Timing error estimation method and device for Nyquist system

Abstract

The present application discloses a timing error estimation method and apparatus for a Nyquist system. The method comprises: generating a randomly modulated signal; inserting zeros into even-numbered bits of the randomly modulated signal to obtain an initial training sequence; generating a target communication frame based on the initial training sequence and a communication signal; and transmitting the target communication frame to a receiving end of the Nyquist system. The receiving end extracts the initial training sequence from the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result. The present application enhances the tolerance of the timing error detection algorithm to chromatic dispersion, polarization mode dispersion effects, and differential group delay impairments, thereby providing accurate timing error estimation even under complex channel conditions. The present application can be widely applied in the field of data communication technology.

Description

Technical Field

[0001] The present application relates to the field of data communication technology, and in particular to a method and device for estimating timing errors of a Nyquist system. Background Art

[0002] In the related technology, there are timing error estimation algorithms. In optical communication systems, timing error estimation is crucial for accurate signal demodulation and system performance. However, due to non-ideal factors such as dispersion effects, polarization mode dispersion and random polarization rotation in the optical fiber channel, the performance of traditional timing error estimation algorithms is limited when facing these complex channel effects, which has a significant impact on the performance of the timing error estimation algorithm. These complex channel effects will cause the spectral correlation of the signal to decrease, thereby affecting the accurate estimation of the timing error.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The embodiments of the present application aim to at least partially address one of the technical problems in the related art. To this end, the main purpose of the embodiments of the present application is to propose a method and apparatus for estimating the timing error of a Nyquist system, which aims to enhance the tolerance of the timing error detection algorithm to chromatic dispersion, polarization mode dispersion effects, and differential group delay impairments, thereby providing accurate timing error estimation even under complex channel conditions.

[0005] To achieve the above-mentioned object, an embodiment of the present application provides a timing error estimation method for a Nyquist system, which is applied to a transmitting end of the Nyquist system. The method includes the following steps:

[0006] Generate a random modulation signal;

[0007] Inserting zeros into even bits of the random modulated signal to obtain an initial training sequence;

[0008] generating a target communication frame based on the initial training sequence and the communication signal;

[0009] The target communication frame is sent to a receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result.

[0010] In some embodiments, generating a target communication frame based on the initial training sequence and the communication signal includes:

[0011] Splicing the initial training sequence and the communication signal to obtain a target spliced ​​signal;

[0012] Mapping the target splicing signal using a target signal mapping method to obtain a target mapping signal;

[0013] Performing Nyquist pulse shaping on the target mapping signal to obtain the target communication frame.

[0014] To achieve the above objectives, an embodiment of the present application provides a timing error estimation method for a Nyquist system, which is applied to a receiving end of the Nyquist system. The method includes the following steps:

[0015] Receiving a target communication frame sent by a transmitting end; wherein the target communication frame includes an initial training sequence and a communication signal;

[0016] performing data extraction processing on the target communication frame to obtain the initial training sequence in the target communication frame;

[0017] performing data processing on the initial training sequence to obtain a target training sequence;

[0018] Constructing a target cyclic autocorrelation function matrix based on the target training sequence;

[0019] Performing timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and the target timing error detection algorithm to obtain a timing error estimation result;

[0020] The target communication frame is determined according to the timing error estimation method of the Nyquist system applied to the transmitting end of the Nyquist system.

[0021] In some embodiments, the performing data processing on the initial training sequence to obtain a target training sequence includes:

[0022] The initial training sequence is resampled using a target resampling rule to obtain the target training sequence; wherein the target training sequence is applied to a timing error detection unit in the receiving end.

[0023] In some embodiments, the target communication frame is represented by a discrete digital signal at the receiving end, and constructing a target cyclic autocorrelation function matrix based on the target training sequence includes:

[0024] intercepting the target training sequence according to a target interception time length to obtain a target discrete digital signal;

[0025] generating a spectrum correlation function based on the target discrete digital signal;

[0026] Performing inverse Fourier transform processing on the spectrum correlation function to obtain an initial cyclic autocorrelation function;

[0027] Constructing an initial cyclic autocorrelation function matrix according to the initial cyclic autocorrelation function;

[0028] The target cyclic autocorrelation function matrix is ​​generated based on the initial cyclic autocorrelation function matrix.

[0029] In some embodiments, generating the target cyclic autocorrelation function matrix based on the initial cyclic autocorrelation function matrix includes:

[0030] The target cyclic autocorrelation function in the initial cyclic autocorrelation function matrix is ​​calculated using a target peak position formula to obtain the target cyclic autocorrelation function matrix at the target peak position.

[0031] In some embodiments, the target peak position formula is:

[0032]

[0033] Among them, τ represents the target peak position point, Expressed as the cyclic autocorrelation function of signal x and signal x, It is represented as the cyclic autocorrelation function of signals x and y, and argmax is represented as the operator that returns the index of the maximum value.

[0034] In some embodiments, performing timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and a target timing error detection algorithm to obtain a timing error estimation result includes:

[0035] Based on the target cyclic autocorrelation function matrix, the timing error of the target communication frame is estimated in combination with the timing error estimation formula in the target timing error detection algorithm to obtain the timing error estimation result; the timing error estimation formula is:

[0036]

[0037] in, is represented as the timing error, CAF is represented as the initial cyclic autocorrelation function matrix, CAF(τ) is represented as the target cyclic autocorrelation function matrix at the τth position, SCF is represented as the spectrum correlation function matrix, det is represented as the matrix determinant operator, and -Im{·} is represented as the imaginary part operator of the complex number.

[0038] To achieve the above-mentioned object, another aspect of an embodiment of the present application provides a timing error estimation device for a Nyquist system, which is applied to a transmitting end of the Nyquist system. The device includes the following modules:

[0039] A random modulation signal generation module, used for generating a random modulation signal;

[0040] an initial training sequence building module, configured to insert zeros into even-numbered bits of the random modulated signal to obtain an initial training sequence;

[0041] A target communication frame generating module, configured to generate a target communication frame based on the initial training sequence and the communication signal;

[0042] The target communication frame sending module is used to send the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and estimates the timing error of the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result.

[0043] To achieve the above-mentioned object, another aspect of an embodiment of the present application provides a timing error estimation device for a Nyquist system, which is applied to a receiving end of the Nyquist system. The device includes the following modules:

[0044] A target communication frame receiving module is configured to receive a target communication frame sent by a transmitting end; wherein the target communication frame includes an initial training sequence and a communication signal;

[0045] An initial training sequence extraction module is used to perform data extraction processing on the target communication frame to obtain the initial training sequence in the target communication frame;

[0046] A target training sequence acquisition module is used to process the initial training sequence to obtain a target training sequence;

[0047] A cyclic autocorrelation function matrix acquisition module, configured to construct a target cyclic autocorrelation function matrix based on the target training sequence;

[0048] A timing error estimation module is used to perform timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and a target timing error detection algorithm to obtain a timing error estimation result;

[0049] The target communication frame is determined according to the timing error estimation device of the Nyquist system applied to the transmitting end of the Nyquist system.

[0050] The embodiments of the present application include at least the following beneficial effects: The present application provides a method and apparatus for estimating the timing error of a Nyquist system, which generates a random modulation signal; inserts zeros into the even bits of the random modulation signal to obtain an initial training sequence; generates a target communication frame based on the initial training sequence and the communication signal; and transmits the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence from the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result. The embodiments of the present application enhance the tolerance of the timing error detection algorithm to dispersion, polarization mode dispersion effects, and differential group delay impairments by designing an RZ code training sequence at the transmitting end and transmitting it to the receiving end, thereby providing accurate timing error estimation even under complex channel conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flowchart of the steps when the timing error estimation method of the Nyquist system provided by an embodiment of the present application is applied to the transmitting end of the Nyquist system;

[0052] Figure 2 This is a flow chart of constructing a communication frame based on a training sequence according to an embodiment of the present application;

[0053] Figure 3 This is a flowchart of the steps when the timing error estimation method of the Nyquist system provided by an embodiment of the present application is applied to the receiving end of the Nyquist system;

[0054] Figure 4 This is a flow chart of a timing error estimation algorithm provided in an embodiment of the present application;

[0055] Figure 5 1 is a schematic structural diagram of a timing error estimation device for a Nyquist system provided by an embodiment of the present application when applied to a transmitting end of the Nyquist system;

[0056] Figure 6 1 is a schematic structural diagram of a timing error estimation device for a Nyquist system provided by an embodiment of the present application when applied to a receiving end of the Nyquist system;

[0057] Figure 7 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0059] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0060] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0062] Before describing the embodiments of the present application in detail, some of the nouns and terms involved in the embodiments of the present application are first explained. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations:

[0063] (1) Coherent Optical Communication: Coherent optical communication is an advanced optical communication technology that uses lasers as light sources and uses coherent detection technology to improve signal reception sensitivity and transmission quality. In a coherent optical communication system, the signal at the transmitter modulates the optical carrier of the laser, while the receiver uses a local laser synchronized with the transmitter for coherent mixing to detect the amplitude and phase information of the transmitted signal. Coherent optical communication technology significantly improves the signal transmission distance and system capacity.

[0064] (2) Chromatic Dispersion (CD) refers to the phenomenon in which the optical signal transmitted in an optical fiber is broadened due to the different speeds of light at different wavelengths. Dispersion distorts the signal's time domain waveform, affecting signal integrity and thus degrading the performance of the communication system. Chromatic dispersion, which includes material dispersion and waveguide dispersion, is one of the damage factors that require special consideration and compensation in long-distance optical fiber communication systems.

[0065] (3) Nyquist System: A Nyquist system is a signal sampling system that satisfies the Nyquist sampling theorem. According to the Nyquist sampling theorem, to avoid aliasing, the sampling frequency of the signal should be at least twice the highest frequency of the signal. In optical fiber communications, a Nyquist system usually refers to a digital signal processing system that meets this condition. The Nyquist system can accurately reconstruct the original analog signal from the sampled data.

[0066] (4) Timing Error Estimation: Timing error estimation is the process of estimating the deviation between the sampling clock of the received signal and the actual clock of the signal in a digital communication system. Accurate timing error estimation is crucial for synchronous demodulation of the signal. It ensures that the signal is sampled at the optimal time, thereby improving the accuracy of signal demodulation and the overall performance of the system.

[0067] (5) DSP technology (Digital Signal Processing Technology) refers to the technology of using digital circuits or computers to process signals. In optical fiber communication systems, DSP technology is widely used in key links such as signal modulation, demodulation, equalization, dispersion compensation, and timing recovery. Through DSP technology, efficient signal processing can be achieved, system performance can be optimized, and transmission quality can be improved.

[0068] (6) Roll-off Factor (ROF). The roll-off factor is a parameter that describes the rate at which a signal spectrum decays. It is used to filter signals in digital communication systems to limit their bandwidth. Signals with smaller roll-off factors have slower decay edges, meaning the signal occupies a wider spectrum; signals with larger roll-off factors have faster decay edges and narrower spectrum occupancy. The choice of roll-off factor affects the system's transmission efficiency and anti-interference capability.

[0069] (7) Polarization Division Multiplexing (PDM) is a technology that improves the capacity of optical fiber communication systems. It uses the polarization characteristics of optical signals to achieve double the data transmission rate by simultaneously transmitting two orthogonal polarization state signals in the same optical fiber. Each polarization state can independently carry information, equivalent to two independent communication channels. Polarization multiplexing technology can be combined with other multiplexing technologies (such as wavelength division multiplexing (WDM)) to further improve the system's spectral efficiency and transmission capacity.

[0070] (8) Rotation of State of Polarization (RSOP) describes the change in the polarization state of an optical signal during transmission in an optical fiber. Due to the imperfections of the optical fiber and external environmental factors (such as temperature and stress), the polarization state of the optical signal may randomly rotate during transmission. This rotation affects the signal quality and the performance of coherent detection. Therefore, the impact of polarization rotation needs to be considered when designing optical fiber communication systems, and appropriate compensation measures must be taken.

[0071] (9) Differential Group Delay (DGD) refers to the difference in arrival time caused by the different path lengths of two orthogonal polarization state optical signals transmitted in an optical fiber due to birefringence in a polarization multiplexing system. DGD is a manifestation of polarization mode dispersion (PMD), which is an indicator of the difference in transmission speed of optical fiber for signals with different polarization states. DGD will cause signal pulse broadening, affecting the integrity of the signal and the bit error rate performance of the system. In high-speed optical fiber communication systems, DGD compensation is one of the key technologies to ensure signal quality.

[0072] (10) Single Sample per Symbol (SPS). In digital signal processing and communication systems, SPS is a sampling strategy that means only one sample is collected in each symbol period. This strategy is very useful in some high-speed communication systems, especially in systems that use Nyquist pulse shaping. In digital communications, signals usually need to be digitized, which involves converting continuous analog signals into discrete digital signals. This process is called sampling, and the sampling rate refers to the number of samples per unit time. Traditional sampling strategies may require multiple samples per symbol period to ensure signal integrity and accuracy; however, the 1-SPS strategy only samples once per symbol period, which can reduce the number of samples, thereby reducing hardware complexity and power consumption. SPS is particularly useful in high-speed optical communication systems because these systems need to process very high data rates. By using Nyquist pulse shaping, spectrum resources can be effectively utilized, and SPS further reduces the required sampling rate, making system design more efficient.

[0073] With the growing demand for the internet and data communications, optical communication systems have become the backbone of modern communication networks due to their advantages, such as high bandwidth, low loss, and resistance to electromagnetic interference. In particular, high-speed coherent optical communication systems, by using advanced modulation formats and digital signal processing techniques, can achieve ultra-high-speed data transmission. Coherent detection technology in coherent optical communication systems can provide higher signal detection sensitivity and accurately measure the amplitude and phase of optical signals, which is crucial for achieving high-speed transmission and improving signal quality. DSP technology plays a core role in coherent optical communication systems, performing necessary processing on received optical signals, including symbol rate estimation, carrier frequency offset correction, dispersion compensation, and adaptive equalization. In optical communication systems, timing error estimation is crucial for accurate signal demodulation and system performance. However, due to non-ideal factors such as chromatic dispersion (CD), polarization mode dispersion (PMD), and random polarization rotation (RSOP) in optical fiber channels, traditional timing error estimation algorithms are limited in the face of these complex channel effects, significantly impacting the performance of timing error estimation algorithms. These effects can reduce the spectral correlation of signals, thereby affecting the accuracy of timing error estimation.

[0074] For example, related timing error estimation algorithms have insufficient tolerance when processing Nyquist signals with low roll-off factors, which limits their application in practical optical communication systems, as practical systems may have lower roll-off factors due to various factors (such as device characteristics, transmission distance, and the pursuit of higher spectral efficiency). In addition, traditional timing error estimation methods are sensitive to chromatic dispersion (CD), polarization mode dispersion (PMD) effects, and differential group delay (DGD) impairments in optical fiber channels, which may lead to algorithm performance degradation in the presence of these effects.

[0075] In view of this, embodiments of the present application provide a method and apparatus for estimating the timing error of a Nyquist system. This scheme generates a randomly modulated signal; inserts zeros into the even bits of the randomly modulated signal to obtain an initial training sequence; generates a target communication frame based on the initial training sequence and the communication signal; and transmits the target communication frame to the receiving end of the Nyquist system. This allows the receiving end to extract the initial training sequence from the target communication frame, perform data processing on the initial training sequence to obtain a target training sequence, and perform timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result. Embodiments of the present application enhance the tolerance of the timing error detection algorithm to dispersion, polarization mode dispersion effects, and differential group delay impairments by designing an RZ code training sequence at the transmitting end and transmitting it to the receiving end, thereby providing accurate timing error estimation even under complex channel conditions.

[0076] The timing error estimation method of the Nyquist system provided in the embodiment of the present application relates to the field of data communication technology. The timing error estimation method of the Nyquist system provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network, content distribution network) and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the timing error estimation method of the Nyquist system, etc., but is not limited to the above forms.

[0077] The present application can be used in numerous general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs (Personal Computers, personal computers), minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0078] See also Figure 1 , Figure 1 This is an optional step flow chart of a timing error estimation method for a Nyquist system provided by an embodiment of the present application, which is applied to the transmitting end of the Nyquist system. Figure 1 The method may include but is not limited to steps S101 to S104.

[0079] Step S101, generating a random modulation signal;

[0080] The transmitter of the Nyquist system is a component of the system, responsible for encoding and modulating information (such as data, voice, etc.) and sending it to the receiver of the Nyquist system.

[0081] As for the random modulation signal, in the embodiment of the present application, it is a random QPSK (Quadrature Phase Shift Keying) signal.

[0082] In a specific implementation, by generating a random modulation signal at the transmitting end of the Nyquist system, the signal characteristics in the actual communication environment can be better simulated to improve the robustness of the entire communication system.

[0083] Step S102, inserting zeros into the even bits of the random modulation signal to obtain an initial training sequence;

[0084] The initial training sequence is obtained by inserting zeros into the even bits of a randomly modulated signal to help the receiving end perform operations such as synchronization, channel estimation, and equalization.

[0085] In the implementation, a random QPSK signal is generated at the transmitter, and zeros are inserted into the even bits of the random QPSK signal to form a training sequence with a block size of 512 bits. This is the same as X-polarization and Y-polarization. X-polarization and Y-polarization refer to two different polarization directions of electromagnetic waves, which can produce two signal components.

[0086] Step S103, generating a target communication frame based on the initial training sequence and the communication signal;

[0087] In some embodiments, step S103 may include: splicing the initial training sequence and the communication signal to obtain a target spliced ​​signal; mapping the target spliced ​​signal using a target signal mapping method to obtain a target mapped signal; and performing Nyquist pulse shaping on the target mapped signal to obtain a target communication frame.

[0088] The communication signal is the actual data signal to be sent, containing the information the user wishes to transmit, such as text, audio, video, or any other form of data. In communication systems, this data is typically encoded and modulated, ready for transmission over a physical medium. It should be noted that the communication signal to be sent to the receiver can be any other modulated signal, but the signal used to construct the initial training sequence is exclusively a QPSK signal.

[0089] The splicing operation is a process of splicing an initial training sequence and a communication signal to obtain a target spliced ​​signal. The target spliced ​​signal includes the training sequence and a communication signal for transmitting actual data.

[0090] In a specific implementation, the target signal mapping method may adopt a 16QAM (Quadrature Amplitude Modulation) mapping method.

[0091] Nyquist pulse shaping is a signal processing technique used to design the waveform of a transmitted signal so that it meets the Nyquist criterion in the frequency domain, allowing the original data to be recovered without error at the receiving end. Nyquist pulse shaping ensures that the signal has no interference between symbols and that its spectrum is within the bandwidth limit.

[0092] The target communication frame is the data finally sent to the receiving end, including the training sequence and the communication signal to be sent.

[0093] See also Figure 2 , Figure 2This is a flow chart of constructing a communication frame based on a training sequence provided by an embodiment of the present application; in order to improve the tolerance of optical communication channel impairments in the Nyquist system, an embodiment of the present application designs a training sequence for TED (Timing Error Detection), such as Figure 2 As shown in the Tx-DSP (Transmitter Digital Signal Processing) module, a random QPSK signal is first generated at the transmitter; then zeros are inserted into the even bits of the random QPSK signal to form a training sequence with a block size of 512 bits, which is the same as the X polarization and Y polarization. For example, Figure 2 As shown, insert "0" between the random QPSK signals "1+li" and "1+li"; then insert the training sequence and the communication signal to be sent (such as Figure 2 The target splicing signal is obtained by splicing the PRBS (Pseudo-Random Binary Sequence) in the target signal to obtain a target splicing signal; the target splicing signal is then mapped by a 16QAM mapping method to obtain a target mapping signal; finally, the target mapping signal is subjected to Nyquist pulse shaping to obtain a target communication frame.

[0094] Step S104: Send the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result.

[0095] The receiver, like the transmitter, is a component of the Nyquist system, responsible for receiving the signal transmitted from the transmitter and converting it back to the original data. In a specific implementation, the receiver can be an oscilloscope.

[0096] At the receiving end, the target communication frame received by the receiving end needs to be extracted to obtain the initial training sequence, and the initial training sequence needs to be processed to obtain the target training sequence. In specific implementation, at the receiver side, a frame of data is first resampled to the target baud rate (2-SPS) for frame synchronization to extract the initial training sequence. After the initial training sequence is extracted, the initial training sequence is resampled to obtain the target training sequence of 1-SPS. In this way, low-baud-rate symbols (1-SPS target training sequence) are obtained and applied to the subsequent clock error detection algorithm unit (also known as the timing error detection unit).

[0097] As for the target timing error detection algorithm, it is a timing error estimation algorithm designed using second-order cyclic statistics, which can more effectively handle the robustness of the timing error detection algorithm to dispersion effects, polarization mode dispersion and differential group delay.

[0098] Steps S101 to S104 shown in the embodiment of the present application are as follows: generating a random modulation signal; inserting zeros into the even bits of the random modulation signal to obtain an initial training sequence; generating a target communication frame based on the initial training sequence and the communication signal; sending the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, and performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and the target timing error detection algorithm to obtain a timing error estimation result. The embodiment of the present application enhances the tolerance of the timing error detection algorithm to dispersion, polarization mode dispersion effect and differential group delay damage by designing an RZ code training sequence at the transmitting end and sending it to the receiving end, thereby providing accurate timing error estimation even under complex channel conditions.

[0099] See also Figure 3 , Figure 3 This is an optional step flow chart of a timing error estimation method for a Nyquist system provided by an embodiment of the present application, which is applied to a receiving end of a Nyquist system. Figure 3 The method may include but is not limited to steps S301 to S305.

[0100] Step S301, receiving a target communication frame sent by a transmitting end; wherein the target communication frame includes an initial training sequence and a communication signal;

[0101] The transmitter of the Nyquist system is a component of the system, responsible for encoding and modulating information (such as data and voice) and then transmitting it to the receiver. The steps completed at the transmitter include: first, generating a random QPSK signal; then, inserting zeros into the even bits of the random QPSK signal to form a training sequence with a block size of 512 bits; then, concatenating the training sequence with the communication signal to be transmitted to obtain the target spliced ​​signal; then, mapping the target spliced ​​signal using the 16QAM mapping method to obtain the target mapped signal; and finally, performing Nyquist pulse shaping on the target mapped signal to obtain the target communication frame, which is then transmitted to the receiver via the optical communication channel.

[0102] Among them, the target communication frame is based on Figure 1 The target communication frame contains the initial training sequence and the communication signal.

[0103] The receiver, like the transmitter, is a component of the Nyquist system, responsible for receiving the signal transmitted from the transmitter and converting it back to raw data for processing. In a specific implementation, the receiver can be an oscilloscope.

[0104] At the receiving end, the target communication frame received by the receiving end needs to be extracted to obtain the initial training sequence, and the initial training sequence needs to be processed to obtain the target training sequence. In specific implementation, at the receiver side, a frame of data is first resampled to the target baud rate (2-SPS) for frame synchronization to extract the initial training sequence. After the initial training sequence is extracted, the initial training sequence is resampled to obtain the target training sequence of 1-SPS. In this way, low-baud-rate symbols (1-SPS target training sequence) are obtained and applied to the subsequent clock error detection algorithm unit (also known as the timing error detection unit).

[0105] Step S302, performing data extraction processing on the target communication frame to obtain the initial training sequence in the target communication frame;

[0106] The target communication frame is represented by a discrete digital signal at the receiving end.

[0107] In a specific implementation, on the receiving end (Receive Digital Signal Processing, or Rx-DSP module), after the oscilloscope (receiving device) obtains the orthogonally polarized discrete digital signals x and y in the target communication frame, the frame of data is resampled to the target baud rate (2-SPS) for frame synchronization and extraction of the initial training sequence. Specifically, the discrete digital signals x and y are first subjected to matched filtering, and then the matched filtered signal data is resampled once to obtain the initial training sequence (2-SPS). It should be noted that the resampled training sequence here is the initial 2-SPS training sequence.

[0108] Among them, matched filtering is an optimal linear filtering technology used to maximize the signal-to-noise ratio of the signal under a given noise background, thereby improving the detection probability of the signal.

[0109] Step S303, performing data processing on the initial training sequence to obtain a target training sequence;

[0110] In some embodiments, step S303 may include: resampling the initial training sequence using a target resampling rule to obtain a target training sequence; wherein the target training sequence is applied to a timing error detection unit in the receiving end.

[0111] The target resampling rule resamples the initial training sequence to obtain a 1-SPS target training sequence. This means that after discarding even bits of the training sequence at the receiving end, a 1-SPS training sequence is obtained and applied to the timing error detection unit. The target training sequence is the 1-SPS training sequence.

[0112] The timing error detection unit is a key component of the receiving end. Its main function is to detect the timing error in the received signal and adjust the sampling clock accordingly to ensure that the data is sampled at the optimal time, thereby reducing bit errors caused by inaccurate sampling time.

[0113] In a specific implementation, after extracting the initial training sequence, the initial training sequence is resampled twice to obtain a 1-SPS target training sequence. This yields low-baud-rate symbols (the 1-SPS target training sequence) and is applied to the subsequent clock error detection algorithm unit (also known as the timing error detection unit). It should be noted that the resampled training sequence here is the 1-SPS target training sequence.

[0114] It's worth noting that the training sequence after Nyquist pulse shaping at the transmitter is a special RZ (Return-to-Zero) code. RZ Code is a digital signal encoding method in which the signal returns to zero at the middle of each bit. This means that within the duration of the bit, the signal starts at zero, transitions to a non-zero level (usually positive or negative), and then returns to zero at the middle of the bit. This encoding method can reduce inter-symbol interference and is used in communication systems to improve signal discernibility and timing information. In Nyquist systems, the use of RZ Code allows for better frame synchronization and clock recovery.

[0115] The embodiment of the present application designs a special RZ code as a training sequence, which has a high tolerance for Nyquist pulse shaping systems or small roll-off factors (ROF<0.2). This is particularly important for actual optical communication systems. Since Nyquist shaping will seriously affect the sideband information integrity of the signal, the timing error algorithm based on sideband correlation will usually fail. The embodiment of the present application enhances the algorithm's tolerance to dispersion, polarization mode dispersion effects and differential group delay damage by designing a training sequence of the RZ code, thereby providing accurate timing error estimation even under complex channel conditions.

[0116] Step S304, constructing a target cyclic autocorrelation function matrix based on the target training sequence;

[0117] In some embodiments, step S304 may include: intercepting the target training sequence according to the target interception time length to obtain a target discrete digital signal; generating a spectrum correlation function based on the target discrete digital signal; performing inverse Fourier transform processing on the spectrum correlation function to obtain an initial cyclic autocorrelation function; constructing an initial cyclic autocorrelation function matrix based on the initial cyclic autocorrelation function; and generating a target cyclic autocorrelation function matrix based on the initial cyclic autocorrelation function matrix.

[0118] In some specific embodiments, generating a target cyclic autocorrelation function matrix based on an initial cyclic autocorrelation function matrix may include: using a target peak position formula to calculate the target cyclic autocorrelation function in the initial cyclic autocorrelation function matrix to obtain a target cyclic autocorrelation function matrix at a target peak position.

[0119] The target interception time length is a specified length of time used to intercept a portion of data from the target training sequence. This intercepted data is used for subsequent function processing. The target discrete digital signal is the signal portion intercepted from the target training sequence with the target interception time length. The target discrete digital signal is represented by signal spectrum X and signal spectrum Y.

[0120] The Spectral Correlation Function (SCF) is a function used to describe the autocorrelation of a signal in the frequency domain, and can be obtained by performing Fourier transform on the signal and calculating its autocorrelation.

[0121] The inverse Fourier transform (IFT) process is the inverse of the Fourier transform, used to convert the frequency domain signal back to the time domain. In this step, the spectral correlation function is converted back to the time domain via the IFT process, resulting in a cyclic autocorrelation function (CAF). The initial CAF is the time domain autocorrelation function obtained by IFT of the spectral correlation function. This function is used to construct an initial CAF matrix, which contains multiple CAFs, each corresponding to a different cyclic shift of the signal.

[0122] In the specific implementation, the calculation process of the cyclic autocorrelation function (CAF) matrix and the spectral correlation function (SCF) matrix in the cyclic correlation theory is as follows:

[0123] After the oscilloscope at the receiving end obtains the orthogonally polarized discrete digital signals x and y in the target communication frame sent by the transmitting end, it first performs matched filtering and resampling on the discrete digital signals x and y to obtain a 1-SPS target training sequence. Then, the target training sequence is signal-cuttered based on a truncation threshold with a truncation time length of W to obtain signal spectra X and signal spectrum Y. It is important to note that x and y in the original orthogonally polarized discrete digital signals x and y are lowercase letters, while X and Y in the signal spectra X and Y obtained after signal-cuttering the target training sequence are uppercase letters. Here, x, y, X, and Y are case-sensitive, and different parameters represent different meanings.

[0124] Among them, the signal spectrum X and the signal spectrum Y are used to calculate four cyclic periodogram functions. The cyclic periodogram function can be regarded as an estimate of the spectrum correlation function (SCF). The four cyclic periodogram functions (P xx 、P xy 、P yx 、P yy ) is calculated as follows:

[0125]

[0126] Where X and Y are the signal spectra obtained by intercepting the target training sequence, α is the cyclic frequency, and f is the frequency. The above cyclic periodogram function can be regarded as an estimate of the spectral correlation function (SCF). The spectral correlation function (SCF) matrix corresponding to the spectral correlation function (SCF) can be calculated using the above four cyclic periodogram functions. The expression of the spectral correlation function matrix SCF(f) is:

[0127]

[0128] It is worth mentioning that the calculation expressions of the above-mentioned cyclic periodogram functions are all obtained by multiplying two parameters, that is, using second-order cyclic statistics. The embodiments of the present application use second-order cyclic statistics to design a timing error estimation algorithm, which can more effectively handle the robustness of the timing error detection algorithm to chromatic dispersion (CD), polarization mode dispersion (PMD) effects and differential group delay (DGD) impairments in the optical fiber channel.

[0129] Therefore, the cyclic autocorrelation function (CAF) can be estimated using its inverse Fourier transform, and the four CAF functions can be written in matrix form, that is, the CAF matrix of the two-polarization multiplexed signal is constructed. The CAF matrix of the two-polarization multiplexed signal contains four variables {CAFxx, CAFxy, CAFyx, CAFyy}, which facilitates the study of the influence of polarization effects on SCF and CAF.

[0130] There are two methods for calculating the CAF sequence: one is to perform an inverse Fourier transform on the spectral correlation function (SCF) estimated from the periodogram to obtain the cyclic autocorrelation function (CAF); the other is to calculate the CAF directly in the time domain based on the definition of the CAF.

[0131] After the CAF sequence is generated, the delay module determines the delay value n based on the peak position calculated by the target peak position formula; then, the first value of the delayed cyclic autocorrelation function (CAF) sequence is used to construct the cyclic autocorrelation function (CAF) matrix.

[0132] For example, you can use The inverse Fourier transform of the cyclic autocorrelation function (CAF) is obtained The same applies to the inverse Fourier transform of other matrix parameters. Considering polarization multiplexing (signal spectrum X polarization and signal spectrum Y polarization), the final CAF matrix (i.e., target cyclic autocorrelation function matrix) is as follows:

[0133]

[0134] Among them, before the delay module calculates the peak position according to the target peak position formula, the CAF matrix is ​​expressed as That is, the initial cyclic autocorrelation function matrix.

[0135] Optionally, the target peak position formula is used to calculate a formula for a target cyclic autocorrelation function at a specific peak position. In the cyclic autocorrelation function, the target position where the expected peak appears is usually related to timing information of the signal.

[0136] Optionally, the target peak position formula is:

[0137]

[0138] Among them, τ represents the target peak position point, Expressed as the cyclic autocorrelation function of signal x and signal x, It is represented as the cyclic autocorrelation function of signals x and y, and argmax is represented as the operator that returns the index of the maximum value.

[0139] Step S305, performing timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and the target timing error detection algorithm to obtain a timing error estimation result;

[0140] In some embodiments, step S305 may include: performing timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and in combination with a timing error estimation formula in a target timing error detection algorithm to obtain a timing error estimation result; the timing error estimation formula is:

[0141]

[0142] in, is represented as the timing error, CAF is represented as the initial cyclic autocorrelation function matrix, CAF(τ) is represented as the target cyclic autocorrelation function matrix at the τth position, SCF is represented as the spectrum correlation function matrix, det is represented as the matrix determinant operator, and -Im{·} is represented as the imaginary part operator of the complex number.

[0143] The number of CAFs is equal to the number of signal points involved in the timing error estimation operation, and IFFT (SCF) is expressed as performing an inverse Fourier transform on each matrix element in the SCF.

[0144] The target timing error detection algorithm, also known as the training sequence-based timing error detection algorithm (i.e., the Return-to-Zero Timing Error Detection (RZ-TED) algorithm), is an algorithm used by the timing error detection unit in the receiving end to detect and estimate the timing error in the received signal. It includes a timing error estimation formula.

[0145] in, The impact of dispersion on CAF matrix clock modulation can be quantified, allowing timing error extraction. This approach allows the timing error estimation algorithm to tolerate the effects of dispersion, polarization mode dispersion, and differential group delay, even in Nyquist systems with a roll-off factor close to zero.

[0146] The timing error estimation result is the output of the timing error estimation process, which gives the clock offset of the received signal relative to the transmitted signal.

[0147] See also Figure 4 , Figure 4 : is a flow chart of a timing error estimation algorithm provided in an embodiment of the present application; Figure 4 As shown, the specific implementation process of the timing error estimation algorithm includes the following steps 1 to 5:

[0148] In step 1, the oscilloscope at the receiving end first acquires the orthogonally polarized discrete digital signals x and y in the target communication frame. Then, the discrete digital signals x and y are matched filtered and resampled to obtain a 1-SPS target training sequence. The target training sequence is then clipped based on a clipping threshold with a clipping time length of W to obtain signal spectra X and Y.

[0149] Step 2: Calculate four cyclic periodogram functions (P xx 、P xy 、P yx 、P yy ), the cyclic periodogram function can be regarded as an estimate of the spectral correlation function (SCF). The spectral correlation function matrix corresponding to the spectral correlation function can be calculated through the calculation expressions of the above four cyclic periodogram functions.

[0150] Step 3: Perform inverse Fourier transform on the spectrum correlation function in the spectrum correlation function matrix to obtain the corresponding cyclic autocorrelation function. The initial cyclic autocorrelation function matrix can be constructed based on the cyclic autocorrelation function obtained by inverse Fourier transform.

[0151] Step 4: Since a 1024-point signal can obtain a 1024-point CAF, we can obtain and The number of points where the maximum absolute value of the square sum is located, we get The specific value of is a positive integer.

[0152] That is, the embodiment of the present application considers polarization multiplexing (signal spectrum X polarization and signal spectrum Y polarization). After the CAF sequence is generated, the function of the delay module is to determine the delay amount n according to the peak position calculated by the target peak position formula; then, the first value of the delayed cyclic autocorrelation function (CAF) sequence is used to construct a cyclic autocorrelation function (CAF) matrix, and the final CAF matrix (i.e., the target cyclic autocorrelation function matrix) is This matrix takes into account the influence of dispersion effect on CAF matrix clock modulation.

[0153] Step 5: According to the timing error estimation formula in the target timing error detection algorithm Calculate the timing error.

[0154] In the embodiment of the present application, steps S301 to S305 are performed by generating a random modulation signal; inserting zeros into the even bits of the random modulation signal to obtain an initial training sequence; generating a target communication frame based on the initial training sequence and the communication signal; sending the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, and performs data processing on the initial training sequence to obtain the target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and the target timing error detection algorithm to obtain a timing error estimation result. In the embodiment of the present application, the training sequence of the RZ code is designed at the transmitting end and sent to the receiving end, thereby enhancing the tolerance of the timing error detection algorithm to dispersion, polarization mode dispersion effect and differential group delay damage, thereby providing accurate timing error estimation under complex channel conditions. At the same time, the timing error estimation algorithm is designed using second-order cyclic statistics, which can more effectively process the robustness of the timing error detection algorithm to dispersion (CD), polarization mode dispersion (PMD) effect and differential group delay (DGD) damage in the optical fiber channel, so as to achieve accurate estimation of the timing error.

[0155] In order to explain the principle of the technical solution of the present invention in detail, the overall process of the present invention is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and cannot be regarded as a limitation of the present invention.

[0156] The general process of the timing error estimation method of the Nyquist system provided in the embodiment of the present application includes two parts: the transmitting end and the receiving end. The specific implementation process is as follows:

[0157] (1) The steps completed at the transmitting end include: first, generating a random QPSK signal at the transmitting end; then, inserting zeros into the even bits of the random QPSK signal to form a training sequence with a block size of 512 bits; then, splicing the training sequence and the communication signal to be sent to obtain a target spliced ​​signal; then, mapping the target spliced ​​signal using the 16QAM mapping method to obtain a target mapped signal; finally, performing Nyquist pulse shaping on the target mapped signal to obtain a target communication frame, so as to transmit the target communication frame to the receiving end through the optical communication channel.

[0158] (2) The steps completed at the receiving end include: 1) first, the oscilloscope at the receiving end is used to collect the orthogonally polarized discrete digital signals x and y in the target communication frame; then, the discrete digital signals x and y are matched filtered and resampled to obtain a 1-SPS target training sequence; then, the target training sequence is signal intercepted according to the interception threshold with an interception time length of W to obtain the signal spectrum X and signal spectrum Y. 2) Four cyclic periodogram functions (P xx 、P xy、P yx 、P yy ), the cyclic periodogram function can be regarded as an estimate of the spectral correlation function (SCF). The spectral correlation function matrix SCF(f) corresponding to the spectral correlation function can be calculated through the calculation expressions of the above four cyclic periodogram functions. 3) The cyclic autocorrelation function in the spectral correlation function matrix can be obtained by inverse Fourier transforming the spectral correlation function. The initial cyclic autocorrelation function matrix can be constructed based on the cyclic autocorrelation function obtained by inverse Fourier transform. 4) Since a 1024-point signal can obtain a 1024-point CAF, it can be obtained by traversing and The number of points where the maximum absolute value of the square sum is located, and the specific value of τ is a positive integer. That is, the embodiment of the present application takes into account polarization multiplexing (signal spectrum X polarization and signal spectrum Y polarization). After the CAF sequence is generated, the function of the delay module is to determine the delay amount n according to the peak position calculated according to the target peak position formula; then, the first value of the delayed cyclic autocorrelation function (CAF) sequence is used to form a cyclic autocorrelation function (CAF) matrix, and the final CAF matrix (that is, the target cyclic autocorrelation function matrix) CAF (τ) is obtained. This matrix takes into account the influence of the dispersion effect on the CAF matrix clock adjustment. 5) According to the timing error estimation formula Calculate the timing error.

[0159] The RZ-TED timing error estimation algorithm provided in the embodiment of the present application will have very small jitter under various optical fiber environment conditions, and has a high tolerance for dispersion effects, polarization effects, differential group delay, low signal-to-noise ratio, etc., highlighting the robustness and adaptability of the RZ-TED timing error estimation algorithm proposed in the embodiment of the present application, which is of great practical significance for improving the performance and reliability of coherent optical communication systems, especially when facing extreme channel conditions.

[0160] It should be noted that this embodiment only briefly illustrates the general process of the timing error estimation method of the Nyquist system. The detailed description of each step can refer to the relevant content in the aforementioned embodiment and will not be repeated here. It can be understood that the present invention is not limited to this.

[0161] The embodiment of the present application provides a timing error estimation method and device for a Nyquist system. The scheme generates a random modulation signal; inserts zeros into the even bits of the random modulation signal to obtain an initial training sequence; generates a target communication frame based on the initial training sequence and a communication signal; and sends the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result. The embodiment of the present application enhances the tolerance of the timing error detection algorithm to chromatic dispersion, polarization mode dispersion effect and differential group delay impairment by designing an RZ code training sequence at the transmitting end and sending it to the receiving end, thereby providing accurate timing error estimation even under complex channel conditions. At the same time, the timing error estimation algorithm is designed using second-order cyclic statistics, which can more effectively handle the robustness of the timing error detection algorithm to chromatic dispersion (CD), polarization mode dispersion (PMD) effect and differential group delay (DGD) impairment in the optical fiber channel, so as to achieve accurate estimation of the timing error.

[0162] The timing error estimation algorithm proposed in the embodiment of the present application can effectively tolerate the dispersion and polarization effects in the system, and provides a timing synchronization solution for optical communication systems in actual complex channel environments. The timing error estimation algorithm of the embodiment of the present application specifically includes the following key points:

[0163] (1) Application of cyclostationary theory: The embodiment of the present application uses second-order cyclic statistics to design a timing error estimation algorithm, which can more effectively handle the robustness of the timing error detection algorithm to chromatic dispersion (CD), polarization mode dispersion (PMD) effects and differential group delay (DGD) impairments in the optical fiber channel.

[0164] (2) Adaptability of the Nyquist system (tolerance to small roll-off factors): The timing error estimation algorithm provided in the embodiment of the present application specifically designs a special RZ code as a training sequence, which has a high tolerance for the Nyquist pulse shaping system or a small roll-off factor (ROF<0.2), which is particularly important for actual optical communication systems.

[0165] (3) Tolerance to polarization effects: The timing error estimation algorithm provided in the embodiments of the present application is specially designed to have a high tolerance to chromatic dispersion (CD), polarization mode dispersion (PMD) effects and differential group delay (DGD) impairments in the optical fiber channel. This is particularly important for actual optical communication systems because these effects can seriously affect the integrity of the signal and the accurate estimation of the timing error.

[0166] (4) Transparency of format and system parameters: The timing error estimation algorithm provided in the embodiments of the present application does not depend on a specific modulation format or system parameters, and provides a universal solution that can adapt to Nyquist systems and non-Nyquist coherent fiber communication scenarios. It provides a timing synchronization solution for optical communication systems in actual complex channel environments and can be applied to different signal modulation formats and system configurations.

[0167] In summary, the embodiments of the present application provide an efficient, accurate and adaptable technical solution with broad application prospects and practical value. Specifically, the timing error estimation algorithm based on high-order cyclic statistics is specially designed to deal with the timing error estimation problem in the severely band-limited Nyquist system, and can more effectively handle the bandwidth-limited situation of the signal. The timing error estimation algorithm provided by the embodiment of the present application utilizes the correlation characteristics of the special training sequence (RZ code) itself to extract the timing error information, which means that even when the spectrum information of the signal is limited, the timing error estimation algorithm provided by the embodiment of the present application can still maintain high performance and accuracy. Moreover, in optical communication systems, dispersion and polarization effects are the main factors affecting signal integrity. The timing error estimation algorithm provided by the embodiment of the present application takes these channel effects into consideration during design, and reduces the negative impact of error estimation during dispersion and polarization rotation through cyclic statistics. By designing a training sequence for the RZ code and applying second-order cyclic statistics, the timing error estimation algorithm's tolerance to dispersion, polarization mode dispersion effects, and differential group delay impairments in the optical fiber channel is enhanced, thereby providing accurate timing error estimation even under complex channel conditions. It also improves tolerance to low roll-off factor signals, enabling the algorithm to operate stably under a wider range of system parameters. In other words, the timing error estimation algorithm provided in the embodiments of the present application can effectively tolerate dispersion and polarization rotation effects while reducing the algorithm's complexity, thereby improving computational efficiency and enhancing the system's adaptability to different channel conditions.

[0168] See also Figure 5 , Figure 5 : This is a schematic diagram of the structure of the timing error estimation device of the Nyquist system provided in an embodiment of the present application when applied to the transmitting end of the Nyquist system. The device includes the following modules:

[0169] Random modulation signal generation module 501, used to generate a random modulation signal;

[0170] An initial training sequence building module 502 is configured to insert zeros into even bits of the random modulated signal to obtain an initial training sequence;

[0171] A target communication frame generating module 503 is configured to generate a target communication frame based on the initial training sequence and the communication signal;

[0172] The target communication frame sending module 504 is used to send the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result.

[0173] See also Figure 6 , Figure 6 : This is a schematic diagram of the structure of the timing error estimation device of the Nyquist system provided by an embodiment of the present application when applied to the receiving end of the Nyquist system. The device includes the following modules:

[0174] The target communication frame receiving module 601 is configured to receive a target communication frame sent by a transmitting end; wherein the target communication frame includes an initial training sequence and a communication signal;

[0175] An initial training sequence extraction module 602 is configured to perform data extraction processing on the target communication frame to obtain the initial training sequence in the target communication frame;

[0176] The target training sequence acquisition module 603 is configured to process the initial training sequence to obtain a target training sequence;

[0177] A cyclic autocorrelation function matrix acquisition module 604 is configured to construct a target cyclic autocorrelation function matrix based on the target training sequence;

[0178] A timing error estimation module 605 is configured to perform timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and a target timing error detection algorithm to obtain a timing error estimation result;

[0179] Wherein, the target communication frame is based on Figure 5 The timing error estimation device of the Nyquist system is determined.

[0180] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0181] An embodiment of the present application further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the timing error estimation method for the Nyquist system. The electronic device can be any smart terminal, such as a tablet computer or an in-vehicle computer.

[0182] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0183] See also Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0184] The processor 701 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0185] The memory 702 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 702 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 702 and is called by the processor 701 to execute the timing error estimation method for the Nyquist system of the embodiments of the present application.

[0186] Input / output interface 703, used to implement information input and output;

[0187] Communication interface 704, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0188] Bus 705 , which transmits information between various components of the device (e.g., processor 701 , memory 702 , input / output interface 703 , and communication interface 704 );

[0189] The processor 701 , the memory 702 , the input / output interface 703 and the communication interface 704 are connected to each other in communication within the device via a bus 705 .

[0190] An embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the timing error estimation method of the Nyquist system is implemented.

[0191] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0192] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0193] The embodiments of the present application provide a timing error estimation method for a Nyquist system, a timing error estimation device for a Nyquist system, an electronic device, and a storage medium. The methods generate a random modulation signal; insert zeros into the even bits of the random modulation signal to obtain an initial training sequence; generate a target communication frame based on the initial training sequence and the communication signal; and send the target communication frame to the receiving end of the Nyquist system. The receiving end extracts the initial training sequence from the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result. The embodiments of the present application enhance the tolerance of the timing error detection algorithm to dispersion, polarization mode dispersion effects, and differential group delay damage by designing an RZ code training sequence at the transmitting end and sending it to the receiving end, thereby providing accurate timing error estimation even under complex channel conditions.

[0194] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0195] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0196] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0197] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0198] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0199] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0200] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0201] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0202] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0203] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0204] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A timing error estimation method for a Nyquist system, characterized in that: Applied to the transmitting end of a Nyquist system, the method comprises the following steps: Generate a random modulation signal; Inserting zeros into even bits of the random modulated signal to obtain an initial training sequence; generating a target communication frame based on the initial training sequence and the communication signal; The target communication frame is sent to a receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and performs timing error estimation on the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result.

2. The method according to claim 1, characterized in that The generating a target communication frame based on the initial training sequence and the communication signal includes: Splicing the initial training sequence and the communication signal to obtain a target spliced ​​signal; Mapping the target splicing signal using a target signal mapping method to obtain a target mapping signal; Performing Nyquist pulse shaping on the target mapping signal to obtain the target communication frame.

3. A timing error estimation method for a Nyquist system, characterized in that: Applied to a receiving end of a Nyquist system, the method comprises the following steps: Receiving a target communication frame sent by a transmitting end; wherein the target communication frame includes an initial training sequence and a communication signal; performing data extraction processing on the target communication frame to obtain the initial training sequence in the target communication frame; performing data processing on the initial training sequence to obtain a target training sequence; Constructing a target cyclic autocorrelation function matrix based on the target training sequence; Performing timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and the target timing error detection algorithm to obtain a timing error estimation result; The target communication frame is determined according to the timing error estimation method of the Nyquist system according to any one of claims 1-2.

4. The method according to claim 3, characterized in that The performing data processing on the initial training sequence to obtain a target training sequence includes: The initial training sequence is resampled using a target resampling rule to obtain the target training sequence; wherein the target training sequence is applied to a timing error detection unit in the receiving end.

5. The method according to claim 3, characterized in that The target communication frame is represented by a discrete digital signal at the receiving end, and constructing a target cyclic autocorrelation function matrix based on the target training sequence includes: intercepting the target training sequence according to a target interception time length to obtain a target discrete digital signal; generating a spectrum correlation function based on the target discrete digital signal; Performing inverse Fourier transform processing on the spectrum correlation function to obtain an initial cyclic autocorrelation function; Constructing an initial cyclic autocorrelation function matrix according to the initial cyclic autocorrelation function; The target cyclic autocorrelation function matrix is ​​generated based on the initial cyclic autocorrelation function matrix.

6. The method according to claim 5, characterized in that The generating the target cyclic autocorrelation function matrix based on the initial cyclic autocorrelation function matrix includes: The target cyclic autocorrelation function in the initial cyclic autocorrelation function matrix is ​​calculated using a target peak position formula to obtain the target cyclic autocorrelation function matrix at the target peak position.

7. The method according to claim 6, characterized in that The target peak position formula is: Among them, τ represents the target peak position point, Expressed as the cyclic autocorrelation function of signal x and signal x, It is represented as the cyclic autocorrelation function of signals x and y, and argmax is represented as the operator that returns the index of the maximum value.

8. The method according to claim 3, characterized in that The performing timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and the target timing error detection algorithm to obtain a timing error estimation result includes: Based on the target cyclic autocorrelation function matrix, the timing error of the target communication frame is estimated in combination with the timing error estimation formula in the target timing error detection algorithm to obtain the timing error estimation result; the timing error estimation formula is: in, is represented as the timing error, CAF is represented as the initial cyclic autocorrelation function matrix, CAF(τ) is represented as the target cyclic autocorrelation function matrix at the τth position, SCF is represented as the spectrum correlation function matrix, det is represented as the matrix determinant operator, and -Im{·} is represented as the imaginary part operator of the complex number.

9. A timing error estimation device for a Nyquist system, characterized in that: Applied to the transmitting end of a Nyquist system, the device includes the following modules: A random modulation signal generation module, used for generating a random modulation signal; an initial training sequence building module, configured to insert zeros into even-numbered bits of the random modulated signal to obtain an initial training sequence; A target communication frame generating module, configured to generate a target communication frame based on the initial training sequence and the communication signal; The target communication frame sending module is used to send the target communication frame to the receiving end of the Nyquist system; so that the receiving end extracts the initial training sequence in the target communication frame, performs data processing on the initial training sequence to obtain a target training sequence, and estimates the timing error of the target communication frame based on the target training sequence and a target timing error detection algorithm to obtain a timing error estimation result.

10. A timing error estimation device for a Nyquist system, characterized in that: Applied to the receiving end of the Nyquist system, the device includes the following modules: A target communication frame receiving module is configured to receive a target communication frame sent by a transmitting end; wherein the target communication frame includes an initial training sequence and a communication signal; An initial training sequence extraction module is used to perform data extraction processing on the target communication frame to obtain the initial training sequence in the target communication frame; A target training sequence acquisition module is used to process the initial training sequence to obtain a target training sequence; A cyclic autocorrelation function matrix acquisition module, configured to construct a target cyclic autocorrelation function matrix based on the target training sequence; A timing error estimation module is used to perform timing error estimation on the target communication frame based on the target cyclic autocorrelation function matrix and a target timing error detection algorithm to obtain a timing error estimation result; The target communication frame is determined according to the timing error estimation device of the Nyquist system according to claim 9.

Images