Inter-symbol interference jitter separation method based on derivative sparsity and cyclostationary characteristic

Abstract

The invention discloses an inter-symbol interference jitter separation method based on derivative sparsity and cyclostationary characteristics, which belongs to the technical field of jitter analysis in high-speed serial communication, and can directly acquire other jitter information without inter-symbol interference from sampling data by constructing a derivative sparsity function and utilizing the cyclostationary characteristics of the function. Compared with a traditional method, the method is not limited by a data code pattern, ISI jitter separation can be carried out on data of any code pattern, the method is not limited by the collected data size, clock signals do not need to be recovered from the data, threshold level does not need to be determined from the data, and the method is simple and convenient to operate. The method does not need an interpolation module to accurately solve the measured data or recover the cross moment of the clock and the threshold level, and is simple in overall implementation and low in calculation complexity.

Description

Technical Field

[0001] This invention relates to the field of jitter analysis technology in high-speed serial communication, specifically to an inter-symbol interference jitter separation method based on derivative sparsity and cyclic stationarity. Background Technology

[0002] In high-speed serial communication systems, jitter analysis is a crucial step in evaluating link performance and guiding system design. Accurate jitter decomposition is fundamental for bit error rate (BER) prediction and extrapolation. With continuously increasing communication rates, inter-symbol interference (ISI) caused by limited transmission channel bandwidth becomes increasingly severe, significantly compressing the timing margin at the receiver. However, due to the blurred boundary between ISI jitter and random jitter (RJ), existing analysis methods often incorrectly classify ISI jitter as RJ jitter, leading to an overestimation of BER extrapolation results. This overestimation can result in excessive margin design at the system level, incurring additional hardware costs, power consumption, and system complexity, ultimately impacting the optimal realization of link performance.

[0003] To meet the compliance testing requirements of the new generation of high-speed interface standards, more stringent stress tests and longer data patterns, such as PRBS31, are needed to stimulate the worst-case response of the channel. However, for signals with such long code patterns, the main bottleneck in jitter decomposition lies in the characterization and separation of ISI jitter. Therefore, how to achieve an efficient, accurate, and code-independent ISI jitter separation method remains a key research problem in current high-speed link jitter analysis.

[0004] The repetition pattern measurement algorithm is currently the most commonly used ISI jitter separation algorithm, mainly suitable for measuring data patterns of PRBS15 length and below. This method effectively filters out the influence of random jitter and periodic jitter by analyzing the time distribution of each transition in a data pattern, usually 10 or more times, and averaging the same transition positions, thus extracting stable ISI jitter. After subtracting the ISI jitter from the total jitter, subsequent jitter decomposition is performed.

[0005] The existing ISI jitter separation steps are as follows: The threshold level reference is established, and the threshold level is calculated using histogram statistics based on the voltage value of the measured data. Clock recovery is performed to extract the clock information embedded in the measured data. The code pattern search is performed on the measured data to determine the code pattern repetition period. Each transition in the measured data pattern is examined in multiple repetitions, usually 10 or more, and the average time of each transition is calculated to measure ISI jitter. The average value of the histogram corresponding to each transition is measured based on the recovered clock. The total TIE jitter is obtained through interpolation. The ISI jitter is subtracted from the total TIE jitter, and subsequent jitter decomposition is performed to obtain a more accurate random jitter. Extrapolation yields a more accurate bit error rate value.

[0006] Deficiencies of existing technology: First, are all other forms of jitter averaged out? The exact length of the data pattern is unknown, and the repetition period obtained through the above search method will consume a lot of computing resources and may even result in an incorrect repetition period. Secondly, existing methods are only applicable to shorter data pattern modes, such as PRBS7. For longer data patterns, such as PRBS21 and above, more than 10 repetition cycles are required, necessitating the acquisition of a large number of waveform sampling points, which far exceeds the storage depth of actual oscilloscopes. Summary of the Invention

[0007] The purpose of this invention is to address the aforementioned problems by providing an inter-symbol interference jitter separation method based on derivative sparsity and cyclic stationarity. This method constructs a derivative sparsity function and utilizes the cyclic stationarity of this function to directly obtain the remaining jitter information excluding inter-symbol interference from the sampled data, thereby achieving the goal of separating inter-symbol interference jitter from the total jitter.

[0008] The technical solution adopted in this invention is as follows: An inter-symbol interference jitter separation method based on derivative sparsity and cyclic stationarity, the method comprising: Based on the transmission model of a high-speed serial link with jitter, the derivative is used to determine the extreme point data of each symbol. A derivative sparse function is constructed using the extreme point data, and the jitter information for inter-symbol interference removal is extracted using the derivative sparse function.

[0009] Furthermore, the transmission model based on a high-speed serial link with jitter is specifically as follows: (1) In the formula, For transmitting symbols, Width when it is a symbol For the first The shaking of a symbol, For width is The formed square wave, This is the channel impulse response; In order to quantify the impact of inter-symbol interference on the time offset of the derivative extremum of equation (1), the derivative of equation (1) is calculated, and the extremum data of each symbol is determined.

[0010] Furthermore, the time offset of the derivative extremum with respect to inter-symbol interference is obtained as follows: In high-speed serial links, for simplicity and clarity, it is usually set that... Zero, and Starting from zero, the derivative yields the following equation: (2) In the formula, the definition is... This illustrates the effect of inter-symbol interference (ISI) on the magnitude of the derivative. Let I represent the time offset of ISI from the extreme values ​​of the derivative. Then in the first The symbol jump point hour, It reaches its maximum value, and its second derivative is in The value is 0, as shown in the following formula: (3) Performing a Taylor expansion on equation (3) above, we obtain the time offset of the derivative extremum caused by inter-symbol interference as follows: (4).

[0011] Furthermore, the determination of the extreme point data is as follows: The analysis of the time offset specifically reveals the channel impulse response. As a low-pass filter, its tail decreases at an exponential rate, and its derivative... Similarly, the amplitude decreases rapidly, approaching zero at positions far from the main code element. It is a relatively large constant, reflecting the channel impulse response. The curvature at the maximum value, therefore The value of is approximately 0, therefore, the location of the extreme points of the derivative is determined by the jitter of each sign. Decision, that is .

[0012] Furthermore, to reduce the amount of data computation, a time-domain sparse function is constructed based on the determined extreme point data, as shown in the following formula: (5).

[0013] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The method of this invention is based on constructing a derivative sparsity function. Utilizing the cyclic stationarity of this function, it directly extracts the remaining jitter information excluding inter-symbol interference (ISI) from the sampled data, achieving the goal of separating ISI jitter from the total jitter. This solves the problem that traditional ISI jitter separation methods heavily rely on repeated code patterns or data reconstruction, and the separation is incomplete, leading to excessively high random jitter results. This results in an excessively high extrapolated bit error rate (BER) from the random jitter, which can lead to excessive margin design at the system level, resulting in additional hardware costs, power consumption, and system complexity, affecting the optimal realization of link performance. Compared to existing methods, this invention's method eliminates the need for complex histogram statistics modules, threshold preset value modules, and clock recovery modules, offering advantages such as simple implementation and low computational complexity. In particular, existing methods rely on data code patterns, requiring large amounts of collected data, making them difficult to handle long-memory channels. This invention's method, however, is not limited by data code patterns and is applicable to collected data of arbitrary length. Attached Figure Description

[0014] Figure 1 This is a flowchart of the inter-symbol interference jitter separation method based on derivative sparsity and cyclic stationarity of the present invention. Figure 2 To verify the time-domain comparison of the total jitter obtained after separating ISI jitter using the method of the present invention and the traditional method in the example; Figure 3 To verify the frequency domain comparison of the total jitter obtained after separating ISI jitter using the method of the present invention and the traditional method in the example; Figure 4 To verify this, a comparison of the histograms of the total jitter obtained after separating ISI jitter using the method of this invention and the traditional method is presented in the example. Detailed Implementation

[0015] The present invention will now be described in detail with reference to the accompanying drawings.

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Example 1 This embodiment provides an ISI jitter separation method based on derivative sparsity and cyclic stationarity characteristics, such as... Figure 1 As shown, this specifically includes constructing a sparse derivative function and extracting jitter based on cyclic stationarity.

[0018] (1) The specific construction of the derivative sparse function is as follows: The transmission model of a high-speed serial link with jitter is as follows: (1) In the formula, It is a transmission symbol. It is the width of the symbol. It is the first The shaking of a symbol, The width is The formed square wave, It is the channel impulse response. For ease of calculation, let's assume... The value is zero, for ease of mathematical processing. and All start from zero, meaning the impact of channel delay is not considered.

[0019] For an ideal square wave There will be an impulse pulse at the moment when the signal changes, which... Differentiate: (2) In the formula, This represents the effect of inter-symbol interference (ISI) on the magnitude of the derivative. Let the time offset of ISI on the extreme value of the derivative be denoted as . Then in the first The symbol jump point hour, It reaches its maximum value, and its second derivative is in The value is 0, as shown in the following formula: (3) Performing a Taylor expansion on the above equation, we get: (4) In the above formula, the channel impulse response Typically a low-pass filter, its tail decreases at an exponential rate, and its derivative is based on this. It will decrease much faster at a rate that is multiple of the time constant of the low-pass filter, and its amplitude will rapidly approach 0 at positions far from the main symbol. It is usually a relatively large constant, reflecting the curvature of the channel impulse response at its maximum value, therefore The value of can be approximated as 0, meaning that the offset caused by ISI to the extreme points of the derivative is negligible, and the position of the extreme points is mainly determined by the jitter of each sign. Decision, that is The extreme point data has been determined.

[0020] After determining the extreme point data, construct the time-domain sparse derivative signal: (5) In the formula, It has stable cyclic characteristics, and the cycle period is ,Right now It is a cycle period of It is a stationary random process, and the sparse function does not contain the effects of ISI jitter.

[0021] (2) Extracting jitter based on cyclic stability characteristics: The method for extracting jitter in a high-speed serial link based on cyclic stationary characteristics, disclosed in ZL202411885558.2, is used to extract total jitter. This includes dividing the acquired sparse function according to the sampling multiple N and calculating the cyclic autocorrelation function of the sparse function at the symbol rate; obtaining the phase of the cyclic autocorrelation function, which contains only other jitter information of each symbol, thereby achieving the purpose of separating inter-symbol interference jitter in the high-speed serial link. The total jitter does not contain ISI jitter, that is, ISI jitter is separated from the total jitter.

[0022] Verification Example To verify the effectiveness of the embodiments of the present invention, the following experimental platform was built. A SerDes signal was simulated using MATLAB 2024b with a symbol rate of 1Gbps, 128 sampling points per symbol, a total of 4096 symbols, a data code of PRBS7, and a modulation scheme of PAM2-NRZ. Random jitter RJ=0.05UI was added to the signal, along with sinusoidal jitter at a frequency of 200kHz and an amplitude of 0.2UI. The ISI jitter information was: channel attenuation of 3dB and frequency of 0.5GHz.

[0023] like Figure 2 As shown, traditional methods remove random jitter and periodic sinusoidal jitter by averaging. However, in the method of this invention, since the period of sinusoidal jitter is less than one cycle, traditional methods cannot remove the influence of sinusoidal jitter by averaging, resulting in a large deviation in the time-domain plot after removing ISI jitter. Furthermore, as... Figure 3 and Figure 4 As shown, it can be seen that the frequency and width of the obtained sinusoidal jitter are both significantly deviated, which will cause the system to be unable to accurately locate the fault frequency.

[0024] In summary, the method proposed in this invention can still correctly identify low-frequency jitter even during non-integer period sinusoidal jitter, effectively remove ISI jitter, retain non-data-related periodic jitter, and provide more accurate system fault location.

[0025] This article uses specific embodiments to illustrate the principles and implementation methods of the present invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that those skilled in the art can make several improvements and modifications to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for separating inter-symbol interference jitter based on derivative sparsity and cyclostationary properties, characterized in that, The method includes: Based on the transmission model of a high-speed serial link with jitter, the derivative is used to determine the extreme point data of each symbol. A derivative sparse function is constructed using the extreme point data, and the jitter information for inter-symbol interference removal is extracted using the derivative sparse function.

2. The inter-symbol interference jitter separation method based on derivative sparsity and cyclic stationarity as described in claim 1, characterized in that, The transmission model based on a high-speed serial link with jitter is specifically as follows: （1） In the formula, For transmitting symbols, Width when it is a symbol For the first The shaking of a symbol, For width is The formed square wave, This is the channel impulse response; Differentiate the above equation (1) to obtain the time offset of the derivative extreme value of inter-symbol interference, and determine the extreme point data of each symbol.

3. The inter-symbol interference jitter separation method based on derivative sparsity and cyclostationary characteristics according to claim 2, characterized in that, The time offset of the derivative extremum obtained from the inter-symbol interference is as follows: set up Zero, and Starting from zero, the derivative yields the following equation: （2） In the formula, This illustrates the effect of inter-symbol interference (ISI) on the magnitude of the derivative. Let I represent the time offset of ISI from the extreme values ​​of the derivative. Then in the first The symbol jump point hour, It reaches its maximum value, and its second derivative is in The value is 0, as shown in the following formula: （3） Performing a Taylor expansion on equation (3) above, we obtain the time offset of the derivative extremum caused by inter-symbol interference as follows: （4）。 4. The inter-symbol interference jitter separation method based on derivative sparsity and cyclic stationarity as described in claim 3, characterized in that, The determination of the extreme point data is as follows: By analyzing the time offset, the time offset of the derivative extremum caused by inter-symbol interference is... If the value of is approximately 0, then the location of the extreme point is determined by the jitter of each sign. Decision, that is .

5. The inter-symbol interference jitter separation method based on derivative sparsity and cyclostationary characteristics according to claim 4, characterized in that, A time-domain sparse function is constructed based on the determined extreme point data, as shown in the following formula: （5）。

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