) and operating frequency. During implementation, the S parameter matrix (such as insertion loss and return loss) of the copper cable is first measured at multiple frequency points (such as 1 MHz to 10 GHz) by a vector network analyzer (VNA), and a frequency domain model is established in combination with the physical parameters of the cable (such as length, diameter, and dielectric constant). At the same time, the signal transmission requirements are clarified, including bandwidth, modulation format (such as PAM4), and bit error rate (BER) targets. Based on the frequency domain characteristic curve and the frequency spectrum overlap area required for transmission, the discrete Fourier transform (DFT) is used to identify the high-frequency dispersion effect and the low-frequency loss dominant interval, and the ideal impedance value of each frequency band is calculated by the segmented impedance compensation algorithm to generate an impedance compensation parameter group. Finally, the equivalent circuit model of the copper cable is reconstructed to complete the optimized transmission channel design to ensure that signal reflection and attenuation are minimized. This process provides a dynamic matching basis for subsequent signal processing, which directly affects the accuracy of equalization processing and crosstalk analysis.

[0020] Step S2: The acquisition of real-time transmission data requires synchronous capture of the time domain signals at the input and output ends of the copper cable, and the signal waveform is recorded at a high resolution (such as 1 GS / s) through a high-speed oscilloscope or digital sampler. The signal is then decomposed into baseband and high-frequency harmonic components, and the amplitude and phase distortion of the signal in different frequency bands are analyzed by combining fast Fourier transform (FFT) and wavelet transform. Based on the frequency domain model of the optimized transmission channel, a dynamic equalization model is constructed: the mathematical relationship between transmission parameters (such as group delay and insertion loss) and equalization coefficients is established through a linear regression algorithm, and the filter coefficients required for forward equalization (FFE) and backward equalization (BFE) are calculated. During the iterative optimization process, the equalized signal is compared with the original signal, and the coefficients are dynamically adjusted through the minimum mean square error (MSE) criterion until the signal distortion is lower than the preset threshold (such as −20 dB). The spectrum flatness of the stable transmission signal output in the end is improved, and the eye diagram opening width is increased, which meets the timing and amplitude tolerance requirements of high-speed signal transmission. This step directly depends on the impedance matching result of S1 to ensure that the equalization processing accurately compensates for the actual channel characteristics.

[0021] Step S3: The crosstalk interference data is collected in a multi-channel wiring environment through a near-field probe or spectrum analyzer, and the coupling strength of adjacent line pairs (such as NEXT / FEXT) is measured and the crosstalk coefficient at different frequency points is recorded. Combined with the physical spacing of the copper cable, the dielectric loss of the insulating material and the skin effect parameters of the conductor, a three-dimensional electromagnetic field simulation model (such as COMSOL Multiphysics) is constructed to simulate the coupling path and attenuation law of the crosstalk during signal transmission. Through spectrum superposition and energy distribution analysis, it is identified that the high frequency band (>1 GHz) is dominated by electromagnetic radiation, and the low frequency band (<1 GHz) is dominated by capacitive coupling. Based on optimizing the shielding effectiveness of the transmission channel (such as metal shielding layer coverage, grounding structure design), a layered suppression strategy is proposed: the high frequency band adopts increasing the number of shielding layers and optimizing the grounding topology structure, and the low frequency band reduces the coupling strength by adjusting the line spacing and using high dielectric constant insulating materials. Finally, a crosstalk suppression measure including physical shielding parameters and signal processing algorithms (such as feedforward cancellation filters) is formed to provide a quantitative basis for subsequent noise optimization.

[0022] Step S4: Although the stable transmission signal has been equalized, it may still be affected by the residual crosstalk and the inherent noise of the channel. When implementing noise optimization, the obtained crosstalk suppression measures (such as physical shielding parameters and filtering algorithms) are combined to further improve the signal-to-noise ratio (SNR) through multi-dimensional signal processing. First, the stable transmission signal is analyzed in the time domain waveform to extract the signal eye diagram characteristics and peak jitter parameters, and quantify the coupling effect of high-frequency noise and low-frequency drift. Based on the autocorrelation function and power spectral density (PSD) analysis, a noise distribution model is constructed: Kalman filtering is used for dynamic estimation and cancellation of low-frequency band (<1 GHz) noise, and wavelet packet decomposition is used to separate signal subbands for high-frequency band (>1 GHz) noise, and the threshold method is used to suppress high-frequency random noise. At the same time, combined with the shielding effectiveness data in the crosstalk suppression measures (such as the electromagnetic field distribution after the grounding structure is optimized), the cutoff frequency and attenuation slope of the filter are adjusted to eliminate the residual crosstalk caused by incomplete shielding. Finally, through multi-channel gain equalization operation, the amplitude difference caused by material inhomogeneity of each channel is compensated, and the initial signal optimization result is output, and its bit error rate (BER) is less than 1×10 -12 , meeting the reliability requirements of high-speed signal transmission.

[0023] Step S5: In order to solve the problem of non-uniform loss in the frequency domain of multi-channel signals, it is necessary to perform deep frequency analysis on the crosstalk interference data (such as NEXT / FEXT spectrum). The interference signal is decomposed into sub-signals of different frequency bands through wavelet decomposition technology, and the energy distribution and attenuation characteristics of each frequency band are quantified to identify the steep drop of the signal caused by dielectric loss in the high frequency band (>5 GHz) and the phase shift caused by the skin effect in the low frequency band (<1 GHz). Based on this, a frequency compensation information library is constructed, including parameters such as band loss coefficient, dispersion slope and Q factor. Further combined with the crosstalk suppression measures of S3 (such as shielding layer thickness and line spacing adjustment), the frequency compensation model is established using transmission line theory: Monte Carlo simulation is used to predict the transmission errors (such as group delay deviation and amplitude attenuation) under different frequencies and different channel conditions, and a dynamic compensation parameter group is generated. This parameter group covers the pre-emphasis coefficient (such as +6dB boost at 5GHz) and the frequency response curve of the equalization filter, ensuring that the compensation process and physical shielding measures work together to effectively offset the nonlinear distortion of the channel frequency response.

[0024] Step S6: The initial signal optimization results need to be optimized through multi-channel collaborative optimization to achieve efficient use of frequency domain resources. First, the frequency compensation parameters are segmented by frequency band (such as low frequency band 0-2GHz, medium frequency band 2-8GHz, high frequency band 8-16GHz), and the corresponding frequency response compensation matrix is ​​constructed. Channel decomposition is performed on the signals in each frequency band, and the multi-channel signals are separated into independent frequency band components through fast Fourier transform (FFT), and the compensation matrix is ​​applied to correct the amplitude-frequency response and phase-frequency response respectively. For example, a pre-emphasis filter is used to compensate for dielectric loss in the high frequency band, and a phase correction algorithm is used to eliminate group delay differences in the low frequency band. After the compensation is completed, the channel coupling analysis is performed on the signals of each frequency band, and the least squares method is used to eliminate the residual crosstalk between channels (such as reducing cross-channel energy leakage through orthogonalization processing). Finally, the signals of each frequency band are recombined into time-domain multi-channel optimized signals through inverse FFT, and the recombined signals are waveform shaped (such as raised cosine filtering) and peak suppressed to ensure that the signals meet the timing tolerance and jitter budget of PCIe5.0 and above standards.

[0025] The multi-channel signal integrity optimization method of the passive copper cable provided by the present invention obtains the channel characteristic parameters and signal transmission requirements of the passive copper cable, performs impedance matching, optimizes the transmission channel, effectively solves the problem of unreasonable impedance matching in the existing signal transmission mode, reduces the reflection and attenuation of the signal during the transmission process, and improves the transmission quality of the signal. The real-time transmission data through the passive copper cable is obtained, and balanced processing is performed in the optimized transmission channel, which can cope with complex and changeable transmission environments, ensure the stable transmission of the signal, and solve the problem of lack of effective balanced processing means for real-time transmission data. At the same time, the present invention also performs shielding analysis on the crosstalk interference data collected by the passive copper cable, obtains crosstalk suppression measures, performs noise optimization on the stable transmission signal, improves the purity of the signal, reduces the noise introduced by the crosstalk interference, and solves the problem of lack of effective suppression measures for the crosstalk interference data. By performing frequency characteristic analysis on the crosstalk interference data, frequency compensation information is obtained, and transmission prediction of the frequency compensation information is performed based on the crosstalk suppression measures to obtain frequency compensation parameters, and further multi-channel optimization is performed on the initial signal optimization result, realizing multi-channel optimization processing of the signal, and improving the efficiency and quality of signal transmission.

[0026] In one embodiment, the channel characteristic parameters and signal transmission requirements of the passive copper cable are obtained, and impedance matching is performed to obtain an optimized transmission channel, including: When collecting channel physical parameters of passive copper cables, the S parameters such as insertion loss, near-end crosstalk, far-end crosstalk, and return loss of passive copper cables are measured by a high-precision network analyzer. During the measurement, the two ends of the passive copper cable are connected to the test port of the network analyzer, and the frequency sweep measurement is performed in the frequency range of 1MHz-10GHz. The sampling interval is set to 1MHz to obtain the channel characteristic parameters corresponding to each frequency point. These parameters fully reflect the transmission characteristics of passive copper cables at different frequencies.

[0027] When measuring the signal transmission rate and bandwidth of a passive copper cable, use a bit error rate tester to input a PRBS31 pseudo-random code sequence signal into the passive copper cable to measure the bit error rate and signal bandwidth during transmission. The tester outputs a differential signal at the transmitter, and the receiver uses an adaptive equalization algorithm to compensate the signal. Continuous testing is used to obtain bit error rate data and effective signal bandwidth, which constitute the basic indicators of signal transmission requirements.

[0028] When performing multi-point impedance testing on passive copper cables according to channel characteristic parameters, a time domain reflectometer is used for measurement. Connect the test port of the time domain reflectometer to one end of the passive copper cable and the other end to a standard matching load. The rise time of the test signal is set to 35ps. Measure the impedance value of a point at a fixed distance along the length of the passive copper cable, and the measurement range covers the entire cable. Record the measured impedance values ​​to form impedance distribution data containing spatial positions and corresponding impedance values.

[0029] When performing discrete spectrum analysis on impedance distribution data, the impedance distribution data is converted to the frequency domain through fast Fourier transform. Set an appropriate frequency resolution, smooth the transformed spectrum, and remove high-frequency noise components. Draw a curve of impedance value changing with frequency to obtain the frequency domain characteristic curve. This curve reflects the distribution characteristics of impedance in the frequency domain.

[0030] When calculating impedance segment compensation according to the frequency domain characteristic curve and signal transmission requirements, the transmission line is divided into multiple sections of equal length. For each section of the line, the impedance deviation is calculated according to its frequency domain characteristic curve, and the corresponding compensation network parameters are designed. The compensation network adopts an LC series-parallel structure, and impedance matching is achieved by adjusting the inductance and capacitance values. The calculated compensation parameters include the inductance and capacitance values ​​required for each section of the line, which constitute an impedance compensation parameter group.

[0031] When performing impedance optimization configuration for passive copper cables according to the impedance compensation parameter group, a compensation network is installed at key locations of the cable. The installation location of the compensation network is determined by the impedance distribution data and is selected at locations where the impedance changes suddenly or where the impedance deviation is large. The component parameters of the compensation network are set to the calculated values ​​to achieve precise impedance adjustment and obtain impedance matching information.

[0032] When performing channel matching on passive copper cables based on impedance matching information, re-measure the S parameters of the optimized cables to verify the optimization effect. Compare the changes in parameters such as insertion loss, near-end crosstalk, far-end crosstalk, and return loss before and after optimization. Verify the transmission performance through bit error rate testing, evaluate the signal integrity performance of the optimized channel, and form an optimized transmission channel with stable performance. Through this series of optimization steps, the transmission performance of passive copper cables has been significantly improved.

[0033] This embodiment achieves a significant improvement in signal transmission quality by adopting a multi-channel signal integrity optimization method for passive copper cables. This method uses a high-precision network analyzer and a time domain reflectometer to collect comprehensive parameters of passive copper cables, establish a complete channel characteristic model, and provide accurate data support for subsequent optimization. Based on the impedance distribution conversion technology of discrete spectrum analysis, the spatial domain impedance characteristics are converted into frequency domain characteristic curves, revealing the inherent law of impedance change. A segmented compensation calculation method is adopted to design customized compensation parameters for the impedance characteristic differences of each section of the cable, and refined impedance matching is achieved. This method does not rely on a specific cable type, is applicable to various passive copper cable systems, and has wide versatility. The optimized transmission channel shows a lower bit error rate and higher signal integrity in high-speed signal transmission, effectively solves the signal distortion problem in high-speed transmission, meets the strict requirements of modern high-speed communication systems for signal quality, and provides an effective technical means for improving the overall performance and reliability of the system.

[0034] In one embodiment, real-time transmission data of a passive copper cable is obtained, and the real-time transmission data is balanced by optimizing a transmission channel to obtain a stable transmission signal, including: When real-time data sampling is performed on the input and output ends of the passive copper cable, the voltage signals at the input and output ends are sampled by a high-speed oscilloscope, with the sampling frequency set to 25 GHz, the sampling time window set to 100 ns, and the number of sampling points set to 2500. The sampled real-time transmission data contains the amplitude and phase information of the input and output ends. During the sampling process, in order to ensure the accuracy of the data, the bandwidth of the oscilloscope is set to 40 GHz, the input impedance is set to 50 Ω, and the trigger level is set to 50% of the signal amplitude.

[0035] In the process of time-frequency domain conversion of real-time transmission data, the fast Fourier transform algorithm is used to process the sampled data and convert the time domain signal into the frequency domain signal. The transmission parameter group obtained after the conversion includes parameters such as frequency response curve, phase response curve, group delay characteristics, etc. When performing Fourier transform, the window function selects the Hamming window, the frequency resolution is set to 1MHz, and the frequency range is 0-12.5GHz. The converted transmission parameter group is used for subsequent signal processing and analysis.

[0036] When performing linear regression analysis of signal amplitude and phase based on the transmission parameter group, the frequency response curve and phase response curve are fitted by the least square method. By establishing a mathematical model, the loss characteristics and dispersion characteristics of the signal during transmission are calculated. The equalization coefficient obtained by linear regression analysis includes two parts: the amplitude equalization coefficient and the phase equalization coefficient. The amplitude equalization coefficient reflects the attenuation degree of the signal at different frequencies, and the phase equalization coefficient reflects the phase delay characteristics of the signal at different frequencies.

[0037] When forward equalization and backward equalization are calculated for real-time transmission data based on the equalization coefficient, forward equalization is implemented at the transmitting end, and the high-frequency components are compensated by the pre-emphasis circuit; backward equalization is implemented at the receiving end, and the signal distortion is compensated by the adaptive equalization algorithm. The equalization calculation process adopts the recursive least squares algorithm, the iteration step is set to 0.01, and the convergence threshold is set to 0.001. The calculated equalization compensation parameters include forward equalization coefficients and backward equalization coefficients, which are used for the subsequent compensation algorithm construction.

[0038] When constructing the compensation algorithm based on the equalization compensation parameters, a digital filter model is established based on the forward equalization coefficient and the backward equalization coefficient. The compensation algorithm adopts a finite impulse response filter structure, the filter order is set to 64, and the cutoff frequency is set to 1.2 times the signal bandwidth. The filter coefficients are adjusted by an iterative optimization method until the signal quality index reaches the set threshold. The optimization parameter group obtained by iterative optimization contains the optimized filter coefficients and equalization parameters.

[0039] When the signal compensation of the real-time transmission data is performed according to the optimized parameter group, the optimized filter coefficients are loaded into the digital signal processing unit to process the input signal in real time. During the compensation process, amplitude compensation and phase compensation are performed simultaneously to eliminate various distortions introduced by the channel.

[0040] This embodiment uses a high-frequency oscilloscope for real-time data acquisition to ensure the accuracy and reliability of signal characteristic analysis. Combining time-frequency domain conversion with linear regression analysis technology, effective modeling of channel loss and dispersion characteristics is achieved, providing an accurate basis for subsequent equalization design. Through the collaborative design strategy of forward equalization and backward equalization, various distortion factors in the channel are compensated in a targeted manner, so that the signal quality is comprehensively improved. The multi-order finite impulse response filter structure is adopted, combined with the iterative optimization algorithm, and the signal quality index is effectively improved while ensuring that the computational complexity is acceptable.

[0041] In one embodiment, crosstalk interference data collected by the passive copper cable is obtained, and shielding analysis is performed on the optimized transmission channel to obtain crosstalk suppression measures, including: In the process of optimizing the multi-channel signal integrity of passive copper cables, obtaining the crosstalk interference data of passive copper cables is the basis for crosstalk suppression. Through field measurements of passive copper cables, the spacing values ​​between channels and various parameters of conductor materials are collected. Spacing collection is carried out by a high-precision laser rangefinder with a measurement accuracy of 0.01mm; conductor material parameters include physical properties such as resistivity and dielectric constant, which are measured and recorded by an impedance analyzer. Based on the collected spacing data and conductor material parameters, the transmission line theory is used to calculate the crosstalk attenuation coefficient, which reflects the attenuation characteristics of the signal during transmission.

[0042] The crosstalk interference sampling process uses a high-speed oscilloscope to collect signals in the passive copper cable, and the sampling rate is set to 20GS / s to ensure the accuracy of the sampled data. The time domain data obtained by sampling is Fourier transformed and converted into frequency domain data to obtain the crosstalk spectrum. The spectrum data clearly shows the distribution of crosstalk interference intensity under different frequency components.

[0043] Based on the obtained crosstalk attenuation coefficient, the energy distribution analysis of the crosstalk spectrum data is performed. By calculating the energy density at different frequency points, a crosstalk energy distribution diagram is drawn. The distribution diagram is presented in three-dimensional form, with the horizontal axis representing the frequency range, the vertical axis representing the spatial position, and the color depth representing the energy intensity. The energy distribution diagram intuitively shows the distribution characteristics of crosstalk interference in the frequency domain and space.

[0044] Multi-channel crosstalk path identification is based on the crosstalk energy distribution diagram. The energy gradient analysis method is used to track the energy propagation direction and establish a crosstalk propagation path model. The model includes the location of the main crosstalk source, the propagation direction, and the attenuation characteristics. The propagation path identification uses the maximum energy tracking algorithm to ensure that the main crosstalk propagation channel is captured.

[0045] Electromagnetic simulation analysis uses the finite element method to model the optimized transmission channel. According to the identified crosstalk propagation path, the simulation boundary conditions and meshing parameters are set. The simulation process calculates the electric field strength, magnetic field strength and their distribution, and generates detailed electromagnetic field distribution data. The simulation results are displayed in the form of an electromagnetic field strength cloud map, which clearly reflects the crosstalk impact area.

[0046] Electromagnetic simulation analysis uses the three-dimensional finite element method to perform high-precision modeling of the optimized transmission channel of passive copper cables. The modeling process is based on measured geometric dimension parameters, including key parameters such as conductor diameter, insulation layer thickness, shielding layer structure, and line pair spacing. After the model is built, the electrical parameters in the material library are imported, including the conductivity of the conductor, the dielectric constant of the insulation material, and the loss tangent.

[0047] The simulation boundary condition settings include Bode boundary conditions and radiation boundary conditions. Bode boundaries are used to simulate signal source excitation, and radiation boundaries are used to ensure that electromagnetic waves do not produce non-physical reflections at the boundaries of the computational domain. The meshing adopts adaptive meshing technology, and the mesh density is high in key areas such as conductor surfaces and insulation layer interfaces. The mesh unit size is controlled to be less than 1 / 10 of the wavelength of the highest frequency of the signal to ensure calculation accuracy.

[0048] The simulation process is divided into two parts: time domain analysis and frequency domain analysis. The time domain analysis uses the finite difference time domain method (FDTD) to solve the Maxwell equations, setting the time step to 1 / 20 of the signal period and the total simulation time to 5 times the signal transmission time to ensure that the complete crosstalk response is captured. The frequency domain analysis uses the finite element method (FEM) to calculate the S parameter matrix at logarithmic intervals within the frequency range of 0.1-40GHz to reflect the crosstalk characteristics between each channel.

[0049] In the process of generating electromagnetic field distribution data, the electric field intensity vector E and magnetic field intensity vector H at each grid node are calculated to form a complete three-dimensional electromagnetic field distribution data set. The data processing process includes field intensity normalization and non-uniform grid interpolation to generate field intensity distribution data on a regular grid.

[0050] The electromagnetic field distribution results are visualized in the form of cloud maps, including the electric field intensity distribution and magnetic field intensity distribution on three orthogonal sections: XY plane, YZ plane, and XZ plane. The color scale ranges from blue (low field strength) to red (high field strength), clearly showing the trend of field strength changes. In addition, electromagnetic energy density distribution maps and Poynting vector streamlines are generated to intuitively display the direction and intensity of energy propagation.

[0051] During the simulation result analysis, the areas where the field strength exceeds the threshold (usually set to 20% of the maximum field strength) are identified as key interference areas. These areas are subjected to a joint time-frequency analysis to determine the main frequency components of the interference and their spatial distribution characteristics, forming a triple data structure of interference source-path-receiver, providing an accurate basis for subsequent suppression measures.

[0052] The accuracy of the electromagnetic simulation results is verified by comparing them with the measured data, requiring the time domain waveform similarity to be no less than 90% and the frequency domain characteristic error to be no more than ±3dB. On the basis of meeting the accuracy requirements, the simulation results are used to guide the design and verification of the crosstalk suppression solution.

[0053] The formulation of crosstalk suppression measures is based on the crosstalk propagation path and electromagnetic field distribution data. By analyzing the electromagnetic field intensity distribution law, the key suppression point position is determined. For different types of crosstalk interference, corresponding suppression strategies are formulated: for crosstalk mainly caused by electric field coupling, the thickness of the shielding layer is increased; for crosstalk mainly caused by magnetic field coupling, magnetic materials are used for shielding; for mixed coupling crosstalk, a multi-layer shielding structure is used in combination.

[0054] This embodiment uses a three-dimensional finite element method for electromagnetic simulation analysis to achieve high-precision modeling of passive copper cable transmission channels and accurately capture the propagation characteristics of crosstalk interference. The grid division scheme based on adaptive grid technology achieves a more detailed grid density in key areas, effectively improving the accuracy of electromagnetic field calculations. Through dual analysis methods in time domain and frequency domain, the crosstalk response characteristics are fully obtained, providing a reliable basis for formulating suppression schemes. In the process of generating electromagnetic field distribution data, field intensity normalization processing and non-uniform grid interpolation technology are used to construct a complete three-dimensional electromagnetic field distribution data set, which intuitively shows the spatial distribution law of interference. Through the joint time-frequency analysis and the establishment of the interference source-path-receiver triple data structure, the key interference area is accurately identified, providing important guidance for the optimization of the crosstalk suppression scheme.

[0055] In one embodiment, noise optimization is performed on a stable transmission signal according to a crosstalk suppression measure to obtain an initial signal optimization result, including: In the passive copper cable transmission system, crosstalk suppression measures are used to stabilize the transmission signal, and noise optimization is performed on the stable transmission signal. The noise optimization process includes multiple steps to ensure that the signal quality reaches the optimal level.

[0056] In the waveform analysis phase, the stable transmission signal is sampled, and the sampling frequency is set to 4 times the signal frequency to collect the signal's amplitude, phase, frequency and other parameters. The sampled data is analyzed through Fourier transform to extract the signal's rise time, fall time, overshoot, undershoot and other waveform characteristic parameters. The signal waveform characteristics output by this phase are used for subsequent noise analysis.

[0057] In the noise spectrum density calculation link, the power spectrum density estimation method is used to calculate the noise component in the signal based on the signal waveform characteristics. The calculation process uses the Welch periodogram method to divide the signal into several overlapping segments, multiply each segment of the signal by the Hanning window function, calculate the periodogram separately and average them. This method obtains the noise distribution parameters such as the frequency distribution and amplitude distribution of the noise. The noise distribution parameters output by this link are used to guide the filtering process.

[0058] In the filtering operation, a Wiener filter is designed according to the noise distribution parameters, and the filter has the minimum mean square error characteristic. The cutoff frequency of the filter is determined according to the signal bandwidth and the noise spectrum distribution, and the filter order is determined by the compromise between signal quality and computational complexity. The effective frequency components of the signal are maintained during the filtering process, while the noise frequency components are suppressed. The filtered signal output by this link has a higher signal-to-noise ratio.

[0059] In the signal compensation calculation phase, the transmission loss of the filtered signal is compensated. The compensation process establishes a compensation model based on the characteristic impedance of the transmission line and the transmission distance. The compensation model includes frequency-related attenuation compensation and phase compensation. The compensation parameters are obtained by measuring the insertion loss and return loss of the cable, and the compensation amount varies nonlinearly with frequency. The compensation signal output by this phase has better amplitude-frequency characteristics.

[0060] The multi-channel gain equalization operation performs gain balancing processing on the compensation signal among multiple channels, and uses an adaptive gain control algorithm to achieve dynamic adjustment of each channel signal. The core of this algorithm is to monitor the amplitude of each channel signal in real time and make corresponding gain adjustments.

[0061] Real-time monitoring of signal amplitude uses high-speed sampling technology to periodically sample each channel signal, with the sampling rate set to 5 times the highest frequency of the signal. The sampled data is transformed by fast Fourier transform to obtain spectrum information, and the amplitude information of each frequency point is extracted by spectrum analysis. The monitoring circuit uses a low-noise amplifier and a high-precision ADC to ensure that the measurement accuracy reaches ±0.1dB.

[0062] The adaptive gain control algorithm is based on statistical decision theory and sets a standard channel amplitude reference model. The model comprehensively considers the average amplitude, median amplitude and weighted average amplitude of multi-channel signals to generate a target amplitude benchmark. Each channel signal is compared with the benchmark value to calculate the amplitude deviation.

[0063] The gain adjustment range is strictly limited to ±3dB to avoid signal distortion caused by overcompensation. The adjustment step size adopts a dynamic change strategy and designs a nonlinear control function: when the amplitude difference is greater than 1.5dB, a 0.3dB step size is used for coarse adjustment; when the difference is between 0.5dB and 1.5dB, a 0.1dB step size is used for medium adjustment; when the difference is less than 0.5dB, a 0.02dB step size is used for fine adjustment. This strategy ensures stability while ensuring adjustment speed.

[0064] The gain adjustment circuit uses a digitally controlled variable gain amplifier with a control accuracy of 0.01dB. The gain control signal is generated by a digital signal processor and drives the gain amplifier after conversion by a DAC. The bandwidth of the gain adjustment circuit covers DC-100MHz, ensuring a flat gain response and phase linearity over the entire signal frequency band.

[0065] Environmental adaptability enhancement measures include temperature drift compensation and aging characteristic compensation. Temperature compensation uses a table lookup method to correct the gain value according to the ambient temperature, and the temperature coefficient is controlled within 0.005dB / °C. Aging characteristic compensation is achieved through periodic self-calibration, and the self-calibration cycle is 100 hours.

[0066] After gain equalization is completed, the signal amplitude deviation of each channel is controlled within the range of ±0.3dB, and the phase difference between channels is less than ±5°. The equalized signal has good channel consistency, and the crosstalk attenuation between channels is greater than 30dB, which effectively improves the signal integrity of the multi-channel system. The equalized signal is subsequently optimized through waveform shaping to form a high-quality transmission signal. In the waveform shaping optimization phase, the equalized signal is finally optimized. The shaping process uses pre-emphasis and de-emphasis technology to enhance the high-frequency components at the transmitting end and perform corresponding attenuation at the receiving end to compensate for the frequency selective fading during the transmission process. The shaping parameters are determined according to the signal bandwidth and transmission distance to ensure that the signal waveform meets the eye diagram index requirements. The initial signal optimization result output by this phase has ideal signal quality.

[0067] This embodiment adopts a multi-channel gain equalization operation method to monitor and dynamically adjust the gain of each channel signal in real time to ensure the consistency of signal amplitude, thereby greatly improving the signal transmission quality. The application of high-speed sampling technology, low-noise amplifiers, and high-precision ADCs enables the measurement accuracy to reach ±0.1dB, ensuring the accuracy of gain adjustment. The adaptive gain control algorithm is based on statistical decision theory, takes into account a variety of signal amplitude characteristics, dynamically adjusts the gain, and achieves signal amplitude deviation control within the range of ±0.3dB, and the phase difference between channels is controlled within ±5°, significantly improving signal consistency and integrity. Environmental adaptability and long-term stability are enhanced through temperature drift compensation and aging characteristic compensation measures.

[0068] In one embodiment, the noise spectrum density of the stable transmission signal is calculated according to the signal waveform characteristics to obtain the noise distribution parameters, including: When performing time domain sampling segmentation processing on the signal waveform characteristics, the sampling period is set to 100ns and the length of each sampling segment is 1000 data points. The sampling device digitizes the input signal and converts the continuous analog signal into a discrete digital sequence. During the sampling process, an anti-aliasing filter is used to eliminate high-frequency interference to ensure the accuracy of the sampled data. After time domain sampling segmentation processing, 50 signal sampling segments are obtained, each of which contains complete signal waveform characteristic information.

[0069] When calculating the autocorrelation function of a stable transmission signal based on multiple signal sampling segments, the autocorrelation analysis method is applied to each sampling segment. Autocorrelation analysis reflects the periodic characteristics and random components of the signal by calculating the correlation between the signal and itself at different time delays. The calculation uses normalization processing to eliminate the influence of amplitude differences. The calculation results of the autocorrelation function show the main periodic components and noise components in the signal, and the frequency resolution of the initial noise power spectrum is 1MHz.

[0070] When performing multi-window spectrum analysis on a stable transmission signal based on the initial noise power spectrum, the Thomson multi-window method is used for spectrum estimation. This method uses an orthogonal window function sequence to perform weighted averaging on the signal to reduce the spectrum leakage effect. Multi-window analysis uses 8 independent orthogonal window functions, and the window function length is equal to the sampling segment length. The improved noise spectrum obtained by multi-window spectrum analysis has higher statistical stability and the frequency resolution is increased to 100kHz.

[0071] When performing frequency band segmentation of a stable transmission signal based on an improved noise spectrum, an adaptive threshold segmentation algorithm is used. This algorithm determines the segmentation threshold according to the local characteristics of the noise spectrum and divides the entire frequency range into multiple sub-bands. The spectrum energy distribution and signal bandwidth characteristics are comprehensively considered during the segmentation process to ensure that the segmentation boundary is consistent with the actual signal characteristics. The result of the frequency band segmentation shows five main frequency bands, each corresponding to a different type of noise source. The multi-band noise distribution mapping reflects the spatial distribution characteristics of the noise in each frequency band.

[0072] When performing noise source identification calculations on the multi-band noise distribution map based on the preset transmission medium characteristic parameters, the feature matching algorithm is used to locate the noise source. The preset transmission medium characteristic parameters include: transmission line impedance of 100Ω, dielectric constant of 4.4, and loss factor of 0.02. During the feature matching process, a noise source model library is established, which contains common noise source characteristic modes such as crosstalk, reflection, and radiation. By calculating the similarity between the noise distribution map and the model characteristics, the type and location of the main noise source are identified. The noise distribution parameters contain the type, intensity, and spatial distribution information of the noise source in each frequency band.

[0073] The frequency bands of the noise distribution parameters obtained are as follows: the low frequency band (0-100MHz) mainly has near-end crosstalk noise with a noise intensity of -60dB; the mid-frequency band (100-500MHz) has reflection noise caused by impedance mismatch with a noise intensity of -50dB; the high frequency band (above 500MHz) has radiation interference noise with a noise intensity of -45dB.

[0074] This embodiment realizes accurate capture of signal waveform characteristics through time domain sampling segmentation processing and setting a reasonable sampling period and sampling segment length, effectively avoiding information loss. The frequency resolution of noise spectrum analysis is significantly improved from 1MHz to 100kHz by combining autocorrelation function calculation and multi-window spectrum analysis, making noise feature identification more accurate. The frequency band segmentation algorithm based on adaptive threshold accurately divides the frequency band range where different types of noise sources are located, providing a reliable basis for subsequent noise source identification. By establishing a noise source model library and combining a feature matching algorithm, accurate positioning and classification of various noise sources such as crosstalk, reflection, and radiation are achieved, and the accuracy of noise source identification is significantly improved. The noise distribution parameters obtained by this method comprehensively reflect the type, intensity and spatial distribution characteristics of noise in each frequency band, providing systematic technical support for signal integrity optimization, and effectively improving the quality and stability of multi-channel signal transmission of passive copper cables.

[0075] In one embodiment, frequency characteristics of crosstalk interference data are analyzed to obtain frequency compensation information, and transmission prediction of the frequency compensation information is performed based on crosstalk suppression measures to obtain frequency compensation parameters, including: When performing wavelet decomposition on crosstalk interference data, discrete wavelet transform is used to perform multi-scale decomposition on the crosstalk interference data. During the decomposition process, the db4 wavelet basis function is used to perform 5-layer decomposition on the signal to obtain wavelet coefficients in different frequency bands. By analyzing the wavelet coefficients, the energy distribution characteristics of the crosstalk signal in different frequency bands are extracted to form the crosstalk spectrum characteristics. The crosstalk spectrum characteristics include the energy distribution of the signal in the high frequency, medium frequency and low frequency regions.

[0076] When dividing the frequency component levels of the crosstalk spectrum characteristics, the spectrum characteristics are grouped based on the energy clustering principle. The spectrum characteristics are divided into three levels according to the energy size: high energy area (>-20dB), medium energy area (-20dB to -40dB) and low energy area (<-40dB). Within each level, sub-groups are set according to the frequency interval to ensure that the frequency components in each sub-group have similar transmission characteristics, thereby obtaining frequency feature grouping. The frequency feature grouping reflects the distribution law of crosstalk interference in different frequency intervals.

[0077] When performing frequency band analysis on frequency feature groups, the frequency band transmission loss is calculated for each group. The signal attenuation at each frequency point is measured using the insertion loss method, and a frequency-loss relationship curve is established. By analyzing the changing trend of the curve, the loss characteristics of the key frequency bands are identified, including parameters such as loss peak, loss slope, and loss bandwidth, to form a frequency band loss characteristic. The frequency band loss characteristic reveals the attenuation law of the signal during transmission.

[0078] The polynomial regression method is used to fit the signal attenuation law of the band loss characteristics. The process first converts the band loss characteristic data into frequency-loss data pairs ( , ),in Indicates the frequency point, Indicates the corresponding loss value. The fitting process selects a 6th-order polynomial model to fit the data. The polynomial form expresses the relationship between the loss L(f) and the frequency f. The polynomial fitting uses the least squares method to determine the polynomial coefficients by minimizing the mean square error between the actual loss value and the fitting value.

[0079] The frequency compensation equation is: , where L(f): represents the loss function, which is a function of frequency f; : Constant term coefficient, representing the basic loss at zero frequency; : First-order coefficient, representing the linear loss component; : The quadratic term coefficient mainly reflects the basic transmission loss characteristics of the copper cable; , , , : High-order coefficient, used to describe nonlinear loss effects, f: frequency variable.

[0080] Constructed objective function , find the coefficient value that minimizes E. Where, E: objective function, representing the mean square error between the actual value and the fitted value; : The actual loss measurement value at the i-th frequency point; ): fitting loss value of the ith frequency point; n: total number of data points.

[0081] In the process of solving the coefficients, the singular value decomposition (SVD) algorithm is used to deal with the ill-conditioned matrix problem and improve the fitting accuracy. For the fitting deviation in the high-frequency area (>5GHz), a weight function is introduced to adjust the fitting weights of different frequency bands. The weight of the high-frequency area is set to 1.5 times that of the low-frequency area to ensure the fitting accuracy of the high-frequency area.

[0082] After the fitting is completed, the goodness of fit is evaluated. 2 When it is greater than 0.95 and the maximum residual is less than 2dB, the fitting result is considered to meet the requirements; otherwise, the polynomial order is adjusted or the segmented fitting strategy is used to refit. The final fitting polynomial is the frequency compensation equation.

[0083] The frequency compensation equation describes the frequency-loss relationship of the signal in the transmission medium and expresses the law of loss change with frequency. This equation not only includes the basic frequency square attenuation (in line with the transmission characteristics of copper cables), but also includes high-order terms to characterize the nonlinear attenuation characteristics caused by factors such as medium inhomogeneity and structural discontinuity.

[0084] The frequency compensation equation also takes into account the combined effects of skin effect and dielectric loss. The low-frequency region mainly reflects the conductor loss characteristics, while the high-frequency region reflects more dielectric loss characteristics. The coefficients of each order in the equation reflect the contribution ratio of different physical mechanisms to the total loss, providing a basis for frequency domain equalization compensation.

[0085] After obtaining the frequency compensation equation, a compensation curve is constructed by calculating the compensation values ​​at different frequency points. This curve reflects the compensation required for each frequency component during signal transmission, providing an accurate parameter basis for subsequent compensation circuit design.

[0086] When numerically solving the frequency compensation equation, the interval iteration method is used to calculate the compensation values ​​at different frequency points. During the solution process, the iteration accuracy is set to 0.01dB and the maximum number of iterations is set to 100 times to ensure the accuracy of the solution results. The compensation amplitude and phase information of each frequency point are obtained by solving the problem to form the frequency compensation information. The frequency compensation information reflects the compensation amount required during the signal transmission process.

[0087] When the transmission error of the frequency compensation information is quantified according to the crosstalk suppression measures, an error evaluation model is established. The model comprehensively considers factors such as crosstalk isolation, return loss, and near-end crosstalk to calculate the transmission error at each frequency point. The transmission error is normalized to obtain a standardized channel error parameter. The channel error parameter characterizes the accuracy of the compensation effect.

[0088] When compensating and predicting channel error parameters, an autoregressive prediction model is used to analyze the error trend. Based on historical compensation data, the error change trend of the next compensation cycle is predicted to generate compensation prediction results. The compensation prediction results contain information about the direction and magnitude of the error change.

[0089] When the frequency compensation information is adaptively updated according to the compensation prediction results, a compensation parameter adjustment algorithm is designed. The algorithm dynamically adjusts the compensation coefficient according to the predicted error trend to achieve continuous optimization of the compensation effect. When the compensation effect reaches the preset threshold, the final frequency compensation parameters are output. The frequency compensation parameters serve as an important basis for optimizing the signal integrity of passive copper cables and provide support for subsequent signal processing.

[0090] This embodiment uses a polynomial regression method to fit the signal attenuation law of the frequency band loss characteristics, thereby achieving an accurate description of the loss characteristics during signal transmission. This method fully considers various loss factors in copper cable transmission, including the skin effect, dielectric loss, and the influence of structural discontinuity, by constructing a 6th-order polynomial model, so that the fitting results are more comprehensive and accurate. The singular value decomposition algorithm is introduced in the coefficient solution process to effectively solve the ill-conditioned matrix problem and improve the calculation accuracy. By introducing a weight function to adjust the fitting weights of different frequency bands, especially using a 1.5-fold weight coefficient for the high-frequency area, the accuracy of high-frequency signal compensation is ensured. The setting of the goodness of fit evaluation mechanism, including the dual judgment criteria of the determination coefficient and the maximum residual, ensures the reliability of the fitting results. The obtained frequency compensation equation not only accurately reflects the law of loss change with frequency, but also provides an accurate parameter basis for the subsequent compensation circuit design, significantly improving the signal transmission quality of the passive copper cable system.

[0091] In one embodiment, performing multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimized signal includes: The frequency compensation parameters are segmented to obtain the frequency segmentation coefficient. The frequency segmentation coefficient is obtained based on the signal transmission bandwidth range. The signal transmission bandwidth range is divided equally into 2GHz between 0-20GHz to obtain 10 frequency segments. Each frequency segment corresponds to a frequency segmentation coefficient, which reflects the signal transmission characteristics of each frequency band. The value range of the frequency segmentation coefficient is between 0.1-1.0, and the value is positively correlated with the signal attenuation degree of the frequency segment.

[0092] According to the obtained frequency segmentation coefficients, the initial signal optimization result is subjected to multi-channel decomposition to obtain channel decomposition data. Multi-channel decomposition uses the S parameter matrix method to decompose the initial signal into corresponding channel data according to the number of channels. The channel decomposition data contains the amplitude and phase information of each channel. In a 4-channel system, the channel decomposition data forms a 4×4 data matrix, and the matrix elements characterize the transmission characteristics between channels.

[0093] The frequency response of the channel decomposition data is calculated to obtain the frequency response parameters. The frequency response calculation uses the fast Fourier transform method to convert the time domain signal to the frequency domain for analysis. The frequency response parameters include two parts: the amplitude response and the phase response. The amplitude response reflects the gain change of the signal at each frequency point, and the phase response reflects the phase change of the signal at each frequency point. The calculation results of the frequency response parameters are expressed in complex form, with the real part representing the amplitude characteristics and the imaginary part representing the phase characteristics.

[0094] Frequency compensation is performed on the channel decomposition data according to the calculated frequency response parameters to obtain frequency compensation data. Frequency compensation uses the reverse filtering method to correct the amplitude and phase of the signal in the frequency domain. The frequency compensation data eliminates the frequency distortion of the signal during transmission, making the signal have better frequency characteristics. The compensated data maintains the effective information of the original signal and improves the frequency response characteristics of the signal.

[0095] Channel coupling is performed on the frequency compensation data to obtain the channel coupling coefficient. The channel coupling adopts the mixed matrix method to analyze the crosstalk effect between channels. The channel coupling coefficient reflects the degree of signal interference between adjacent channels. The larger the coefficient value, the more serious the crosstalk. In a 4-channel system, the channel coupling coefficient forms a 4×4 coupling matrix. The diagonal elements of the matrix represent the intrinsic characteristics of the channel, and the non-diagonal elements represent the coupling characteristics between channels.

[0096] Channel isolation is performed on the frequency compensation data according to the channel coupling coefficient to obtain channel isolation data. Channel isolation uses an orthogonalization method to reduce crosstalk interference between channels. Channel isolation data eliminates the coupling effect of signals during multi-channel transmission and improves signal transmission quality. The isolated data maintains the independence of each channel signal and reduces the mutual influence between channels.

[0097] The channel isolation data is reorganized to obtain reorganized signal data. Signal reorganization uses the inverse S parameter matrix method to recombine the data of each channel into a complete signal. The reorganized signal data contains optimized amplitude and phase information, maintaining the integrity of the signal. The frequency characteristics and channel characteristics are comprehensively considered in the reorganization process, so that the reorganized signal has good transmission performance.

[0098] The recombined signal data is compensated and optimized according to the frequency segmentation coefficient to obtain a multi-channel optimized signal. The compensation optimization uses an adaptive equalization method to fine-tune the recombined signal in each frequency band. The multi-channel optimized signal has better frequency characteristics and channel characteristics, meeting the requirements of high-speed signal transmission. The optimized signal shows good characteristics in both the time domain and the frequency domain, with an increased eye opening and improved jitter indicators.

[0099] This embodiment achieves accurate signal processing for different frequency bands by frequency segmenting the frequency compensation parameters and obtaining frequency segmentation coefficients, effectively solving the distortion problem of wide-band signals during transmission. The multi-channel decomposition technology combined with the S parameter matrix method enables the characteristics of each channel to be fully characterized, laying the foundation for subsequent optimization. The frequency response calculation and frequency compensation links eliminate the distortion of the signal in the frequency domain, so that the signal maintains stable transmission characteristics throughout the frequency band. Channel coupling analysis and channel isolation processing significantly reduce crosstalk interference in multi-channel transmission and improve the signal independence between adjacent channels. The signal reconstruction and compensation optimization steps ensure that the optimized signal has better time domain and frequency domain characteristics.

[0100] Reference Figure 2 The present invention further provides a multi-channel signal integrity optimization system for a passive copper cable, which is applied to any of the multi-channel signal integrity optimization methods for a passive copper cable described above, comprising: The acquisition module is used to obtain the channel characteristic parameters and signal transmission requirements of the passive copper cable, and perform impedance matching to obtain an optimized transmission channel; An analysis module is used to obtain real-time transmission data of the passive copper cable, and to perform equalization processing on the real-time transmission data by optimizing the transmission channel to obtain a stable transmission signal; The correlation module is used to obtain the crosstalk interference data collected by the passive copper cable, perform shielding analysis on the optimized transmission channel, and obtain crosstalk suppression measures; A processing module, the processing module is used to perform noise optimization on the stable transmission signal according to the crosstalk suppression measures to obtain an initial signal optimization result; A control module, the control module is used to perform frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, perform transmission prediction on the frequency compensation information based on the crosstalk suppression measures to obtain frequency compensation parameters; The execution module is used to perform multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimized signal.

[0101] The multi-channel signal integrity optimization system of the passive copper cable provided by the present invention obtains the channel characteristic parameters and signal transmission requirements of the passive copper cable, performs impedance matching, optimizes the transmission channel, effectively solves the problem of unreasonable impedance matching in the existing signal transmission mode, reduces the reflection and attenuation of the signal during the transmission process, and improves the transmission quality of the signal. The real-time transmission data through the passive copper cable is obtained, and balanced processing is performed in the optimized transmission channel, which can cope with complex and changeable transmission environments, ensure the stable transmission of the signal, and solve the problem of lack of effective balanced processing means for real-time transmission data. At the same time, the present invention also performs shielding analysis on the crosstalk interference data collected by the passive copper cable, obtains crosstalk suppression measures, performs noise optimization on the stable transmission signal, improves the purity of the signal, reduces the noise introduced by the crosstalk interference, and solves the problem of lack of effective suppression measures for the crosstalk interference data. By performing frequency characteristic analysis on the crosstalk interference data, frequency compensation information is obtained, and transmission prediction of the frequency compensation information is performed based on the crosstalk suppression measures to obtain frequency compensation parameters, and further multi-channel optimization is performed on the initial signal optimization result, realizing multi-channel optimization processing of the signal, and improving the efficiency and quality of signal transmission.

[0102] 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.

[0103] 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 optimizing multi-channel signal integrity of a passive copper cable, characterized in that: include: Obtain the channel characteristic parameters and signal transmission requirements of the passive copper cable, and perform impedance matching to obtain the optimized transmission channel; Acquiring real-time transmission data of the passive copper cable, and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; Obtaining crosstalk interference data collected by the passive copper cable, performing shielding analysis with the optimized transmission channel, and obtaining crosstalk suppression measures; Performing noise optimization on the stable transmission signal according to the crosstalk suppression measure to obtain an initial signal optimization result; Performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain frequency compensation parameters; The initial signal optimization result is subjected to multi-channel optimization according to the frequency compensation parameter to obtain a multi-channel optimized signal.

2. The multi-channel signal integrity optimization method of passive copper cable according to claim 1, characterized in that: The step of obtaining the channel characteristic parameters and signal transmission requirements of the passive copper cable and performing impedance matching to obtain an optimized transmission channel includes: Collecting channel physical parameters of the passive copper cable to obtain the channel characteristic parameters; Measuring the signal transmission rate and bandwidth of the passive copper cable to obtain the signal transmission requirement; Performing a multi-point impedance test on the passive copper cable according to the channel characteristic parameters to obtain impedance distribution data; Performing discrete spectrum analysis on the impedance distribution data to obtain a frequency domain characteristic curve; Perform impedance segmentation compensation calculation according to the frequency domain characteristic curve and the signal transmission requirement to obtain an impedance compensation parameter group; Performing impedance optimization configuration on the passive copper cable according to the impedance compensation parameter group to obtain impedance matching information; Channel matching is performed on the passive copper cable based on the impedance matching information to obtain an optimized transmission channel.

3. The multi-channel signal integrity optimization method of passive copper cable according to claim 1, characterized in that: The acquiring the real-time transmission data of the passive copper cable and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal includes: Performing real-time data sampling on the input end and the output end of the passive copper cable to obtain the real-time transmission data; Performing time-frequency domain conversion on the real-time transmission data to obtain a transmission parameter group; Performing linear regression analysis of signal amplitude and phase based on the transmission parameter group to obtain an equalization coefficient; Performing forward equalization and backward equalization calculation on the real-time transmission data according to the equalization coefficient to obtain an equalization compensation parameter; Constructing a compensation algorithm according to the equalization compensation parameters, and iteratively optimizing the real-time transmission data to obtain an optimized parameter group; Signal compensation is performed on the real-time transmission data according to the optimization parameter group to obtain the stable transmission signal.

4. The multi-channel signal integrity optimization method of passive copper cable according to claim 1, characterized in that: The obtaining of the crosstalk interference data collected by the passive copper cable and performing shielding analysis on the optimized transmission channel to obtain crosstalk suppression measures includes: Collecting the spacing and material parameters of the passive copper cable to obtain channel spacing data and conductor material parameters, and performing crosstalk attenuation calculation to obtain a crosstalk attenuation coefficient; Performing crosstalk interference sampling and signal decomposition on the passive copper cable to obtain crosstalk spectrum data; Performing energy distribution analysis on the crosstalk spectrum data according to the crosstalk attenuation coefficient to obtain a crosstalk energy distribution diagram; Perform multi-channel crosstalk path identification according to the crosstalk energy distribution diagram to obtain a crosstalk propagation path; Performing electromagnetic simulation on the optimized transmission channel according to the crosstalk propagation path to obtain electromagnetic field distribution data; A suppression analysis is performed on the crosstalk propagation path and the electromagnetic field distribution data to obtain crosstalk suppression measures.

5. The multi-channel signal integrity optimization method of passive copper cable according to claim 1, characterized in that: The step of performing noise optimization on the stable transmission signal according to the crosstalk suppression measure to obtain an initial signal optimization result includes: Performing waveform analysis on the stable transmission signal to obtain signal waveform characteristics; Calculate the noise spectrum density of the stable transmission signal according to the signal waveform characteristics to obtain noise distribution parameters; Performing a filtering operation on the stable transmission signal based on the noise distribution parameter to obtain a filtered signal; Performing signal compensation calculation on the filtered signal to obtain a compensated signal; Performing a multi-channel gain equalization operation on the compensation signal to obtain an equalized signal; The equalized signal is subjected to waveform shaping optimization to obtain an initial signal optimization result.

6. The multi-channel signal integrity optimization method of passive copper cable according to claim 5, characterized in that: The step of calculating the noise spectrum density of the stable transmission signal according to the signal waveform characteristics to obtain noise distribution parameters includes: Performing time domain sampling segmentation processing on the signal waveform feature to obtain multiple signal sampling segments; Calculating the autocorrelation function of the stable transmission signal according to the multiple signal sampling segments to obtain an initial noise power spectrum; Performing multi-window spectrum analysis on the stable transmission signal according to the initial noise power spectrum to obtain an improved noise spectrum; Based on the improved noise spectrum, the stable transmission signal is divided into frequency bands to obtain a multi-band noise distribution map; A noise source identification calculation is performed on the multi-band noise distribution mapping according to preset transmission medium characteristic parameters to obtain noise distribution parameters.

7. The multi-channel signal integrity optimization method of passive copper cable according to claim 1, characterized in that: The performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain frequency compensation parameters, includes: Performing wavelet decomposition on the crosstalk interference data to obtain crosstalk spectrum characteristics; Dividing the crosstalk spectrum characteristics into frequency component levels to obtain frequency characteristic groups; Performing frequency band analysis on the frequency feature groups to obtain frequency band loss characteristics; Fitting the frequency band loss characteristics with a signal attenuation law to obtain a frequency compensation equation; Numerically solving the frequency compensation equation to obtain the frequency compensation information; quantifying the transmission error of the frequency compensation information according to the crosstalk suppression measure to obtain a channel error parameter; Performing compensation prediction on the channel error parameter to obtain a compensation prediction result; The frequency compensation information is adaptively updated according to the compensation prediction result to obtain the frequency compensation parameter.

8. The multi-channel signal integrity optimization method of passive copper cable according to claim 1, characterized in that: The performing multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimized signal includes: Performing frequency segmentation on the frequency compensation parameter to obtain frequency segmentation coefficients; Perform multi-channel decomposition on the initial signal optimization result according to the frequency segmentation coefficient to obtain channel decomposition data; Performing frequency response calculation on the channel decomposition data to obtain frequency response parameters; Performing frequency compensation on the channel decomposition data according to the frequency response parameter to obtain frequency compensation data; Performing channel coupling on the frequency compensation data to obtain a channel coupling coefficient; Performing channel isolation on the frequency compensation data according to the channel coupling coefficient to obtain channel isolation data; Performing signal reorganization on the channel isolation data to obtain reorganized signal data; The recombined signal data is compensated and optimized according to the frequency segmentation coefficients to obtain a multi-channel optimized signal.

9. A multi-channel signal integrity optimization system for passive copper cables, characterized in that: The multi-channel signal integrity optimization method for a passive copper cable as described in any one of claims 1 to 8 above comprises: An acquisition module, which is used to obtain channel characteristic parameters and signal transmission requirements of the passive copper cable, and perform impedance matching to obtain an optimized transmission channel; An analysis module, the analysis module is used to obtain real-time transmission data of the passive copper cable, and perform equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; An association module, the association module is used to obtain the crosstalk interference data collected by the passive copper cable, perform shielding analysis with the optimized transmission channel, and obtain crosstalk suppression measures; A processing module, the processing module is used to perform noise optimization on the stable transmission signal according to the crosstalk suppression measure to obtain an initial signal optimization result; A control module, the control module is used to perform frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and perform transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain frequency compensation parameters; An execution module is used to perform multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimized signal.

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