A method for rate calibration of pulse amplitude modulation signals and a computer storage medium

Abstract

A method for rate calibration of pulse amplitude modulation (PAM) signals and a computer storage medium are disclosed, belonging to the field of communication signal processing technology. The method includes: edge detection of the PAM signal based on all voltage thresholds, and determination of four sets of edge feature data; determination of a histogram based on the four sets of edge feature data; determination of the center value of the interval range corresponding to the initial rate as the initial search center, and determination of the first and second characteristic peaks based on the initial search center; determination of the termination boundary of the target feature region based on the number of edge widths contained between the center values ​​of the two characteristic peaks; determination of the starting boundary based on the termination boundary and the center value of the interval range of the first characteristic peak; determination of the midpoint of the target feature region as the target center value; determination of the estimated sampling point value based on the target center value and the single symbol period; and determination of the final rate of the PAM signal based on the estimated sampling point value and the sampling rate.

Description

Technical Field

[0001] This invention relates to the field of communication signal processing technology, specifically to a method for rate calibration of pulse amplitude modulation signals and a computer storage medium. Background Technology

[0002] With the rapid development of high-speed serial communication technology, pulse amplitude modulation (PAM) has become a key technology for improving data transmission bandwidth. In the reception and processing of PAM signals, accurate signal rate estimation is a crucial step in ensuring communication quality.

[0003] Currently, rate estimation for pulse amplitude modulation (PAM) signals typically employs a "two-step estimation" strategy: first, an inaccurate initial rate is obtained through preliminary detection; then, this initial rate is used as prior information for a second, more precise refinement process to obtain the accurate signal rate. However, in actual transmission, signals are often affected by severe jitter and inter-symbol interference (ISI), making accurate signal rate acquisition a significant challenge.

[0004] To address the aforementioned issues, existing research directions mainly fall into two categories: histogram statistical detection methods and data fitting methods.

[0005] In histogram statistical detection, the interval length of the detection edges is directly statistically analyzed to construct a histogram, and the characteristic peak value is used as the estimated rate. However, in practical applications, the peak value of the histogram is greatly affected by signal jitter, often resulting in offset and inaccurate estimation results. Furthermore, the accuracy of this method depends on the amount of data, which limits its application in high-speed real-time processing scenarios.

[0006] In the data fitting method, curve fitting is performed on the acquired edge intervals, and the estimated rate is determined by the fitted feature parameters. However, when the signal has large jitter and inter-symbol interference, the fitting model is easily affected by abnormal distributions, resulting in the output estimate usually being higher than the true rate, indicating a significant measurement bias.

[0007] Furthermore, existing technologies often ignore the differences caused by odd and even edges during the calculation process, making it difficult to balance the stability and accuracy of rate estimation in complex communication environments. Therefore, how to eliminate the effects of jitter, crosstalk, and hardware non-ideal factors and achieve a more accurate and adaptive signal rate calibration method is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0008] The main technical problem solved by this invention is to provide a rate calibration method for pulse amplitude modulation signals that can reduce signal jitter and inter-symbol interference.

[0009] According to a first aspect, one embodiment provides a rate calibration method for a pulse amplitude modulation signal, comprising:

[0010] The initial rate of the pulse amplitude modulation signal and a voltage judgment threshold group of the pulse amplitude modulation signal are obtained, wherein the voltage judgment threshold group includes at least one voltage judgment threshold.

[0011] The pulse amplitude modulation signal is edge-detected based on all voltage judgment thresholds in the voltage judgment threshold group to determine the edge time of each edge.

[0012] According to the timing sequence of the pulse amplitude modulation signal, each edge of the pulse amplitude modulation signal is divided into an odd edge group and an even edge group; the rising edge and falling edge of the odd edge group and the rising edge and falling edge of the even edge group are extracted respectively to determine four sets of edge feature data.

[0013] Calculate the edge difference between adjacent edges in each group of edge feature data, and determine the resolution interval based on the initial rate;

[0014] The edge width is determined based on the edge difference, the interval range is determined based on the edge width in the four sets of edge feature data and the resolution interval, and the histogram is determined based on the interval range and the number of edge widths in the four sets of edge feature data contained in each interval range.

[0015] The center value of the interval range corresponding to the initial rate is determined as the initial search center, and the first feature peak and the second feature peak are determined according to the preset search conditions based on the initial search center.

[0016] The termination boundary is determined based on the center value of the interval range of the first characteristic peak and the center value of the interval range of the second characteristic peak, wherein the termination boundary is the termination boundary of the target feature region;

[0017] The starting boundary of the target feature region is determined based on the termination boundary and the center value of the interval range of the first feature peak.

[0018] The midpoint of the target feature region is determined as the target center value based on the number of edge widths contained in each interval range of the target feature region.

[0019] The estimated value of the sampling point is determined based on the multiple relationship between the target center value and the single symbol period;

[0020] The final rate of the pulse amplitude modulation signal is determined based on the estimated sampling point value and the sampling rate of the pulse amplitude modulation signal.

[0021] In one embodiment, obtaining the initial rate of the pulse amplitude modulation signal includes:

[0022] For each voltage, a threshold is determined:

[0023] Edge detection is performed on each edge of the pulse amplitude modulation signal according to the voltage judgment threshold to determine the initial rising edge group and the initial falling edge group;

[0024] Initial target center value detection is performed on the initial rising edge group and the initial falling edge group respectively. The initial target center value detection includes:

[0025] Obtain the time difference between two adjacent edges to determine the initial edge difference value accordingly;

[0026] The initial edge width is determined based on the difference between each initial edge, and the initial interval range is determined based on each initial edge width and the preset initial resolution interval. The initial histogram is determined based on the initial interval range and the number of initial edge widths contained in each initial interval range.

[0027] Based on the range of each initial interval in the initial histogram from smallest to largest, an initial target feature region is determined in the initial histogram to represent the minimum time interval of the same-direction edge.

[0028] Based on the number of initial edge widths contained in each initial interval range of the initial target feature region, the midpoint of the initial target feature region is determined as the initial target center value;

[0029] Obtain the initial target center value of the initial rising edge group and the initial target center value of the initial falling edge group corresponding to each voltage judgment threshold;

[0030] The estimated values ​​of the initial sampling points are determined based on the multiple relationship between the initial target center values ​​and the single symbol period;

[0031] The initial rate of the pulse amplitude modulation signal is determined based on the estimated initial sampling point value and the sampling rate of the pulse amplitude modulation signal.

[0032] In one embodiment, obtaining the initial rate of the pulse amplitude modulation signal includes:

[0033] From the original sampled data of the pulse amplitude modulation signal, the transition time corresponding to each transition edge in the pulse amplitude modulation signal is obtained, and the first initial frequency is determined according to the time difference between adjacent transition times;

[0034] The number of sampling points for frequency domain analysis of the pulse amplitude modulation signal is determined based on the sampling rate of the pulse amplitude modulation signal and the first initial frequency, and the reference frequency for frequency domain analysis of the pulse amplitude modulation signal is determined based on the first initial frequency.

[0035] From the original sampled data, continuous data of length equal to the number of sample points is obtained as sampled data. The sampled data is then subjected to a nonlinear transformation to obtain nonlinear transformed data, and the nonlinear transformed data is converted into frequency domain amplitude spectrum data.

[0036] In the frequency domain amplitude spectrum data, peak retrieval is performed based on the reference frequency to determine a second initial frequency after correcting the first initial frequency;

[0037] Based on the second initial frequency, multiple candidate frequencies are determined, and for each candidate frequency:

[0038] The clock sequence number is determined based on the candidate frequency and the transition time, and the transition time is linearly fitted based on the clock sequence number to determine the fitting parameters accordingly; the initial rate of the pulse amplitude modulation signal is determined based on the fitting parameters of each candidate frequency.

[0039] In one embodiment, determining the edge width based on the edge difference and determining the interval range based on the edge width in the four sets of edge feature data and the resolution interval includes:

[0040] Obtain the sampling rate of the pulse amplitude modulation signal, and determine the edge width based on the sampling rate and the edge difference;

[0041] Obtain the maximum and minimum edge widths from four sets of edge feature data. Determine the maximum value of the histogram interval edge based on the maximum edge width, and determine the minimum value of the histogram interval edge based on the minimum edge width.

[0042] The range of each interval in the histogram is determined based on the minimum value of the histogram interval edge, the maximum value of the histogram interval edge, and the preset resolution interval.

[0043] In one embodiment, determining the first feature peak and the second feature peak based on the initial search center and preset search conditions includes:

[0044] A peak search is performed within a preset neighborhood range of the initial search center to determine the first feature peak.

[0045] The theoretical center value is determined based on the center value of the interval range of the first feature peak and a preset multiple; the first feature search range is determined based on the theoretical center value and a preset unilateral search length.

[0046] The maximum value of the number of edge widths contained in each interval within the first feature search range is the peak value of the second feature.

[0047] In one embodiment, determining the starting boundary of the target feature region based on the termination boundary and the center value of the interval range of the first feature peak includes:

[0048] The temporary starting boundary of the target feature region is determined based on the termination boundary and the center value of the interval range of the first feature peak.

[0049] The starting boundary of the target feature region is determined based on the temporary starting boundary and the minimum interval center value in the histogram.

[0050] In one embodiment, determining the starting boundary of the target feature region based on the temporary starting boundary and the minimum interval center value in the histogram includes:

[0051] When the temporary starting boundary is less than the minimum interval center value in the histogram, the minimum interval center value in the histogram is the starting boundary.

[0052] When the temporary starting boundary is greater than the minimum interval center value in the histogram, the sum of the number of edge widths contained in each interval between the temporary starting boundary and the ending boundary is calculated as a first sum value, and the sum of the number of edge widths contained in each interval between the minimum interval center value in the histogram and the temporary starting boundary is calculated as a second sum value; the starting boundary of the target feature region is determined based on the first sum value and the second sum value.

[0053] In one embodiment, determining the starting boundary of the target feature region based on the first sum value and the second sum value includes:

[0054] Determine whether the ratio of the second sum value to the first sum value exceeds a preset threshold; if it does not exceed the threshold, the minimum interval center value in the histogram is the starting boundary; if it exceeds the threshold, determine the maximum value of the number of edge widths contained in each interval between the minimum interval center value in the histogram and the temporary starting boundary as the third feature peak value, and determine the minimum value of the number of edge widths contained in each interval between the interval center value of the third feature peak value and the interval center value of the first feature peak value as the starting boundary of the target feature region.

[0055] In one embodiment, determining the midpoint of the target feature region as the target center value based on the number of edge widths contained in each interval range of the target feature region includes:

[0056] Calculate the sum of the number of edge widths contained in each interval range of the target feature region;

[0057] According to the order of the range of each interval in the target feature region from smallest to largest, the number of edge widths contained in each interval range is accumulated one by one to determine the dynamic accumulation value;

[0058] When the dynamic accumulated value is greater than or equal to half of the sum value for the first time, the accumulation stops; the center value of the interval range corresponding to the current dynamic accumulated value is determined as the target center value.

[0059] In one embodiment, determining the estimated value of the sampling point based on the multiple relationship between the target center value and the single symbol period includes:

[0060] The estimated value of the sampling point is determined based on the relationship between the target center value and the period of a single symbol, which is twice the ratio of the period of a single symbol.

[0061] According to a second aspect, one embodiment provides a computer-readable storage medium including a program that can be executed by a processor to perform the methods as described in any of the embodiments herein.

[0062] According to the above embodiment, a pulse amplitude modulation signal rate calibration method and computer storage medium are disclosed. The method first acquires the initial rate of the signal and performs edge detection on the signal based on each voltage threshold. Then, the captured edge data is decomposed into four independent sets of edge feature data according to odd / even timing and rising / falling edges. A statistical histogram is constructed by combining this with the resolution interval determined by the initial rate. Next, based on the initial rate, the first and second characteristic peaks are determined in the histogram. The termination boundary of the target feature region is determined using the first and second characteristic peaks, and the starting boundary is deduced from the termination boundary of the target feature region. Finally, the target center value is determined within the target feature region using a point-by-point accumulation and area bisector median algorithm. The target center value is then converted using the physical multiple relationship of the symbol period and the target center value to determine the estimated sampling point value. Finally, the calibration rate is calculated using the estimated sampling point value.

[0063] This application effectively isolates and cancels deviations caused by duty cycle distortion and channel asymmetry by dividing the timing into even and odd times and rising / falling edges, greatly improving the accuracy of the calibration reference. Furthermore, this application uses the initial rate to determine the resolution interval, thereby controlling the unit interval of the histogram and avoiding the waste of computational power caused by blind statistics. Secondly, by using the termination boundary determined by the first and second characteristic peaks, and the starting boundary determined by the termination boundary, it avoids the false peak interference generated by high-order long consecutive codes in the histogram. Finally, the median area bisector algorithm is used to find the target center value of the target feature region, which effectively removes random noise caused by the limited sampling rate of the test instrument, resulting in higher precision and stability of the final acquired signal rate. Attached Figure Description

[0064] Figure 1 This is a flowchart of a rate calibration method for a pulse amplitude modulation signal in one embodiment;

[0065] Figure 2 This is a flowchart of step S1 in the first method of a pulse amplitude modulation signal rate calibration method in one embodiment;

[0066] Figure 3 This is a flowchart of step S13 in the first method of a pulse amplitude modulation signal rate calibration method in one embodiment;

[0067] Figure 4 This is a flowchart of step S133 in the first method of a pulse amplitude modulation signal rate calibration method in one embodiment;

[0068] Figure 5 This is a flowchart of step S135 in the first method of a pulse amplitude modulation signal rate calibration method in one embodiment;

[0069] Figure 6 This is a flowchart of step S1, the second method in a pulse amplitude modulation signal rate calibration method in one embodiment;

[0070] Figure 7 This is a flowchart of step S10 in the second method of the pulse amplitude modulation signal rate calibration method in one embodiment;

[0071] Figure 8 This is a flowchart of step S104 in the second method of the pulse amplitude modulation signal rate calibration method in one embodiment;

[0072] Figure 9 This is a flowchart of step S12 in the second method of the pulse amplitude modulation signal rate calibration method in one embodiment;

[0073] Figure 10 This is a flowchart of step S18 in the second method of the pulse amplitude modulation signal rate calibration method in one embodiment;

[0074] Figure 11 This is a flowchart of step S5 in a pulse amplitude modulation signal rate calibration method in one embodiment;

[0075] Figure 12 This is a flowchart of step S6 in a pulse amplitude modulation signal rate calibration method in one embodiment;

[0076] Figure 13 The waveform diagram of PAM4 is shown in one specific implementation.

[0077] Figure 14 The histogram corresponding to PAM4;

[0078] Figure 15 The waveform diagram of PAM5 is shown in another specific embodiment;

[0079] Figure 16 This is the histogram corresponding to PAM5. Detailed Implementation

[0080] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0081] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0082] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this invention, unless otherwise specified, include both direct and indirect connections (linkages).

[0083] This application targets pulse amplitude modulation (PAM) signals, which are continuously varying voltage waveforms in the time domain. PAM signals in this application encompass basic two-level signals (i.e., binary pulse amplitude modulation, PAM2 / NRZ signals) and higher-order multi-level PAM signals (such as PAM3, PAM4, and PAM5 signals). From a physical level mapping logic perspective, PAM signals are divided into multiple different stepped voltage levels in the vertical voltage dimension. Specifically, for binary NRZ signals, only high and low voltage states are defined in the vertical voltage dimension, and the waveform switches between these two levels according to binary logic (0 or 1). For higher-order multi-level PAM signals, three or more stepped voltage levels are defined in the vertical voltage dimension. Taking PAM4 as an example, when transmitting data, the waveform presents four different high and low voltage levels in space based on its four different logic state combinations (00, 01, 10, 11).

[0084] Whether it's an NRZ signal or a high-order multi-level PAM signal, when the signal switches between different logic states, the voltage waveform will jump up and down between its corresponding level layers, thus forming alternating rising and falling edge waveforms on the time axis. The method provided in this application determines the transmission rate of various pulse amplitude modulation signals by accurately capturing the time-domain characteristics of these transition edges, which will be described in detail below.

[0085] Please refer to Figure 1 In one embodiment, this application provides a rate calibration method for a pulse amplitude modulation signal, comprising the following steps S1-S8.

[0086] Step S1: Obtain the initial rate of the pulse amplitude modulation signal and the voltage judgment threshold group.

[0087] In one embodiment, the pulse amplitude modulation signal has multiple different discrete voltage levels during transmission. Let the number of pulse amplitude modulation signal levels be M (M is a positive integer greater than or equal to 2). In order to completely distinguish these M different levels, there are M-1 voltage judgment thresholds between adjacent levels, and the M-1 voltage judgment thresholds constitute a voltage judgment threshold group.

[0088] It should be noted that when determining the voltage judgment threshold based on the pulse amplitude modulation signal, a pulse amplitude modulation signal of a preset length is first acquired, and the voltage values ​​of all sampling points in this signal are extracted. The voltage values ​​of all sampling points of this preset length pulse amplitude modulation signal are then statistically analyzed on the voltage axis. Because the waveform of the pulse amplitude modulation signal has the longest dwell time at each standard voltage level (in PAM4 as an example, the standard voltage levels are the discrete voltage levels corresponding to "00", "01", "10", and "11") during transmission, the voltage positions corresponding to each standard voltage level contain the largest number of sampling points, thus forming multiple distinct peak values ​​in the amplitude distribution. The voltage value corresponding to each peak value represents a discrete voltage level. The average of the voltage values ​​corresponding to two adjacent peak values ​​is then calculated to obtain the voltage judgment threshold.

[0089] In one embodiment, this application can employ various methods to obtain the initial rate of the pulse amplitude modulation signal, and these methods are described below.

[0090] Please refer to Figure 2 The first method performs the following steps S11-S17 when obtaining the initial rate of the pulse amplitude modulation signal.

[0091] Step S11: Perform edge detection on each edge in the pulse amplitude modulation signal according to the voltage judgment threshold to determine the initial rising edge group and the initial falling edge group.

[0092] In one embodiment, for each voltage judgment threshold in the voltage judgment threshold group, when the voltage amplitude in the waveform of the pulse amplitude modulation signal crosses the corresponding voltage judgment threshold, it indicates that a transition edge has been captured. At this time, the precise time point at which the transition edge crosses the corresponding voltage judgment threshold is recorded, and this is used as the initial edge time of the transition edge. Simultaneously, the voltage direction of the transition edge is used to determine whether it is an initial rising edge group or an initial falling edge group. That is, when the voltage amplitude of the transition edge changes from below the voltage judgment threshold to above the voltage judgment threshold, then the transition edge is determined to be in the initial rising edge group; when the voltage amplitude of the transition edge changes from above the voltage judgment threshold to below the voltage judgment threshold, then the transition edge is determined to be in the initial falling edge group.

[0093] Step S13: Detect the target center value for the initial rising edge group and the initial falling edge group respectively.

[0094] Please refer to Figure 3 In one embodiment, when performing initial target center value detection for the initial rising edge group and the initial falling edge group in step S13, the following steps S131-S137 are executed.

[0095] Step S131: Obtain the time difference between two adjacent edges to determine the initial edge difference value accordingly.

[0096] In one embodiment, due to jitter and crosstalk during signal transmission, the initial edge time in a single direction often contains asymmetric phase deviation. Therefore, in the first method for obtaining the initial rate of the pulse amplitude modulation signal, not all edges are mixed; instead, the target center value is detected separately for the rising edge group and the falling edge group.

[0097] In one embodiment, taking the processing of the initial rising edge group as an example (the processing of the initial falling edge is similar), assuming that after edge detection, there are a total of T initial edge times (T is a positive integer greater than 1) extracted from the pulse amplitude modulation signal waveform and classified into the initial rising edge group, they are arranged sequentially on the time axis as: t1, t2, ..., t t , ..., t T Subtracting the time of the preceding initial edge from the time of the next initial edge on the timeline yields the corresponding initial edge difference. The specific mathematical formula is as follows:

[0098]

[0099] Where t represents the sequence number of the initial edge time, and 2≤t≤T, This represents the calculated initial edge difference value of the kth edge.

[0100] By subtracting adjacent data within the initial rising edge group or the initial falling edge group, T-1 initial edge differences will be calculated for a sequence containing T initial edge times.

[0101] Step S133: Determine the initial edge width according to the difference between each initial edge, determine the initial interval range according to each initial edge width and the preset initial resolution interval, and determine the initial histogram according to the initial interval range and the number of initial edge widths contained in each initial interval range.

[0102] Please refer to Figure 4 In one embodiment, when performing step S133 to determine the initial edge width based on the difference between each initial edge, and to determine the initial interval range based on each initial edge width and the preset initial resolution interval, and to determine the initial histogram based on the initial interval range and the number of initial edge widths contained in each initial interval range, the following steps S1331-S1333 are executed.

[0103] Step S1331: Determine the initial edge width based on the sampling rate and the initial edge difference.

[0104] In one embodiment, since the initial edge difference is a physical quantity with absolute time units, it needs to be converted into a relative quantity in terms of the number of sampling points for easy statistical processing by the digital signal processing system. Therefore, the sampling rate of the pulse amplitude modulation signal needs to be obtained, and multiplying each initial edge difference by the sampling rate yields the corresponding initial edge width. Thus, the initial edge width in this application numerically represents the number of signal sampling points contained between two adjacent edges of the same polarity, thereby eliminating the limitation of absolute time.

[0105] Step S1333: Determine the initial interval range of each interval in the initial histogram based on the minimum value of the initial histogram interval edge, the maximum value of the initial histogram interval edge, and the preset initial resolution interval.

[0106] In one embodiment, the maximum and minimum edge widths among the initial edge widths are obtained. The maximum value of the edge of the initial histogram interval is determined based on the maximum edge width, which is the rightmost boundary of the horizontal axis of the initial histogram. The minimum value of the edge of the initial histogram interval is determined based on the minimum edge width, which is the leftmost boundary of the horizontal axis of the initial histogram.

[0107] In one embodiment, a preset initial resolution interval is obtained, which serves as the unit width or step size of the initial interval range on the horizontal axis of the initial histogram. Starting from the minimum value at the interval edge, the values ​​are accumulated with the initial resolution interval as a fixed step size until the maximum value at the interval edge is reached, thereby dividing multiple initial interval ranges. Simultaneously, the median value of each initial interval edge value is calculated as the center value of the corresponding initial interval range.

[0108] In one embodiment, after determining the range of each initial interval, all calculated initial edge widths are iterated, and each initial edge width is compared with the defined edge values ​​of the initial intervals to determine which initial edge width belongs to its corresponding initial interval range. The number of initial edge widths falling within each initial interval range is counted. Thus, an initial histogram is determined based on the initial interval range and the number of initial edge widths contained in each initial interval, constructing an initial histogram with the initial interval edge values ​​or center values ​​as the horizontal axis and the statistical count of the corresponding initial edge widths within each interval as the vertical axis.

[0109] Step S135: Determine the initial target feature region in the initial histogram to represent the minimum time interval of the same-direction edge.

[0110] Please refer to Figure 5 In one embodiment, when performing step S135 to determine the initial target feature region in the initial histogram for representing the minimum time interval of the same-direction edge, the following steps S1351-S1357 are executed.

[0111] Step S1351: Determine the first extreme point that satisfies the preset peak condition in the initial histogram as the first peak value.

[0112] In one embodiment, after determining the initial histogram representing the rate distribution of the pulse amplitude modulation signal, due to inter-symbol interference and consecutive code phenomena in the signal, the initial histogram often presents multiple peak points of varying sizes. The absolute maximum value (i.e., the global peak) with the largest number of statistical occurrences in the initial histogram often physically corresponds to a unit interval width of 2, 3, or even higher, rather than the minimum reference interval that truly represents a single symbol period. Therefore, in order to determine the minimum reference interval for a single symbol period, it is necessary to determine the initial target feature region in the initial histogram to characterize the minimum reference interval for the same-direction edge, according to the ascending order of the initial interval ranges in the initial histogram.

[0113] It should be noted that the rate of the pulse amplitude modulation signal is determined by the single symbol period. In this application, the pulse amplitude modulation signal is grouped into rising edge groups and falling edge groups, constructing an initial histogram of the same-direction edges. Therefore, based on the symmetry of the waveform transition, there is at least one complete alternation of positive and negative pulses between the same-direction edges. Thus, the same-direction waveform transition representing a single symbol period corresponds to twice the unit interval in the time domain. That is, in the initial histogram of the same-direction edges, twice the unit interval is the minimum reference interval constituting the entire signal transition, and other peaks appearing on the right side of the initial histogram (such as three times the unit interval, four times the unit interval, etc.) are essentially integer multiples of this minimum reference interval. Therefore, this application needs to determine the initial target feature region based on the minimum reference interval in order to eliminate false peak interference caused by multiple periods due to continuous identical code patterns at the source, thereby enabling a more accurate calculation of the pulse amplitude modulation signal rate.

[0114] Specifically, the initial histogram is first scanned to obtain the candidate peak value (the candidate peak value is the point with the most statistical count in the entire initial histogram, which usually represents a certain edge transition interval that occurs most frequently in the signal), and the center value of the initial interval range corresponding to the candidate peak value is obtained as the candidate center value.

[0115] Since we need to find the first valid peak in ascending order, i.e., the leftmost valid peak, we need to use the initial candidate center value as a benchmark and then move to a smaller initial interval (i.e., to the left) to determine if there are any ignored feature peaks. Therefore, we need to multiply the candidate center value by a preset scaling factor (the preset scaling factor is less than 1) to determine the test center value.

[0116] After determining the test center value, obtain the smallest initial interval range center value in the initial histogram (i.e., the leftmost starting point of the horizontal axis). If the test center value is greater than the smallest initial interval range center value in the initial histogram, it means that there is enough space to the left of the candidate center value to accommodate another smaller peak value, so it is necessary to search the left boundary.

[0117] When searching for the left boundary, the center value of the smallest initial interval range in the initial histogram is first determined as the left boundary of the target peak search range. To identify any potential small peaks on the left, the center value of the initial interval range offset to the right by a preset number of initial interval ranges is determined as the right boundary of the target peak search range. Thus, a target peak search range is defined in the left region of the initial histogram.

[0118] After defining the target peak search range, it is determined whether there exists a target peak within the search range that is greater than a preset threshold for the candidate peaks. This preset threshold is typically set to half the value of the candidate peaks. If it exists, the target peak is the first peak, indicating that to the left of the absolute maximum value, there is indeed a small but sufficiently significant feature peak (greater than half the maximum value), which truly represents the minimum time interval of the same-direction edge. If it does not exist, the candidate peak is the first peak, indicating that the left side consists entirely of random noise or useless data, and the first peak itself is the first valid peak. The center value of the initial interval range corresponding to the first peak is the center value of the first interval.

[0119] Step S1353: Determine the first search range based on the initial interval center value of the first peak value, and determine the second peak value within the first search range.

[0120] In one embodiment, the initial interval center value of the first peak is multiplied by a preset factor to calculate a theoretical signal jump aggregation point in a longer time interval region on the horizontal axis, which is then used as the theoretical center value. To accommodate phase shifts caused by signal jitter, a preset single-sided search length is extended to the left and right, respectively, with the theoretical center value as the geometric center, thereby determining the left and right boundaries of the first search range. In the actual horizontal axis coordinates of the initial histogram, the actual initial interval range closest to the calculated left and right boundaries is determined as the actual left and right boundaries, thus forming the first search range.

[0121] In one embodiment, after determining the first search range, a maximum value scan is performed within the first search range. The statistical point with the largest number of initial edge widths contained within the first search range is taken as the second peak value, and the center value of the initial interval range corresponding to the second peak value is recorded as the center value of the second interval.

[0122] Step S1355: Determine the second search range based on the first peak and the second peak, and determine the valley center value by the minimum number of initial edge widths contained in each initial interval range within the second search range.

[0123] In one embodiment, between the first peak and the second peak, there must exist a relatively sparse point caused by the alternating transitions of the signal state. This relatively sparse point corresponds to the valley, which physically usually corresponds to the signal decision boundary. Therefore, the initial interval range between the center value of the initial interval range corresponding to the first peak (i.e., the center value of the first interval) and the center value of the initial interval range corresponding to the second peak (i.e., the center value of the second interval) is set as the second search range. After defining the second search range, the minimum value of the number of initial edge widths contained in each interval within this range is searched, and the center value of the initial interval range corresponding to this minimum value is determined as the valley center value (i.e., the center value of the third interval).

[0124] It should be noted that if the number of minimum initial edge widths within the second search range is equal to 1, meaning there is only one unique minimum initial edge width within the second search range, then the position corresponding to this minimum value is directly determined as the valley center value. If, due to data flatness or noise interference, the number of minimum initial edge widths within the second search range is greater than 1, meaning there are multiple identical minimum initial edge widths within the second search range, then the initial interval range corresponding to all minimum initial edge widths is obtained, and the geometric center point of the initial interval range corresponding to all minimum initial edge widths is determined as the valley center value. In other words, the minimum value in the middle among all identical minimum values ​​is selected, and its corresponding initial interval range center value is recorded as the valley center value.

[0125] Step S1357: Determine the region between the smallest initial interval center value and the valley center value in the initial histogram as the initial target feature region.

[0126] In one embodiment, after determining the valley center value, the region between the smallest initial interval center value in the initial histogram and the valley center value is defined as the initial target feature region. Specifically, the smallest initial interval center value on the horizontal axis of the initial histogram is directly retrieved as the left boundary, and the calculated valley center value is used as the right boundary. The initial target feature region is determined between these two boundaries. Because the right boundary is blocked by the valley, the false peaks and large-cycle interference data caused by consecutive codes on the right side are isolated, while the area between the left boundary and the valley completely encompasses all the data of the most basic single-symbol period. Therefore, a more accurate signal rate can be calculated.

[0127] Step S137: Determine the midpoint of the initial target feature region as the initial target center value based on the number of initial edge widths contained in each initial interval range of the initial target feature region.

[0128] In one embodiment, all initial histogram data contained within the initial target feature region are acquired, and the initial sum of the number of initial edge widths contained in each initial interval range within the initial target feature region is calculated. This initial sum represents the total number of samples within the initial target feature region. Following the ascending order of the initial interval ranges within the initial target feature region—that is, starting from the minimum center value of the left boundary interval of the horizontal axis of the initial target feature region and traversing to the right—the number of initial edge widths contained in each initial interval range is accumulated to determine the initial dynamic accumulation value. During the accumulation process, the initial dynamic accumulation value is compared with half of the initial sum value in real time using a threshold comparison. Accumulation stops when the initial dynamic accumulation value is first greater than or equal to half of the initial sum value. At this point, the center value of the initial interval range corresponding to the current initial dynamic accumulation value is determined as the initial target center value.

[0129] It should be noted that, since the voltage judgment threshold group may contain at least one voltage judgment threshold, the target center value is determined for each voltage judgment threshold. After the target center value of each voltage judgment threshold is determined, the voltage judgment threshold is updated to the remaining voltage judgment thresholds in the voltage judgment threshold group. The process returns to step S11 based on the updated voltage judgment threshold until the target center value of all voltage judgment thresholds in the voltage judgment threshold group has been determined.

[0130] Step S15: Determine the estimated value of the initial sampling point based on the multiple relationship between the initial target center value and the single symbol period.

[0131] In one embodiment, after solving for the corresponding initial target center value for each voltage judgment threshold and the initial rising edge group and the initial falling edge group, it is necessary to fuse the local features of the rate corresponding to these values ​​into a global feature of the rate.

[0132] Specifically, a baseline center value is first determined from the multiple initial target center values ​​obtained. This can be achieved by comparing the calculated initial target center values ​​and selecting the minimum as the baseline center value. Alternatively, the average of all obtained initial target center values ​​can be calculated and used as the baseline center value. Regardless of the method used to obtain the baseline center value, the absolute difference between each initial target center value and the baseline center value is calculated. If the absolute difference corresponding to a certain initial target center value is determined to be greater than the product of the baseline center value and the preset tolerance ratio, it means that a temporal abrupt change has occurred at that point. Therefore, the corresponding initial target center value needs to be identified as an outlier and removed from the calculation of the sample point estimation value.

[0133] After removing outliers, to improve the stability and noise resistance of the final estimation results, a statistical average is calculated for all remaining valid initial target center values. This effectively offsets the edge asymmetric offset caused by inter-symbol interference, achieving smooth noise reduction of time-domain statistical data.

[0134] Since the initial histogram counts the spacing between edges in the same direction (such as from rising edge to the next rising edge, or from falling edge to the next falling edge), in the most basic alternating transition pattern, there must be at least two symbols between two edges in the same direction. Therefore, the initial target center value physically represents the number of sampling points corresponding to twice the single-symbol period. Thus, the average of the effective initial target center values ​​needs to be divided by the multiplier factor 2 to determine the estimated number of initial sampling points contained in a single symbol period.

[0135] Step S17: Determine the initial rate of the pulse amplitude modulation signal based on the initial sampling point estimate and the sampling rate of the pulse amplitude modulation signal.

[0136] In one embodiment, the sampling rate and initial sampling point estimate of the pulse amplitude modulation signal are obtained. Based on the principle of time-domain to frequency-domain conversion, the absolute time length of a single symbol period can be represented by dividing the initial sampling point estimate by the sampling rate. Furthermore, the signal rate in a communication system is physically equal to the reciprocal of the absolute time length of a single symbol period. Therefore, dividing the sampling rate of the pulse amplitude modulation signal by the initial sampling point estimate yields the initial rate of the pulse amplitude modulation signal.

[0137] Please refer to Figure 6 The second method executes the following steps S10-S18 when obtaining the initial rate of the pulse amplitude modulation signal.

[0138] Step S10: Obtain the first initial frequency.

[0139] Please refer to Figure 7 In one embodiment, when performing step S10 to obtain the first initial frequency, the following steps S102-S104 are executed.

[0140] Step S102: Determine the initial histogram of the pulse amplitude modulation signal.

[0141] In one embodiment, edge detection is performed on the raw sampled data of the input pulse amplitude modulation signal to obtain the transition times corresponding to all transition edges. The transition times are physically arranged in ascending order, and the time difference between adjacent transition times is calculated. The sampling rate of the pulse amplitude modulation signal is obtained, and the time difference between each adjacent transition time is multiplied by the sampling rate to convert the absolute time into an initial edge width in units of the number of sampling points. The maximum and minimum values ​​of each initial edge width are obtained to determine the rightmost and leftmost boundaries of the initial histogram's horizontal axis. A preset initial resolution interval is obtained, and an initial histogram is constructed based on the initial resolution interval, the rightmost boundary of the initial histogram's horizontal axis, and the leftmost boundary of the initial histogram's horizontal axis.

[0142] It should be noted that the method for determining the initial histogram in the second method is similar to that in the first method. The difference lies in that the first method requires distinguishing between the initial rising edge group and the initial falling edge group, while the second method does not. Regardless of whether adjacent transition edges are rising or falling, it is only necessary to obtain the time difference between adjacent transition times to determine the initial histogram in the second method. Based on this, the specific steps for determining the initial histogram in the second method will not be repeated here; refer to the first method. The same terms in both methods represent the same meaning; only the numerical values ​​corresponding to each term differ between the two methods.

[0143] Step S104: Determine the first initial frequency in the initial histogram.

[0144] Please refer to Figure 8 In one embodiment, when performing step S104 to determine the first initial frequency in the initial histogram, the following steps S1042-S1048 are also performed.

[0145] Step S1042: Obtain the center value of the initial interval range corresponding to the maximum number of initial edge widths contained in each initial interval range in the initial histogram as the initial search value.

[0146] In one embodiment, the initial histogram is scanned to obtain the maximum value of the number of initial edge widths contained in each initial interval range of the initial histogram, and the center value of the initial interval range corresponding to the maximum value is the initial search value P1.

[0147] Step S1044: Determine the data filtering interval within the preset offset range, centered on the initial search value.

[0148] In one embodiment, with the initial search value P1 as the center of the time domain axis, M1 initial interval ranges of the initial histogram are selected on both the left and right sides. All these selected initial interval ranges together constitute a data filtering interval, namely [P1-M1, P1+M1].

[0149] Step S1046: Sum the number of initial edge widths of each initial interval range and the number of initial interval ranges in the preset neighborhood range of each initial interval range respectively, so as to determine the total initial edge width of each initial interval range.

[0150] In one embodiment, within the data filtering interval, the sum of the number of initial edge widths contained in each initial interval range and each of the M2 initial interval ranges to its left and right is calculated, thereby determining the total initial edge width of each initial interval range.

[0151] Step S1048: Determine the first initial frequency based on the largest total initial edge width among the total initial edge widths of each initial interval range.

[0152] In one embodiment, the range of the initial interval with the largest total width of the initial edge of the data filtering interval is taken as the first initial period. Then, by using the reciprocal mapping relationship in the time-frequency domain, the sampling rate is divided by the first initial period to calculate and determine the first initial frequency.

[0153] Step S12: Determine the number of sampling points for frequency domain analysis of the pulse amplitude modulation signal based on the sampling rate and the first initial frequency, and determine the reference frequency for frequency domain analysis of the pulse amplitude modulation signal based on the first initial frequency.

[0154] Please refer to Figure 9 In one embodiment, when performing step S12 to determine the number of sampling points for frequency domain analysis of the pulse amplitude modulation signal based on the sampling rate of the pulse amplitude modulation signal and the first initial frequency, and to determine the reference frequency for frequency domain analysis of the pulse amplitude modulation signal based on the first initial frequency, the following steps S122-S124 are also performed.

[0155] Step S122: Determine the number of sampling points for frequency domain analysis of the pulse amplitude modulation signal based on the number of sampling points in a single unit interval and the total number of unit intervals.

[0156] In one embodiment, the total number of unit intervals for frequency domain analysis is obtained from a preset pulse amplitude modulation signal. Based on the time-frequency mapping principle, the specific calculation method for the number of sampling points corresponding to a single unit interval is as follows: dividing the fixed sampling rate by the coarsely measured first initial frequency yields the number of sampling points per unit interval. Multiplying the number of sampling points per unit interval by the total number of unit intervals determines the number of sampling points for frequency domain analysis.

[0157] The specific formula is as follows:

[0158]

[0159] Where N represents the number of sampling points in the frequency domain analysis, The number of sampling points per unit interval is represented by L, which represents the total number of unit intervals when performing frequency domain analysis on the pulse amplitude modulation signal. fs represents the sampling rate, and f1 represents the first initial frequency.

[0160] Step S124: Determine the reference frequency based on the first initial frequency and the screening threshold.

[0161] In one embodiment, a preset screening threshold for frequency analysis of the pulse amplitude modulation signal is obtained, and the first initial frequency measured coarsely is multiplied by the screening threshold to determine the reference frequency.

[0162] Step S14: Obtain continuous data with a length equal to the number of sampling points from the original sampling data as sampling data, perform nonlinear transformation on the sampling data to obtain nonlinear transformed data, and convert the nonlinear transformed data into frequency domain amplitude spectrum data.

[0163] In one embodiment, a continuous data segment with a length equal to the number of sampling points is obtained from the original sampling data as the sampling data. Since the captured waveform data is a completely continuous segment without time discontinuity, its time domain length corresponds exactly to the preset unit interval. This ensures that the frequency resolution can accurately match the sampling data when performing Fourier transform, thereby improving the accuracy of pulse amplitude modulation signal rate acquisition.

[0164] In one implementation, after acquiring continuous sampled data of a length equal to the number of sampling points, a spectral transform is not performed directly. Instead, each data value in the sampled data is squared. After squaring, a Fast Fourier Transform (FFT) is used to transform the squared nonlinear data to the frequency domain, obtaining complex spectral data. Subsequently, the modulus (i.e., the absolute value) of the complex spectral data is calculated to obtain frequency domain amplitude spectrum data. The horizontal axis of the amplitude spectrum data corresponds to frequency, and the vertical axis corresponds to the amplitude of each frequency.

[0165] Step S16: In the frequency domain amplitude spectrum data, perform peak retrieval based on the reference frequency to determine the second initial frequency after correcting the first initial frequency.

[0166] In one embodiment, the generated frequency domain amplitude spectrum data is filtered and scanned, that is, a set of candidate spectrum data with frequencies greater than the reference frequency is obtained from the frequency domain amplitude spectrum data. After locking the candidate spectrum data set, the vertical axis amplitude values ​​of all frequency points in the set are compared, and the frequency corresponding to the maximum amplitude value is determined as the second initial frequency. This second initial frequency is used as the frequency after fine correction of the first initial frequency obtained from the coarse time-domain measurement.

[0167] Step S18: Determine multiple candidate frequencies based on the second initial frequency, determine the clock sequence number based on the candidate frequencies and the transition time, and perform linear fitting on the transition time based on the clock sequence number to determine the fitting parameters accordingly; determine the rate of the pulse amplitude modulation signal based on the fitting parameters of each candidate frequency.

[0168] Please refer to Figure 10 In one embodiment, when performing step S18 to determine multiple candidate frequencies based on the second initial frequency, determine the clock sequence number based on the candidate frequencies and the transition time, and perform linear fitting on the transition time based on the clock sequence number to determine the fitting parameters accordingly; and when determining the rate of the pulse amplitude modulation signal based on the fitting parameters of each candidate frequency, the following steps S182-S186 are also performed.

[0169] Step S182: Calculate candidate frequencies.

[0170] In one embodiment, a set of scaling coefficients for frequency domain analysis of a preset pulse amplitude modulation signal is obtained, wherein the set of scaling coefficients includes at least two different scaling coefficients. For each scaling coefficient: the second initial frequency is multiplied by the scaling coefficient in the current loop to determine a finely perturbed scaling frequency, which is then used as a candidate frequency.

[0171] Step S184: Determine the clock sequence number based on the candidate frequency and the transition time, and perform least squares fitting on the transition time based on the clock sequence number to determine the slope and variance as fitting parameters.

[0172] In one embodiment, assuming the current candidate frequency is the actual signal rate, it is necessary to reverse-engineer the clock cycle corresponding to that candidate frequency to determine which transition edge occurs on. Therefore, each transition moment needs to be multiplied by the current candidate frequency, and then the product is rounded down to obtain the clock sequence number of each transition edge under the current candidate clock cycle.

[0173] It should be noted that the clock corresponding to the candidate frequency is physically equivalent to a time scale with a standard clock cycle, each cycle of which constitutes a discrete clock scale on the time axis. Due to limitations imposed by channel noise and sampling jitter in actual testing, the transition moments captured from the pulse amplitude modulation signal are typically a set of discrete time data with bias. To map these discrete transition moments to a specific clock beat, this application maps each transition moment onto the clock axis corresponding to the candidate frequency. By multiplying the transition moment by the candidate frequency and rounding, it is possible to deduce which cycle of the clock corresponding to the candidate frequency each actual transition edge falls on, i.e., which clock scale, thus determining the clock sequence number corresponding to each transition moment.

[0174] In one embodiment, after determining the one-to-one correspondence between the clock sequence number and the jump time, ideally, the two should present a linear proportional relationship. In this case, the clock sequence number is used as the independent variable and the jump time is used as the dependent variable. The two variables are then fed into the least squares linear regression model for approximation fitting, thereby solving and recording the slope and variance values ​​obtained from the fitting as fitting parameters under the current candidate frequency.

[0175] It's important to note that when determining the fitting parameters, it's necessary to check if each scaling factor has a corresponding fitting parameter. If the current scaling factor doesn't have a corresponding fitting parameter—meaning the current loop branch calculation failed or didn't generate a valid slope and variance—then the current scaling factor is updated with the remaining scaling factors in the scaling factor group, and the fitting parameters are recalculated based on the updated scaling factors. This allows skipping failed scaling factors and refitting using valid scaling factors until all valid scaling factors successfully output their corresponding slopes and variances.

[0176] Step S186: Determine the rate of the pulse amplitude modulation signal based on the fitting parameters of the candidate frequency.

[0177] In one embodiment, after all scaling coefficients are included in the calculation, the slope corresponding to the smallest variance among the fitting parameters corresponding to each scaling coefficient is obtained. This slope is the estimated period of the pulse amplitude modulation signal, and the reciprocal of the slope is the rate of the pulse amplitude modulation signal.

[0178] In one embodiment, in addition to the first and second methods described above, other methods, such as FFT and least squares methods, can be used to obtain the initial rate of the pulse amplitude modulation signal.

[0179] It should be noted that after obtaining the initial rate of the pulse amplitude modulation signal in step S1, it is necessary to re-perform edge detection based on all voltage judgment thresholds in the voltage judgment threshold group to determine the new edge time, and thus determine the new histogram accordingly. This will be explained in detail below.

[0180] Step S2: Perform edge detection on the pulse amplitude modulation signal based on all voltage judgment thresholds in the voltage judgment threshold group to determine the edge time of each edge. Extract the rising and falling edges of the odd-numbered edge group and the rising and falling edges of the even-numbered edge group to determine four sets of edge feature data.

[0181] In one embodiment, all voltage judgment thresholds in the acquired voltage judgment threshold group are invoked to scan the input continuous pulse amplitude modulation signal waveform. When the voltage amplitude of the waveform crosses any voltage judgment threshold, a transition event is identified, and the time of the transition event is recorded synchronously to determine the edge time of each edge. All captured edge times are sequentially numbered according to the order of the pulse amplitude modulation signal on the time axis. To isolate nonlinear crosstalk, edge times with odd numbers are categorized and grouped into odd-numbered edge groups, and edge times with even numbers are categorized and grouped into even-numbered edge groups.

[0182] It should be noted that in serial digital signals, odd and even edges typically correspond to different half-cycles of the clock cycle. By grouping odd and even edges separately in timing, asymmetric jitter caused by clock duty cycle distortion or parity channel imbalance can be effectively isolated.

[0183] In one embodiment, after the odd-even current split is completed, for the odd-numbered edge groups, the rising and falling edges are extracted by identifying the direction of the voltage transition, thereby constructing an odd-numbered rising edge subgroup and an odd-numbered falling edge subgroup. For the even-numbered edge groups, the same method is used to identify and extract the rising and falling edges, thereby constructing an even-numbered rising edge subgroup and an even-numbered falling edge subgroup, thus ultimately determining four sets of completely independent edge feature data.

[0184] It should be noted that the first method in step S1 performs edge detection on any one of the voltage judgment thresholds, while the method in step S2 performs edge detection on all voltage judgment thresholds. That is, edge detection is performed once for each voltage judgment threshold, and finally all edge detections are statistically classified to determine the odd rising edge subgroup, the odd falling edge subgroup, the even rising edge subgroup, and the even falling edge subgroup.

[0185] Step S3: Calculate the edge difference between adjacent edges in each group of edge feature data, and determine the resolution interval based on the initial rate.

[0186] In one embodiment, after determining four sets of edge feature data, the edge difference between adjacent edges in each set of edge feature data is calculated, and the resolution interval is determined based on the initial rate. The process of calculating the edge difference is the same as in step S1, and will not be repeated here. The initial edge difference in step S1 is calculated based on the time difference between two adjacent edges in the initial rising edge group and the initial falling edge group determined by any one of the voltage judgment thresholds. In step S3, the time difference between two adjacent edges is extracted from each of the four sets of edge feature data determined by all voltage judgment thresholds. The methods are the same; only the data samples differ.

[0187] In one embodiment, to ensure that the constructed histogram accurately represents the rate, an optimal resolution interval needs to be set as the unit width of the histogram's horizontal axis data. This embodiment does not use a blindly fixed constant, but rather calculates the resolution interval based on the initial rate; that is, the resolution interval is determined by dividing the number of sampling points of the pulse amplitude modulation signal in a single symbol period by the sampling rate of the pulse amplitude modulation signal.

[0188] It should be noted that the absolute time length of a single symbol period of a pulse amplitude modulated signal is equal to the number of sampling points divided by the sampling rate; while the signal rate is the physical reciprocal of the absolute time length of a single symbol period. Therefore, the resolution interval in step S3 is essentially determined by the initial rate of the pulse amplitude modulated signal.

[0189] Step S4: Determine the edge width based on the edge difference, determine the interval range based on the edge width and resolution interval in the four sets of edge feature data, and determine the histogram based on the interval range and the number of edge widths in the four sets of edge feature data contained in each interval range.

[0190] In one embodiment, since the calculated edge difference is a physical quantity with absolute time units, it needs to be converted into a relative quantity in terms of the number of sampling points. Each edge difference in the four sets of edge feature data is multiplied by the sampling rate to obtain the corresponding edge width. The edge width at this point directly represents the number of signal sampling points contained between two adjacent edges. The largest edge width in the four sets of edge feature data is obtained, and the maximum value of the histogram interval edge is determined based on this largest edge width; this maximum value is the rightmost endpoint of the histogram axis. Then, the smallest edge width in the four sets of edge feature data is obtained, and the minimum value of the histogram interval edge is determined based on this minimum edge width; this minimum value is the leftmost starting point of the histogram axis.

[0191] In one embodiment, after determining the minimum and maximum values ​​of the histogram interval edges, the range of each interval in the histogram is determined based on these values ​​and the resolution interval. Specifically, starting from the minimum value of the histogram interval edge, the values ​​are incremented to the right at a fixed step size of the resolution interval until the maximum value of the histogram interval edge is reached. This determines the center values ​​of all histogram interval ranges and their corresponding ranges.

[0192] In one embodiment, after the horizontal axis grid is divided, the edge widths in the four sets of edge feature data are merged. Each edge width data is compared with the divided interval range, and the edge width is assigned to its corresponding interval range. The number of samples falling into each interval range is then counted. Finally, a histogram is determined based on the interval range and the number of edge widths in the four sets of edge feature data contained in each interval range. The constructed histogram uses the interval range as the horizontal axis data and the total number of edge widths in each interval range as the vertical axis data.

[0193] It should be noted that the initial histogram determined in step S1 is based on the initial edge difference and the initial resolution interval, while the histogram determined in step S3 is a new histogram determined based on the four sets of edge feature data, using new edge differences and resolution intervals. For ease of distinction, the histogram in step S1 is named the initial histogram, while the histograms mentioned after step S4 are the determined new histograms.

[0194] Step S5: Determine the center value of the interval range corresponding to the initial rate as the initial search center, and determine the first feature peak and the second feature peak based on the preset search conditions according to the initial search center.

[0195] Please refer to Figure 11 In one embodiment, when performing step S5 to determine the center value of the interval range corresponding to the initial rate as the initial search center, and determining the first feature peak and the second feature peak based on the preset search conditions according to the initial search center, the following steps S51-S52 are executed.

[0196] Step S51: Perform a peak search within a preset neighborhood of the initial search center to determine the first feature peak.

[0197] In one embodiment, on the horizontal axis of the constructed histogram, the center value of the interval range corresponding to the initial rate is determined based on the initial rate of the acquired pulse amplitude modulation signal. The initial search center physically represents the approximate landing point of the initial rate of the pulse amplitude modulation signal.

[0198] In one embodiment, a peak search is performed within a preset neighborhood of the initial search center to determine the first feature peak. Specifically, with the initial search center as the axis, a preset number of interval units of width are extended to the left and right as the neighborhood. Within this neighborhood, the number of edge widths along the vertical axis is traversed. The maximum value of the number of edge widths contained in each interval within this neighborhood is determined as the first feature peak, and its corresponding horizontal axis coordinate is recorded as the center value of the interval range of the first feature peak.

[0199] Step S52: Determine the first feature search range and determine the second feature peak value based on the first feature search range.

[0200] In one embodiment, after determining the first characteristic peak, the distribution law of integer multiples of the communication code pattern is used to capture higher-order characteristic peaks caused by consecutive codes. The center value of the interval range of the first characteristic peak is obtained and multiplied by a preset multiple to determine the theoretical center value, where the preset multiple is usually configured as 1.5 or 2. Due to the presence of inter-symbol interference and jitter in the channel, the actual code peaks often shift around the theoretical point. Therefore, based on the calculated theoretical center value, a preset one-sided search length is extended to the right (or bidirectionally) to determine the first characteristic search range. After determining the first characteristic search range, the maximum number of edge widths contained in each interval range within the first characteristic search range is obtained as the second characteristic peak, and the horizontal axis coordinate corresponding to the second characteristic peak is recorded as the center value of the interval range of the second characteristic peak.

[0201] Step S6: Determine the termination boundary of the target feature region based on the center value of the interval range of the first feature peak and the center value of the interval range of the second feature peak, and determine the starting boundary of the target feature region based on the termination boundary.

[0202] Please refer to Figure 12 In one embodiment, when performing step S6 to determine the termination boundary of the target feature region based on the center value of the interval range of the first feature peak and the center value of the interval range of the second feature peak, and to determine the starting boundary of the target feature region based on the termination boundary, the following steps S61-S62 are executed.

[0203] Step S61: Determine the termination boundary.

[0204] In one embodiment, the center value of the interval range corresponding to the first feature peak and the center value of the interval range corresponding to the second feature peak are read. The range between the center value of the interval range corresponding to the first feature peak and the center value of the interval range corresponding to the second feature peak is set as the second feature search range, with the center value of the interval range corresponding to the first feature peak as the left starting point and the center value of the interval range corresponding to the second feature peak as the right ending point.

[0205] In one embodiment, if there exists only one unique interval with the fewest edge widths within the second feature search range, then that interval is directly determined as the trough center. In practical engineering applications, due to quantization accuracy or burst noise, the bottom of a trough may present a flat area with the same amount of data, all at their lowest values, thus resulting in multiple minimum values ​​with the fewest edge widths. In this case, the intervals corresponding to all these minimum values ​​are obtained, and the middle minimum value is taken as the unique trough point. After determining the trough point, the center value of the interval corresponding to that trough point is used as the termination boundary (i.e., the right boundary) for determining the target feature region.

[0206] Step S62: Determine the starting boundary based on the termination boundary.

[0207] In one embodiment, the determined right boundary and the center value of the interval range of the first characteristic peak are obtained. Utilizing the time symmetry of the waveform transition, the center value of the interval range of the first characteristic peak is subtracted from the termination boundary to calculate the interval distance. Subtracting the interval distance from the center value of the interval range of the first characteristic peak yields a symmetrical reference point on the left side of the coordinate axis, which is then determined as the temporary starting boundary (i.e., the temporary left boundary).

[0208] In one embodiment, when the temporary left boundary is less than the minimum interval center value in the histogram, that is, the symmetric reference point derived from the symmetry has exceeded the effective data boundary of the histogram, the minimum interval center value in the histogram is determined as the starting boundary (i.e., the left boundary) of the target feature region.

[0209] In one embodiment, when the temporary left boundary is greater than the center value of the smallest interval range in the histogram, it indicates that the histogram still retains some data further to the left of the temporary left boundary. To determine whether this portion of the sample is random noise or interference, the sum of the number of edge widths contained in each interval range between the temporary left and right boundaries is calculated and determined as the first sum value; simultaneously, the sum of the center value of the smallest interval range in the histogram and the sum of the number of edge widths contained in each interval range between the temporary left boundary are calculated and determined as the second sum value. It is then determined whether the ratio of the second sum value to the first sum value exceeds a preset threshold. If the ratio does not exceed the preset threshold, it indicates that there is relatively little data remaining to the left of the temporary left boundary, which is normal random noise. In this case, the center value of the smallest interval range in the histogram is determined as the starting boundary of the target feature region. If the ratio exceeds the preset threshold, it indicates that there is an abnormal interference on the far left caused by severe nonlinear crosstalk or clock anomalies. At this point, a peak scan needs to be performed between the minimum interval center value and the temporary left boundary in the histogram. The maximum value of the number of edge widths contained in each interval within this region is the third characteristic peak value, and its corresponding horizontal axis coordinate is recorded as the interval center value of the third characteristic peak value.

[0210] In one embodiment, since there must be a physical transition zone between the third feature peak and the first feature peak, the range between the center value of the interval range of the third feature peak and the center value of the interval range of the first feature peak is obtained and set as the third feature search range. Within the third feature search range, the minimum value of the number of edge widths contained in each interval range is searched. If there are multiple minimum values ​​within the third search range, the middle minimum value is taken, and the center value of the interval range corresponding to the middle minimum value is determined as the final starting boundary (i.e., the left boundary) of the target feature region.

[0211] Step S7: Determine the midpoint of the target feature region as the target center value based on the number of edge widths contained in each interval range of the target feature region.

[0212] In one embodiment, the region between the starting boundary and the ending boundary is defined as the target feature region, the number of edge widths contained in each interval range within the region is obtained, and the sum of the number of edge widths contained in each interval range within the target feature region is calculated.

[0213] In one embodiment, the number of edge widths contained in each interval range within the target feature region is accumulated sequentially in ascending order (i.e., starting from the center value of the smallest interval range corresponding to the initial boundary and traversing it incrementally to the right along the horizontal axis) to determine the dynamic accumulated value. Accumulation stops when the dynamic accumulated value is first greater than or equal to half of the total sum. This means that the geometric area of ​​the statistical histogram in that region is now exactly halved. At this point, the center value of the interval range corresponding to the current dynamic accumulated value (i.e., the center value of the interval range corresponding to the step that triggered the cessation of accumulation) is determined as the target center value.

[0214] Step S8: Determine the estimated sampling point value based on the multiple relationship between the target center value and the single symbol period. Determine the final rate of the pulse amplitude modulation signal based on the estimated sampling point value and the sampling rate of the pulse amplitude modulation signal.

[0215] In one embodiment, since the histogram constructed in this application counts the temporal interval between edges in the same direction (such as rising edge to adjacent rising edge, or falling edge to adjacent falling edge), in the most basic alternating transition code pattern, there must be two complete symbols (i.e., one positive pulse width and one negative pulse width) between two adjacent edges in the same direction. Therefore, the target center value actually represents the number of sampling points corresponding to twice the single symbol period. It is necessary to divide the target center value by 2 to calculate and determine the estimated number of sampling points contained in a single symbol period.

[0216] In one embodiment, after obtaining the estimated sampling point value representing a single symbol period, the absolute time length of the single symbol period is the reciprocal of the signal transmission rate. Therefore, dividing the sampling rate of the pulse amplitude modulation signal by the estimated sampling point value allows for the calculation and output of the pulse amplitude modulation signal rate, as shown in the formula:

[0217]

[0218] Where B represents the rate of the pulse amplitude modulation signal, fs represents the sampling rate of the pulse amplitude modulation signal, and Nest represents the estimated value of the sampling point.

[0219] The present application will be further explained below with reference to a specific embodiment.

[0220] Taking PAM4 signal as an example, the sampling rate is 40GHz and the signal rate is 1GHz, meaning that each unit interval contains 40 sampling points, and the signal contains deterministic jitter, random jitter and inter-symbol interference.

[0221] Please refer to Figure 13 The initial rate of the PAM4 signal is obtained, which in this example is 40.5 sampling points, or 987.65MHz. Three voltage judgment thresholds are then calculated (i.e., Figure 13 The voltage thresholds V11, V12, and V13 are used to perform edge detection on the PAM4 signal, and the time and type of each edge are obtained.

[0222] The edge times are categorized into two groups based on odd and even order, resulting in odd-numbered edges and even-numbered edges. Rising and falling edges are extracted from the odd-numbered edges, and rising and falling edges are extracted from the even-numbered edges, resulting in a total of four groups of edge feature data. In this example, there are 17995 rising edges in odd order, 19747 rising edges in even order, 19498 falling edges in odd order, and a total of 17745 falling edges in even order. The difference between two adjacent edge times in each group of edge feature data is then calculated to obtain the edge difference value.

[0223] Please refer to Figure 14 The edge width is obtained by multiplying the edge difference by the sampling rate fs = 40 GHz. The initial rate corresponds to a signal period of 40.5 sampling points, therefore the resolution interval is calculated as follows: `round` indicates rounding. This retrieves the maximum and minimum edge widths; in this example, the minimum value among the four edge widths is [value to be filled in]. The maximum value is Thus, the minimum and maximum edge widths are 60.7346 and 930.5898, respectively. Aligning these with the resolution interval and rounding, the maximum value of the histogram interval edge is obtained. The minimum value of the histogram interval edge is ,according to The edge values ​​of each interval in the histogram are calculated to be [59.5, 60.5, ..., 930.5, 931.5], and the corresponding center values ​​of the intervals are [60, 61, ..., 930, 931]. The edge widths are then mapped to the intervals to form the histogram.

[0224] In this example, the signal period corresponding to the initial rate is 40.5 sampling points. Multiplying this by 2 gives the initial search center in the histogram, which is 81. The first characteristic peak is searched for near this value. The condition for determining the peak is: if the number of edge widths contained within a certain interval is greater than the number of edge widths contained in the three intervals on the left and three on the right of the histogram, then it is taken as the first characteristic peak. In this example, the statistical value corresponding to 81 is 2147, which is less than the statistical value 2302 corresponding to the first value on the left, 80. 2302 satisfies the peak determination condition, so it is taken as the first characteristic peak. The center value of the interval corresponding to the first characteristic peak is recorded as 80. Based on the center value of the interval corresponding to the first characteristic peak, the center of the first feature search range is set to the center value of the interval corresponding to the first characteristic peak multiplied by 1.5, which is 120. The single-sided search length is set to 16, meaning the left boundary of the first feature search range is 104, the right boundary is 136, and the first feature search range is [104, 136]. The first feature search range of the histogram data is used to find the peak value, which is the second feature peak value. In this example, the second feature peak value is 1000, and the center value of the interval range corresponding to the second feature peak value is 121. The range between the center value of the interval range corresponding to the first feature peak value and the center value of the interval range corresponding to the second feature peak value is set as the second feature search range, i.e., [80, 121]. Within the second feature search range, the interval range with the fewest edge widths is found. In this example, the minimum value is 0, and there are a total of 2 minimum values. That is, the first minimum value, which records the interval range with the fewest edge widths, with the center value of 100 as the right boundary (i.e., Figure 14 The right boundary is subtracted from the center value of the interval range of the first characteristic peak to obtain an interval distance of 20. The interval distance is then subtracted from the center value of the interval range of the first characteristic peak to obtain a temporary left boundary of 60.

[0225] Determine if the temporary left boundary 60 is greater than the minimum interval center value 60 in the histogram. If they are equal, set the minimum interval center value 60 as the left boundary (i.e., ...). Figure 14The starting boundary is defined as follows: The range between the left and right boundaries is set as the target feature region. In this example, the target feature region is [60, 100]. All data in the histogram within this range are extracted and summed to obtain the total value. In this example, the total value is 23494. Starting from the left boundary 60, the number of edge widths contained in each interval range is gradually accumulated. When the accumulation reaches the 21st interval range of the histogram, the accumulated value is 12871, which is greater than half of the total value 11747. The current data 2302 is recorded as the final value, and the corresponding interval range center value 80 is recorded, which is also the target center value. The target center value 80 is divided by 2 to obtain the sampling point estimation result of 40GHz. Dividing fs=40GHz by the sampling point estimation result, the final rate of the multilevel signal is obtained as 1GHz. This rate is the same as the signal rate of 1GHz corresponding to the PAM4 signal, thus indicating that the method provided in this application is effective.

[0226] The present application will be further explained below with reference to another specific embodiment.

[0227] Taking the PAM5 signal as an example, the sampling rate is 50GHz and the signal rate is 4GHz, meaning that each unit interval contains 12.5 sampling points, and the signal contains deterministic jitter, random jitter and inter-symbol interference.

[0228] Please refer to Figure 15 The initial rate of the PAM5 signal is obtained in advance, which in this example is 12.10 sampling points, or 4.132 GHz. Four voltage judgment thresholds are then calculated (i.e., Figure 15 The voltage thresholds V21, V22, V23, and V24 are used to perform edge detection on the PAM5 signal and obtain the time and type of each edge.

[0229] The edge times are categorized into two groups based on odd and even order, resulting in odd-numbered edges and even-numbered edges. Rising and falling edges are extracted from the odd-numbered edges, and rising and falling edges are extracted from the even-numbered edges, resulting in a total of four groups of edge data. In this example, there are 5269 rising edges in odd order, 5218 rising edges in even order, 5387 falling edges in odd order, and a total of 5438 falling edges in even order. The difference between two adjacent edge times in each group of edge feature data is then calculated to obtain the edge difference value.

[0230] Please refer to Figure 16 The edge width is obtained by multiplying the edge difference by the sampling rate fs = 50 GHz. The signal period corresponding to the initial rate is 12.10 sampling points, therefore the resolution interval is calculated as follows: Round to one decimal place. To obtain the maximum and minimum edge widths, in this example, the minimum value among the four edge widths is... The maximum value is Thus, the minimum and maximum edge widths are 9.1124 and 260.5502, respectively. Aligning these with the resolution interval and rounding, the maximum value of the histogram interval edge is obtained. The minimum value of the histogram interval edge is ,according to The edge values ​​of each interval in the histogram are calculated to be [8.9, 9.1, ..., 260.5, 260.7], and the corresponding center values ​​of the intervals are [9.0, 9.2, ..., 260.4, 260.6]. The edge widths are then mapped to the intervals to form the histogram.

[0231] In this example, the signal period corresponding to the initial rate is 12.10 sampling points. Multiplying this by 2 gives the initial search center in the histogram, which is 24.2. The first characteristic peak is searched for near 24.2. The condition for determining the peak is set as follows: if a value is greater than the number of edge widths contained in the four intervals on the left and four on the right of the histogram, it is taken as the first characteristic peak. In this example, the first peak on the right that satisfies the condition is 222, and the corresponding interval center value is 25.8. Therefore, 222 is taken as the first characteristic peak, and the interval center value corresponding to the first characteristic peak is recorded as 25.8. Based on the interval center value corresponding to the first characteristic peak, the center of the first feature search range is set to the interval center value corresponding to the first characteristic peak multiplied by 1.5, which is 38.7. The single-sided search length is set to 4, meaning the left boundary of the first feature search range is 34.7, the right boundary is 42.7, and the first feature search range is [34.7, 42.7]. The first feature search range of the histogram data is used to find the peak value, which is the second feature peak value. In this example, the second feature peak value is 183, and the center value of the interval range corresponding to the second feature peak value is 38.2. The range between the center value of the interval range corresponding to the first feature peak value and the center value of the interval range corresponding to the second feature peak value is set as the second feature search range, i.e., [25.8, 38.2]. The interval range with the fewest edge widths is found within the second feature search range. In this example, the minimum value is 20, and there is only one minimum value. The center value of the interval range with the fewest edge widths, 30.8, is recorded as the right boundary (i.e., Figure 16 The right boundary is subtracted from the center value of the first interval range to obtain an interval distance of 5. The interval distance is subtracted from the center value of the interval range of the first characteristic peak to obtain a temporary left boundary of 20.8.

[0232] To determine if the temporary left boundary value of 20.8 is greater than the minimum interval center value of the histogram of 9.0, we calculate the total number of statistical data points of 30.8 between the temporary left boundary of 20.8 and the right boundary as the first sum value M1 = 5277, and calculate the total number of statistical data points to the left of the temporary left boundary as the second sum value M2 = 1557. We then determine if the ratio of M2 to M1 (0.29) exceeds the threshold of 0.1, which is clearly exceeded. Therefore, we find the peak value of 78 to the left of the temporary left boundary of the histogram, and denote it as the third feature peak value. We denote the interval center value of the third feature peak value as 15.8. We set the range between the interval center value of the third feature peak value (15.8) and the interval center value of the first feature peak value (25.8) as the third feature search range. Within the third feature search range, we find the minimum number of edge widths contained in each interval range. The minimum number of edge widths contained in each interval range is 8. We record the interval center value of 19.2 corresponding to this minimum value as the left boundary (i.e.,...). Figure 16 (The initial boundary in the middle).

[0233] The range between the left and right boundaries is defined as the target feature region. In this example, the target feature region is [19.2, 30.8]. All data in the histogram within this range are extracted and summed to obtain a total value. In this example, the total value is 5462. Starting from the left boundary 19.2, the number of edge widths contained in each interval range is gradually accumulated. When the accumulation reaches the 81st interval range of the histogram, the accumulated value is 2792, which is greater than half of the total value 2731. The current data 2792 is recorded as the final value, and its corresponding interval range center value 25.0 is also recorded, which is the target center value. Dividing the target center value 25.0 by 2 gives the sampling point estimation result of 12.5. Dividing fs=50GHz by the sampling point estimation result gives the initial rate of the pulse amplitude modulation signal as 4GHz. This rate is the same as the signal rate of 4GHz corresponding to the PAM5 signal, thus indicating that the method provided in this application is effective.

[0234] In the above embodiments, implementation can be achieved, in whole or in part, by software, hardware, firmware, or any combination thereof. Furthermore, as those skilled in the art will understand, the principles herein can be reflected in a computer program product on a computer-readable storage medium pre-loaded with computer-readable program code. Any tangible, non-transitory computer-readable storage medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CDs, DVDs, Blu-ray discs, etc.), flash memory, and / or the like. These computer program instructions can be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to form a machine, such that instructions executing on the computer or other programmable data processing apparatus can generate means for performing a specified function. These computer program instructions can also be stored in a computer-readable storage medium that can instruct the computer or other programmable data processing apparatus to operate in a particular manner, such that instructions stored in the computer-readable storage medium can form an article of manufacture, including means for implementing the specified function. The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to perform a series of operational steps on the computer or other programmable apparatus to produce a computer-implemented process, such that instructions executing on the computer or other programmable apparatus can provide steps for implementing the specified function.

[0235] This document describes various exemplary embodiments with reference to them. However, those skilled in the art will recognize that changes and modifications can be made to the exemplary embodiments without departing from the scope of this document. For example, various operational steps and components for performing operational steps can be implemented in different ways depending on the specific application or considering any number of cost functions associated with the operation of the system (e.g., one or more steps can be deleted, modified, or combined with other steps).

[0236] While the principles herein have been illustrated in various embodiments, numerous modifications to the structures, arrangements, proportions, elements, materials, and components, particularly suited to specific environments and operational requirements, may be used without departing from the principles and scope of this disclosure. These modifications and other alterations or alterations will be included within the scope of this document. Those skilled in the art will recognize that many changes can be made to the details of the above embodiments without departing from the fundamental principles of the invention.

Claims

1. A method for rate calibration of a pulse amplitude modulation signal, characterized in that, include: The initial rate of the pulse amplitude modulation signal and a voltage judgment threshold group of the pulse amplitude modulation signal are obtained, wherein the voltage judgment threshold group includes at least one voltage judgment threshold. The pulse amplitude modulation signal is edge-detected based on all voltage judgment thresholds in the voltage judgment threshold group to determine the edge time of each edge. According to the timing sequence of the pulse amplitude modulation signal, each edge of the pulse amplitude modulation signal is divided into an odd edge group and an even edge group; the rising edge and falling edge of the odd edge group and the rising edge and falling edge of the even edge group are extracted respectively to determine four sets of edge feature data. Calculate the edge difference between adjacent edges in each group of edge feature data, and determine the resolution interval based on the initial rate; The edge width is determined based on the edge difference, the interval range is determined based on the edge width in the four sets of edge feature data and the resolution interval, and the histogram is determined based on the interval range and the number of edge widths in the four sets of edge feature data contained in each interval range. The center value of the interval range corresponding to the initial rate is determined as the initial search center, and the first feature peak and the second feature peak are determined according to the preset search conditions based on the initial search center. The termination boundary is determined based on the center value of the interval range of the first characteristic peak and the center value of the interval range of the second characteristic peak, wherein the termination boundary is the termination boundary of the target feature region; The starting boundary of the target feature region is determined based on the termination boundary and the center value of the interval range of the first feature peak. The midpoint of the target feature region is determined as the target center value based on the number of edge widths contained in each interval range of the target feature region. The estimated value of the sampling point is determined based on the multiple relationship between the target center value and the single symbol period; The final rate of the pulse amplitude modulation signal is determined based on the estimated sampling point value and the sampling rate of the pulse amplitude modulation signal.

2. The rate calibration method for pulse amplitude modulation signals as described in claim 1, characterized in that, The step of obtaining the initial rate of the pulse amplitude modulation signal includes: For each voltage, a threshold is determined: Edge detection is performed on each edge of the pulse amplitude modulation signal according to the voltage judgment threshold to determine the initial rising edge group and the initial falling edge group; Initial target center value detection is performed on the initial rising edge group and the initial falling edge group respectively. The initial target center value detection includes: Obtain the time difference between two adjacent edges to determine the initial edge difference value accordingly; The initial edge width is determined based on the difference between each initial edge, and the initial interval range is determined based on each initial edge width and the preset initial resolution interval. The initial histogram is determined based on the initial interval range and the number of initial edge widths contained in each initial interval range. Based on the range of each initial interval in the initial histogram from smallest to largest, an initial target feature region is determined in the initial histogram to represent the minimum time interval of the same-direction edge. Based on the number of initial edge widths contained in each initial interval range of the initial target feature region, the midpoint of the initial target feature region is determined as the initial target center value; Obtain the initial target center value of the initial rising edge group and the initial target center value of the initial falling edge group corresponding to each voltage judgment threshold; The estimated values ​​of the initial sampling points are determined based on the multiple relationship between the initial target center values ​​and the single symbol period; The initial rate of the pulse amplitude modulation signal is determined based on the estimated initial sampling point value and the sampling rate of the pulse amplitude modulation signal.

3. The rate calibration method for pulse amplitude modulation signals as described in claim 1, characterized in that, The step of obtaining the initial rate of the pulse amplitude modulation signal includes: From the original sampled data of the pulse amplitude modulation signal, the transition time corresponding to each transition edge in the pulse amplitude modulation signal is obtained, and the first initial frequency is determined according to the time difference between adjacent transition times; The number of sampling points for frequency domain analysis of the pulse amplitude modulation signal is determined based on the sampling rate of the pulse amplitude modulation signal and the first initial frequency, and the reference frequency for frequency domain analysis of the pulse amplitude modulation signal is determined based on the first initial frequency. From the original sampled data, continuous data of length equal to the number of sample points is obtained as sampled data. The sampled data is then subjected to a nonlinear transformation to obtain nonlinear transformed data, and the nonlinear transformed data is converted into frequency domain amplitude spectrum data. In the frequency domain amplitude spectrum data, peak retrieval is performed based on the reference frequency to determine a second initial frequency after correcting the first initial frequency; Based on the second initial frequency, multiple candidate frequencies are determined, and for each candidate frequency: The clock sequence number is determined based on the candidate frequency and the transition time, and the transition time is linearly fitted based on the clock sequence number to determine the fitting parameters accordingly; the initial rate of the pulse amplitude modulation signal is determined based on the fitting parameters of each candidate frequency.

4. The rate calibration method for pulse amplitude modulation signals as described in claim 1, characterized in that, The step of determining the edge width based on the edge difference and determining the interval range based on the edge width in the four sets of edge feature data and the resolution interval includes: Obtain the sampling rate of the pulse amplitude modulation signal, and determine the edge width based on the sampling rate and the edge difference; Obtain the maximum and minimum edge widths from four sets of edge feature data. Determine the maximum value of the histogram interval edge based on the maximum edge width, and determine the minimum value of the histogram interval edge based on the minimum edge width. The range of each interval in the histogram is determined based on the minimum value of the histogram interval edge, the maximum value of the histogram interval edge, and the resolution interval.

5. The rate calibration method for pulse amplitude modulation signals as described in claim 4, characterized in that, The step of determining the first feature peak and the second feature peak based on the initial search center and preset search conditions includes: A peak search is performed within a preset neighborhood range of the initial search center to determine the first feature peak. The theoretical center value is determined based on the center value of the interval range of the first feature peak and a preset multiple; the first feature search range is determined based on the theoretical center value and a preset unilateral search length. The maximum value of the number of edge widths contained in each interval within the first feature search range is the peak value of the second feature.

6. The rate calibration method for pulse amplitude modulation signals as described in claim 5, characterized in that, Determining the starting boundary of the target feature region based on the termination boundary and the center value of the interval range of the first feature peak includes: The temporary starting boundary of the target feature region is determined based on the termination boundary and the center value of the interval range of the first feature peak. The starting boundary of the target feature region is determined based on the temporary starting boundary and the minimum interval center value in the histogram.

7. The rate calibration method for pulse amplitude modulation signals as described in claim 6, characterized in that, Determining the starting boundary of the target feature region based on the temporary starting boundary and the minimum interval center value in the histogram includes: When the temporary starting boundary is less than the minimum interval center value in the histogram, the minimum interval center value in the histogram is the starting boundary. When the temporary starting boundary is greater than the minimum interval center value in the histogram, the sum of the number of edge widths contained in each interval between the temporary starting boundary and the ending boundary is calculated as a first sum value, and the sum of the number of edge widths contained in each interval between the minimum interval center value in the histogram and the temporary starting boundary is calculated as a second sum value; the starting boundary of the target feature region is determined based on the first sum value and the second sum value.

8. The rate calibration method for pulse amplitude modulation signals as described in claim 7, characterized in that, Determining the starting boundary of the target feature region based on the first sum value and the second sum value includes: Determine whether the ratio of the second sum value to the first sum value exceeds a preset threshold; if it does not exceed the threshold, the minimum interval center value in the histogram is the starting boundary; if it exceeds the threshold, determine the maximum value of the number of edge widths contained in each interval between the minimum interval center value in the histogram and the temporary starting boundary as the third feature peak value, and determine the minimum value of the number of edge widths contained in each interval between the interval center value of the third feature peak value and the interval center value of the first feature peak value as the starting boundary of the target feature region.

9. The rate calibration method for pulse amplitude modulation signals as described in claim 1, characterized in that, The step of determining the midpoint of the target feature region as the target center value based on the number of edge widths contained in each interval range of the target feature region includes: Calculate the sum of the number of edge widths contained in each interval range of the target feature region; According to the order of the range of each interval in the target feature region from smallest to largest, the number of edge widths contained in each interval range is accumulated one by one to determine the dynamic accumulation value; When the dynamic accumulated value is greater than or equal to half of the sum value for the first time, the accumulation stops; the center value of the interval range corresponding to the current dynamic accumulated value is determined as the target center value.

10. The rate calibration method for pulse amplitude modulation signals as described in claim 9, characterized in that, The step of determining the estimated value of the sampling point based on the multiple relationship between the target center value and the single symbol period includes: The estimated value of the sampling point is determined based on the relationship between the target center value and the period of a single symbol, which is twice the ratio of the period of a single symbol.

11. A computer-readable storage medium, characterized in that, Includes a program that can be executed by a processor to implement the method as described in any one of claims 1-10.

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