Filter implementation method, noise suppression method, device and computer equipment

Abstract

The application relates to a filter implementation method, a noise suppression method and device, computer equipment, a storage medium and a computer program product. The filter implementation method comprises the following steps: acquiring a first signal frequency spectrum and a first power spectrum corresponding to a first test signal; determining a noise spectrum of the first test signal according to the first signal frequency spectrum and the first power spectrum; acquiring a second signal frequency spectrum corresponding to a second test signal; and determining a frequency response of a Wiener filter according to the second signal frequency spectrum and the noise spectrum of the first test signal. The frequency response of the Wiener filter under MPI noise can be obtained by combining the frequency domain method, so that the optimal filter for MPI noise can be realized, and MPI noise can be maximally suppressed to avoid the influence of MPI noise on PAM signal transmission.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method for implementing a filter, a method for suppressing noise, an apparatus, a computer device, a storage medium, and a computer program product. Background Technology

[0002] With the development of communication technology, Pulse Amplitude Modulation (PAM) technology has been widely used in optical interconnects. Since PAM signals can be generated by intensity modulation, the receiver can perform Intensity Modulation-Direct Detection (IMDD). Compared to traditional Non-Return-to-Zero (NRZ) binary modulation signals, each symbol of a PAM signal transmits more bits, thus enabling higher bit rates under the same channel bandwidth.

[0003] However, in practical transmission systems, reflections occur at the optical transmitter, optical receiver, and fiber optic connectors. As the optical signal passes through multiple reflective surfaces during transmission, multipath interference (MPI) occurs. The MPI noise generated by MPI can negatively impact the detection of transmitted signals. Particularly under PAM modulation, MPI noise significantly increases the bit error rate, resulting in a substantial increase in optical power consumption for the optical transmission system.

[0004] In traditional technologies, based on the impact of MPI noise on PAM signal transmission, several methods exist for suppressing MPI noise. These include digital and analog filtering schemes. However, experience has shown that these schemes are not optimal for MPI noise and are ineffective in filtering out MPI noise under high noise levels or when the PAM transmission signal has significant chirp. High-pass filtering, while effective for low-frequency MPI noise, is only effective for chirp-free PAM signals. For PAM signals generated by directly modulated lasers (DMLs), the inherent chirp causes the MPI noise spectrum to extend to higher frequencies, rendering simple high-pass filtering largely ineffective in reducing MPI noise. Furthermore, high-pass filtering at the receiving end introduces DC drift, which negatively impacts PAM signal recovery. Therefore, to avoid significant impacts of MPI noise on PAM transmission systems, strict limits are currently placed on the return loss of optical modules and fiber optic connectors. For example, international standards such as IEEE 802.3 (a network protocol) and 100G Lambda MSA (Optical Interconnect System Specification) have reduced the maximum return loss of fiber optic connectors from -26 dB to -35 dB. However, in actual deployed fiber optic links, due to the large number of connectors and the difficulty in controlling their return loss below -35 dB, MPI noise can significantly reduce optical power consumption in PAM transmission. In some practical links, even with complex forward error correction (FEC) codes, error-free PAM signal transmission is still not achievable. Therefore, minimizing the impact of MPI noise on PAM signal transmission is a pressing issue that needs to be addressed. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for implementing a filter, a noise suppression method, an apparatus, a computer device, a storage medium, and a computer program product that can effectively suppress the influence of MPI noise on PAM signal transmission, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for implementing a Wiener filter, the method comprising:

[0007] Obtain the first signal spectrum and first power spectrum corresponding to the first test signal, wherein the first test signal is an optical signal after the signal transmitting end modulates the first test data and transmits it through an optical fiber;

[0008] Based on the first signal spectrum and the first power spectrum, determine the noise spectrum of the first test signal;

[0009] Obtain the spectrum of the second test signal corresponding to the second test signal. The second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber. The period of the second test data is greater than the period of the first test data.

[0010] The frequency response of the Wiener filter is determined based on the spectrum of the second signal and the noise spectrum of the first test signal.

[0011] In one embodiment, determining the frequency response of the Wiener filter based on the spectrum of the second signal and the noise spectrum of the first test signal includes: obtaining a first difference between the spectrum of the second signal and the noise spectrum of the first test signal; obtaining a sum between the first difference and the noise spectrum of the first test signal; and determining the frequency response of the Wiener filter based on the first difference and the sum.

[0012] In one embodiment, determining the frequency response of the Wiener filter based on the first difference and the sum includes: determining the ratio of the first difference to the sum as the frequency response of the Wiener filter.

[0013] In one embodiment, determining the noise spectrum of the first test signal based on the first signal spectrum and the first power spectrum includes: obtaining a second difference between the first signal spectrum and the first power spectrum, and using the second difference as the noise spectrum of the first test signal.

[0014] In one embodiment, after determining the noise spectrum of the first test signal, the method further includes: acquiring a received noise spectrum, the received noise spectrum being used to characterize the noise of the signal receiving end itself; and determining a multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum.

[0015] In one embodiment, obtaining the received noise spectrum includes: obtaining a second power spectrum corresponding to the detection signal, using the second power spectrum as the received noise spectrum, wherein the detection signal is the noise generated by the signal receiver when there is no light signal input.

[0016] In one embodiment, determining the multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum includes: obtaining a third difference between the noise spectrum of the first test signal and the received noise spectrum, and using the third difference as the multipath interference noise spectrum.

[0017] Secondly, this application provides a method for suppressing multipath interference noise, which is applied to the signal receiving end of an optical communication system. The method includes: acquiring a received signal; filtering the received signal according to the frequency response of a Wiener filter to obtain a filtered target signal; wherein the frequency response of the Wiener filter is implemented according to the method described in the first aspect above.

[0018] In one embodiment, the step of filtering the received signal using the frequency response of a Wiener filter to obtain a filtered target signal includes: performing spectral decomposition on the frequency response of the Wiener filter to obtain the frequency response of a causal Wiener filter; and filtering the received signal based on the frequency response of the causal Wiener filter to obtain the filtered target signal.

[0019] Thirdly, this application provides an apparatus for implementing a Wiener filter, the apparatus comprising: a first spectrum acquisition module, configured to acquire a first signal spectrum and a first power spectrum corresponding to a first test signal, wherein the first test signal is an optical signal after the signal transmitting end modulates the first test data and transmits it through an optical fiber; a noise spectrum determination module, configured to determine the noise spectrum of the first test signal based on the first signal spectrum and the first power spectrum; a second spectrum acquisition module, configured to acquire a second signal spectrum corresponding to a second test signal, wherein the second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber, wherein the period of the second test data is greater than the period of the first test data; and a filter determination module, configured to determine the frequency response of the Wiener filter based on the second signal spectrum and the noise spectrum of the first test signal.

[0020] Fourthly, this application provides a multipath interference noise suppression device, which is applied to the receiving end of an optical communication system. The device includes: a received signal acquisition module for acquiring a received signal; and a filtering module for filtering the received signal according to the frequency response of a Wiener filter to obtain a filtered target signal. The frequency response of the Wiener filter is implemented according to any one of the embodiments of this disclosure.

[0021] Fifthly, this application provides an optical communication system, the system comprising: a signal transmitting end for modulating input transmission data to generate an optical signal; an optical fiber for transmitting the optical signal generated by the signal transmitting end; and a signal receiving end for receiving the optical signal transmitted by the optical fiber and filtering the optical signal using the frequency response of a Wiener filter implemented as described in the first aspect above to obtain a filtered target signal.

[0022] In a sixth aspect, this application provides a computer device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method as described in any one of the first or second aspects above.

[0023] In a seventh aspect, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any one of the first or second aspects above.

[0024] Eighthly, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps of the method as described in any one of the first or second aspects above.

[0025] The aforementioned filter implementation method, noise suppression method, apparatus, computer equipment, storage medium, and computer program product acquire the first signal spectrum and first power spectrum corresponding to the first test signal, and determine the noise spectrum of the first test signal based on the first signal spectrum and first power spectrum. Then, they acquire the second signal spectrum corresponding to the second test signal, and determine the frequency response of the Wiener filter based on the second signal spectrum and the noise spectrum of the first test signal. By combining frequency domain methods, the frequency response of the Wiener filter under MPI noise can be obtained, thereby achieving an optimal filter for MPI noise and maximally suppressing MPI noise to avoid its impact on PAM signal transmission. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the implementation method of a Wiener filter in one embodiment;

[0027] Figure 2A This is a schematic diagram of the signal spectrum received by the receiver at -36dB in one embodiment of the MPI;

[0028] Figure 2B This is a schematic diagram of the signal spectrum received by the receiver at -33dB in one embodiment of the MPI;

[0029] Figure 2C This is a schematic diagram of the signal spectrum received by the receiver at -30dB in one embodiment of MPI;

[0030] Figure 3 This is a flowchart illustrating the steps for determining the frequency response of a Wiener filter in one embodiment;

[0031] Figure 4 This is a flowchart illustrating the steps for determining the multipath interference noise spectrum in one embodiment;

[0032] Figure 5 This is a flowchart illustrating the implementation method of the Wiener filter in another embodiment;

[0033] Figure 6 This is a flowchart illustrating a method for suppressing multipath interference noise in one embodiment;

[0034] Figure 7 This is a block diagram of an optical communication system in one embodiment;

[0035] Figure 8 This is a schematic diagram illustrating the working principle of an optical communication system in one embodiment;

[0036] Figure 9 This is a structural block diagram of a Wiener filter implementation device in one embodiment;

[0037] Figure 10 This is a structural block diagram of a multipath interference noise suppression device in one embodiment;

[0038] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0039] 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.

[0040] Currently, how to minimize the interference of MPI noise in fiber optic communication systems and separate the useful signal is a pressing problem for receivers. The purpose of signal processing is to reduce MPI noise interference and recover the interference-free signal as much as possible; this is a classic problem in signal processing, often referred to as the optimal filter problem. Digital and analog filtering schemes for MPI noise have been validated to some extent, but they are not effective at filtering out MPI noise under high noise levels or when the PAM transmission signal has significant chirp. Furthermore, these schemes are not optimal for MPI noise. While high-pass filtering can remove low-frequency MPI noise, it is only effective for chirp-free PAM signals. For PAM signals generated by directly modulated lasers, the inherent chirp causes the MPI noise spectrum to extend to higher frequencies, making simple high-pass filtering largely ineffective in reducing MPI noise. Additionally, high-pass filtering at the receiving end can cause DC drift in the PAM signal, negatively impacting PAM signal recovery.

[0041] While the Wiener filter can achieve optimal filtering with minimum mean square error, implementing it for MPI noise in fiber optic communication is a very challenging problem. Generally, there are two implementation schemes for the Wiener filter: time-domain methods and frequency-domain methods.

[0042] The time-domain method can be achieved by solving the Wiener-Hopf equation: The weighting coefficients of the digital Wiener filter can be obtained through this equation. However, how do we determine the autocorrelation matrix of the received signal x? And the cross-correlation vector of the transmitted signal d and the received signal x However, certain difficulties exist. Although the autocorrelation matrix The cross-correlation vector can be obtained from the autocorrelation function. The autocorrelation function and the cross-correlation function can be obtained from the cross-correlation function. However, both the autocorrelation function and the cross-correlation function are statistical quantities, requiring a large number of sample functions or the statistical distribution of the signal. In optical communication systems, it is difficult for the signal receiver to estimate the autocorrelation function of the received signal and the cross-correlation function of the transmitted and received signals in real time. Even if the wide stationarity and ergodicity of the transmitted and received signals are utilized to estimate the autocorrelation and cross-correlation functions from sample functions, it still requires as many sample function values ​​as possible and a large amount of computation, which is impractical in real-time high-speed optical communication.

[0043] In the frequency domain approach, the frequency response of the Wiener filter is generally obtained through the power spectrum of the received signal. and MPI noise spectrum Confirmed. However, if the receiver itself has noise... If it cannot be ignored, then the MPI noise spectrum should be referenced. and receiver noise spectrum The sum is the total noise spectrum. Determine the frequency response of the filter. Then the frequency response of the Wiener filter is:

[0044]

[0045] During signal transmission, due to the presence of MPI noise and the receiver's own noise, the signal spectrum measured by the receiver is... The actual power spectrum includes the received signal. and MPI noise spectrum and the noise spectrum of the receiver itself .Right now Among them, the power spectrum of the received signal. This is from the signal spectrum The energy spectrum after removing noise, i.e. Specifically, power spectrum It can be obtained using power spectrum estimation methods, or, for commonly used signals such as NRZ and PAM4, it can also be obtained using the corresponding analytical expressions of the power spectrum.

[0046] However, for the MPI noise spectrum Its corresponding expression is usually:

[0047]

[0048] in It's frequency. R is the linewidth of the laser, and R is the equivalent MPI generated by multiple reflections in the optical fiber. The MPI noise spectrum given in the above formula is for a linewidth of . The MPI noise spectrum generated by a continuous-wave laser in an optical fiber link is complex. However, in actual optical fiber communication systems, due to large-signal modulation, the chirp introduced by the modulator at the transmitting end causes the MPI noise spectrum to no longer satisfy the above expression. Currently, there is no analytical expression for the MPI noise spectrum of chirped signals, and because the MPI noise spectrum and the spectrum of the modulated signal are superimposed at the receiving end, it is currently impossible to obtain actual measurement results for the MPI noise spectrum of chirped signals. Therefore, obtaining the MPI noise spectrum of chirped signals and using frequency domain methods to obtain the frequency response of a Wiener filter under MPI noise is crucial.

[0049] Based on this, in one embodiment, such as Figure 1 As shown, a method for implementing a Wiener filter is provided. Taking the application of this method in an optical communication system as an example, the specific steps include:

[0050] Step 102: Obtain the first signal spectrum and the first power spectrum corresponding to the first test signal.

[0051] The first test signal is an optical signal that modulates the first test data at the signal transmitting end and transmits it through an optical fiber. Specifically, the first test data includes any one of the following: a periodic pseudo-random bit stream (PRBS), a square wave, a sawtooth wave, a triangular wave, or other periodic signals.

[0052] The first signal spectrum corresponding to the first test signal is the signal spectrum obtained by the signal receiver sampling the received first test signal and performing power spectrum estimation, i.e. It is understandable that the signal spectrum includes the noise spectrum generated during the transmission of the first test signal, i.e. .

[0053] The first power spectrum is a noise-free energy spectrum obtained based on the first test signal. Since the first test data is periodic, the corresponding signal spectrum obtained by the receiver only contains signal energy at some discrete frequency points. For example, taking a 25Gbaud PAM4 optical communication system and a PRBS7 binary code stream as an example, its period is... The PAM4 signal spectrum only has energy at frequency points within the range of N×393.7MHz (where N is an integer, N = 0, 1, 2, 3…). The energy spectral lines corresponding to these frequency points constitute the first power spectrum of the first test signal. Specifically, the first power spectrum can be obtained based on the received first test signal using a power spectrum estimation method, or, for signals such as NRZ and PAM4, it can be calculated using the corresponding analytical expression of the power spectrum.

[0054] In this embodiment, before formal data transmission, such as during the power-on period of the optical communication system, the signal transmitting end can generate a first test signal by pulse amplitude modulation of the first test data and transmit the first test signal to the optical fiber. The optical communication system then obtains the first signal spectrum and the first power spectrum corresponding to the first test signal received by the signal receiving end from the optical fiber.

[0055] Step 104: Determine the noise spectrum of the first test signal based on the first signal spectrum and the first power spectrum.

[0056] Since the first power spectrum is a noise-free energy spectrum estimated based on the first test signal, and the first signal spectrum includes the noise spectrum generated during the transmission of the first test signal, the noise spectrum of the first test signal can be determined based on the first signal spectrum and the first power spectrum.

[0057] In one scenario, considering that the chirp generated by the optical modulator at the transmitting end is related to the specific signal bitstream, and since MPI noise is related to the time-domain waveform of the signal and the chirp, and that the PRBS (pseudo-random binary code) bitstream is closer to the actual transmitted data bitstream, a PRBS bitstream with a shorter period can be used as the first test data. For example, PRBS7, PRBS9, or PRBS11 bitstreams can be used as the first test data. This makes the noise spectrum of the first test signal modulated based on the first test data closer to the actual noise spectrum.

[0058] Specifically, taking a 25Gbaud PAM4 optical communication system and a PRBS7 binary code stream as an example, since the first test data is periodic data, its period is... Therefore, the PAM4 signal spectrum obtained by the receiver only has energy at frequency points of N×393.7MHz (N is an integer, N = 0,1,2,3…), while the energy of MPI noise is continuously distributed across the entire spectrum.

[0059] like Figure 2A , Figure 2B as well as Figure 2C As shown, the signal spectrum received by the receiver at different MPIs (-36dB, -33dB, and -30dB) of a 25GPAM4 optical communication system with a transmission code stream of PRBS7 is presented (e.g., Figure 2A , Figure 2B as well as Figure 2C The left figure shows the spectrum, where the horizontal axis represents frequency and the vertical axis represents energy. As can be seen from the figure, the spectrum contains discrete spectral lines, which are the power spectrum of the PAM4 signal (i.e., the received signal based on the PRBS7 codestream). Besides these discrete spectral lines, the spectrum also contains a relatively low-energy continuous spectrum, which is the MPI noise spectrum and the receiver noise spectrum. Therefore, if we consider… Figure 2A , Figure 2B as well as Figure 2C By removing the discrete signal spectral lines from the signal spectrum in the left figure, we can obtain the following: Figure 2A , Figure 2B as well as Figure 2C The right figure shows the noise spectrum of the received signal (this noise spectrum includes the MPI noise spectrum and the receiver noise spectrum). Since discrete signal spectral lines have been removed from the spectrum, the resulting noise spectrum has no measurement values ​​at these discrete frequency points. To compensate for the missing noise spectrum at these discrete spectral lines, the above steps can be repeated based on another PRBS code stream with a different period to measure the missing noise spectrum at those discrete frequency points.

[0060] Specifically, in this embodiment, the noise spectrum of the first test signal can be obtained by removing the discrete first power spectrum from the first signal spectrum based on the first signal spectrum and the first power spectrum obtained above. For example, the difference between the first signal spectrum and the first power spectrum can be calculated, and this difference can be used as the noise spectrum of the first test signal.

[0061] Step 106: Obtain the spectrum of the second signal corresponding to the second test signal.

[0062] The second test signal is an optical signal modulated by the signal transmitter using the second test data and transmitted through an optical fiber. Specifically, the period of the second test data is longer than the period of the first test data. For example, the first test data can use a code stream with a shorter period, such as PRBS7, PRBS9, or PRBS11, while the second test data can use a code stream with a longer period, such as PRBS31. In this embodiment, the second test data can be used to simulate the data to be transmitted. The signal transmitter modulates the second test data, and after obtaining the second test signal, it is transmitted through an optical fiber. The optical communication system then acquires the second test signal sampled from the optical fiber by the signal receiver and obtains the second signal spectrum corresponding to the sampled second test signal through power spectrum estimation. It is understood that the second signal spectrum has a similar meaning to the first signal spectrum, the difference being that they correspond to different received signals. In this embodiment, to distinguish the signal spectra of different received signals, the signal spectrum corresponding to the received first test signal is defined as the first signal spectrum, and the signal spectrum corresponding to the received second test signal is defined as the second signal spectrum.

[0063] Step 108: Determine the frequency response of the Wiener filter based on the spectrum of the second signal and the noise spectrum of the first test signal.

[0064] In this embodiment, the optical communication system determines the frequency response of the Wiener filter based on the frequency domain method of the Wiener filter and the obtained second signal spectrum and the noise spectrum of the first test signal. Since filters are primarily characterized by their frequency response, determining the frequency response of the Wiener filter also determines the corresponding Wiener filter.

[0065] The aforementioned Wiener filter implementation method involves the optical communication system acquiring the first signal spectrum and first power spectrum corresponding to the first test signal, determining the noise spectrum of the first test signal based on these spectra, then acquiring the second signal spectrum corresponding to the second test signal, and finally determining the frequency response of the Wiener filter based on the second signal spectrum and the noise spectrum of the first test signal. By combining frequency domain methods, the frequency response of the Wiener filter under MPI noise can be obtained, thereby achieving an optimal filter for MPI noise and maximally suppressing MPI noise to avoid its impact on PAM signal transmission.

[0066] In one embodiment, such as Figure 3 As shown, the frequency response of the Wiener filter is determined based on the spectrum of the second signal and the noise spectrum of the first test signal. Specifically, this may further include:

[0067] Step 302: Obtain the first difference between the spectrum of the second signal and the noise spectrum of the first test signal.

[0068] The second signal spectrum is derived by simulating the data to be transmitted using the second test data. The second test data is modulated by the signal transmitter, and the resulting second test signal is transmitted via optical fiber. The signal receiver samples the second test signal from the optical fiber and estimates the corresponding signal spectrum using the power spectrum. Since this signal spectrum includes both the power spectrum and the corresponding noise spectrum of the second test signal, and the noise spectrum of the first test signal can approximate the actual noise spectrum, the power spectrum of the second test signal can be obtained by calculating the difference between the second signal spectrum and the noise spectrum of the first test signal.

[0069] Specifically, the optical communication system obtains a first difference between the spectrum of the second signal and the noise spectrum of the first test signal. This first difference is the power spectrum of the second test signal, which is the energy spectrum after removing noise from the spectrum of the second signal.

[0070] Step 304: Obtain the sum between the first difference and the noise spectrum of the first test signal.

[0071] Step 306: Determine the frequency response of the Wiener filter based on the first difference and sum.

[0072] Specifically, the optical communication system determines the frequency response of the Wiener filter by the ratio of the first difference to the sum, based on the frequency response expression of the Wiener filter.

[0073] Specifically, if the spectrum of the first signal corresponding to the first test signal is The first power spectrum corresponding to the first test signal is Then from the spectrum of the first signal Remove the first power spectrum This allows us to obtain the noise spectrum of the first test signal. That is, the noise spectrum of the first test signal The spectrum of the first signal With the first power spectrum The second difference between them. Since the noise spectrum of the first test signal can approximate the actual noise spectrum, the noise spectrum of the first test signal can be used as the noise spectrum of any signal received by the receiver.

[0074] Based on this, if the spectrum of the second signal corresponding to the second test signal is Then from the spectrum of the second signal The energy spectrum after noise removal is , that is Based on the above expression, the frequency response of the Wiener filter can be obtained as:

[0075]

[0076] Among them, the molecule is the spectrum of the second signal. Noise spectrum of the first test signal The first difference between them, that is, from the spectrum of the second signal Remove noise spectrum The energy spectrum after The denominator is the first difference. With noise spectrum The sum between them, i.e., the denominator is .

[0077] In the above embodiments, the optical communication system determines the noise spectrum of the signal based on the first signal spectrum and the first power spectrum corresponding to the first test signal, and determines the frequency response of the Wiener filter based on the noise spectrum and the second signal spectrum corresponding to the second test signal, thereby improving the accuracy of the frequency response of the Wiener filter.

[0078] In one scenario, the Wiener filter described above can be implemented at the signal receiver in an actual link, thus exhibiting adaptive characteristics. While numerous adaptive filter implementations exist, such as Least Mean Square (LMS) and Recursive Least Square (RLS) adaptive filtering, which statistically approximate Wiener filtering, their performance falls short of the Wiener filter due to estimation errors. Furthermore, adaptive filters suffer from convergence issues; an inappropriate adaptive step size can lead to non-convergence or slow convergence. In contrast, the Wiener filter implemented in this disclosure is the optimal filter for MPI noise, exhibiting excellent stability and avoiding convergence problems.

[0079] In one embodiment, such as Figure 4 As shown, after determining the noise spectrum of the first test signal, the above method may further include:

[0080] Step 402: Obtain the received noise spectrum.

[0081] The received noise spectrum is used to characterize the noise of the signal receiver itself. Since the noise spectrum of the first test signal includes both the noise of the receiver itself and the MPI noise transmitted in the optical fiber, if the noise of the receiver itself can be determined, the MPI noise can also be determined.

[0082] Since MPI noise is generated when a signal propagates in an optical fiber, there is neither MPI noise nor a useful signal when no optical signal is transmitted in the fiber. Based on this, the optical communication system determines its own noise by detecting the second power spectrum of the detected signal at the signal receiver when there is no optical signal. Because there is no signal transmission in the fiber at this time, meaning there is neither MPI noise nor a useful signal in the detected signal, the second power spectrum of the detected signal is the receiver's own noise, i.e., the received noise spectrum. Specifically, in this embodiment, the second power spectrum corresponding to the detected signal can be used as the receiver's own received noise spectrum.

[0083] Step 404: Determine the multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum.

[0084] Specifically, the optical communication system acquires a third difference between the noise spectrum of the first test signal and the received noise spectrum, and uses this third difference as the multipath interference noise spectrum. For example, if the received noise spectrum is... The noise spectrum of the first test signal is The corresponding multipath interference noise spectrum That is, the multipath interference noise spectrum is the difference between the noise spectrum of the first test signal and the received noise spectrum. For ease of explanation and to distinguish different differences, this difference is defined as the third difference.

[0085] In the above embodiments, the optical communication system obtains the received noise spectrum and determines the multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum. That is, the multipath interference noise spectrum is determined to be the difference between the noise spectrum of the first test signal and the received noise spectrum, thereby enabling accurate measurement of the MPI noise spectrum.

[0086] In one embodiment, such as Figure 5 As shown, the implementation method of the Wiener filter described above is further explained below, taking its application in an optical communication system as an example. The optical communication system includes a signal transmitter and a signal receiver. The specific steps of the method may include the following:

[0087] Step 502: The signal transmitting end sends the first test signal.

[0088] The first test signal is obtained by the signal transmitting end modulating the first test data with pulse amplitude before transmitting the actual signal, such as during the power-on of the optical module.

[0089] Step 504: The signal receiver samples the received first test signal and performs power spectrum estimation to obtain the first signal spectrum and the discrete first power spectrum.

[0090] Among them, the first power spectrum The frequency can be determined by the baud rate, number of bits in the symbol, and sequence length of the period of the first test signal. Since the power spectrum is a function of frequency, the corresponding power spectrum can be obtained once the frequency is determined.

[0091] Specifically, in this embodiment, the frequency of the first power spectrum can be determined using the following formula:

[0092]

[0093] in The number of bits in a symbol. M is the size of the transmitted symbol set. For a PAM4 signal, M=4, m=2. N is the harmonic number. This indicates the frequency corresponding to the Nth harmonic of the periodic first test signal. The PRBS code length is the sequence length of the corresponding period; for example, the PRBS code length can be 2^N. 7 -1 (i.e., 127), 2 9 -1 (i.e., 511), 2 11 -1 (i.e., 2047), etc. Furthermore, since the PRBS signal is a periodic signal, the PRBS signal only occurs in... Signals at certain frequencies have energy, while signals at other frequencies have zero energy.

[0094] First signal spectrum This is the signal spectrum obtained by sampling the received first test signal and then estimating its power spectrum. This signal spectrum includes the noise spectrum generated during the transmission of the first test signal. .Right now .

[0095] Step 506: The signal receiver removes the discrete first power spectrum from the first signal spectrum to obtain the noise spectrum of the first test signal.

[0096] Specifically, the noise spectrum of the first test signal .

[0097] Step 508: The signal transmitting end begins to send the second test signal.

[0098] The second test signal is an optical signal obtained by modulating the second test data at the signal transmitting end and transmitting it through an optical fiber. Specifically, the period of the second test data is longer than the period of the first test data.

[0099] Step 510: The signal receiver samples the received second test signal and performs power spectrum estimation to obtain the spectrum of the second signal.

[0100] Second signal spectrum This is the signal spectrum obtained by sampling the received second test signal and then estimating its power spectrum. This signal spectrum includes the noise spectrum generated during the transmission of the second test signal. And the spectrum of the actual signal.

[0101] Step 512: The signal receiver determines the frequency response of the Wiener filter based on the spectrum of the second signal and the noise spectrum of the first test signal.

[0102] Due to the noise spectrum of the first test signal Having been determined by the aforementioned method, the true spectrum of the second test signal, i.e., the spectrum after noise removal, can then be determined. .

[0103] The frequency response of the Wiener filter is then:

[0104]

[0105] Step 514: The signal receiver performs spectral decomposition on the frequency response of the Wiener filter to obtain the frequency response of the causal Wiener filter.

[0106] The signal receiver measures the frequency response of the Wiener filter. Spectral decomposition is performed to obtain the frequency response of the causal Wiener filter. .

[0107] Step 516: The signal receiver obtains the corresponding causal Wiener filter based on the frequency response of the causal Wiener filter.

[0108] Since filters are primarily characterized by their frequency response, the corresponding causal Wiener filter is determined when the frequency response of the causal Wiener filter is known.

[0109] In this embodiment, the power spectrum and noise spectrum of the received signal can be estimated at the signal receiver, and then the frequency response of the Wiener filter can be obtained using the frequency domain method. Finally, the causal Wiener filter is derived by the spectral decomposition method. Thus, the optimal filter with minimum mean square error can be achieved for MPI noise in the link. The method is not only simple and stable, but also adaptive to different MPI noise.

[0110] In one embodiment, such as Figure 6 As shown, this disclosure also provides a method for suppressing multipath interference noise. This embodiment uses the application of this method to the signal receiving end of an optical communication system as an example for illustration, and it may specifically include the following steps:

[0111] Step 602: Obtain the received signal.

[0112] The received signal is an optical signal that is pulse amplitude modulated by the signal transmitting end, transmitted through optical fiber, and then received by the signal receiving end.

[0113] Step 604: Filter the received signal according to the frequency response of the Wiener filter to obtain the filtered target signal.

[0114] The frequency response of the Wiener filter is based on Figures 1 to 5 The method described above is used to achieve this. In this embodiment, the signal receiving end uses the frequency response of the Wiener filter implemented by the above method to filter the received signal, thereby obtaining the target signal after noise removal, so as to achieve error-free PAM signal transmission.

[0115] In one embodiment, since the frequency response of the Wiener filter obtained by frequency domain methods is typically a non-causal filter, it cannot be implemented in a real-time receiver. Therefore, in this embodiment, when the optical communication system is a real-time communication system, i.e., the signal receiving end is a real-time receiver, the frequency response of the Wiener filter is used to filter the received signal to obtain the filtered target signal. Specifically, this may include: performing spectral decomposition on the frequency response of the Wiener filter to obtain the frequency response of the causal Wiener filter, and then filtering the received signal based on the frequency response of the causal Wiener filter to obtain the filtered target signal. This achieves an optimal filter for MPI noise at the real-time receiver and maximizes the suppression of MPI noise.

[0116] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0117] In one embodiment, such as Figure 7 As shown, this disclosure also provides an optical communication system, the system comprising:

[0118] The signal transmitter 702 is used to modulate the input transmission data to generate an optical signal.

[0119] Fiber 704 is used to transmit optical signals generated at the signal transmitting end.

[0120] The signal receiver 706 is used to receive optical signals transmitted through optical fiber and to filter the optical signals using the frequency response of the Wiener filter implemented by the above method, thereby obtaining the filtered target signal.

[0121] Specifically, such as Figure 8 As shown, the signal transmitting end further includes a PAM4 encoding module and an electroabsorption modulated laser (EML). The signal receiving end further includes a photodiode (PD), a transimpedance amplifier (TIA), an analog-to-digital converter (ADC), and a digital signal processing module (DSP).

[0122] In this embodiment, in conjunction with the above... Figure 8 The illustrated optical communication system further explains its working principle. Specifically, after the binary code stream is PAM4 encoded at the transmitting end, the encoded PAM4 signal is intensity modulated and loaded onto the optical carrier using EML. The modulated optical PAM4 signal is transmitted through optical fiber to the optical receiver. The optical receiver uses PD to convert the optical signal into an electrical signal, which is then amplified by TIA, converted into a digital signal by ADC, and processed by DSP to obtain the spectrum of the received signal. Due to reflections in the optical fiber link, multiple reflections from different paths superimposed at the receiving end will form multipath interference noise (MPI). Although most of the energy of the MPI noise spectrum is concentrated in the low-frequency band, transient chirping caused by EML modulation at the transmitting end extends the MPI noise spectrum into the high-frequency band.

[0123] Therefore, at the receiving end, the MPI noise spectrum and the PAM4 signal spectrum are superimposed. Based on this, this embodiment is based on... Figures 1 to 5 The method shown implements a Wiener filter in the DSP at the receiving end. Specifically, it determines the frequency response of the Wiener filter and performs spectral decomposition on the frequency response of the Wiener filter to obtain the frequency response of the causal Wiener filter. Based on the frequency response of the causal Wiener filter, the corresponding causal Wiener filter is obtained. Then, the causal Wiener filter is used to filter the received signal to suppress MPI noise and the noise of the receiving end itself to the maximum extent, thereby achieving optimal filtering for MPI noise.

[0124] Based on the same inventive concept, this application also provides a Wiener filter implementation apparatus for implementing the Wiener filter implementation method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations of one or more Wiener filter implementation apparatus embodiments provided below can be found in the limitations of the Wiener filter implementation method described above, and will not be repeated here.

[0125] In one embodiment, such as Figure 9 As shown, an apparatus for implementing a Wiener filter is provided, the apparatus comprising:

[0126] The first spectrum acquisition module 902 is used to acquire the first signal spectrum and the first power spectrum corresponding to the first test signal. The first test signal is an optical signal after the signal transmitting end modulates the first test data and transmits it through an optical fiber.

[0127] The noise spectrum determination module 904 is used to determine the noise spectrum of the first test signal based on the first signal spectrum and the first power spectrum;

[0128] The second spectrum acquisition module 906 is used to acquire the spectrum of the second signal corresponding to the second test signal. The second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber. The period of the second test data is greater than the period of the first test data.

[0129] The filter determination module 908 is used to determine the frequency response of the Wiener filter based on the spectrum of the second signal and the noise spectrum of the first test signal.

[0130] In one embodiment, the filter determination module may further include: a first difference acquisition unit, configured to acquire a first difference between the spectrum of the second signal and the noise spectrum of the first test signal; and an acquisition unit, configured to acquire the sum between the first difference and the noise spectrum of the first test signal; and a frequency response determination unit, configured to determine the frequency response of the Wiener filter based on the first difference and the sum.

[0131] In one embodiment, the frequency response determination unit is further configured to determine the ratio of the first difference to the sum as the frequency response of the Wiener filter.

[0132] In one embodiment, the noise spectrum determination module is further configured to obtain a second difference between the first signal spectrum and the first power spectrum, and use the second difference as the noise spectrum of the first test signal.

[0133] In one embodiment, the apparatus further includes: a received noise spectrum acquisition module, configured to acquire a received noise spectrum, the received noise spectrum being used to characterize the noise of the signal receiving end itself; and a multipath interference noise spectrum acquisition module, configured to determine a multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum.

[0134] In one embodiment, the receiving noise spectrum acquisition module is specifically used to acquire the second power spectrum corresponding to the detection signal, and use the second power spectrum as the receiving noise spectrum, wherein the detection signal is the signal output by the signal receiving end when there is no light signal input.

[0135] In one embodiment, the multipath interference noise spectrum acquisition module is specifically used to acquire a third difference between the noise spectrum of the first test signal and the received noise spectrum, and to use the third difference as the multipath interference noise spectrum.

[0136] The modules in the aforementioned Wiener filter implementation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0137] Based on the same inventive concept, this application also provides a multipath interference noise suppression device for implementing the multipath interference noise suppression method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more multipath interference noise suppression device embodiments provided below can be found in the limitations of the multipath interference noise suppression method described above, and will not be repeated here.

[0138] In one embodiment, such as Figure 10 As shown, a multipath interference noise suppression device is provided. The device is applied at the receiving end of an optical communication system, and the device includes:

[0139] The signal acquisition module 1002 is used to acquire the received signal.

[0140] Filtering module 1004 is used to filter the received signal according to the frequency response of the Wiener filter to obtain the filtered target signal; the frequency response of the Wiener filter is based on the above... Figures 1 to 5 The method described in any embodiment is implemented.

[0141] In one embodiment, the filtering module is further configured to perform spectral decomposition on the frequency response of the Wiener filter to obtain the frequency response of the causal Wiener filter; and to filter the received signal according to the frequency response of the causal Wiener filter to obtain the filtered target signal.

[0142] Each module in the aforementioned multipath interference noise suppression 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.

[0143] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data such as noise spectrum and filter frequency response. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a Wiener filter implementation method or a multipath interference noise suppression method.

[0144] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0145] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0146] Obtain the first signal spectrum and first power spectrum corresponding to the first test signal, wherein the first test signal is an optical signal after the signal transmitting end modulates the first test data and transmits it through an optical fiber;

[0147] Based on the first signal spectrum and the first power spectrum, determine the noise spectrum of the first test signal;

[0148] Obtain the spectrum of the second test signal corresponding to the second test signal. The second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber. The period of the second test data is greater than the period of the first test data.

[0149] The frequency response of the Wiener filter is determined based on the spectrum of the second signal and the noise spectrum of the first test signal.

[0150] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a first difference between the spectrum of the second signal and the noise spectrum of the first test signal; obtaining a sum between the first difference and the noise spectrum of the first test signal; and determining the frequency response of the Wiener filter based on the first difference and the sum.

[0151] In one embodiment, when the processor executes a computer program, it further performs the following step: determining the ratio of the first difference to the sum as the frequency response of the Wiener filter.

[0152] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a second difference between the first signal spectrum and the first power spectrum, and using the second difference as the noise spectrum of the first test signal.

[0153] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring a received noise spectrum, the received noise spectrum being used to characterize the noise of the signal receiving end itself; and determining a multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum.

[0154] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring a second power spectrum corresponding to the detection signal, and using the second power spectrum as the received noise spectrum, wherein the detection signal is the noise detected by the signal receiver when there is no light signal input.

[0155] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining a third difference between the noise spectrum of the first test signal and the received noise spectrum, and using the third difference as the multipath interference noise spectrum.

[0156] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring a received signal; filtering the received signal according to the frequency response of the Wiener filter to obtain a filtered target signal; wherein the frequency response of the Wiener filter is implemented according to the method described in the first aspect above.

[0157] In one embodiment, when the processor executes the computer program, it further performs the following steps: spectral decomposition of the frequency response of the Wiener filter to obtain the frequency response of the causal Wiener filter; filtering the received signal according to the frequency response of the causal Wiener filter to obtain the filtered target signal.

[0158] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0159] Obtain the first signal spectrum and first power spectrum corresponding to the first test signal, wherein the first test signal is an optical signal after the signal transmitting end modulates the first test data and transmits it through an optical fiber;

[0160] Based on the first signal spectrum and the first power spectrum, determine the noise spectrum of the first test signal;

[0161] Obtain the spectrum of the second test signal corresponding to the second test signal. The second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber. The period of the second test data is greater than the period of the first test data.

[0162] The frequency response of the Wiener filter is determined based on the spectrum of the second signal and the noise spectrum of the first test signal.

[0163] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: obtaining a first difference between the spectrum of the second signal and the noise spectrum of the first test signal; obtaining a sum between the first difference and the noise spectrum of the first test signal; and determining the frequency response of the Wiener filter based on the first difference and the sum.

[0164] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining the ratio of the first difference to the sum as the frequency response of the Wiener filter.

[0165] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a second difference between the first signal spectrum and the first power spectrum, and using the second difference as the noise spectrum of the first test signal.

[0166] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring a received noise spectrum, the received noise spectrum being used to characterize the noise of the signal receiving end itself; and determining a multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum.

[0167] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring a second power spectrum corresponding to the detection signal, and using the second power spectrum as the received noise spectrum, wherein the detection signal is the noise detected by the signal receiver when there is no light signal input.

[0168] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a third difference between the noise spectrum of the first test signal and the received noise spectrum, and using the third difference as the multipath interference noise spectrum.

[0169] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: acquiring a received signal; filtering the received signal according to the frequency response of a Wiener filter to obtain a filtered target signal; wherein the frequency response of the Wiener filter is implemented according to the method described in the first aspect above.

[0170] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing spectral decomposition on the frequency response of the Wiener filter to obtain the frequency response of the causal Wiener filter; filtering the received signal according to the frequency response of the causal Wiener filter to obtain the filtered target signal.

[0171] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0172] Obtain the first signal spectrum and first power spectrum corresponding to the first test signal, wherein the first test signal is an optical signal transmitted to the optical fiber after the signal transmitting end modulates the first test data;

[0173] Based on the first signal spectrum and the first power spectrum, determine the noise spectrum of the first test signal;

[0174] Obtain the spectrum of the second test signal corresponding to the second test signal. The second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber. The period of the second test data is greater than the period of the first test data.

[0175] The frequency response of the Wiener filter is determined based on the spectrum of the second signal and the noise spectrum of the first test signal.

[0176] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: obtaining a first difference between the spectrum of the second signal and the noise spectrum of the first test signal; obtaining a sum between the first difference and the noise spectrum of the first test signal; and determining the frequency response of the Wiener filter based on the first difference and the sum.

[0177] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining the ratio of the first difference to the sum as the frequency response of the Wiener filter.

[0178] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a second difference between the first signal spectrum and the first power spectrum, and using the second difference as the noise spectrum of the first test signal.

[0179] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring a received noise spectrum, the received noise spectrum being used to characterize the noise of the signal receiving end itself; and determining a multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum.

[0180] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring a second power spectrum corresponding to the detection signal, and using the second power spectrum as the received noise spectrum, wherein the detection signal is the noise detected by the signal receiver when there is no light signal input.

[0181] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining a third difference between the noise spectrum of the first test signal and the received noise spectrum, and using the third difference as the multipath interference noise spectrum.

[0182] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: acquiring a received signal; filtering the received signal according to the frequency response of a Wiener filter to obtain a filtered target signal; wherein the frequency response of the Wiener filter is implemented according to the method described in the first aspect above.

[0183] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing spectral decomposition on the frequency response of the Wiener filter to obtain the frequency response of the causal Wiener filter; filtering the received signal according to the frequency response of the causal Wiener filter to obtain the filtered target signal.

[0184] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0185] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0186] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0187] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for implementing a Wiener filter, characterized in that, The method includes: The first test signal is obtained by acquiring the first signal spectrum and the first power spectrum corresponding to the first test signal. The first test signal is an optical signal after the first test data is modulated by the signal transmitting end and transmitted through optical fiber. The first test data is periodic data. The corresponding signal spectrum obtained by the signal receiving end has signal energy at the target discrete frequency points. The energy spectral line corresponding to the target discrete frequency points is taken as the first power spectrum corresponding to the first test data. Based on the first signal spectrum and the first power spectrum, determine the noise spectrum of the first test signal; Obtain the spectrum of the second test signal corresponding to the second test signal. The second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber. The period of the second test data is greater than the period of the first test data. The frequency response of the Wiener filter is determined based on the spectrum of the second signal and the noise spectrum of the first test signal.

2. The method according to claim 1, characterized in that, Determining the frequency response of the Wiener filter based on the spectrum of the second signal and the noise spectrum of the first test signal includes: Obtain the first difference between the spectrum of the second signal and the noise spectrum of the first test signal; Obtain the sum between the first difference and the noise spectrum of the first test signal; The frequency response of the Wiener filter is determined based on the first difference and the sum.

3. The method according to claim 2, characterized in that, Determining the frequency response of the Wiener filter based on the first difference and the sum includes: The ratio of the first difference to the sum is determined as the frequency response of the Wiener filter.

4. The method according to claim 1, characterized in that, Determining the noise spectrum of the first test signal based on the first signal spectrum and the first power spectrum includes: Obtain the second difference between the first signal spectrum and the first power spectrum, and use the second difference as the noise spectrum of the first test signal.

5. The method according to any one of claims 1 to 4, characterized in that, After determining the noise spectrum of the first test signal, the method further includes: Obtain the received noise spectrum, which is used to characterize the noise of the signal receiver itself; The multipath interference noise spectrum is determined based on the noise spectrum of the first test signal and the received noise spectrum.

6. The method according to claim 5, characterized in that, The acquisition of the received noise spectrum includes: The second power spectrum corresponding to the detection signal is obtained, and the second power spectrum is used as the received noise spectrum. The detection signal is the noise generated by the signal receiving end when there is no light signal input.

7. The method according to claim 5, characterized in that, Determining the multipath interference noise spectrum based on the noise spectrum of the first test signal and the received noise spectrum includes: Obtain a third difference between the noise spectrum of the first test signal and the received noise spectrum, and use the third difference as the multipath interference noise spectrum.

8. A method for suppressing multipath interference noise, characterized in that, The method is applied to the signal receiving end of an optical communication system, and the method includes: Acquire received signals; The received signal is filtered according to the frequency response of the Wiener filter to obtain the filtered target signal. The frequency response of the Wiener filter is achieved by the method according to any one of claims 1 to 7.

9. The method according to claim 8, characterized in that, The step of filtering the received signal according to the frequency response of the Wiener filter to obtain the filtered target signal includes: The frequency response of the Wiener filter is subjected to spectral decomposition to obtain the frequency response of the causal Wiener filter; The received signal is filtered according to the frequency response of the causal Wiener filter to obtain the filtered target signal.

10. A device for implementing a Wiener filter, characterized in that, The device includes: The first spectrum acquisition module is used to acquire the first signal spectrum and the first power spectrum corresponding to the first test signal. The first test signal is an optical signal after the signal transmitting end modulates the first test data and transmits it through an optical fiber. The first test data is periodic data. The corresponding signal spectrum obtained by the signal receiving end has signal energy at the target discrete frequency points. The energy spectral line corresponding to the target discrete frequency points is used as the first power spectrum corresponding to the first test data. The noise spectrum determination module is used to determine the noise spectrum of the first test signal based on the first signal spectrum and the first power spectrum. The second spectrum acquisition module is used to acquire the spectrum of the second signal corresponding to the second test signal. The second test signal is an optical signal after the signal transmitting end modulates the second test data and transmits it through an optical fiber. The period of the second test data is greater than the period of the first test data. The filter determination module is used to determine the frequency response of the Wiener filter based on the spectrum of the second signal and the noise spectrum of the first test signal.

11. A device for suppressing multipath interference noise, characterized in that, The device is used at the receiving end of an optical communication system, and the device includes: A signal acquisition module is used to acquire received signals; A filtering module is used to filter the received signal according to the frequency response of the Wiener filter to obtain a filtered target signal; the frequency response of the Wiener filter is implemented according to the method according to any one of claims 1 to 7.

12. An optical communication system, characterized in that, The system includes: The signal transmitting end is used to modulate the input transmission data to generate an optical signal; Optical fiber, used to transmit the optical signal generated by the signal transmitting end; The signal receiving end is used to receive the optical signal transmitted by the optical fiber and to filter the optical signal using the frequency response of the Wiener filter implemented by any one of claims 1 to 7 to obtain the filtered target signal.

13. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 9.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

15. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

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