Passive copper cable transmission rate optimization method, device and equipment

Abstract

The invention relates to a transmission rate optimization method, device and equipment for a passive copper cable, and the method comprises the steps: obtaining physical characteristic parameters of the passive copper cable, and carrying out the performance evaluation, and obtaining reference transmission parameters; acquiring a transmission signal of the passive copper cable, and performing modulation optimization on the transmission signal and the reference transmission parameter to obtain an optimized transmission signal; obtaining interference characteristic data of the passive copper cable, and performing interference analysis on the optimized transmission signal to obtain an interference elimination result; performing rate evaluation analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter; performing trend analysis on the attenuation characteristic information based on the interference elimination result to obtain a rate attenuation prediction result; and performing rate optimization on the transmission signal according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimization signal. According to the invention, various interferences in the transmission process can be effectively identified and eliminated.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a method, device and equipment for optimizing the transmission rate of a passive copper cable. Background Art

[0002] As a common data transmission medium, passive copper cable plays an important role in communication networks. With the continuous growth of data transmission demand, how to improve the transmission rate of passive copper cables and achieve efficient and stable data transmission has become one of the key research topics. Existing transmission rate optimization methods usually only focus on a single transmission parameter, such as signal strength or line attenuation, while ignoring the coupling effect of multiple factors in the transmission process and the comprehensive impact of various interferences on transmission performance. This single-dimensional optimization method is difficult to meet the actual needs of high-speed data transmission, which may lead to unsatisfactory transmission rate optimization results and affect the overall network performance. Summary of the invention

[0003] The main purpose of the present invention is to provide a method, device and equipment for optimizing the transmission rate of a passive copper cable, which can effectively identify and eliminate various interferences during the transmission process, thereby improving the quality and stability of signal transmission.

[0004] To achieve the above object, the present invention provides a method for optimizing the transmission rate of a passive copper cable, comprising: Obtain the physical characteristic parameters of the passive copper cable, conduct performance evaluation, and obtain benchmark transmission parameters; Acquire the transmission signal of the passive copper cable, and perform modulation optimization with the reference transmission parameter to obtain an optimized transmission signal; Obtaining interference characteristic data of the passive copper cable, performing interference analysis on the optimized transmission signal, and obtaining an interference elimination result; Performing rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter; Performing transmission path detection on the interference characteristic data to obtain attenuation characteristic information, and performing trend analysis on the attenuation characteristic information based on the interference elimination result to obtain a rate attenuation prediction result; The transmission signal is rate optimized according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimized signal.

[0005] Furthermore, the obtaining of physical characteristic parameters of the passive copper cable and performing performance evaluation to obtain reference transmission parameters includes: Performing end-to-end measurement on the passive copper cable to obtain a signal attenuation value, a crosstalk coefficient, and a line impedance; Performing frequency domain analysis on the passive copper cable according to the signal attenuation value to obtain a frequency domain characteristic curve; Performing noise analysis on the frequency domain characteristic curve according to the crosstalk coefficient to obtain noise distribution data; Performing channel capacity calculation on the noise distribution data according to the line impedance to obtain channel characteristic parameters; Performing fusion evaluation on the channel characteristic parameters and the frequency domain characteristic curve to obtain a comprehensive evaluation index; Performing transmission performance analysis on the passive copper cable according to the comprehensive evaluation index to obtain a performance reference value; Perform parameter iterative calculation on the performance reference value to obtain the benchmark transmission parameter.

[0006] Further, the acquiring the transmission signal of the passive copper cable and performing modulation optimization with the reference transmission parameter to obtain the optimized transmission signal includes: Inputting detection signals of different frequencies to the passive copper cable, collecting returned signal waveform data, and obtaining the transmission signal; Performing signal-to-noise ratio calculation and bandwidth analysis on the transmission signal according to the reference transmission parameters to obtain a channel capacity parameter; Perform coding and modulation mapping on the transmission signal according to the channel capacity parameter to obtain an initial modulation parameter; Performing multi-concatenated coding on the initial modulation parameters to obtain channel adjustment coding; Perform carrier modulation processing on the transmission signal according to the channel adjustment code to obtain a modulated signal; Performing cyclic prefix insertion and frame synchronization processing on the modulated signal to obtain a frame synchronization signal; Performing equalizer parameter configuration on the frame synchronization signal to obtain equalization processing parameters; The frame synchronization signal is equalized and compensated according to the equalization processing parameters to obtain the optimized transmission signal.

[0007] Further, the obtaining of interference characteristic data of the passive copper cable, performing interference analysis on the optimized transmission signal, and obtaining an interference elimination result includes: Performing channel spectrum scanning on the passive copper cable to obtain signal energy distribution data; Performing frequency band signal-to-noise ratio calculation on the signal energy distribution data to obtain frequency band signal-to-noise ratio data; Performing interference identification on the frequency band signal-to-noise ratio data to obtain interference feature data; Performing time domain sampling on the optimized transmission signal based on the interference characteristic data to obtain a sampling sequence; Preliminarily reconstructing the sampling sequence to obtain a preliminary denoised signal; Performing phase compensation on the optimized transmission signal according to the preliminary noise elimination signal to obtain a phase compensation result; An interference elimination operation is performed on the phase compensation result according to the preliminary noise elimination signal to obtain the interference elimination result.

[0008] Further, the performing rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter includes: Performing waveform analysis on the optimized transmission signal to obtain signal characteristic data; Calculating the channel bandwidth utilization rate according to the signal characteristic data, and performing capacity calculation to obtain a theoretical transmission rate; Performing a sampling residual interference measurement on the interference elimination result to obtain interference distribution data; Performing transmission section index evaluation based on the interference distribution data to obtain effective transmission quality parameters; Performing a coding bit error rate test on the optimized transmission signal to obtain a coding efficiency factor; Calculate the achievable rate parameter based on the theoretical transmission rate, the effective transmission quality parameter and the coding efficiency factor to obtain rate estimation data; Performing interval screening and abnormal correction on the rate estimation data to obtain a stable transmission rate value; Dynamically iteratively predict the stable transmission rate value to obtain the initial rate parameter.

[0009] Further, the performing transmission path detection on the interference characteristic data to obtain attenuation characteristic information, and performing trend analysis on the attenuation characteristic information based on the interference elimination result to obtain a rate attenuation prediction result, includes: Performing dual analysis of the interference characteristic data in time domain and frequency domain to obtain interference time-frequency attributes; Performing transmission segmentation processing on the time-frequency attributes to obtain a segmented transmission structure; Performing signal propagation simulation on the segmented transmission structure to obtain a signal attenuation factor of each segment; Performing overall transmission attenuation analysis on the attenuation factors of the signals in each segment to obtain attenuation conditions; Correlation analysis is performed on the attenuation situation and the interference elimination result to obtain attenuation interference corresponding data; Performing trend deduction on the data corresponding to the attenuation interference to obtain an attenuation change function; Performing curve fitting and data extrapolation according to the attenuation change function to obtain an attenuation estimated value; Performing a critical comparison on the attenuation estimate according to a preset attenuation threshold to obtain a rate attenuation critical point; The rate attenuation prediction result is obtained by performing rate prediction according to the rate attenuation critical point and the attenuation estimated value.

[0010] Further, the rate optimization of the transmission signal according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimization signal includes: Decomposing the transmission signal according to the initial rate parameter to obtain a multi-level signal component; Adjusting the coefficients of the multi-level signal components according to the rate attenuation prediction result to obtain an adjusted signal component; Constructing a rate improvement algorithm based on the adjustment signal composition, and performing rate adjustment on the transmission signal to obtain an initial adjustment transmission signal; Performing a multi-dimensional joint analysis on the initially adjusted transmission signal to obtain multi-dimensional feature data; Dynamically balancing the initially adjusted transmission signal according to the multi-dimensional feature data to obtain a balanced transmission signal; Performing multi-channel signal integration on the balanced transmission signal to obtain a composite signal; Performing transmission channel allocation according to the composite signal to obtain an allocation channel signal; Performing global coding modulation on the allocation channel signal to obtain a global optimization signal.

[0011] Further, the constructing of a rate improvement algorithm based on the adjustment signal composition and rate adjustment of the transmission signal to obtain an initial adjustment transmission signal includes: Performing a multi-scale rate decomposition operation on the adjustment signal composition to obtain a rate variation mode; Constructing a time-frequency joint distribution function according to the rate variation mode to obtain a rate time-frequency matrix; Performing envelope demodulation on the rate time-frequency matrix to obtain a rate analysis sequence; Performing fractional differential operation according to the rate analysis sequence to obtain rate derivative characteristics; Performing a multi-dimensional trajectory transformation on the rate derivative feature to obtain rate trajectory data; Constructing a rate compensation polynomial according to the rate trajectory data to obtain a compensation basis function; Performing nonlinear rate reconstruction optimization on the compensation basis function to obtain a rate reconstruction function; Parameters of the transmission signal are optimized according to the rate reconstruction function and the adjustment signal composition to obtain the initial adjustment transmission signal.

[0012] The present invention also provides a transmission rate optimization device for a passive copper cable, which is applied to any one of the above-mentioned transmission rate optimization methods for a passive copper cable, comprising: An acquisition module, the acquisition module is used to obtain physical characteristic parameters of the passive copper cable, and perform performance evaluation to obtain reference transmission parameters; An analysis module, the analysis module is used to obtain the transmission signal of the passive copper cable, and perform modulation optimization with the reference transmission parameter to obtain an optimized transmission signal; An association module, the association module is used to obtain interference characteristic data of the passive copper cable, perform interference analysis on the optimized transmission signal, and obtain an interference elimination result; A processing module, the processing module is used to perform rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter; A control module, the control module is used to perform transmission path detection on the interference characteristic data to obtain attenuation characteristic information, and perform trend analysis on the attenuation characteristic information based on the interference elimination result to obtain a rate attenuation prediction result; An execution module is used to optimize the rate of the transmission signal according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimization signal.

[0013] The present invention also provides a transmission rate optimization device for a passive copper cable, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned methods for optimizing the transmission rate of a passive copper cable.

[0014] The present invention provides a method, device and equipment for optimizing the transmission rate of a passive copper cable, which has the following beneficial effects: By obtaining the physical characteristic parameters of passive copper cables and performing performance evaluation, the basic transmission capacity of copper cables can be accurately grasped, providing a reliable reference benchmark for subsequent transmission optimization. By optimizing the modulation of transmission signals and combining the analysis of interference feature data, effective identification and elimination of various types of interference during transmission can be achieved, improving the quality and stability of signal transmission. Based on the interference feature data, transmission path detection and attenuation feature analysis can accurately predict the attenuation trend of the transmission rate, thereby achieving dynamic adjustment and optimization of the transmission rate. By conducting a comprehensive rate evaluation of the optimized transmission signal and interference elimination results, and combining the attenuation prediction results for global optimization, the optimal configuration of the transmission rate is achieved, effectively improving the overall transmission performance of the passive copper cable. Through multi-dimensional parameter analysis and optimization strategies, the limitations of traditional single optimization methods are overcome, and a significant increase in transmission rate is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a method for optimizing the transmission rate of a passive copper cable provided by the present invention; Figure 2 It is a structural diagram of a transmission rate optimization device for a passive copper cable provided by the present invention; Figure 3 The present invention provides a structure diagram of a transmission rate optimization device for a passive copper cable.

[0016] 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

[0017] 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.

[0018] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Reference Figure 1 As shown, the present invention also provides 1. a method for optimizing the transmission rate of a passive copper cable, comprising: Step S1: obtaining physical characteristic parameters of the passive copper cable, and performing performance evaluation to obtain reference transmission parameters; Step S2: acquiring a transmission signal of the passive copper cable, performing modulation optimization with a reference transmission parameter, and obtaining an optimized transmission signal; Step S3: Obtain interference characteristic data of the passive copper cable, perform interference analysis on the optimized transmission signal, and obtain interference elimination results; Step S4: performing rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter; Step S5: performing transmission path detection on the interference feature data to obtain attenuation feature information, performing trend analysis on the attenuation feature information based on the interference elimination result to obtain a rate attenuation prediction result; Step S6: Optimize the transmission signal rate according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimized signal.

[0020] Based on the above steps, the detailed steps are as follows: Step S1: At this stage, it is critical to fully understand the physical characteristics of the passive copper cable, including resistance, conductor diameter, insulation material, temperature coefficient, and other factors that may affect the quality of signal transmission. By using professional measuring instruments, these parameters of the copper cable are accurately measured to ensure the accuracy of the data. The evaluation process includes testing the transmission characteristics of the copper cable at different frequencies and recording the signal attenuation, distortion, and noise level. Through these tests, baseline transmission parameters are established, which will serve as reference standards in the subsequent optimization process.

[0021] In performance evaluation, using appropriate test methods and instruments, such as network analyzers, oscilloscopes, and signal generators, can effectively analyze the performance of copper cables under actual use conditions. The evaluation results should not only consider the transmission rate, but also the integrity and stability of the signal. The determination of the benchmark transmission parameters lays the foundation for subsequent modulation optimization, so that subsequent steps can effectively adjust and optimize the signal on this basis. By comparing the test results under different conditions, the key factors affecting the transmission performance can be identified, providing data support for subsequent optimization plans.

[0022] Step S2: In this stage, the focus is on analyzing the current passive copper cable transmission signal and comparing it with the benchmark transmission parameters to identify the deficiencies in the signal. By analyzing the spectrum of the transmission signal, the main components and interference components of the signal are determined, and modulation optimization is performed in a targeted manner. Modulation optimization can use a variety of technologies, such as phase modulation, amplitude modulation, and frequency modulation, to select a modulation method that suits the current signal characteristics to improve the signal's anti-interference ability and transmission efficiency.

[0023] During the implementation process, software tools are used for simulation and optimization to evaluate the impact of different modulation methods on signal transmission rate and quality. By comparing the signal characteristics before and after optimization, it is ensured that the optimized signal can achieve higher transmission rate and lower bit error rate in passive copper cables. At the same time, the parameters and algorithms used in the optimization process are recorded for subsequent analysis and adjustment. Ultimately, the optimized transmission signal obtained will provide basic data for subsequent interference analysis, ensuring that the transmission performance can be effectively improved in practical applications.

[0024] Step S3: In this stage, the focus is on identifying and analyzing various interference factors that affect the transmission signal of the passive copper cable. By monitoring and recording the interference characteristic data of the copper cable in actual operation, possible interference sources are identified, including external electromagnetic interference, signal crosstalk and environmental noise. Using advanced signal processing technology, the interference characteristic data is deeply analyzed to evaluate its impact on optimizing the transmission signal.

[0025] The interference analysis process usually includes spectrum analysis and time domain analysis of the interference signal to determine the frequency characteristics and duration of the interference. By comparing the characteristics of the optimized transmission signal and the interference signal, the specific impact of the interference on the signal is identified, and the corresponding interference elimination strategy is proposed. This may include measures such as adding filters, adjusting signal modulation methods, or changing transmission paths. Ultimately, after the interference elimination process, the results obtained will provide clear signal quality data for subsequent rate evaluation and analysis, ensuring that the optimized signal can effectively resist interference during transmission and improve the overall transmission rate.

[0026] Step S4: The rate evaluation and analysis phase integrates the data of optimized transmission signals and interference elimination results, and conducts a comprehensive analysis through professional test equipment and evaluation tools. A variety of test methods are used in the evaluation process, including bit error rate testing, signal-to-noise ratio analysis, and transmission delay measurement. Through statistical analysis of these test data, the actual achievable transmission rate range is calculated.

[0027] The evaluation and analysis uses multiple sets of different test data and conducts tests under different transmission distances and environmental conditions to ensure the accuracy and reliability of the evaluation results. Mathematical modeling and analysis are performed on the test data to establish a relationship model between the transmission rate and various influencing factors. Through these analyses, an initial rate parameter set including the maximum transmission rate, stable transmission rate, and minimum acceptable rate is obtained. These parameters will serve as an important reference for the subsequent optimization process and provide data support for achieving optimal transmission performance.

[0028] Step S5: The entire transmission path of the passive copper cable is analyzed in detail during the transmission path detection phase to identify key points that may cause signal attenuation. By using professional equipment such as a time domain reflectometer, the impedance change and signal attenuation on the transmission path are measured, and the attenuation characteristic information is recorded. The collected attenuation data is systematically analyzed to establish a relationship model between attenuation and factors such as transmission distance and frequency.

[0029] Combined with the interference elimination results obtained previously, an in-depth trend analysis of the attenuation feature information is performed. Through mathematical models and statistical methods, the signal attenuation trend under different usage conditions is predicted. During the analysis process, the impact of external factors such as temperature changes and ambient humidity on attenuation is considered to establish a complete attenuation prediction model. Through these analyses, accurate rate attenuation prediction results are obtained, providing a scientific basis for the final rate optimization.

[0030] Step S6: In the global optimization stage, the initial rate parameters and rate attenuation prediction results are comprehensively used to perform the final rate optimization on the transmission signal. The optimization process uses an adaptive algorithm to dynamically adjust the transmission parameters according to different transmission scenarios. By establishing a mathematical optimization model, the transmission rate is maximized while ensuring signal quality. The balance of multiple factors is considered during the optimization process, including transmission reliability, signal stability, and system complexity. Through repeated testing and verification, it is ensured that the optimized signal can maintain stable high-speed transmission in practical applications. The final globally optimized signal has higher transmission efficiency and stronger anti-interference ability, and can maintain good transmission performance in various complex environments. The optimization results are verified through actual tests to ensure that the expected performance indicators are met and a significant increase in the transmission rate of passive copper cables is achieved.

[0031] The present invention provides a method for optimizing the transmission rate of a passive copper cable. By acquiring the physical characteristic parameters of the passive copper cable and performing performance evaluation, the basic transmission capacity of the copper cable can be accurately grasped, and a reliable reference benchmark can be provided for subsequent transmission optimization. By optimizing the modulation of the transmission signal and combining the analysis of the interference characteristic data, the effective identification and elimination of various interferences in the transmission process are achieved, and the quality and stability of the signal transmission are improved. By performing transmission path detection and attenuation characteristic analysis based on the interference characteristic data, the attenuation trend of the transmission rate can be accurately predicted, thereby realizing dynamic adjustment and optimization of the transmission rate. By performing a comprehensive rate evaluation on the optimized transmission signal and the interference elimination results, and performing global optimization in combination with the attenuation prediction results, the optimal configuration of the transmission rate is achieved, and the overall transmission performance of the passive copper cable is effectively improved. Through multi-dimensional parameter analysis and optimization strategies, the limitations of the traditional single optimization method are overcome, and a significant improvement in the transmission rate is achieved.

[0032] In one embodiment, physical characteristic parameters of a passive copper cable are obtained, and performance evaluation is performed to obtain reference transmission parameters, including: When performing end-to-end measurements on passive copper cables, a network analyzer is used to apply standard test signals to both ends of the passive copper cable within the frequency range of 1MHz to 10GHz, and the attenuation of the signal during transmission is recorded. The measured signal attenuation value is in decibels (dB), which reflects the degree of signal loss during transmission. At the same time, a differential pair crosstalk tester is used to measure near-end crosstalk (NEXT) and far-end crosstalk (FEXT) to obtain the crosstalk coefficient. The impedance of the cable is measured by a time domain reflectometer (TDR) to obtain the characteristic impedance value, which reflects the basic transmission characteristics of the passive copper cable.

[0033] In the process of frequency domain analysis based on the signal attenuation value, the measured attenuation data is converted into a frequency domain characteristic curve. This curve describes the relationship between signal attenuation and frequency, which is specifically expressed as the insertion loss value at different frequency points. By mathematically modeling the frequency domain characteristic curve, the functional relationship between insertion loss and frequency is established to obtain the frequency domain characteristic curve. This curve has important guiding significance for the subsequent optimization of transmission performance.

[0034] When performing noise analysis on the frequency domain characteristic curve according to the crosstalk coefficient, the measured near-end crosstalk and far-end crosstalk data are combined with the frequency domain characteristic curve to construct a noise model. This model takes into account the impact of various crosstalk interference sources on signal transmission, and obtains the noise power spectrum density distribution at different frequencies through mathematical analysis to form complete noise distribution data.

[0035] When calculating the channel capacity based on the line impedance and noise distribution data, the line characteristic impedance, signal attenuation characteristics and noise distribution data are combined based on Shannon's theorem to calculate the channel capacity in different frequency bands. In the calculation process, factors such as signal power, noise power and channel bandwidth are fully considered, and finally the channel characteristic parameters reflecting the transmission capacity of the passive copper cable are obtained.

[0036] When fusion evaluation is performed on the channel characteristic parameters and frequency domain characteristic curves, a multi-dimensional evaluation method is used to weightedly fuse parameters of multiple dimensions such as channel capacity, frequency domain response, and noise characteristics. The evaluation process uses a standardized processing method to unify parameters of different dimensions to the same scale, and obtains a comprehensive evaluation index that reflects the overall performance of the passive copper cable through a specific evaluation algorithm.

[0037] When the transmission performance of passive copper cables is analyzed based on comprehensive evaluation indicators, the evaluation results are compared with standard transmission indicators to analyze the current transmission performance status of passive copper cables. This analysis process combines the requirements of actual application scenarios, quantitatively evaluates key performance indicators such as transmission rate, bit error rate, and transmission delay, and finally obtains performance reference values.

[0038] When performing parameter iterative calculations on the performance reference value, the optimization algorithm is used to perform multiple rounds of iterative optimization on the transmission parameters. In each round of iteration, the transmission parameters are adjusted according to the performance reference value, and the effect of the adjusted parameters is verified by simulation. When the iterative results converge to a stable state, the final benchmark transmission parameters are obtained. The benchmark transmission parameters serve as the basis for optimizing the transmission performance of the passive copper cable and guide the subsequent transmission rate optimization process.

[0039] This embodiment obtains multi-dimensional physical parameters through end-to-end measurement, combines frequency domain analysis and noise analysis, and establishes a complete performance evaluation system, which effectively improves the accuracy of transmission performance evaluation. A multi-dimensional fusion evaluation method is adopted to comprehensively analyze parameters such as channel capacity, frequency domain response, and noise characteristics, overcoming the limitations of traditional single parameter evaluation methods. Based on the iterative optimization algorithm, the transmission parameters are dynamically adjusted to improve the transmission rate while ensuring the signal quality, thereby enhancing the applicability of passive copper cables in high-speed transmission scenarios. By establishing a standardized performance optimization process, reliable technical support is provided for the optimization of the transmission rate of passive copper cables, which has strong practical value.

[0040] In one embodiment, a transmission signal of a passive copper cable is obtained, and modulation optimization is performed with a reference transmission parameter to obtain an optimized transmission signal, including: When optimizing the transmission rate of the passive copper cable, the frequency range of the detection signal is set to 1MHz to 100MHz, and the frequency interval is 1MHz. The detection signal is generated by the signal generator, converted by the D / A converter and input into the passive copper cable. The sampling rate of the signal acquisition device is set to 200MHz, the sampling depth is 14 bits, and the acquisition time window is 100 microseconds. The returned signal waveform data is stored in the data buffer after A / D conversion to form the transmission signal.

[0041] During the signal-to-noise ratio calculation process, the amplitude of the transmission signal is selected as the effective signal and its root mean square value is calculated; the signal baseline is selected as the noise and its standard deviation is calculated. The bandwidth analysis adopts the -3dB bandwidth method. By performing Fourier transform on the transmission signal, the signal power spectrum is obtained and the frequency range corresponding to the half-power point is found. The benchmark transmission parameters include the channel characteristic curve, channel attenuation curve, and phase response curve. The channel capacity parameters are calculated according to the Shannon formula. The parameters include indicators such as maximum transmission rate, signal bandwidth utilization, and signal transmission loss.

[0042] The coded modulation mapping process uses adaptive modulation coding technology, and adopts different modulation methods for different frequency bands according to the channel capacity parameters. 256-QAM modulation is used in high signal-to-noise ratio areas, 64-QAM modulation is used in medium signal-to-noise ratio areas, and 16-QAM modulation is used in low signal-to-noise ratio areas. The initial modulation parameters include constellation mapping rules, modulation depth, symbol rate and other parameters.

[0043] The multi-cascade coding adopts a three-level coding structure: RS coding is used on the outside, convolutional interleaving coding is used in the middle, and LDPC coding is used on the inside. The RS coding parameters are (255, 239), and the correction capability is 8 symbol errors. The convolutional interleaving depth is 16, and the constraint length is 7. The LDPC coding rate is 5 / 6, and the code length is 648 bits. The channel adjustment code is obtained by cascading these three levels of coding.

[0044] Carrier modulation processing Carrier modulation uses orthogonal frequency division multiplexing technology, with 256 subcarriers and a subcarrier interval of 250kHz. The modulated signal is transformed into a time domain waveform through inverse Fourier transform. The power of each subcarrier is dynamically allocated according to the channel characteristics to ensure that the signal power spectrum density meets the transmission requirements.

[0045] In the frame synchronization process, the cyclic prefix length is set to 1 / 4 of the symbol period and inserted before each OFDM symbol. The frame synchronization signal is realized by adding a synchronization sequence to the header of the data frame. The synchronization sequence uses the Zadoff-Chu sequence with a length of 64. The frame structure includes fields such as the preamble, frame header information, data payload, and tail code.

[0046] The equalizer adopts an adaptive decision feedback equalizer structure, with 32 and 16 taps in the feedforward and feedback stages respectively. The equalization processing parameters include tap coefficients, step size factors, forgetting factors, etc. The tap coefficients are initialized using a zero-forcing algorithm, the step size factor uses a variable step size strategy, and the forgetting factor is set to 0.99.

[0047] The optimized transmission signal is obtained through equalization compensation processing, and the compensation process adopts the minimum mean square error criterion. The compensation result includes performance indicators such as the signal waveform after equalization, bit error rate, and signal quality factor. The entire transmission rate optimization process is a closed-loop control system, and each processing link cooperates with each other to jointly achieve the improvement of passive copper cable transmission performance.

[0048] This embodiment uses adaptive modulation and coding technology to dynamically select the modulation mode according to the channel capacity, and adopts 256-QAM, 64-QAM and 16-QAM modulation in different signal-to-noise ratio areas to make full use of channel resources. The three-stage cascade coding structure combines the advantages of RS coding, convolutional interleaving coding and LDPC coding to effectively improve the anti-interference and error correction capabilities. Orthogonal frequency division multiplexing technology cooperates with the dynamic power allocation strategy to ensure the efficient use of spectrum resources. The adaptive decision feedback equalizer adopts a variable step size strategy to achieve accurate tracking and compensation of channel characteristics. By building a complete closed-loop control system, each processing link cooperates and continuously dynamically optimizes the system parameters, so that the passive copper cable can achieve a higher transmission rate while maintaining stable transmission.

[0049] In one embodiment, interference characteristic data of a passive copper cable is obtained, interference analysis is performed on an optimized transmission signal, and an interference elimination result is obtained, including: When performing channel spectrum scanning on passive copper cables, a spectrum analyzer is used to perform full-band scanning on the transmission channel of passive copper cables. During the scanning process, the spectrum analyzer scans the channel in the frequency range of 0-1000MHz with a step frequency of 0.1MHz, and records the signal energy value of each frequency point. After the scanning is completed, a signal energy distribution data matrix containing frequency points and corresponding energy values ​​is formed. The matrix contains energy data of 10,000 sampling points, and each data point contains a frequency value and a corresponding signal energy value.

[0050] When calculating the frequency band signal-to-noise ratio of the signal energy distribution data, the full frequency band is divided into 100 sub-frequency bands, each of which contains 100 sampling points. In each sub-frequency band, the mean of the signal energy is calculated as the signal power, and the standard deviation of the energy fluctuation is calculated as the noise power. The signal-to-noise ratio data of each sub-frequency band is obtained based on the ratio of the signal power to the noise power. The calculation results form a frequency band signal-to-noise ratio data vector containing 100 elements.

[0051] In the process of interference identification of frequency band signal-to-noise ratio data, the signal-to-noise ratio threshold is set to 20dB. For frequency bands with signal-to-noise ratios lower than the threshold, characteristic parameters such as the center frequency, bandwidth, and signal-to-noise ratio of the frequency band are extracted. By clustering these characteristic parameters, the type and characteristics of the interference source are identified. The interference characteristic data finally obtained contains information such as the location, bandwidth, and intensity of the interference frequency band.

[0052] When sampling the optimized transmission signal in the time domain based on the interference characteristic data, a high-speed digital-to-analog converter is used to sample the transmission signal. The sampling rate is set to 2.5 times the highest frequency of the interference frequency band, and the sampling time is 10ms. The timestamp information of the sampling time is recorded at the same time during the sampling process. The sampling sequence obtained after the sampling is completed contains the signal amplitude and the corresponding time information.

[0053] When the sampling sequence is initially reconstructed, the wavelet transform method is used to decompose the sampling sequence at multiple scales. In the wavelet domain, the disturbed wavelet coefficients are determined based on the interference feature data, and these coefficients are threshold processed. After processing, the signal is reconstructed through the inverse wavelet transform to obtain a preliminary denoised signal. This signal retains the main features of the original signal while suppressing some interference components.

[0054] When performing phase compensation on the optimized transmission signal based on the preliminary noise elimination signal, a phase distortion model is established by comparing the phase difference between the preliminary noise elimination signal and the original signal. The phase compensation amount of each frequency point is calculated based on the model, and the phase of the original signal is corrected. The phase compensation process takes into account the dispersion effect of the signal during transmission, and the phase compensation result is obtained after compensation.

[0055] When performing interference elimination operation on the phase compensation result based on the preliminary denoising signal, an adaptive filter is constructed. The preliminary denoising signal is used as the reference input, the phase compensation result is used as the signal to be processed, and the filter coefficient is optimized by the minimum mean square error criterion. After filtering, the final interference elimination result is obtained, which significantly reduces the interference component in the signal.

[0056] This embodiment realizes the accurate extraction of the interference characteristics of the transmission channel by scanning the channel spectrum of the passive copper cable and obtaining the signal energy distribution data, combined with the frequency band signal-to-noise ratio calculation and interference identification, and provides reliable data support for subsequent signal optimization. The signal is preliminarily reconstructed by combining time domain sampling and wavelet transform, which effectively suppresses the interference component and retains the main characteristics of the original signal. Phase compensation and interference elimination operations are performed based on the preliminary noise reduction signal, and an adaptive signal processing mechanism is established, which significantly improves the signal quality. By adopting a multi-level signal processing scheme, corresponding parameter thresholds and optimization criteria are set at different processing stages to ensure the stability and reliability of the processing results. The entire optimization process forms a complete signal processing link, and the processing results of each step are interconnected, which improves the efficiency of transmission rate optimization.

[0057] In one embodiment, the rate evaluation analysis is performed on the optimized transmission signal and the interference elimination result to obtain the initial rate parameter, including: When performing waveform analysis on the optimized transmission signal, a digital oscilloscope is used to sample the signal, with the sampling rate set to 10 times the signal frequency and the sampling time not less than 1ms. The amplitude, phase, rise time, fall time and other characteristic quantities of the signal are obtained through sampling, and the spectrum characteristics of the signal are obtained by combining Fourier transform, and the obtained data constitutes the signal characteristic data.

[0058] The calculation of channel bandwidth utilization is based on the spectrum distribution in the signal characteristic data. The signal energy distribution curve is convolved with the channel transmission characteristic curve to obtain the frequency domain utilization efficiency curve. The curve is integrated and compared with the theoretical capacity of the channel to obtain the actual channel capacity value. This capacity value, together with parameters such as signal-to-noise ratio and modulation mode, constitutes the theoretical transmission rate data.

[0059] The residual interference measurement of the interference elimination result is carried out using a high-precision data acquisition card, with a sampling accuracy of no less than 14 bits and a sampling window width that is an integer multiple of the signal period. The sampled data is statistically analyzed to obtain the characteristics of interference amplitude distribution, frequency distribution, phase correlation, etc., to form interference distribution data.

[0060] During the transmission section indicator evaluation process, the interference distribution data is compared with the preset transmission quality threshold. The evaluation indicators include parameters such as signal-to-noise ratio, crosstalk, return loss, and insertion loss. A comprehensive score is obtained through weighted calculation, and the mapping relationship between the score and the transmission distance determines the effective transmission quality parameters.

[0061] The coding error rate test uses a pseudo-random code sequence as the test sequence. The decoding error probability is recorded under different signal-to-noise ratio conditions, and the functional relationship between the bit error rate and the signal-to-noise ratio is established. The functional relationship is normalized to obtain the coding efficiency factor.

[0062] The achievable rate parameter calculation uses the theoretical transmission rate as the reference value and modifies the reference value in combination with the effective transmission quality parameter. The modification process uses a nonlinear mapping function whose coefficient is determined by the coding efficiency factor. The data obtained after the modification calculation is the rate estimation data.

[0063] The sliding window method is used to filter the interval of rate estimation data, and the window size is 10% of the total data. The mean and standard deviation of the data are calculated in each window, and data points that deviate from the mean by more than 3 standard deviations are eliminated. After multiple rounds of iterative filtering, the data are averaged to obtain the stable transmission rate value.

[0064] Dynamic iterative prediction uses an autoregressive moving average model to predict future trends using historical data sequences. The order of the prediction model is determined by the autocorrelation characteristics of the data, and the confidence level of the prediction interval is set to 95%. The prediction results are combined with the current stable transmission rate value to obtain the final initial rate parameter.

[0065] This embodiment achieves a comprehensive evaluation of transmission performance through multi-dimensional data collection and analysis, avoiding the one-sidedness that may be caused by a single indicator evaluation. High-precision sampling and complete frequency domain analysis are used in the signal feature extraction process to ensure the accuracy and reliability of the evaluation data. By establishing an interference distribution model and a transmission quality evaluation system, an accurate characterization of the actual transmission environment is achieved, and the practicality of the rate evaluation results is improved. An adaptive screening mechanism and prediction algorithm are introduced in the data processing process, which effectively reduces the volatility of the evaluation results and enhances the stability of system optimization. By combining theoretical analysis with actual testing, a complete evaluation link from signal characteristics to final rate parameters is established, providing reliable technical support for the performance improvement of passive copper cable transmission systems.

[0066] In one embodiment, transmission path detection is performed on interference feature data to obtain attenuation feature information, and trend analysis is performed on the attenuation feature information based on the interference elimination result to obtain a rate attenuation prediction result, including: The interference time-frequency attributes are obtained by performing dual analysis of the interference feature data in time domain and frequency domain. The time domain analysis uses signal amplitude detection to obtain the signal change characteristics over time, and the frequency domain analysis uses fast Fourier transform to obtain the signal's spectral distribution characteristics. The time domain characteristics are combined with the frequency domain characteristics to form an interference time-frequency attribute data matrix.

[0067] When the time-frequency attributes are processed for transmission segmentation to obtain a segmented transmission structure, multiple detection points are set based on the signal transmission distance. The intervals between the detection points are dynamically adjusted according to the degree of signal attenuation. The greater the attenuation, the smaller the interval between the detection points. The transmission path is divided into multiple transmission segments through the detection points. The length of each transmission segment is determined by the distance between adjacent detection points to form segmented transmission structure data.

[0068] In the process of simulating signal propagation of the segmented transmission structure to obtain the signal attenuation factor of each segment, the transmission line model is used to simulate the signal propagation characteristics in each transmission segment. The distributed parameters of the transmission line are considered in the simulation process, including resistance, inductance, capacitance and conductance per unit length. The transmission function of each transmission segment is calculated based on these parameters, and the signal attenuation factor is extracted from the transmission function.

[0069] When the attenuation situation is obtained by performing overall transmission attenuation analysis on the attenuation factors of each segment of the signal, the attenuation factors of each transmission segment are combined in series to calculate the cumulative attenuation effect. During the analysis, factors such as energy loss, crosstalk effect, and reflection loss of the signal during transmission are comprehensively considered to generate an overall transmission attenuation characteristic curve.

[0070] In the process of correlating the attenuation situation with the interference elimination results to obtain the corresponding data of attenuation interference, the mapping relationship between attenuation characteristics and interference characteristics is established. The influence of interference on signal attenuation is determined through correlation analysis, and the attenuation-interference correlation matrix is ​​generated, which describes the contribution of different types of interference to signal attenuation.

[0071] When the attenuation-interference corresponding data is trended and the attenuation change function is obtained, the regression analysis method is used to fit the attenuation-interference correlation data. A suitable mathematical model is selected to describe the trend of attenuation changing with interference, and a mathematical expression of attenuation change is established, which reflects the quantitative relationship between attenuation and interference.

[0072] In the process of obtaining the estimated attenuation value by curve fitting and data extrapolation based on the attenuation change function, the least squares method is used to fit the existing data points to obtain the parameters of the fitting curve. Data extrapolation is performed based on the fitting parameters to predict the signal attenuation value at future time points.

[0073] When the rate attenuation critical point is obtained by critically comparing the attenuation estimated value according to the preset attenuation threshold, multiple attenuation threshold levels are set, each level corresponds to a different transmission rate requirement. The estimated attenuation value is compared with each level of threshold to determine the time point when the attenuation reaches the critical state.

[0074] When the rate attenuation prediction result is obtained by performing rate prediction based on the rate attenuation critical point and the attenuation estimated value, a corresponding relationship between the attenuation value and the transmission rate is established. Based on the attenuation estimated value at the critical point, the corresponding maximum available transmission rate is calculated to generate a prediction curve of the rate changing over time.

[0075] This embodiment establishes a complete transmission rate optimization prediction system by performing dual analysis of the interference feature data in the time domain and frequency domain, combined with transmission segmentation processing and signal propagation simulation. By utilizing the dynamic adjustment mechanism of the detection point interval, the area with severe attenuation is monitored more intensively, thereby improving the accuracy of the transmission performance evaluation. By establishing an attenuation-interference correlation matrix, a quantitative analysis of the impact of different types of interference is achieved, providing accurate data support for the performance optimization of the transmission system. Based on the setting of multi-level attenuation thresholds, the critical state of transmission performance can be identified in a timely manner, and a forward-looking prediction of transmission rate changes can be achieved. Through a systematic data analysis and modeling process, the optimization of the transmission rate has a scientific basis and predictability, providing a reliable decision-making basis for the maintenance and upgrade of the passive copper cable transmission system.

[0076] In one embodiment, the transmission signal is rate optimized according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimization signal, including: In practical applications, the transmission signal is rate optimized according to the initial rate parameter and the rate attenuation prediction result. Specifically, the following steps are performed to obtain a global optimized signal.

[0077] When the transmission signal is decomposed according to the initial rate parameter, the wavelet transform method is used to decompose the transmission signal into three frequency bands: high frequency signal, medium frequency signal and low frequency signal, forming a multi-level signal composition. In the signal decomposition process, the db4 wavelet basis function is selected to decompose the signal to the third layer to obtain the energy distribution characteristics of the signal in different frequency bands.

[0078] When adjusting the coefficients of the multi-level signal composition according to the rate attenuation prediction results, an attenuation model is established based on the transmission distance, and the attenuation characteristics of signals in different frequency bands are predicted and analyzed. The temperature correction factor α (0<α<1) is introduced into the attenuation model, and the influence of ambient temperature on signal attenuation is comprehensively considered to obtain the adjusted signal composition. The attenuation prediction adopts an exponential attenuation model, and the attenuation coefficient is nonlinearly related to frequency, distance and temperature.

[0079] When constructing a rate improvement algorithm based on the adjustment signal composition, an adaptive equalization algorithm is used to pre-compensate the signal. The algorithm performs gain compensation on the high-frequency component through a feedforward filter and suppresses inter-symbol interference through a feedback filter to achieve rate adjustment of the transmission signal and obtain the initial adjustment transmission signal. The convergence step size μ of the equalization algorithm is dynamically adjusted within the range of 0.01-0.1.

[0080] When performing multi-dimensional joint analysis on the initial adjustment transmission signal, signal features are extracted from three dimensions: time domain, frequency domain, and phase. Time domain features include parameters such as signal amplitude, overshoot, and rise time; frequency domain features include parameters such as spectrum bandwidth and harmonic distortion; and phase features include parameters such as group delay and phase jitter, forming multi-dimensional feature data. Feature extraction uses a sliding window method with a window length of 1024 points.

[0081] When the transmission signal is dynamically balanced for the first adjustment based on multi-dimensional feature data, a feature parameter weight matrix is ​​established to weight the features of each dimension. The weight coefficient is adjusted through an iterative optimization algorithm to achieve a dynamic balance among the various performance indicators of the signal and obtain a balanced transmission signal. The value range of the weight coefficient is between 0 and 1, and the sum of the weights of each dimension is 1.

[0082] When integrating multiple balanced transmission signals, orthogonal frequency division multiplexing technology is used to orthogonally modulate multiple signals in the frequency domain. Through subcarrier mapping and cyclic prefix addition, effective multiplexing of multiple signals is achieved to obtain a composite signal. The number of subcarriers is dynamically allocated according to the system bandwidth, and the cyclic prefix length does not exceed 1 / 4 of the effective symbol length.

[0083] When allocating transmission channels according to composite signals, adaptive resource allocation is performed based on channel status information. The water filling algorithm is used to evaluate the channel capacity, and channel allocation is performed in combination with the signal-to-noise ratio distribution characteristics to obtain the allocated channel signal. The channel status information is updated every 100ms and includes parameters such as signal-to-noise ratio and bit error rate.

[0084] When performing global coding modulation on the allocated channel signal, a cascade coding scheme is used, including external RS coding and internal LDPC coding. Interleaving is used to improve the ability to resist burst interference, and adaptive modulation is used to select the optimal modulation order, ultimately obtaining a globally optimized signal. The coding efficiency is dynamically adjusted with the channel conditions, and the modulation order is switched between QPSK, 16QAM and 64QAM.

[0085] More specifically, when performing global coding modulation on the distribution channel signal, the coding modulation scheme adopts a double-layer cascade coding structure, wherein the outer layer adopts RS (255, 239) coding and the inner layer adopts quasi-cyclic LDPC coding with a code rate of 0.8, and the two layers of coding are connected by a matrix interleaver.

[0086] During the outer RS ​​encoding process, the input data stream is grouped into groups of 239 bytes, and 16 bytes of check bits are added to each group of data to form a 255-byte codeword. RS encoding uses a generating polynomial on the Galois field GF(28) and has the ability to correct 8 random errors. The encoded data is hashed through an interleaving matrix with a depth of 4, and the interleaving depth is adapted to the burst error length.

[0087] The inner LDPC code is based on a quasi-cyclic structure of the (3, 6) rule, with a code length of 64800 bits. The check matrix H is constructed by a cyclic permutation matrix with a size of 360×360 and 24 sub-matrices. The LDPC code uses a serial decoding algorithm with a maximum number of iterations set to 50 and an early stopping threshold of 10. -6 .

[0088] The interleaving process uses block interleaving, and the interleaving matrix size is 64×64. Data is written in row order and read out in column order, which effectively breaks up burst errors. A pseudo-random sequence is introduced in the interleaving process to adjust the position and enhance the anti-interference performance. The interleaving depth matches the channel coherence time to ensure that the error after deinterleaving is within the decoding capability.

[0089] During the modulation selection process, the signal-to-noise ratio threshold is established based on the channel state information. When the signal-to-noise ratio is lower than 8dB, QPSK modulation is used; when the signal-to-noise ratio is in the range of 8-15dB, 16QAM modulation is used; when the signal-to-noise ratio is higher than 15dB, 64QAM modulation is used. The modulation constellation diagram uses Gray mapping to reduce the bit difference between adjacent code elements.

[0090] Pre-equalization technology is introduced in the modulation process to perform pre-distortion compensation on the constellation points. The pre-equalization parameters are obtained through channel estimation, and the compensation coefficients are updated every 1ms. For 64QAM modulation, the pre-distortion compensation intensity of the outer constellation points is higher than that of the inner constellation points to balance the error performance of different constellation points.

[0091] During the dynamic adjustment of coding efficiency, appropriate coding parameters are selected based on the channel quality index. When the channel conditions are good, the LDPC code rate is increased to 0.9, and the number of check bytes for RS coding is reduced to 12 bytes; when the channel conditions deteriorate, the LDPC code rate is reduced to 0.7, and the number of check bytes for RS coding is increased to 20 bytes. The adjustment period of the coding parameters matches the channel coherence time.

[0092] Through the above global coding and modulation processing, the distribution channel signal is effectively transmitted under the premise of ensuring a certain reliability, forming a globally optimized signal. This signal combines the advantages of error correction coding, interleaving protection and adaptive modulation, and can still maintain stable transmission performance under changing channel conditions.

[0093] This embodiment adopts a double-layer cascade coding structure and combines the advantages of RS coding and LDPC coding to significantly improve the error correction capability of the system. The design of the generating polynomial of RS coding on the GF(28) field enables the system to have the ability to correct 8 random errors and effectively deal with random interference during transmission. Quasi-cyclic LDPC coding adopts a (3,6) rule structure and cooperates with a serial decoding algorithm to ensure good error correction performance while ensuring high decoding efficiency. By introducing an interleaving matrix with a depth of 4 and a 64×64 block interleaving structure, burst errors are effectively scattered and the anti-interference performance of the system is enhanced. Combined with the position adjustment mechanism of the pseudo-random sequence, the system can still maintain stable transmission performance in the face of complex channel environments. The adaptive modulation scheme based on the signal-to-noise ratio threshold realizes dynamic switching of the modulation mode and can obtain the optimal transmission efficiency under different channel conditions. The introduction of pre-equalization technology effectively balances the error performance of different constellation points by pre-distortion compensation of constellation points. The dynamic coding efficiency adjustment mechanism enables the system to adaptively adjust the coding parameters according to the channel quality, maximizing the transmission efficiency while ensuring transmission reliability.

[0094] In one embodiment, a rate improvement algorithm is constructed based on the adjustment signal composition, and a transmission signal is rate adjusted to obtain an initial adjustment transmission signal, including: When performing multi-scale rate decomposition operations on the adjustment signal components, the empirical mode decomposition method is used to decompose the signal into several intrinsic mode functions (IMFs) and a residual signal. The decomposition process extracts the inherent vibration mode based on the local characteristic time scale of the signal, and decomposes the complex signal into IMF components with different characteristic scales. The upper and lower envelopes are obtained by performing cubic spline interpolation on all extreme points, and the average envelope is calculated by calculating the mean value. The average envelope is subtracted from the original signal to obtain the IMF candidate component. Repeat this process until the IMF condition is met, and the acquisition of the rate variation mode is completed.

[0095] When constructing the time-frequency joint distribution function based on the rate variation mode, the Hilbert transform is performed on each IMF component to obtain the analytical signal. The analytical signal is expressed as the product of the instantaneous amplitude and the instantaneous phase, and the instantaneous frequency is obtained by differentiating the instantaneous phase. The instantaneous amplitude and instantaneous frequency of all IMF components are combined into a two-dimensional matrix to form a rate time-frequency matrix. This matrix reflects the energy distribution characteristics of the signal at different times and frequencies.

[0096] In the process of envelope demodulation of the rate time-frequency matrix, the time-frequency matrix is ​​demodulated using the orthogonal modulation principle. The signal analysis expression is obtained through Hilbert transform, the instantaneous amplitude is extracted as the envelope feature, and the rate analysis sequence is reconstructed in combination with the phase information. This sequence retains the amplitude and phase characteristics of the original signal.

[0097] When performing fractional differential operations based on rate-analytic sequences, the Grünwald-Letnikov fractional differential definition is used. The sequence is operated based on the fractional differential operator, and the order selection range is between 0 and 1. The operation result reflects the non-integer derivative characteristics of the signal, and the rate derivative characteristics are obtained. This feature reflects the change trend and fluctuation law of the signal.

[0098] When the rate derivative characteristics are transformed into multidimensional trajectories, the one-dimensional time series is reconstructed into a multidimensional phase space trajectory. The appropriate embedding dimension and time delay are selected to construct the trajectory matrix. The trajectory matrix is ​​subjected to singular value decomposition, the principal component trajectory is extracted, and the rate trajectory data is obtained. This data reflects the dynamic characteristics of the system.

[0099] When constructing the rate compensation polynomial based on the rate trajectory data, the compensation polynomial takes the form of an n-order Chebyshev polynomial, where the value of n ranges from 3 to 8. The Chebyshev polynomial has the minimum and maximum value characteristics, which can effectively reduce the fitting error. Based on the distribution characteristics of the rate trajectory data, the Chebyshev polynomial coefficients are optimized. The optimization process uses the weighted least squares method, and the weight function is related to the reliability of the data point. The residual sum of squares is calculated in each iterative step, and the optimization is stopped when the residual is less than the preset threshold or the maximum number of iterations is reached. The obtained compensation basis function is determined by the optimized Chebyshev polynomial coefficients, and the function form is a linear combination of Chebyshev polynomials of each order. The compensation basis function contains the main characteristics of the rate trajectory data and shows good numerical stability in the interval [-1,1].

[0100] When the compensation basis function is optimized for nonlinear rate reconstruction, an iterative optimization algorithm based on gradient descent is constructed. The optimization objective function includes two parts: reconstruction error term and regularization term. The reconstruction error is in the form of mean square error, and the regularization term is in the form of L1 norm. In each iteration, the gradient of the objective function with respect to the optimization parameter is calculated, and the parameter value is updated according to the step size. The step size is selected by line search method to ensure that the objective function value can be reduced in each iteration. In order to avoid local optimal solution, momentum term is introduced in the optimization process to improve the convergence performance of the algorithm. At the same time, the early stopping criterion is set, and the optimization is stopped when the improvement of the objective function for multiple consecutive iterations is less than the given threshold. The final rate reconstruction function has good nonlinear fitting ability and can accurately describe the distortion characteristics of the signal. The reconstruction function shows a stable compensation effect under different working conditions, and the distortion of the compensated signal is significantly reduced. The form of the reconstruction function maintains the main characteristics of the compensation basis function, and has stronger adaptability and robustness.

[0101] When optimizing the parameters of the transmission signal based on the rate reconstruction function and the adjusted signal composition, the reconstruction function is applied to the original signal. The transmission parameters are adjusted to minimize the error between the reconstructed signal and the ideal signal. The optimization process considers the balance between signal integrity and system stability, and finally obtains the first adjusted transmission signal. This signal has better transmission characteristics.

[0102] This embodiment uses the empirical mode decomposition method to perform multi-scale rate decomposition operations, so that the signal can be decomposed into IMF components with different characteristic scales, thereby improving the ability to extract signal features. Based on the Hilbert transform, the time-frequency joint distribution function is constructed to achieve accurate expression of the time-frequency characteristics of the signal and enhance the accuracy of signal analysis. The envelope demodulation is performed using the orthogonal modulation principle, which retains the amplitude and phase characteristics of the signal and improves the integrity of signal reconstruction. The Grünwald-Letnikov fractional differential operation is used to effectively capture the non-integer derivative characteristics of the signal and enhance the ability to characterize the trend of signal changes. The system dynamics characteristics are extracted through multidimensional trajectory transformation, and the compensation basis function is constructed in combination with Chebyshev polynomials to achieve accurate compensation for signal distortion. The gradient descent iterative optimization algorithm is used for nonlinear rate reconstruction, which improves the adaptability and robustness of the reconstruction function, so that the signal can maintain a stable compensation effect under different working conditions, and finally obtains an adjustment signal with better transmission characteristics.

[0103] Reference Figure 2 As shown, the present invention also provides a transmission rate optimization device for a passive copper cable, which is applied to any one of the above-mentioned transmission rate optimization methods for a passive copper cable, comprising: The acquisition module is used to obtain the physical characteristic parameters of the passive copper cable, and to perform performance evaluation to obtain the benchmark transmission parameters; An analysis module, the analysis module is used to obtain the transmission signal of the passive copper cable, perform modulation optimization with the reference transmission parameters, and obtain an optimized transmission signal; The correlation module is used to obtain interference characteristic data of the passive copper cable, perform interference analysis on the optimized transmission signal, and obtain interference elimination results; A processing module, the processing module is used to perform rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter; A control module, the control module is used to perform transmission path detection on the interference feature data to obtain attenuation feature information, perform trend analysis on the attenuation feature information based on the interference elimination result, and obtain a rate attenuation prediction result; The execution module is used to optimize the transmission signal rate according to the initial rate parameters and the rate attenuation prediction result to obtain a global optimized signal.

[0104] The present invention provides a transmission rate optimization device for a passive copper cable. By acquiring the physical characteristic parameters of the passive copper cable and performing performance evaluation, the basic transmission capacity of the copper cable can be accurately grasped, and a reliable reference benchmark can be provided for subsequent transmission optimization. By optimizing the modulation of the transmission signal and combining the analysis of the interference characteristic data, the effective identification and elimination of various interferences in the transmission process are achieved, and the quality and stability of signal transmission are improved. By performing transmission path detection and attenuation characteristic analysis based on the interference characteristic data, the attenuation trend of the transmission rate can be accurately predicted, thereby realizing dynamic adjustment and optimization of the transmission rate. By performing a comprehensive rate evaluation on the optimized transmission signal and the interference elimination results, and performing global optimization in combination with the attenuation prediction results, the optimal configuration of the transmission rate is achieved, and the overall transmission performance of the passive copper cable is effectively improved. Through multi-dimensional parameter analysis and optimization strategies, the limitations of the traditional single optimization method are overcome, and a significant improvement in the transmission rate is achieved.

[0105] Reference Figure 3 As shown, the present invention also provides a transmission rate optimization device for a passive copper cable, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned methods for optimizing the transmission rate of a passive copper cable.

[0106] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0107] 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.

[0108] 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 the transmission rate of a passive copper cable, characterized in that: include: Obtain the physical characteristic parameters of the passive copper cable, conduct performance evaluation, and obtain benchmark transmission parameters; Acquire the transmission signal of the passive copper cable, and perform modulation optimization with the reference transmission parameter to obtain an optimized transmission signal; Obtaining interference characteristic data of the passive copper cable, performing interference analysis on the optimized transmission signal, and obtaining an interference elimination result; Performing rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter; Performing transmission path detection on the interference characteristic data to obtain attenuation characteristic information, and performing trend analysis on the attenuation characteristic information based on the interference elimination result to obtain a rate attenuation prediction result; The transmission signal is rate optimized according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimized signal.

2. The method for optimizing the transmission rate of a passive copper cable according to claim 1, characterized in that: The obtaining of the physical characteristic parameters of the passive copper cable and the performance evaluation to obtain the reference transmission parameters include: Performing end-to-end measurement on the passive copper cable to obtain a signal attenuation value, a crosstalk coefficient, and a line impedance; Performing frequency domain analysis on the passive copper cable according to the signal attenuation value to obtain a frequency domain characteristic curve; Performing noise analysis on the frequency domain characteristic curve according to the crosstalk coefficient to obtain noise distribution data; Performing channel capacity calculation on the noise distribution data according to the line impedance to obtain channel characteristic parameters; Performing fusion evaluation on the channel characteristic parameters and the frequency domain characteristic curve to obtain a comprehensive evaluation index; Performing transmission performance analysis on the passive copper cable according to the comprehensive evaluation index to obtain a performance reference value; Perform parameter iterative calculation on the performance reference value to obtain the benchmark transmission parameter.

3. The method for optimizing the transmission rate of a passive copper cable according to claim 1, characterized in that: The step of acquiring the transmission signal of the passive copper cable and performing modulation optimization with the reference transmission parameter to obtain an optimized transmission signal includes: Inputting detection signals of different frequencies to the passive copper cable, collecting returned signal waveform data, and obtaining the transmission signal; Performing signal-to-noise ratio calculation and bandwidth analysis on the transmission signal according to the reference transmission parameters to obtain a channel capacity parameter; Perform coding and modulation mapping on the transmission signal according to the channel capacity parameter to obtain an initial modulation parameter; Performing multi-concatenated coding on the initial modulation parameters to obtain channel adjustment coding; Perform carrier modulation processing on the transmission signal according to the channel adjustment code to obtain a modulated signal; Performing cyclic prefix insertion and frame synchronization processing on the modulated signal to obtain a frame synchronization signal; Performing equalizer parameter configuration on the frame synchronization signal to obtain equalization processing parameters; The frame synchronization signal is equalized and compensated according to the equalization processing parameters to obtain the optimized transmission signal.

4. The method for optimizing the transmission rate of a passive copper cable according to claim 1, characterized in that: The obtaining of interference characteristic data of the passive copper cable, performing interference analysis on the optimized transmission signal, and obtaining an interference elimination result includes: Performing channel spectrum scanning on the passive copper cable to obtain signal energy distribution data; Performing frequency band signal-to-noise ratio calculation on the signal energy distribution data to obtain frequency band signal-to-noise ratio data; Performing interference identification on the frequency band signal-to-noise ratio data to obtain interference feature data; Performing time domain sampling on the optimized transmission signal based on the interference characteristic data to obtain a sampling sequence; Preliminarily reconstructing the sampling sequence to obtain a preliminary denoised signal; Performing phase compensation on the optimized transmission signal according to the preliminary noise elimination signal to obtain a phase compensation result; An interference elimination operation is performed on the phase compensation result according to the preliminary noise elimination signal to obtain the interference elimination result.

5. The method for optimizing the transmission rate of a passive copper cable according to claim 1, characterized in that: The performing rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter includes: Performing waveform analysis on the optimized transmission signal to obtain signal characteristic data; Calculating the channel bandwidth utilization rate according to the signal characteristic data, and performing capacity calculation to obtain a theoretical transmission rate; Performing sampling residual interference measurement on the interference elimination result to obtain interference distribution data; Performing transmission section index evaluation based on the interference distribution data to obtain effective transmission quality parameters; Performing a coding bit error rate test on the optimized transmission signal to obtain a coding efficiency factor; Calculate the achievable rate parameter based on the theoretical transmission rate, the effective transmission quality parameter and the coding efficiency factor to obtain rate estimation data; Performing interval screening and abnormal correction on the rate estimation data to obtain a stable transmission rate value; Dynamically iteratively predict the stable transmission rate value to obtain the initial rate parameter.

6. The method for optimizing the transmission rate of a passive copper cable according to claim 1, characterized in that: The performing transmission path detection on the interference characteristic data to obtain attenuation characteristic information, and performing trend analysis on the attenuation characteristic information based on the interference elimination result to obtain a rate attenuation prediction result, includes: Performing dual analysis of the interference characteristic data in time domain and frequency domain to obtain interference time-frequency attributes; Performing transmission segmentation processing on the time-frequency attributes to obtain a segmented transmission structure; Performing signal propagation simulation on the segmented transmission structure to obtain a signal attenuation factor of each segment; Performing overall transmission attenuation analysis on the attenuation factors of the signals in each segment to obtain attenuation conditions; Correlation analysis is performed on the attenuation situation and the interference elimination result to obtain attenuation interference corresponding data; Performing trend deduction on the data corresponding to the attenuation interference to obtain an attenuation change function; Performing curve fitting and data extrapolation according to the attenuation change function to obtain an attenuation estimated value; Performing a critical comparison on the attenuation estimate according to a preset attenuation threshold to obtain a rate attenuation critical point; The rate attenuation prediction result is obtained by performing rate prediction according to the rate attenuation critical point and the attenuation estimated value.

7. The method for optimizing the transmission rate of a passive copper cable according to claim 1, characterized in that: The step of optimizing the transmission signal rate according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimization signal includes: Decomposing the transmission signal according to the initial rate parameter to obtain a multi-level signal component; Adjusting the coefficients of the multi-level signal components according to the rate attenuation prediction result to obtain an adjusted signal component; Constructing a rate improvement algorithm based on the adjustment signal composition, and performing rate adjustment on the transmission signal to obtain an initial adjustment transmission signal; Performing a multi-dimensional joint analysis on the initially adjusted transmission signal to obtain multi-dimensional feature data; Dynamically balancing the initially adjusted transmission signal according to the multi-dimensional feature data to obtain a balanced transmission signal; Performing multi-channel signal integration on the balanced transmission signal to obtain a composite signal; Performing transmission channel allocation according to the composite signal to obtain an allocation channel signal; Performing global coding modulation on the allocation channel signal to obtain a global optimization signal.

8. The method for optimizing the transmission rate of a passive copper cable according to claim 7, characterized in that: The constructing of a rate improvement algorithm based on the adjustment signal composition and rate adjusting the transmission signal to obtain an initial adjustment transmission signal includes: Performing a multi-scale rate decomposition operation on the adjustment signal composition to obtain a rate variation mode; Constructing a time-frequency joint distribution function according to the rate variation mode to obtain a rate time-frequency matrix; Performing envelope demodulation on the rate time-frequency matrix to obtain a rate analysis sequence; Performing fractional differential operation according to the rate analysis sequence to obtain rate derivative characteristics; Performing a multi-dimensional trajectory transformation on the rate derivative feature to obtain rate trajectory data; Constructing a rate compensation polynomial according to the rate trajectory data to obtain a compensation basis function; Performing nonlinear rate reconstruction optimization on the compensation basis function to obtain a rate reconstruction function; Parameters of the transmission signal are optimized according to the rate reconstruction function and the adjustment signal composition to obtain the initial adjustment transmission signal.

9. A transmission rate optimization device for a passive copper cable, characterized in that: The transmission rate optimization method for the passive copper cable according to any one of claims 1 to 8 comprises: An acquisition module, the acquisition module is used to obtain physical characteristic parameters of the passive copper cable, and perform performance evaluation to obtain reference transmission parameters; An analysis module, the analysis module is used to obtain the transmission signal of the passive copper cable, and perform modulation optimization with the reference transmission parameter to obtain an optimized transmission signal; An association module, the association module is used to obtain interference characteristic data of the passive copper cable, perform interference analysis on the optimized transmission signal, and obtain an interference elimination result; A processing module, the processing module is used to perform rate evaluation and analysis on the optimized transmission signal and the interference elimination result to obtain an initial rate parameter; A control module, the control module is used to perform transmission path detection on the interference characteristic data to obtain attenuation characteristic information, and perform trend analysis on the attenuation characteristic information based on the interference elimination result to obtain a rate attenuation prediction result; An execution module is used to optimize the rate of the transmission signal according to the initial rate parameter and the rate attenuation prediction result to obtain a global optimization signal.

10. A transmission rate optimization device for a passive copper cable, characterized in that: include: Memory, used to store programs; The processor is used to execute the program to implement the various steps of the transmission rate optimization method of a passive copper cable as described in any one of claims 1-8.

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