Dispersion estimation method and apparatus, computer device and readable storage medium

Abstract

The application relates to a dispersion estimation method, a dispersion estimation device, a computer device, a computer readable storage medium and a computer program product. The method comprises the following steps: obtaining a shaped digital signal and test dispersion values, and performing weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is used for recovering the phase of the shaped digital signal; a cost function curve is constructed according to the test dispersion values and the weighted digital signal, and a first dispersion estimation result is determined from the test dispersion values based on the cost function curve; and the first dispersion estimation result is corrected according to a dispersion correction value range to obtain a dispersion value. The method can improve the accuracy of the dispersion estimation method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital signal processing, and particularly relates to a chromatic dispersion estimation method and device, computer equipment, computer readable storage medium and computer program product. BACKGROUND

[0002] With the development of optical network technology, optical fiber links are usually used to transmit data. An optical receiver receives optical signals transmitted by an optical fiber link, and performs photoelectric conversion and digital-to-analog conversion on the optical signals to obtain digital signals. Then, according to digital signal processing technology, loss compensation is performed on the digital signals to obtain digital signals transmitted by an optical transmitter. In the process of digital signal processing technology, it is necessary to determine the chromatic dispersion of the optical fiber based on a chromatic dispersion estimation method.

[0003] The current chromatic dispersion estimation method is the FD CD est algorithm (Frequency Domain Chromatic Dispersion Estimation). Specifically, Fourier transform is performed on the digital signals, and the phase slope of the transformed digital signals is determined. Then, the chromatic dispersion value of the optical fiber is determined according to the wavelength-dispersion characteristics of the optical fiber and the phase slope.

[0004] However, in the current chromatic dispersion estimation method, the transmitter shapes the occurrence probability of the digital signals and transmits the shaped digital signals. The current chromatic dispersion estimation method can only determine the chromatic dispersion value of the optical fiber for digital signals with uniformly distributed occurrence probabilities. Therefore, the accuracy of the current chromatic dispersion estimation method is low. SUMMARY

[0005] Therefore, it is necessary to provide a chromatic dispersion estimation method, device, computer equipment, computer readable storage medium and computer program product to solve the above technical problems.

[0006] In a first aspect, the present application provides a chromatic dispersion estimation method, comprising:

[0007] Obtaining shaped digital signals and test chromatic dispersion values, and performing weighted processing on the shaped digital signals according to a probability weighting algorithm to obtain weighted digital signals; the probability weighting algorithm is used to restore the phase of the shaped digital signals;

[0008] Constructing a cost function curve according to the test chromatic dispersion values and the weighted digital signals, and determining a first chromatic dispersion estimation result in the test chromatic dispersion values based on the cost function curve;

[0009] According to the chromatic dispersion correction value range, the first chromatic dispersion estimation result is corrected to obtain a chromatic dispersion value.

[0010] In one of the embodiments, the obtaining of the shaped digital signal and the test dispersion values comprises:

[0011] obtaining the shaped digital signal, the search range of the dispersion value and the step value;

[0012] performing data processing on the search range of the dispersion value and the step value to obtain the test dispersion values.

[0013] In one of the embodiments, the constructing of the cost function curve according to the test dispersion values and the weighted digital signal comprises:

[0014] constructing a filter function corresponding to each of the test dispersion values based on a filter function algorithm, and performing exploratory filtering processing on the weighted digital signal based on the filter function to obtain a filtered weighted digital signal;

[0015] constructing a cyclic autocorrelation function corresponding to the filtered weighted digital signal according to a cyclic autocorrelation function algorithm, and determining an absolute value corresponding to the cyclic autocorrelation function;

[0016] constructing a cost function curve based on the absolute values.

[0017] In one of the embodiments, the determining of the first dispersion estimation result from the test dispersion values based on the cost function curve comprises:

[0018] determining an extreme point in the cost function curve;

[0019] determining the test dispersion value corresponding to the extreme point in the test dispersion values as the first dispersion estimation result.

[0020] In one of the embodiments, the correcting of the first dispersion estimation result according to the dispersion correction value range to obtain the dispersion value comprises:

[0021] determining a target dispersion correction value in the dispersion value correction range;

[0022] performing addition operation on the target dispersion correction value and the first dispersion estimation result to obtain a second estimation result, and determining the second estimation result as the dispersion value.

[0023] In one of the embodiments, after the correcting of the first dispersion estimation result according to the dispersion correction value range to obtain the dispersion value, the method further comprises:

[0024] performing conversion on the shaped digital signal according to a Fourier transform algorithm to obtain a frequency domain digital signal;

[0025] construct a dispersion compensation filter according to the dispersion value, and compensate the frequency domain digital signal based on the dispersion compensation filter to obtain a compensated frequency domain digital signal;

[0026] convert the compensated frequency domain digital signal according to an inverse Fourier transform algorithm to obtain a compensated digital signal.

[0027] In a second aspect, the present application further provides a dispersion estimation device, comprising:

[0028] a processing module configured to acquire a shaped digital signal and a plurality of test dispersion values, and perform weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is configured to recover a phase of the shaped digital signal;

[0029] a constructing module configured to construct a cost function curve according to the plurality of test dispersion values and the weighted digital signal, and determine a first dispersion estimation result from the plurality of test dispersion values based on the cost function curve;

[0030] a correcting module configured to correct the first dispersion estimation result according to a dispersion correction value range to obtain a dispersion value.

[0031] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0032] acquire a shaped digital signal and a plurality of test dispersion values, and perform weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is configured to recover a phase of the shaped digital signal;

[0033] construct a cost function curve according to the plurality of test dispersion values and the weighted digital signal, and determine a first dispersion estimation result from the plurality of test dispersion values based on the cost function curve;

[0034] correct the first dispersion estimation result according to a dispersion correction value range to obtain a dispersion value.

[0035] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0036] acquire a shaped digital signal and a plurality of test dispersion values, and perform weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is configured to recover a phase of the shaped digital signal;

[0037] construct a cost function curve according to each of the test dispersion values and the weighted digital signal, and determine a first dispersion estimation result among each of the test dispersion values based on the cost function curve;

[0038] correct the first dispersion estimation result according to a dispersion correction value range, to obtain a dispersion value.

[0039] In a fifth aspect, the present application also provides a computer program product, comprising a computer program which, when executed by a processor, implements the following steps:

[0040] obtain a shaped digital signal and each test dispersion value, and perform weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is used to recover a phase of the shaped digital signal;

[0041] construct a cost function curve according to each of the test dispersion values and the weighted digital signal, and determine a first dispersion estimation result among each of the test dispersion values based on the cost function curve;

[0042] correct the first dispersion estimation result according to a dispersion correction value range, to obtain a dispersion value.

[0043] The dispersion estimation method, device, computer device, computer readable storage medium and computer program product described above obtain a shaped digital signal and each test dispersion value, and perform weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is used to recover a phase of the shaped digital signal; a cost function curve is constructed according to each of the test dispersion values and the weighted digital signal, and a first dispersion estimation result is determined among each of the test dispersion values based on the cost function curve; the first dispersion estimation result is corrected according to a dispersion correction value range, to obtain a dispersion value. By using the method, the shaped digital signal is weighted processed according to the probability weighting algorithm, the phase of the shaped digital signal is preliminarily recovered, the weighted shaped digital signal after preliminary recovery is obtained, a cost function curve is constructed based on the weighted digital signal, the steepness of the cost function curve is recovered, and the accuracy of the cost function curve is improved. Then, a first dispersion estimation result is determined among each of the test dispersion values based on the cost function curve, and the first dispersion estimation result is corrected according to a dispersion correction value range, to further compensate for errors caused by channel loss and preliminary recovery of the phase of the shaped digital signal, to obtain an accurate dispersion value, and to improve the accuracy of the dispersion estimation method. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0045] Figure 1 The application environment diagram of the dispersion estimation method in one embodiment;

[0046] Figure 2 The relationship curve between the MSE (Mean Squared Error) performance of the estimation result of the FD CD est algorithm on the PS 64QAM signal and the SNR in one embodiment;

[0047] Figure 3 The schematic diagram of the influence of the probability shaping technology on the MSE performance of the FD CD est scheme in one embodiment;

[0048] Figure 4 The flowchart of the dispersion estimation method in one embodiment;

[0049] Figure 5 The flowchart of obtaining the shaped digital signal and the test dispersion value in one embodiment;

[0050] Figure 6 The flowchart of constructing the cost function curve in one embodiment;

[0051] Figure 7 The flowchart of determining the first dispersion estimation result in one embodiment;

[0052] Figure 8 The flowchart of determining the dispersion value in one embodiment;

[0053] Figure 9 The flowchart of dispersion compensation based on the dispersion value in one embodiment;

[0054] Figure 10 The application effect diagram of the dispersion estimation method in one embodiment in the dispersion estimation system;

[0055] Figure 11 The flowchart of the dispersion estimation method in one exemplary embodiment;

[0056] Figure 12 The structural block diagram of the dispersion estimation device in one embodiment;

[0057] Figure 13This is an internal structural diagram of a communication device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] The dispersion estimation method provided in this application can be applied to, for example... Figure 1 The dispersion estimation system 100 shown includes an optical transmitter 110 and an optical receiver 120. An optical fiber link exists between the optical transmitter 110 and the optical receiver 120 to transmit optical signals. This dispersion estimation system 100 is also a coherent optical communication system. The coherent optical communication system uses probabilistic shaped QAM modulation format for transmission. The optical transmitter 110 includes a laser, an I / Q (I for In-phase, Q for Quadrature) electro-optic modulator, a digital-to-analog converter, and a probabilistic shaped QAM (Quadrature Amplitude Modulation) signal sequence generator. The laser is used to generate a laser carrier; the digital-to-analog converter is used to convert the generated probabilistic shaped QAM digital signal sequence into an analog signal; and the I / Q modulator is used to complete the electro-optic conversion. The optical receiver 120 includes a coherent receiver, a local oscillator laser, an analog-to-digital converter, and receiver-side digital signal processing. Local oscillator lasers provide the local laser source; coherent receivers perform photoelectric conversion; analog-to-digital converters convert analog signals into digital signals; and digital signal processing at the receiver compensates for channel impairments. The digital signal processing flow mainly includes normalization, dispersion estimation and recovery, clock recovery, dispersion estimation and compensation, orthogonalization, polarization demultiplexing, frequency offset estimation, and phase recovery.

[0060] With the rapid development of optoelectronic devices, photonic integrated circuit technology, and digital signal processing technology, optical communication networks are gradually evolving towards intelligence. Under this trend, future optical networks need to support adaptive rate and optical path adjustment, as well as flexible optical wavelength switching. This necessitates the research of digital signal processing algorithms capable of dynamic adaptation.

[0061] The digital signal processing algorithm is applied in a data transmission process. The data transmission process specifically includes: an optical transmitter converts digital signals of to-be-transmitted data into analog signals and photoelectric signals, obtains optical signals, and transmits the optical signals to an optical receiver through an optical fiber link. The optical receiver receives the optical signals transmitted by the optical fiber link, converts the optical signals into photoelectric signals and digital signals, and then compensates for loss of the digital signals according to the digital signal processing algorithm to obtain the digital signals sent by the optical transmitter. In the process of compensating for the loss of the digital signals, the dispersion of the optical fiber needs to be determined based on a dispersion estimation method.

[0062] Because the lengths of optical fibers of different paths are different, the optical signals are affected by different degrees of dispersion after being transmitted through different optical paths. In addition, the dispersion coefficient of the optical fiber is not a constant value, and even in the same link, the existence of the dispersion slope causes the signals to encounter different damage caused by different accumulations of dispersion before and after wavelength exchange.

[0063] The traditional dispersion estimation method, the FD CD est algorithm based on the improved discrete cyclic autocorrelation function in the frequency domain, has become a potential choice in the field of dispersion estimation because of its advantages in low complexity and strong robustness. Specifically, the optical receiver performs Fourier transform on the digital signals and is based on the phase slope of the transformed digital signals. Then, the dispersion value of the optical fiber is determined according to the wavelength-dispersion characteristics of the optical fiber and the phase slope.

[0064] However, in order to improve the transmission rate of the optical signals, the transmitter shapes the occurrence probability of the digital signals of the to-be-transmitted data and transmits the shaped digital signals. The current dispersion estimation method can only determine the dispersion value of the optical fiber of the digital signals with uniformly distributed occurrence probability. Therefore, the accuracy of the current dispersion estimation method is low.

[0065] Specifically, the transmitter shapes the occurrence probability of the digital signals of the to-be-transmitted data based on a probability constellation shaping algorithm. The probability constellation shaping (PS) algorithm can adjust the occurrence probability of different amplitude constellation points in the constellation diagram with fine granularity, thereby changing the entropy value of the signal source, and has become a very promising solution. Compared with the traditional rate adaptive scheme based on uniformly distributed QAM signals, the probability shaping scheme can provide additional energy efficiency (sensitivity) benefits. By making the occurrence probability of each constellation point in the signal constellation obey the Maxwell-Boltzmann distribution, a shaping gain of up to 1.53 dB can be theoretically achieved, thereby making the transmission performance closer to the Shannon limit.

[0066] Although the research on probability shaping technique and chromatic dispersion estimation algorithm lays a theoretical foundation for the intelligent optical network of adaptive rate adjustment and optical path adjustment, the coexistence problem of the two techniques in practical application cannot be ignored. Existing research reports that there is significant incompatibility between the probability shaping technique and the traditional DSP algorithm, such as the carrier phase recovery algorithm, the forward error correction algorithm, the frequency offset estimation algorithm, the blind equalization algorithm, and the clock recovery algorithm, which cannot be effectively compatible. Through Monte Carlo simulation, the present application analyzes the accuracy performance of the low-complexity FD CD est algorithm when processing signals using the probability shaping technique.

[0067] Figure 2 The figure shows the relationship between the MSE (Mean Squared Error) performance of the estimation results of the FD CD est algorithm for the PS 64QAM signal and the SNR in one embodiment. As shown in Figure 2 For the 64QAM signal using the PS technique, as the shaping degree increases, the reliability of the estimation results obtained using the frequency domain chromatic dispersion estimation algorithm will decrease, which is manifested as the degradation of the mean square error (MSE) index.

[0068] Figure 3 The figure shows the influence of the probability shaping technique on the MSE performance of the FD CD est scheme in one embodiment. Figure 3 The figure includes (3a) and (3b). Figure 3 Uniform 64QAM in the figure (3a) and (3b) represents an unshaped digital signal, and PS 64QAM (V=0.05) represents a digital signal with a shaping parameter of 0.05. Figure (3a) is a statistical diagram of the estimation results of multiple independent random simulations; Figure (3b) is a comparison diagram of the cost function in the FD CD est algorithm under different shaping coefficient values. In order to clarify the specific reasons for the MSE performance degradation of the FD CD est algorithm due to the incompatibility with the PS technique, the present application analyzes the results of multiple independent random simulations, as shown in the statistical results shown in Figure (3a).

[0069] Based on Figure (3a), for the uniformly distributed 64QAM signal, the estimation results of the FD CD est algorithm are closely gathered around the actual value in each estimation attempt. In contrast, the estimation results of the PS-64QAM signal show significant variability. Although a small part of the estimation results are close to the actual value, a considerable number of estimation results show a large estimation error. Therefore, the overall MSE performance is significantly worse than that of the uniformly distributed signal.

[0070] To further clarify why PS technique tends to cause greater estimation error in FD CD est, a set of uniformly distributed 64QAM and PS-64QAM signals are selected under the same random seed setting, and the cost function curves corresponding to the two types of signals when using FD CD est algorithm are plotted as shown in (3b).

[0071] Based on the graph (3b), it can be seen that the cost function of the uniformly distributed 64QAM signal presents a clear concave trend at different test dispersion values, and has a unique and easily distinguishable minimum value at the actual dispersion value (i.e. 10,000 ps / nm, ps / nm represents the pulse spreading time (in picoseconds) per nanometer of wavelength per kilometer of optical fiber). In contrast, the cost function curve of the PS 64QAM signal is obviously flatter, and the function values at different test dispersion values are relatively close to each other. Therefore, the PS 64QAM signal is prone to fall into the estimated result deviating from the actual value corresponding to the minimum value. For example, in the graph (3b), for the PS 64QAM signal with a shaping parameter of 0.05, an estimation error of up to 400 ps / nm is observed.

[0072] Based on the above-mentioned conventional dispersion estimation method, the dispersion estimation method provided by the present application performs weighted processing on the shaped digital signal through a probability weighting method, preliminarily restores the phase of the shaped digital signal, obtains a preliminarily restored weighted shaped digital signal, and constructs a cost function curve based on the weighted digital signal, thereby restoring the steepness of the cost function curve and improving the accuracy of the cost function curve. Then, the first dispersion estimation result is determined based on the cost function curve at each test dispersion value, and the first dispersion estimation result is corrected through the dispersion correction value range, thereby further compensating for the error caused by the channel loss and the preliminary restoration of the phase of the shaped digital signal, obtaining an accurate dispersion value, and improving the accuracy of the dispersion estimation method.

[0073] Moreover, the dispersion estimation algorithm of the present application is compatible with the probability-shaped signal, improves the compatibility between the FD CD est algorithm and the PS QAM signal, makes the cost function obtained by the FD CD est algorithm when estimating the PS QAM become steeper, and further improves the accuracy of the dispersion estimation and compensation recovery of the PS QAM signal using the FD CD est algorithm, which has important significance for improving the transmission performance and application reliability of future intelligent flexible optical networks.

[0074] Before introducing the specific embodiments of the present application, the professional terms involved in the present application are explained:

[0075] QAM (Quadrature Amplitude Modulation), quadrature amplitude modulation.

[0076] PS (Probabilistic Shaping), probabilistic shaping.

[0077] CD (Chromatic Dispersion), chromatic dispersion.

[0078] FD CD est (Frequency Domain Chromatic Dispersion Estimation), frequency domain chromatic dispersion estimation.

[0079] SNR (Signal-to-Noise Ratio), signal-to-noise ratio.

[0080] MI (Mutual Information), mutual information.

[0081] DSP (Digital Signal Processing), digital signal processing.

[0082] FFT (Fast Fourier Transform), fast Fourier transform.

[0083] MSE (Mean Squared Error), mean squared error.

[0084] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes may not be described again in some examples. The embodiments of the present application will be described below with reference to the drawings.

[0085] In an exemplary embodiment, as shown in Figure 4 , a dispersion estimation method is provided, which is applied to an optical receiver 120 (hereinafter referred to as an optical receiver) in Figure 1 , and includes the following steps 402 to 406. Among them:

[0086] Step 402, obtaining the shaped digital signal and each test dispersion value, and performing weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal.

[0087] Among them, the probability weighting algorithm is used to recover the phase of the shaped digital signal.

[0088] In implementation, the optical receiver receives the shaped optical signal, and converts the shaped optical signal into a shaped digital signal. Meanwhile, the optical receiver acquires a search range and a step value of the dispersion value, and determines each test dispersion value according to the search range and the step value of the dispersion value. Then, the optical receiver performs weighted processing on the shaped digital signal according to a probability weighting algorithm, and obtains a weighted digital signal.

[0089] Specifically, the shaped digital signal is a sequence of shaped digital signals. The optical receiver determines a probability of occurrence of each shaped digital signal according to the probability weighting algorithm, and performs weighted processing on the shaped digital signal according to the probability of occurrence, to obtain a sequence of weighted digital signals. The optical receiver gives a lower weight value to a shaped digital signal with a higher probability of occurrence but a lower amplitude, and gives a higher weight value to a shaped digital signal with a lower probability of occurrence but a higher amplitude. The probability weighting algorithm is shown in the following formula (1):

[0090] x Pr o bability - weig h ted [k]=x[k] × exp ( v × abs(x[k] ) 2 ) ÷ ∑ k exp ( v × abs(x[k] ) 2 ) (1)

[0091] In the above formula (1), x[k] denotes the sequence of shaped digital signals, x Pr o bability - weig h ted [k] denotes the sequence of weighted digital signals, denotes a shaping coefficient in probability shaping, denotes a calculation of a modulus value of a complex signal. The weighted processing on the shaped digital signal by the probability weighting algorithm makes the cost function curve corresponding to the subsequent FDCD est algorithm have a more steep cost function curve.

[0092] In step 404, a cost function curve is constructed according to each test dispersion value and the weighted digital signal, and a first dispersion estimation result is determined among the test dispersion values based on the cost function curve.

[0093] In implementation, the optical receiver performs exploratory filtering processing on the weighted digital signal according to a filtering function algorithm, to obtain a filtered weighted digital signal. Then, the optical receiver determines an absolute value according to a cyclic autocorrelation function algorithm and the filtered weighted digital signal, and constructs a cost function curve according to each absolute value. The optical receiver determines the first dispersion estimation result among the test dispersion values according to an extreme point of the cost function curve.

[0094] Specifically, the optical receiver determines the filter function corresponding to each test dispersion value according to the filter function algorithm, and performs trial filtering on the weighted digital signal according to each filter function to obtain the filtered weighted digital signal. Then, the optical receiver determines the cyclic autocorrelation function corresponding to the filtered weighted digital signal according to the cyclic autocorrelation function algorithm, and determines the absolute value of the cyclic autocorrelation function. Then, the optical receiver constructs a cost function curve based on each absolute value, and determines the first dispersion estimation result among each dispersion value based on the extreme value in the cost function curve.

[0095] Step 406: Correct the first dispersion estimation result according to the dispersion correction value range to obtain the dispersion value.

[0096] In practice, the optical receiver determines the target dispersion correction value within the dispersion correction value range, and corrects the first dispersion estimation result based on the target dispersion correction value to obtain the dispersion value.

[0097] In an optional embodiment, the optical receiver performs dispersion compensation on the shaped digital signal based on the dispersion value to obtain a dispersion-compensated digital signal. This dispersion-compensated digital signal should be a dispersion-compensated digital signal sequence. It should be clarified that the dispersion-compensated digital signal sequence is still a shaped digital signal sequence. The optical receiver performs clock recovery, orthogonalization, polarization demultiplexing, frequency offset estimation, phase recovery, and probabilistic shaping QAM demodulation processing on the dispersion-compensated digital signal sequence in a preset order to obtain the data to be transmitted. The data to be transmitted after probabilistic shaping QAM demodulation is unshaped data.

[0098] In the aforementioned dispersion estimation method, the shaped digital signal is weighted using a probability weighting method to initially recover the phase of the shaped digital signal, resulting in a preliminarily recovered weighted digital signal. A cost function curve is then constructed based on this weighted digital signal, restoring its steepness and improving its accuracy. Next, a first dispersion estimation result is determined from each test dispersion value based on the cost function curve. This first dispersion estimation result is then corrected using a dispersion correction value range, further compensating for errors caused by channel loss and the preliminarily recovered phase of the shaped digital signal. This yields an accurate dispersion value and improves the accuracy of the dispersion estimation method.

[0099] In one exemplary embodiment, such as Figure 5 As shown, the specific processing procedure of step 402 includes steps 502 to 504. Wherein:

[0100] Step 502: Obtain the shaped digital signal, the search range of the dispersion value, and the step value.

[0101] The search range of the dispersion value refers to an interval of the dispersion value determined when the dispersion value is attempted to be found. The step value is a distance between each test dispersion value in the search range of the dispersion value. The smaller the step value is, the more test dispersion values are determined, and the more accurate the dispersion value obtained is. The larger the step value is, the fewer test dispersion values are determined, and the less accurate the dispersion value obtained is.

[0102] In implementation, the optical receiver receives the reshaped optical signal through the optical fiber link. Then, the optical receiver performs signal conversion on the reshaped optical signal to obtain a reshaped digital signal. Meanwhile, the optical receiver acquires the search range of the dispersion value and the step value. The search range of the dispersion value contains the lowest dispersion value and the highest dispersion value.

[0103] Specifically, the optical receiver receives the reshaped optical signal through the optical fiber link. Then, the optical receiver performs photoelectric conversion on the reshaped optical signal through the coherent receiver to obtain a reshaped electrical signal. The optical receiver converts the reshaped electrical signal into a reshaped digital signal through the analog-to-digital converter. Then, the optical receiver acquires the search range of the dispersion value and the step value.

[0104] In an optional embodiment, the optical receiver is connected with the host computer. The host computer acquires the step value and the search range of the dispersion value input by the user. Then, the host computer transmits the step value and the search range of the dispersion value to the optical receiver, so that the optical receiver acquires the search range of the dispersion value and the step value.

[0105] Optionally, the search range of the dispersion value and the step value are determined according to the estimation requirement of the dispersion value, and the search range of the dispersion value and the step value are not limited in the application.

[0106] In step 504, the search range of the dispersion value and the step value are processed to obtain each test dispersion value.

[0107] In implementation, the optical receiver takes the step value as an increment, and performs addition processing on the step value and the lowest dispersion value to obtain each test dispersion value.

[0108] Specifically, the optical receiver takes the step value as an increment, and performs addition processing on the step value and the lowest dispersion value to obtain each test dispersion value. Then, the optical receiver judges whether the test dispersion value reaches the highest dispersion value. If the test dispersion value does not reach the highest dispersion value, the optical receiver performs addition operation on the step value and the test dispersion value to obtain the next test dispersion value, until the test dispersion value reaches the highest dispersion value.

[0109] In the embodiment, the reshaped digital signal is obtained by acquiring the reshaped digital signal, and each initial dispersion value is obtained by determining each test dispersion value, so that the first dispersion estimation result is determined in each test dispersion value in the subsequent process.

[0110] In one exemplary embodiment, as shown in FIG. 4, the step 404 of constructing the cost function curve according to the test dispersion values and the weighted digital signal includes steps 602-606. In which: Figure 6

[0111] In step 602, a filter function corresponding to each test dispersion value is constructed based on a filter function algorithm, and a weighted digital signal is processed by tentative filtering based on the filter function to obtain a filtered weighted digital signal.

[0112] In which, the filter function algorithm is a tentative filter function algorithm.

[0113] In implementation, for each test dispersion value, the optical receiver constructs a tentative filter function corresponding to each test dispersion value according to the filter function algorithm. Then, the optical receiver processes the weighted digital signal by tentative filtering according to the tentative filter function to obtain a filtered weighted digital signal. By processing the weighted digital signal by tentative filtering, the noise in the weighted digital signal can be eliminated, and the accuracy of the weighted digital signal can be improved.

[0114] Optionally, the form of the tentative filter function can be, but is not limited to, a low-pass filter, a high-pass filter, etc., and the form of the tentative filter function is not limited in the embodiments of the present application.

[0115] In step 604, a cyclic autocorrelation function corresponding to the filtered weighted digital signal is constructed according to a cyclic autocorrelation function algorithm, and an absolute value corresponding to the cyclic autocorrelation function is determined.

[0116] In implementation, the optical receiver is pre-provided with a cyclic autocorrelation function (CAF) algorithm. The optical receiver constructs a cyclic autocorrelation function corresponding to the weighted digital signal based on the pre-set cyclic autocorrelation function algorithm. In which, the cyclic autocorrelation function algorithm is shown in the following formula (2):

[0117] (2)

[0118] In which, in the above formula (2), denotes a time delay, denotes a cyclic frequency, denotes a cyclostationary signal. denotes a cyclic autocorrelation function. is a time window, denotes a signal time delay, denotes a signal time delay complex conjugate. is a complex exponential function, is a normalization factor. ​denotes the value of the cyclic autocorrelation function when the time window T tends to infinity.

[0119] The cyclic autocorrelation function corresponding to the weighted digital signal is shown in the following formula (3):

[0120] (3)

[0121] In the above formula (3), denotes the cyclic autocorrelation function corresponding to the weighted digital signal. denotes the cyclic autocorrelation function corresponding to the weighted digital signal. denotes the offset in time, denotes the cyclic frequency, denotes the Fourier transform of the weighted digital signal at the frequency . denotes the conjugate value of the Fourier transform of the weighted digital signal at the frequency . is a complex exponential function, is a normalization factor.

[0122] Then, the optical receiver performs a modulus operation on the cyclic autocorrelation function corresponding to the weighted digital signal, and obtains the absolute value corresponding to the cyclic autocorrelation function.

[0123] Step 606, constructing a cost function curve based on each absolute value.

[0124] In implementation, the optical receiver draws a cost function curve of the weighted digital signal sequence based on the absolute value corresponding to each cyclic autocorrelation function.

[0125] Specifically, the optical receiver is pre-provided with a drawing tool. The optical receiver inputs each absolute value into the drawing tool, and draws a cost function curve of the weighted digital signal sequence through the drawing tool.

[0126] In this embodiment, the weighted digital signal is tentatively filtered through the filter function, the noise in the weighted digital signal is eliminated, and the cost function curve is constructed according to the filtered weighted digital signal, which improves the steepness of the cost function and improves the accuracy of the cost function curve.

[0127] In an exemplary embodiment, as shown in Figure 7 , the specific processing process of determining the first dispersion estimation result in each test dispersion value based on the cost function curve in step 404 includes steps 702 to 704. Among them:

[0128] Step 702, determining an extreme point in the cost function curve.

[0129] In implementation, the optical receiver determines the extreme point in the cost function curve. The extreme point is the absolute value corresponding to the cyclic autocorrelation function.

[0130] Specifically, since the shaped digital signal is weighted in step 402 to obtain a weighted digital signal, and the cost function curve is constructed based on the weighted digital signal, a steeper cost function curve is obtained, so that the optical receiver can more accurately determine the extreme point in the steep cost function curve.

[0131] Step 704: determining the test dispersion value corresponding to the extreme point in each test dispersion value as the first dispersion estimation result.

[0132] In implementation, the optical receiver determines the test dispersion value corresponding to the extreme point in each test dispersion value. Then, the optical receiver determines the test dispersion value as the first dispersion estimation result.

[0133] Specifically, the abscissa of the cost function curve is each test dispersion value, and the ordinate is the absolute value corresponding to the cyclic autocorrelation function. The optical receiver takes the abscissa corresponding to the extreme point of the cost function curve as the first dispersion estimation value, that is, the test dispersion value corresponding to the extreme point of the cost function curve is determined as the first dispersion estimation result.

[0134] In this embodiment, by determining the first dispersion estimation result in the steep cost function curve, the first dispersion estimation result can be more accurately determined, and the accuracy of the first dispersion estimation result is improved.

[0135] In an exemplary embodiment, as shown in FIG. 8, the specific processing process of step 406 includes steps 802 to 804. Among them: Figure 8

[0136] Step 802: determining a target dispersion correction value in the dispersion value correction range.

[0137] Among them, the dispersion value correction range is determined based on an empirical formula.

[0138] In implementation, the optical receiver randomly determines a dispersion correction value in the dispersion value correction range as the target dispersion correction value.

[0139] In an exemplary embodiment, the optical receiver determines the dispersion value correction range according to the empirical formula. The correction range of the dispersion value is generally 400~430 ps / nm. The optical receiver determines 410 ps / nm as the target dispersion correction value in the dispersion value correction range.

[0140] Step 804: performing addition operation on the target dispersion correction value and the first dispersion estimation result to obtain a second estimation result, and determining the second estimation result as the dispersion value.

[0141] ​The target dispersion correction value is a dispersion correction value.

[0142] In the implementation, the probability weighting algorithm and the optical signal in the transmission process will affect the dispersion value, thus causing errors. Therefore, the first dispersion estimation result needs to be corrected. The optical receiver determines the sum of the target dispersion correction value and the first dispersion estimation result as a second estimation result, and determines the second estimation result as the dispersion value. The generation process of the dispersion value is shown in the following formula (4):

[0143] δ est2 = δ est1 + δ Correct-Factor = δ min +Δδ arg min i J[i] -1 + δ Correct-Factor (4)

[0144] In the above formula (4), the first dispersion estimation result is represented by , the dispersion value after correction is represented by , and the dispersion correction term obtained by the empirical formula is represented by is the minimum dispersion value. is the dispersion step. arg min i J[i] is the cost function J[i] is the index when the cost function takes the minimum value. -1 is an adjustment for the index offset.

[0145] In the embodiment, the correction coefficient based on the empirical formula is used to correct the first dispersion estimation result corresponding to the extreme value point of the cost function, so as to obtain an accurate dispersion value. Furthermore, the dispersion compensation is performed according to the size, so as to improve the recovery effect of the blind dispersion estimation and compensation of the reshaped digital signal.

[0146] In an exemplary embodiment, after obtaining the dispersion value, the reshaped digital signal also needs to be compensated for dispersion according to the dispersion value. As Figure 9 shown, after step 406 is executed, the specific processing process of the dispersion estimation method includes steps 902 to 906. Among them:

[0147] In step 902, the reshaped digital signal is converted to obtain a frequency domain digital signal according to the Fourier transform algorithm.

[0148] ​In implementation, the optical receiver performs frequency domain conversion on the shaped digital signal according to a Fourier transform algorithm to obtain a frequency domain digital signal. By converting the shaped digital signal to the frequency domain, the composition and intensity of the shaped digital signal at different frequencies can be analyzed.

[0149] Specifically, the Fourier transform algorithm is a fast Fourier transform (FFT). The fast Fourier transform algorithm is shown in the following formula (5):

[0150] (5)

[0151] In the above formula (5), is the shaped digital signal. is the frequency domain digital signal. N represents the number of the shaped digital signal.

[0152] Step 904, constructing a dispersion compensation filter according to the dispersion value, and compensating the frequency domain digital signal based on the dispersion compensation filter to obtain a compensated frequency domain digital signal.

[0153] In implementation, the optical receiver constructs a dispersion compensation filter according to the dispersion value. Then, the optical receiver performs multiplication operation on the transfer function in the dispersion compensation filter and the frequency domain digital signal to obtain the compensated frequency domain digital signal.

[0154] In the above formula (6),

[0155] (6)

[0156] In the above formula (6), is the transfer function, denotes an imaginary unit, is a phase function, which is determined by the dispersion value, as shown in the following formula (7):

[0157] (7)

[0158] In the above formula (7), is the dispersion value, is the frequency. Based on the above formula (6) and formula (7), the transfer function of the dispersion value compensation filter can be determined as shown in the following formula (8):

[0159] (8)

[0160] In the above formula (8), is the transfer function, is an index, is the dispersion value, is the frequency. denotes the imaginary unit.

[0161] Step 906, converting the compensated frequency domain digital signal according to the inverse Fourier transform algorithm to obtain a compensated digital signal.

[0162] The compensated digital signal is still a shaped digital signal.

[0163] In implementation, the inverse Fourier transform algorithm (IDFT, Inverse Discrete Fourier Transform) preset by the optical receiver is used to perform time domain conversion on the compensated frequency domain digital signal to obtain the compensated digital signal. The inverse Fourier transform algorithm is shown in the following formula (9):

[0164] (9)

[0165] In the above formula (9), is the compensated digital signal. is the compensated frequency domain digital signal. N represents the number of shaped digital signals.

[0166] In this embodiment, the shaped digital signal is compensated based on the accurate dispersion value to obtain an accurate compensated digital signal. Moreover, the application solves the problem of incompatibility between the shaped digital signal and the traditional dispersion estimation method, so that the digital signal can be shaped when transmitting data subsequently, thereby improving the data transmission efficiency and the transmission performance of the coherent optical communication system.

[0167] In an exemplary embodiment, Figure 10 is an application effect diagram of the dispersion estimation method in the dispersion estimation system in one embodiment. Figure 10 contains Figure 10 (a) and Figure 10 (b). The recovery accuracy of the dispersion estimation and compensation of the PS-64QAM (shaping coefficient v=0.05) signal using the dispersion estimation method of the application and using the frequency domain autocorrelation dispersion estimation algorithm is analyzed respectively through Monte Carlo simulation. From the simulation results in Figure 10 (a), it can be seen that after applying the dispersion estimation method proposed in the application, the cost function presents a more clear and uniform distribution concave trend under different test dispersion values (i.e. the triangular line in Figure 10 ). Although there is a fixed deviation between the minimum value position of the cost function and the actual dispersion value (i.e. 10,000 ps / nm), this deviation can be corrected by the dispersion correction value range. Further, Figure 10The dispersion estimation results corresponding to the traditional FD CDest scheme and the new dispersion estimation scheme suitable for the probability shaped signal are shown in (b) after multiple independent random simulations. Figure 10 As can be seen from the results in (b), the fluctuation of the estimation results of multiple random experiments can be effectively reduced by using the scheme of the present application, and the consistency of the estimation results of the uniformly distributed signal is comparable to that of the traditional scheme. Moreover, after the correction of the probability weighted estimation results, the accuracy of the estimation results is obviously improved, which proves the effectiveness of the present application.

[0168] In an exemplary embodiment, Figure 11 A flowchart of the dispersion estimation method in an exemplary embodiment is shown in (a). As shown in (a), the dispersion estimation method comprises: Figure 11

[0169] Step 1101, combining the input signal of the dual polarization to obtain the shaped digital signal.

[0170] Step 1102, performing the signal preprocessing on the shaped digital signal based on the probability weighting algorithm to obtain the weighted digital signal, so as to improve the weight value of the high modulus signal point.

[0171] Step 1103, defining the search range and step value of the test dispersion value to obtain each test dispersion value.

[0172] Step 1104, using the tentative filtering function based on different test dispersion values on the weighted digital signal to obtain the filtered weighted digital signal.

[0173] Step 1105, constructing the cyclic autocorrelation function corresponding to the filtered weighted digital signal.

[0174] Step 1106, determining the absolute value corresponding to each cyclic autocorrelation function, and constructing the cost function curve corresponding to different test dispersion values according to each absolute value.

[0175] Step 1107, selecting the test dispersion value corresponding to the extreme point of the cost function as the first dispersion estimation result.

[0176] Step 1108, correcting the first dispersion estimation result according to the dispersion correction value range to obtain the second dispersion estimation result.

[0177] Step 1109, determining the second dispersion estimation result as the dispersion value.

[0178] Step 1110, performing the dispersion compensation on the shaped digital signal based on the dispersion value to obtain the dispersion compensated digital signal.

[0179] ​The application provides a new dispersion estimation scheme based on probability weighting and coefficient correction, which is suitable for a probability shaping signal, solves the problem that the FD CDest algorithm cannot be compatible with the probability shaping QAM signal, and further improves the estimation accuracy of the FD CDest algorithm for the PS QAM signal, and solves the problem that the existing dispersion estimation algorithm cannot be applied to the intelligent flexible optical network realized based on the probability shaping technology. Moreover, the dispersion estimation method provided by the application can extend the application range of the FD CDest algorithm from being suitable for only the conventional QAM signal subjected to uniform distribution to also supporting the probability shaping QAM signal subjected to Maxwell-Boltzmann distribution, and has a positive significance for improving the recovery performance of the dispersion estimation and dispersion compensation process in the future coherent optical communication system and reducing the calculation complexity.

[0180] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0181] Based on the same inventive concept, the application also provides a dispersion estimation device for implementing the dispersion estimation method described above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more dispersion estimation device embodiments provided below can refer to the limitations of the dispersion estimation method described above, which will not be repeated here.

[0182] In one exemplary embodiment, as shown in Figure 12 A dispersion estimation device 1200 is provided, which includes a processing module 1201, a construction module 1202 and a correction module 1203, wherein:

[0183] The processing module 1201 is configured to obtain the shaped digital signal and the test dispersion values, and perform weighting processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is used to recover the phase of the shaped digital signal.

[0184] The constructing module 1202 is configured to construct a cost function curve according to the test dispersion values and the weighted digital signal, and determine the first dispersion estimation result among the test dispersion values based on the cost function curve.

[0185] The correcting module 1203 is configured to correct the first dispersion estimation result according to the dispersion correction value range, to obtain the dispersion value.

[0186] In an example embodiment, the processing module 1201 includes a first obtaining sub-module and a first processing sub-module. The first obtaining sub-module includes:

[0187] The second obtaining sub-module is configured to obtain the shaped digital signal, the search range of the dispersion value, and the step value.

[0188] The second processing sub-module is configured to perform data processing on the search range of the dispersion value and the step value, to obtain the test dispersion values.

[0189] In an example embodiment, the constructing module 1202 includes a first constructing sub-module and a first determining sub-module. The first constructing sub-module includes:

[0190] The second constructing sub-module is configured to construct a filter function corresponding to each test dispersion value based on a filter function algorithm, and perform exploratory filter processing on the weighted digital signal based on the filter function, to obtain the filtered weighted digital signal.

[0191] The third constructing sub-module is configured to construct a cyclic autocorrelation function corresponding to the filtered weighted digital signal according to a cyclic autocorrelation function algorithm, and determine an absolute value corresponding to the cyclic autocorrelation function.

[0192] The fourth constructing sub-module is configured to construct a cost function curve based on the absolute values.

[0193] In an example embodiment, the constructing module 1202 includes a first constructing sub-module and a first determining sub-module. The first determining sub-module includes:

[0194] The second determining sub-module is configured to determine an extreme point in the cost function curve.

[0195] The third determining sub-module is configured to determine that a test dispersion value corresponding to the extreme point among the test dispersion values is the first dispersion estimation result.

[0196] In an example embodiment, the correcting module 1203 includes:

[0197] The fourth determining sub-module is configured to determine a target dispersion correction value in the dispersion correction value range.

[0198] The first calculation submodule is used to perform an addition operation on the target dispersion correction value and the first dispersion estimation result to obtain the second estimation result, and to determine the second estimation result as the dispersion value.

[0199] In one exemplary embodiment, the dispersion estimation device 1200 further includes:

[0200] The first conversion module is used to convert the shaped digital signal according to the Fourier transform algorithm to obtain a frequency domain digital signal.

[0201] The compensation module is used to construct a dispersion compensation filter based on the dispersion value, and to compensate the frequency domain digital signal based on the dispersion compensation filter to obtain the compensated frequency domain digital signal.

[0202] The second conversion module is used to convert the compensated frequency domain digital signal according to the inverse Fourier transform algorithm to obtain the compensated digital signal.

[0203] Each module in the aforementioned dispersion estimation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0204] In one embodiment, a communication device is provided, see [link to previous document]. Figure 13 . Figure 13 This is a schematic diagram of the structure of the terminal device provided in an embodiment of the present invention. Figure 13 The terminal device 1300 shown includes at least one processor 1301, a memory 1302, at least one network interface 1304, and a user interface 1303. The various components in the terminal device 1300 are coupled together via a bus system 1305. It is understood that the bus system 1305 is used to implement communication between these components. In addition to a data bus, the bus system 1305 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 13 Various buses are designated as bus system 1305. Additionally, this embodiment of the invention includes a transceiver 1306, which may consist of multiple components, including a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium.

[0205] The user interface 1303 may include a display, keyboard, or clicking device (e.g., mouse, trackball, touchpad, or touchscreen).

[0206] It is to be appreciated that the memory 1302 in the embodiments of the present application can be a volatile memory or a nonvolatile memory, or can include both volatile and nonvolatile memory. Among them, the nonvolatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous Dynamic RAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1302 of the system and method described in the embodiments of the present application is intended to include, without being limited to, these and any other suitable types of memory.

[0207] In some embodiments, the memory 1302 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 13021 and an application program 13022.

[0208] Among them, the operating system 13021 contains various system programs, such as framework layer, core library layer, driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program 13022 contains various application programs, such as Media Player, Browser, etc., for implementing various application services. The program for implementing the method of the embodiments of the present application can be contained in the application program 13022.

[0209] In the embodiment of the present application, the processor is configured to acquire the shaped digital signal and the test dispersion values, and perform weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is used to recover the phase of the shaped digital signal; a cost function curve is constructed according to the test dispersion values and the weighted digital signal, and a first dispersion estimation result is determined from the test dispersion values based on the cost function curve; and the first dispersion estimation result is corrected according to the dispersion correction value range to obtain the dispersion value.

[0210] The processor 1301 can be an integrated circuit chip having a signal processing capability. In the implementation process, the steps of the above method can be completed by hardware integrated logic circuit or software form of instructions in the processor 1301. The processor 1301 can be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The methods, steps and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware code processor to execute, or be executed by a combination of hardware and software modules in the code processor. The software module can be located in a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable read only memory, a register, or other mature storage medium in the art. The storage medium is located in the memory 1302, and the processor 1301 reads the information in the memory 1302 and combines the hardware to complete the steps of the above method.

[0211] It can be understood that the embodiments described in the embodiments of the present application can be implemented by hardware, software, firmware, middleware, microcode or combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described in the present application, or a combination thereof.

[0212] For software implementation, the technologies described in the embodiments of the present application can be implemented by modules (for example, processes, functions, etc.) for performing the functions described in the embodiments of the present application. The software code can be stored in the memory and executed by the processor 1301. The memory can be implemented in the processor 1301 or implemented outside the processor 1301.

[0213] Those skilled in the art can understand that the technical solutions in the embodiments of the present application can be implemented by means of hardware only, or by means of software only, or by means of a combination of software and hardware. Figure 13 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0214] In one embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0215] In one embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0216] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0217] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0218] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the present application.

[0219] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method of dispersion estimation, characterized by, The method comprises: obtaining a shaped digital signal and each test dispersion value, and performing weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is used to recover the phase of the shaped digital signal; constructing a cost function curve according to each test dispersion value and the weighted digital signal, and determining a first dispersion estimation result in each test dispersion value based on the cost function curve; correcting the first dispersion estimation result according to a dispersion correction value range to obtain a dispersion value.

2. The method of claim 1, wherein, The method comprises: obtaining a shaped digital signal, a search range of a dispersion value and a step value; performing data processing on the search range of the dispersion value and the step value to obtain each test dispersion value.

3. The method of claim 1, wherein, The method comprises: constructing a filter function corresponding to each test dispersion value based on a filter function algorithm, and performing exploratory filter processing on the weighted digital signal based on the filter function to obtain a filtered weighted digital signal; constructing a cyclic autocorrelation function corresponding to the filtered weighted digital signal according to a cyclic autocorrelation function algorithm, and determining an absolute value corresponding to the cyclic autocorrelation function; constructing a cost function curve based on each absolute value.

4. The method of claim 1, wherein, The method comprises: determining an extreme point in the cost function curve; determining a test dispersion value corresponding to the extreme point in each test dispersion value as a first dispersion estimation result.

5. The method of claim 1, wherein, The method comprises: determining a target dispersion correction value in the dispersion correction range; performing addition operation on the target dispersion correction value and the first dispersion estimation result to obtain a second estimation result, and determining the second estimation result as a dispersion value.

6. The method of claim 1, wherein, After the method of correcting the first dispersion estimation result according to the dispersion correction value range to obtain a dispersion value, the method further comprises: converting the shaped digital signal according to a Fourier transform algorithm to obtain a frequency domain digital signal; constructing a dispersion compensation filter according to the dispersion value, and compensating the frequency domain digital signal based on the dispersion compensation filter to obtain a compensated frequency domain digital signal; converting the compensated frequency domain digital signal according to an inverse Fourier transform algorithm to obtain a compensated digital signal.

7. A dispersion estimation apparatus characterized by comprising: The device comprises: a processing module configured to obtain a shaped digital signal and each test dispersion value, and perform weighted processing on the shaped digital signal according to a probability weighting algorithm to obtain a weighted digital signal; the probability weighting algorithm is used to recover the phase of the shaped digital signal; a construction module configured to construct a cost function curve according to each test dispersion value and the weighted digital signal, and determine a first dispersion estimation result in each test dispersion value based on the cost function curve; a correction module configured to correct the first dispersion estimation result according to a dispersion correction value range to obtain a dispersion value.

8. A communication device comprising a memory and a processor, the memory storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

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