Forward transmission network performance monitoring method, optical module, equipment and storage medium

Abstract

The invention relates to a performance monitoring method of a forward transmission network, an optical module, equipment and a storage medium. The method comprises the following steps: acquiring an optical signal which is sent from a far-end radio frequency unit and is transmitted to a receiving unit of a performance monitoring optical module, and converting the optical signal into an electric signal; performing eye pattern analysis on the electric signal to generate an eye pattern distribution histogram; the eye pattern distribution histogram is used for representing distribution conditions of different signal amplitudes in different intervals; determining a multipath interference deformation factor according to the deformation condition of the eye pattern distribution histogram of the electric signal compared with the eye pattern distribution histogram before signal transmission; and determining a signal-to-noise ratio estimation value according to the multipath interference deformation factor, and determining a performance monitoring result of the forward transmission network according to the signal-to-noise ratio estimation value. By adopting the method, the link quality of the forward network can be accurately evaluated.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a performance monitoring method, optical module, device and storage medium for a fronthaul network. Background Art

[0002] In the 5G Advanced era, with the significant increase in radio bandwidth and the gradual expansion of the wireless spectrum from Sub-6 GHz to U6 GHz or millimeter wave, transceiver speed requirements in fronthaul networks are also increasing from 10G / 25G to 50G or higher, increasing potential risks in fronthaul networks. In this context, traditionally, fronthaul network quality assessment and risk identification are primarily based on bit error rate (BER). However, directly measuring real-time BER during service operation is difficult because it inevitably leads to service signal interruption. Especially when the BER is very low, longer interruption durations are required to achieve accurate BER measurements. In real-world network operations, prolonged service interruption is unacceptable, limiting the application of real-time BER measurement during service operation.

[0003] At the device level, the digital signal processing (DSP) chips in current transceivers do not yet have forward error correction (FEC) enabled; instead, FEC functionality is integrated into the device's application-specific integrated circuit (ASIC). If real-time BER reporting to network management systems is required, the device must include supplementary features such as error correction and counting. This requires additional hardware modules or software algorithms to implement these features, which inevitably increases costs, including hardware, R&D, and maintenance.

[0004] When it comes to link risk identification, multipath interference (MPI) caused by aging links or dirty connectors is a major risk that cannot be ignored in next-generation fronthaul links. However, existing operations and maintenance procedures typically monitor only received optical power, not BER. While monitoring received optical power can provide a certain degree of insight into the overall link status, MPI can cause changes in signal amplitude, phase, and other characteristics. Therefore, relying solely on received optical power alone cannot fully and accurately assess link quality degradation caused by MPI. Therefore, traditional methods cannot accurately assess link quality in forward networks. Summary of the Invention

[0005] Based on this, it is necessary to provide a performance monitoring method, apparatus, computer equipment, computer-readable storage medium and computer program product for a fronthaul network to address the above technical issues, which can accurately evaluate the link quality of the forward network.

[0006] In a first aspect, the present application provides a performance monitoring method for a fronthaul network, which is applied to a performance monitoring optical module of the fronthaul network, comprising:

[0007] Obtaining an optical signal sent from a remote radio frequency unit and transmitted to a receiving unit of a performance monitoring optical module, and converting the optical signal into an electrical signal;

[0008] Perform eye diagram analysis on electrical signals to generate eye diagram distribution histograms; eye diagram distribution histograms are used to characterize the distribution of different signal amplitudes in different intervals;

[0009] Determining a multipath interference deformation factor based on a deformation of an eye diagram distribution histogram of the electrical signal compared to an eye diagram distribution histogram before signal transmission;

[0010] A signal-to-noise ratio estimation value is determined based on the multipath interference deformation factor, and a performance monitoring result of the fronthaul network is determined based on the signal-to-noise ratio estimation value.

[0011] In one embodiment, determining a multipath interference deformation factor based on a deformation of an eye diagram distribution histogram of an electrical signal compared to an eye diagram distribution histogram before signal transmission includes:

[0012] Obtaining target mean values ​​and target standard deviations for multiple levels in an eye diagram distribution histogram of an electrical signal;

[0013] The multipath interference deformation factor is determined based on the target mean value, the target standard deviation, and the reference mean values ​​and reference standard deviations of multiple levels in the eye diagram distribution histogram before signal transmission.

[0014] In one embodiment, determining a multipath interference deformation factor based on a target mean, a target standard deviation, and reference means and reference standard deviations of a plurality of levels in an eye diagram distribution histogram before signal transmission includes:

[0015] For each level in the eye diagram distribution histogram of the electrical signal, obtaining a first ratio between a reference mean and a reference standard deviation corresponding to the current level, and obtaining a second ratio between a target mean and a target standard deviation corresponding to the current level; and using the difference between the first ratio and the second ratio as a deformation factor corresponding to the current level;

[0016] The deformation factors corresponding to the multiple levels are summed up, and the result is used as the multipath interference deformation factor.

[0017] In one embodiment, determining a signal-to-noise ratio estimate based on a multipath interference deformation factor includes:

[0018] determining an initial signal-to-noise ratio according to target means and target standard deviations of a plurality of levels in an eye diagram distribution histogram of the electrical signal;

[0019] The error of the initial signal-to-noise ratio is compensated according to the multipath interference deformation factor to obtain the estimated signal-to-noise ratio.

[0020] In one embodiment, determining a performance monitoring result of a fronthaul network based on a signal-to-noise ratio estimate includes:

[0021] When the estimated signal-to-noise ratio value is lower than a preset threshold, it is determined that the performance monitoring result of the fronthaul network is that multipath interference exists.

[0022] In one embodiment, the performance monitoring method of the fronthaul network further includes:

[0023] Determine a mapping relationship between a multipath interference deformation factor and a multipath interference intensity;

[0024] Determine the multipath interference intensity based on the mapping relationship and the multipath interference deformation factor;

[0025] When the estimated signal-to-noise ratio is lower than a preset threshold, a performance monitoring alarm message for the fronthaul network is generated based on the multipath interference intensity and the performance monitoring result.

[0026] In a second aspect, the present application also provides a performance monitoring optical module for a fronthaul network, including:

[0027] The receiving unit is used to obtain the optical signal sent from the remote radio frequency unit and transmitted to the receiving unit, and convert the optical signal into an electrical signal;

[0028] a digital signal processing chip connected to the receiving unit and configured to perform eye diagram analysis on the electrical signal and generate an eye diagram distribution histogram; the eye diagram distribution histogram is configured to characterize the distribution of different signal amplitudes in different intervals; a multipath interference deformation factor is determined based on the deformation of the eye diagram distribution histogram of the electrical signal compared to the eye diagram distribution histogram before signal transmission; and a signal-to-noise ratio estimation value is determined based on the multipath interference deformation factor;

[0029] The controller unit is connected to the digital signal processing chip and is used to determine the performance monitoring result of the fronthaul network according to the signal-to-noise ratio estimation value.

[0030] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Obtaining an optical signal sent from a remote radio frequency unit and transmitted to a receiving unit of a performance monitoring optical module, and converting the optical signal into an electrical signal;

[0032] Perform eye diagram analysis on electrical signals to generate eye diagram distribution histograms; eye diagram distribution histograms are used to characterize the distribution of different signal amplitudes in different intervals;

[0033] Determining a multipath interference deformation factor based on a deformation of an eye diagram distribution histogram of the electrical signal compared to an eye diagram distribution histogram before signal transmission;

[0034] A signal-to-noise ratio estimation value is determined based on the multipath interference deformation factor, and a performance monitoring result of the fronthaul network is determined based on the signal-to-noise ratio estimation value.

[0035] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0036] Obtaining an optical signal sent from a remote radio frequency unit and transmitted to a receiving unit of a performance monitoring optical module, and converting the optical signal into an electrical signal;

[0037] Perform eye diagram analysis on electrical signals to generate eye diagram distribution histograms; eye diagram distribution histograms are used to characterize the distribution of different signal amplitudes in different intervals;

[0038] Determining a multipath interference deformation factor based on a deformation of an eye diagram distribution histogram of the electrical signal compared to an eye diagram distribution histogram before signal transmission;

[0039] A signal-to-noise ratio estimation value is determined based on the multipath interference deformation factor, and a performance monitoring result of the fronthaul network is determined based on the signal-to-noise ratio estimation value.

[0040] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0041] Obtaining an optical signal sent from a remote radio frequency unit and transmitted to a receiving unit of a performance monitoring optical module, and converting the optical signal into an electrical signal;

[0042] Perform eye diagram analysis on electrical signals to generate eye diagram distribution histograms; eye diagram distribution histograms are used to characterize the distribution of different signal amplitudes in different intervals;

[0043] Determining a multipath interference deformation factor based on a deformation of an eye diagram distribution histogram of the electrical signal compared to an eye diagram distribution histogram before signal transmission;

[0044] A signal-to-noise ratio estimation value is determined based on the multipath interference deformation factor, and a performance monitoring result of the fronthaul network is determined based on the signal-to-noise ratio estimation value.

[0045] The above-mentioned fronthaul network performance monitoring method, optical module, computer equipment, computer-readable storage medium and computer program product obtain an optical signal sent from a remote radio frequency unit and transmitted to the receiving unit of the performance monitoring optical module, converting the optical signal into an electrical signal; performing eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; by comparing the deformation of the eye diagram distribution histogram before and after transmission, the multipath interference deformation factor is determined, and the multipath interference deformation factor indicates the impact of multipath interference on the link quality of the fronthaul network; determining the signal-to-noise ratio estimation value based on the multipath interference deformation factor is conducive to correcting the signal-to-noise ratio calculation error caused by multipath interference, so that the signal-to-noise ratio estimation value is closer to the actual link quality; determining the performance monitoring results of the fronthaul network based on the signal-to-noise ratio estimation value, thereby improving the accuracy of the performance monitoring results of the fronthaul network. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 This is a diagram illustrating an application environment of a method for monitoring performance of a fronthaul network in one embodiment;

[0048] Figure 2 1 is a flow chart of a method for monitoring performance of a fronthaul network according to an embodiment;

[0049] Figure 3 is a schematic diagram of an eye diagram distribution histogram of an electrical signal in one embodiment;

[0050] Figure 4 is a schematic diagram of an eye diagram distribution histogram before signal transmission in one embodiment;

[0051] Figure 5 1 is a schematic diagram of the overall flow of a method for monitoring performance of a fronthaul network in one embodiment;

[0052] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0054] It should be noted that the terms "first", "second", etc. used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "including" and "having" used in this application and any variations thereof are intended to cover non-exclusive inclusions. The term "plurality" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions or any combination of multiple solutions.

[0055] The performance monitoring method of the fronthaul network provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the performance monitoring method of the fronthaul network can be applied to the performance monitoring optical module of the fronthaul network. The performance monitoring optical module 100 of the fronthaul network includes a receiving unit 101, a digital signal processing chip 102, and a controller unit 103. The receiving unit 101 is used to obtain the optical signal λ2 sent from the remote radio frequency unit and transmitted to the receiving unit 101, and convert the optical signal into an electrical signal; the digital signal processing chip 102,2 is connected to the receiving unit 101, and is used to perform eye diagram analysis on the electrical signal and generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; according to the deformation of the eye diagram distribution histogram of the electrical signal compared with the eye diagram distribution histogram before the signal transmission, the multipath interference deformation factor is determined; according to the multipath interference deformation factor, the signal-to-noise ratio estimation value is determined; the controller unit 103 is connected to the digital signal processing chip 102, and is used to determine the performance monitoring result of the fronthaul network according to the signal-to-noise ratio estimation value. In some embodiments, the digital signal processing chip 102 is further configured to perform noise elimination processing on the electrical signal to obtain a processed electrical signal. The performance monitoring optical module 100 further includes a transmitting unit 104 connected to the digital signal processing chip 102 and configured to transmit the processed electrical signal λ1 to a baseband unit (DU).

[0056] In an exemplary embodiment, Figure 2 As shown, a performance monitoring method for a fronthaul network is provided, and the method is applied to Figure 1 The performance monitoring optical module 100 of the fronthaul network in FIG. 1 is used as an example to illustrate the method, which includes the following steps 202 to 208. Among them:

[0057] Step 202: Acquire an optical signal sent from a remote radio frequency unit and transmitted to a receiving unit of a performance monitoring optical module, and convert the optical signal into an electrical signal.

[0058] The fronthaul network refers to the signal transmission link between the baseband unit (BBU / DU / CU) and the remote radio unit (RRU / AAU) in the radio access network. The remote radio unit (RRU) is the device responsible for radio frequency signal processing and transmission in the radio access network. It is typically deployed at the base station antenna and connected to the baseband unit through the fronthaul network. In 5G Advanced, it has evolved into the active antenna unit (AAU), which integrates antenna and radio frequency processing functions.

[0059] Performance monitoring optical modules are used in fronthaul networks and integrate signal monitoring and analysis functions. In addition to traditional optoelectronic conversion, they can also collect eye diagrams, estimate SNR, and monitor bit error rates in real time for performance evaluation of fronthaul networks.

[0060] The receiving unit is a functional module in the performance monitoring optical module that is used to receive the optical signal transmitted to the fronthaul network and complete the optoelectronic conversion. In some embodiments, the receiving unit includes a series of precise optical components and circuits that can pre-process the received optical signal. These pre-processing operations include but are not limited to the preliminary adjustment of the optical power, the filtering of the optical signal, and the necessary preparations before optoelectronic conversion. The purpose is to adjust the optical signal to a state suitable for subsequent processing and ensure that the optical signal can be efficiently and accurately converted into an electrical signal. The receiving unit may also include an optoelectronic conversion device for converting the pre-processed optical signal into an electrical signal. The converted electrical signal contains various characteristic information of the original optical signal, but is also mixed with noise and interference introduced during the transmission process.

[0061] Step 204 , performing eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to represent the distribution of different signal amplitudes in different intervals.

[0062] After the receiving unit converts the optical signal into an electrical signal, the electrical signal is fed into the digital signal processing chip. The digital signal processing chip integrates a signal-to-noise ratio estimation algorithm for estimating the signal-to-noise ratio of the electrical signal. In some embodiments, the digital signal processing chip also integrates a noise removal algorithm for performing noise removal on the electrical signal to obtain a processed electrical signal; the digital signal processing chip then estimates the signal-to-noise ratio of the processed electrical signal. The signal-to-noise ratio estimation algorithm includes eye diagram analysis, deformation comparison, deformation factor determination, and deformation factor compensation.

[0063] The eye diagram is a core tool for evaluating signal quality in communication systems. It forms an "eye"-like pattern by superimposing the waveforms of high-speed serial signals within multiple bit periods. Eye diagram analysis refers to counting the sampling points of the signal amplitude in the eye diagram, dividing them into amplitude intervals, and generating an eye diagram distribution histogram. Specific steps may include: collecting the amplitude values ​​of all sampling points in the eye diagram, dividing the amplitude range into multiple amplitude intervals, counting the number of sampling points in each amplitude interval, and obtaining an eye diagram distribution histogram. The eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals. For example Figure 3 FIG2 is a schematic diagram of an eye diagram distribution histogram of an electrical signal in some embodiments. The horizontal axis of the eye diagram distribution histogram represents the signal amplitude, i.e., the signal level, and the vertical axis represents the distribution density.

[0064] Multipath interference (MPI) occurs when a signal reaches the receiver via two or more transmission paths. The signals from each path overlap due to time delays and phase differences, resulting in distortion of the received signal waveform. If the eye diagram distribution histogram exhibits multiple peaks, offsets, or broadening, this indicates that the signal may be experiencing multipath interference, leading to amplitude distortion. Eye diagrams provide a visual representation of timing and amplitude impairments, while eye distribution histograms quantify the extent of impairments through statistical analysis.

[0065] Step 206 : Determine a multipath interference deformation factor based on a deformation of the eye diagram distribution histogram of the electrical signal compared to the eye diagram distribution histogram before signal transmission.

[0066] The pre-transmission eye diagram distribution histogram refers to the eye diagram distribution histogram of the signal at the transmitter (e.g., the remote radio unit) before transmission through the fronthaul network channel. This diagram reflects the ideal signal amplitude characteristics. Specifically, the remote radio unit, as the transmitter, sends the signal to the fronthaul network. Eye diagram analysis is performed on the signal before transmission through the fronthaul network channel to obtain the pre-transmission eye diagram distribution histogram.

[0067] The signal received by the receiving unit of the performance monitoring optical module is a signal transmitted through the channel of the fronthaul network. Since there may be multipath interference in the fronthaul network, which may cause noise in the signal, the eye diagram distribution histogram of the electrical signal is deformed to a certain extent compared with the eye diagram distribution histogram before the signal transmission. The deformation is mainly reflected in the changes in shape, peak position, amplitude distribution density, symmetry, etc., which can accurately determine the type and intensity of interference, thereby realizing the performance monitoring of the fronthaul network. For example, deformation can include peak offset, distribution broadening (i.e., amplitude variance becomes larger), multi-peak phenomenon, skewness change (i.e., distribution symmetry is destroyed), etc. Figure 4FIG is a schematic diagram of an eye diagram distribution histogram before signal transmission in some embodiments. It can be seen that the eye diagram distribution histogram of the electrical signal (such as Figure 3 ) Compared with the eye diagram distribution histogram before signal transmission, there are deformations such as peak shift and distribution broadening.

[0068] The multipath interference deformation factor refers to a dimensionless parameter that characterizes the degree of histogram deformation caused by multipath interference and can be determined based on the deformation situation.

[0069] Step 208: Determine a signal-to-noise ratio estimation value based on the multipath interference deformation factor, and determine a performance monitoring result of the fronthaul network based on the signal-to-noise ratio estimation value.

[0070] The signal-to-noise ratio (SNR) is the ratio of signal power to noise power and is used to measure signal quality. Signals transmitted through the fronthaul network may be subject to multipath interference, which can cause distortion in the signal's eye diagram distribution histogram. The SNR estimate is calculated based on the multipath interference distortion factor. This SNR estimate reflects the combined effects of noise and multipath interference on the signal during transmission.

[0071] Performance monitoring results refer to the evaluation conclusions on the performance of the fronthaul network. Performance monitoring results can be determined based on the estimated signal-to-noise ratio to assess the link quality of the fronthaul network. For example, performance monitoring results may include the presence of multipath interference in the fronthaul network, the intensity of multipath interference, and whether the link performance of the fronthaul network is good.

[0072] In some embodiments, after the digital signal processing chip determines the signal-to-noise ratio estimation value, the controller unit determines the performance monitoring result of the fronthaul network according to the signal-to-noise ratio estimation value.

[0073] In the above-mentioned performance monitoring method of the fronthaul network, the optical signal is converted into an electrical signal by obtaining the optical signal sent from the remote radio frequency unit and transmitted to the receiving unit of the performance monitoring optical module; the eye diagram analysis is performed on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; by comparing the deformation of the eye diagram distribution histogram before and after transmission, the multipath interference deformation factor is determined, and the multipath interference deformation factor indicates the impact of multipath interference on the link quality of the fronthaul network; the signal-to-noise ratio estimation value is determined according to the multipath interference deformation factor, which is conducive to correcting the signal-to-noise ratio calculation error caused by multipath interference, so that the signal-to-noise ratio estimation value is closer to the actual link quality; the performance monitoring result of the fronthaul network is determined according to the signal-to-noise ratio estimation value, thereby improving the accuracy of the performance monitoring result of the fronthaul network.

[0074] In an exemplary embodiment, a multipath interference deformation factor is determined based on the deformation of an eye diagram distribution histogram of an electrical signal compared to the eye diagram distribution histogram before signal transmission, including: obtaining a target mean and a target standard deviation of each of multiple levels in the eye diagram distribution histogram of the electrical signal; and determining the multipath interference deformation factor based on the target mean, the target standard deviation, and the reference mean and the reference standard deviation of each of multiple levels in the eye diagram distribution histogram before signal transmission.

[0075] The eye diagram distribution histogram divides the amplitude values ​​of an electrical signal into multiple intervals and counts the number of signal samples within each interval to form a histogram. The multiple levels represent a quantitative representation of the signal amplitude, corresponding to the multiple histograms in the eye diagram distribution histogram. The target mean is the average value of the signal amplitude within the amplitude interval corresponding to the level. The target standard deviation is the dispersion of the signal amplitude within the amplitude interval corresponding to the level relative to the target mean, reflecting the fluctuation range of the signal amplitude.

[0076] The reference mean is the average value of the signal amplitude within the amplitude range of the corresponding level in the eye diagram distribution histogram before signal transmission. The reference standard deviation is the dispersion of the signal amplitude within the amplitude range of the corresponding level in the eye diagram distribution histogram before signal transmission relative to the reference mean, reflecting the fluctuation range of the signal amplitude.

[0077] The multipath interference deformation factor can be determined by comparing the mean shift and standard deviation changes of each level in the eye diagram distribution histogram before and after transmission. The multipath interference deformation factor is used to quantify the degree of signal waveform distortion caused by multipath interference.

[0078] In this embodiment, by determining the mean and standard deviation of each level in the eye diagram distribution histogram before and after transmission and comparing the change in the mean and standard deviation, the impact of multipath interference is converted into a quantifiable multipath interference deformation factor, which is conducive to accurately evaluating the link quality of the fronthaul network.

[0079] In an exemplary embodiment, a multipath interference deformation factor is determined based on a target mean, a target standard deviation, and reference means and reference standard deviations of multiple levels in an eye diagram distribution histogram before signal transmission, including: for each level in the eye diagram distribution histogram of the electrical signal, obtaining a first ratio between a reference mean and a reference standard deviation corresponding to the current level, and obtaining a second ratio between a target mean and a target standard deviation corresponding to the current level; using the difference between the first ratio and the second ratio as the deformation factor corresponding to the current level; and summing the deformation factors corresponding to multiple levels to obtain a result as the multipath interference deformation factor.

[0080] The deformation factor is calculated for each level in the eye diagram distribution histogram of the electrical signal, and the deformation factors of multiple levels are summed to obtain the multipath interference deformation factor.

[0081] The first ratio is the ratio of the reference mean to the reference standard deviation before transmission, which represents the concentration of the level distribution under ideal conditions. The second ratio is the ratio of the target mean to the target standard deviation after transmission, which represents the concentration of the level distribution under multipath interference. The distortion factor measures the change in the level distribution of a single level due to multipath interference. The multipath interference distortion factor measures the impact of multipath interference on the entire signal.

[0082] In some embodiments, the calculation formula of the multipath interference deformation factor can be expressed as follows:

[0083]

[0084] in, represents the multipath interference deformation factor, represents the reference mean corresponding to level i, represents the reference standard deviation corresponding to level i, represents the target mean corresponding to level i, represents the target standard deviation corresponding to level i, Indicates the number of levels.

[0085] In this embodiment, since the mean offset reflects the change in amplitude position and the standard deviation offset reflects the change in discreteness, the deformation factor is determined by taking the difference between the ratio of the mean and the standard deviation before and after transmission, which can reflect the amplitude change caused by multipath interference. The deformation factors of multiple levels are superimposed to avoid interference from a single level on the results, thereby improving the accuracy of overall performance monitoring.

[0086] In an exemplary embodiment, determining a signal-to-noise ratio estimate based on a multipath interference deformation factor includes: determining an initial signal-to-noise ratio based on target means and target standard deviations of multiple levels in an eye diagram distribution histogram of an electrical signal; and performing error compensation on the initial signal-to-noise ratio based on the multipath interference deformation factor to obtain a signal-to-noise ratio estimate.

[0087] The initial signal-to-noise ratio (SNR) can be determined by determining the Q value based on the target mean and target standard deviation, and then the average bit error rate (BER) can be determined based on the Q value. The Q value refers to the ratio of the amplitude difference between adjacent levels to the amplitude fluctuation range. The higher the Q value, the stronger the signal's noise immunity and the lower the BER.

[0088] For example, the calculation formula of the initial signal-to-noise ratio is as follows:

[0089]

[0090]

[0091]

[0092] in, represents the Q value of level i, represents the average bit error rate, represents the initial signal-to-noise ratio.

[0093] The initial SNR is determined based on the target mean and target standard deviation of the electrical signal, without considering the influence of factors such as multipath interference. The initial SNR is adjusted using the multipath interference deformation factor to correct the estimation bias caused by multipath interference, making the SNR estimate closer to the actual transmission quality of the fronthaul network.

[0094] For example, the calculation formula for error compensation can be expressed as follows:

[0095]

[0096] in, represents the initial signal-to-noise ratio, represents the estimated signal-to-noise ratio, represents multipath interference, Represents an electrical signal, Represents a noise signal.

[0097] In this embodiment, the initial signal-to-noise ratio is determined based on the target mean and target standard deviation of the electrical signal, and the multipath interference deformation factor is used to compensate for the error of the initial signal-to-noise ratio, so that the estimated signal-to-noise ratio is closer to the actual channel state, thereby improving the accuracy of link instruction evaluation in the fronthaul network.

[0098] In an exemplary embodiment, determining the performance monitoring result of the fronthaul network according to the signal-to-noise ratio estimation value includes: when the signal-to-noise ratio estimation value is lower than a preset threshold, determining that the performance monitoring result of the fronthaul network is that multipath interference exists.

[0099] The preset threshold is a pre-set signal-to-noise ratio (SNR) critical value used to assess whether the fronthaul network link quality meets the quality standards. If the SNR estimate falls below the preset threshold, it indicates that the fronthaul network link quality is abnormal, and the performance monitoring result is determined to indicate the presence of multipath interference.

[0100] In this embodiment, by comparing the estimated signal-to-noise ratio value with a preset threshold, multipath interference is automatically determined, thereby improving the efficiency and accuracy of performance monitoring.

[0101] In an exemplary embodiment, the performance monitoring method of the fronthaul network further includes:

[0102] Determine a mapping relationship between a multipath interference deformation factor and a multipath interference intensity;

[0103] Determine the multipath interference intensity based on the mapping relationship and the multipath interference deformation factor;

[0104] When the estimated signal-to-noise ratio is lower than a preset threshold, a performance monitoring alarm message for the fronthaul network is generated based on the multipath interference intensity and the performance monitoring result.

[0105] The controller unit pre-establishes a mapping relationship between the multipath interference deformation factor and the multipath interference intensity, which can be obtained through theoretical modeling or fitting of measured data. By querying this mapping relationship, the multipath interference intensity corresponding to the multipath interference deformation factor can be determined. The multipath interference intensity represents the strength of multipath interference; larger values ​​indicate stronger multipath interference.

[0106] Since the estimated signal-to-noise ratio is lower than the preset threshold, it indicates that multipath interference exists in the fronthaul network. The multipath interference intensity and performance monitoring results are used together as performance monitoring alarm information to instruct operation and maintenance personnel to quickly locate the fault point and cause in the fronthaul network.

[0107] In this embodiment, the mapping relationship between the multipath interference deformation factor and the multipath interference intensity is used to facilitate determination of the multipath interference intensity; and the multipath interference intensity and the performance monitoring result are used together as performance monitoring alarm information, which is conducive to rapid fault location in the fronthaul network.

[0108] To explain the performance monitoring method and optical module of the fronthaul network in detail, the following is a detailed example:

[0109] The performance monitoring method of a fronthaul network is applied to a performance monitoring optical module of the fronthaul network. The optical module includes a receiving unit, which is used to obtain an optical signal sent from a remote radio frequency unit and transmitted to the receiving unit, and convert the optical signal into an electrical signal; a digital signal processing chip, connected to the receiving unit, is used to perform eye diagram analysis on the electrical signal and generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; based on the deformation of the eye diagram distribution histogram of the electrical signal compared to the eye diagram distribution histogram before signal transmission, a multipath interference deformation factor is determined; based on the multipath interference deformation factor, a signal-to-noise ratio estimation value is determined; and a controller unit, connected to the digital signal processing chip, is used to determine the performance monitoring result of the fronthaul network based on the signal-to-noise ratio estimation value.

[0110] like Figure 5 The figure shows a schematic diagram of the overall flow of a fronthaul network performance monitoring method in some embodiments. The method obtains an eye diagram distribution histogram of the transmitting end (remote radio unit) and an eye diagram distribution histogram of the electrical signal received by the receiving unit to determine whether deformation has occurred. If no deformation has occurred, the initial signal-to-noise ratio is determined. If deformation has occurred, a multipath interference deformation factor is determined based on the deformation. Error compensation is performed on the initial signal-to-noise ratio based on the multipath interference deformation factor to obtain an estimated signal-to-noise ratio. The performance monitoring result of the fronthaul network is determined based on the estimated signal-to-noise ratio.

[0111] In some embodiments, the estimated SNR is stored in a register on the optical module. A network management system can access this value through a specific communication protocol and interface. When the SNR falls below a preset threshold, the network management system can issue an alert, prompting network maintenance personnel to inspect and maintain the fronthaul link, effectively preventing network failures and service interruptions caused by link quality issues.

[0112] The above-mentioned performance monitoring method and optical module of the fronthaul network obtain an optical signal sent from a remote radio frequency unit and transmitted to the receiving unit of the performance monitoring optical module, converting the optical signal into an electrical signal; performing eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; by comparing the deformation of the eye diagram distribution histogram before and after transmission, the multipath interference deformation factor is determined, and the multipath interference deformation factor indicates the impact of multipath interference on the link quality of the fronthaul network; determining the signal-to-noise ratio estimation value based on the multipath interference deformation factor is conducive to correcting the signal-to-noise ratio calculation error caused by multipath interference, so that the signal-to-noise ratio estimation value is closer to the actual link quality; determining the performance monitoring result of the fronthaul network based on the signal-to-noise ratio estimation value, thereby improving the accuracy of the performance monitoring result of the fronthaul network. In addition, to address the problem that traditional SNR estimation methods are not applicable to 5G Advanced fronthaul links containing MPI, this optical module can overcome the limitations of traditional methods, accurately assess link quality in fronthaul links containing MPI, and estimate SNRs that are closer to theoretical values, helping operators to promptly and accurately understand the quality status of the fronthaul link. The calculated SNR value can be used to prompt network maintenance personnel to inspect and maintain the link, effectively avoiding network failures and service interruptions caused by link quality issues and ensuring stable network operation. Through accurate link quality assessment and timely warning, the operation and maintenance workload caused by link problems is reduced, the operation and maintenance difficulty of the optical module is reduced, and the operation and maintenance efficiency is improved. At the same time, this adaptive adjustment mechanism based on real-time link quality information makes the optical module more intelligent and can better adapt to complex and changing network environments.

[0113] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps. It is understandable that the various steps in different embodiments can be freely combined as needed, and the various non-contradictory schemes formed by the combination all fall within the scope of protection of this application.

[0114] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means. The wireless means can be implemented via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for monitoring the performance of a fronthaul network. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0115] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0116] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0117] Obtain an optical signal sent from a remote radio frequency unit and transmitted to the receiving unit of a performance monitoring optical module, and convert the optical signal into an electrical signal; perform eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; determine the multipath interference deformation factor based on the deformation of the eye diagram distribution histogram of the electrical signal compared to the eye diagram distribution histogram before signal transmission; determine a signal-to-noise ratio estimate based on the multipath interference deformation factor, and determine the performance monitoring results of the fronthaul network based on the signal-to-noise ratio estimate.

[0118] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0119] Obtain target mean values ​​and target standard deviations for each of the multiple levels in the eye diagram distribution histogram of the electrical signal; and determine a multipath interference deformation factor based on the target mean value, target standard deviation, and reference mean values ​​and reference standard deviations for each of the multiple levels in the eye diagram distribution histogram before signal transmission.

[0120] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0121] For each level in the eye diagram distribution histogram of the electrical signal, a first ratio between a reference mean and a reference standard deviation corresponding to the current level is obtained, and a second ratio between a target mean and a target standard deviation corresponding to the current level is obtained; the difference between the first ratio and the second ratio is used as a deformation factor corresponding to the current level; the deformation factors corresponding to multiple levels are summed, and the result obtained is used as a multipath interference deformation factor.

[0122] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0123] An initial signal-to-noise ratio is determined according to target means and target standard deviations of multiple levels in an eye diagram distribution histogram of the electrical signal. Error compensation is performed on the initial signal-to-noise ratio according to a multipath interference deformation factor to obtain an estimated signal-to-noise ratio value.

[0124] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0125] When the estimated signal-to-noise ratio value is lower than a preset threshold, it is determined that the performance monitoring result of the fronthaul network is that multipath interference exists.

[0126] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0127] Determine a mapping relationship between a multipath interference deformation factor and multipath interference intensity; determine the multipath interference intensity based on the mapping relationship and the multipath interference deformation factor; and generate performance monitoring alarm information for the fronthaul network based on the multipath interference intensity and performance monitoring results when the signal-to-noise ratio estimate is lower than a preset threshold.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0129] Obtain an optical signal sent from a remote radio frequency unit and transmitted to the receiving unit of a performance monitoring optical module, and convert the optical signal into an electrical signal; perform eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; determine the multipath interference deformation factor based on the deformation of the eye diagram distribution histogram of the electrical signal compared to the eye diagram distribution histogram before signal transmission; determine a signal-to-noise ratio estimate based on the multipath interference deformation factor, and determine the performance monitoring results of the fronthaul network based on the signal-to-noise ratio estimate.

[0130] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0131] Obtain target mean values ​​and target standard deviations for each of the multiple levels in the eye diagram distribution histogram of the electrical signal; and determine a multipath interference deformation factor based on the target mean value, target standard deviation, and reference mean values ​​and reference standard deviations for each of the multiple levels in the eye diagram distribution histogram before signal transmission.

[0132] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0133] For each level in the eye diagram distribution histogram of the electrical signal, a first ratio between a reference mean and a reference standard deviation corresponding to the current level is obtained, and a second ratio between a target mean and a target standard deviation corresponding to the current level is obtained; the difference between the first ratio and the second ratio is used as a deformation factor corresponding to the current level; the deformation factors corresponding to multiple levels are summed, and the result obtained is used as a multipath interference deformation factor.

[0134] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0135] An initial signal-to-noise ratio is determined according to target means and target standard deviations of multiple levels in an eye diagram distribution histogram of the electrical signal. Error compensation is performed on the initial signal-to-noise ratio according to a multipath interference deformation factor to obtain an estimated signal-to-noise ratio value.

[0136] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0137] When the estimated signal-to-noise ratio value is lower than a preset threshold, it is determined that the performance monitoring result of the fronthaul network is that multipath interference exists.

[0138] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0139] Determine a mapping relationship between a multipath interference deformation factor and multipath interference intensity; determine the multipath interference intensity based on the mapping relationship and the multipath interference deformation factor; and generate performance monitoring alarm information for the fronthaul network based on the multipath interference intensity and performance monitoring results when the signal-to-noise ratio estimate is lower than a preset threshold.

[0140] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0141] Obtain an optical signal sent from a remote radio frequency unit and transmitted to the receiving unit of a performance monitoring optical module, and convert the optical signal into an electrical signal; perform eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; determine the multipath interference deformation factor based on the deformation of the eye diagram distribution histogram of the electrical signal compared to the eye diagram distribution histogram before signal transmission; determine a signal-to-noise ratio estimate based on the multipath interference deformation factor, and determine the performance monitoring results of the fronthaul network based on the signal-to-noise ratio estimate.

[0142] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0143] Obtain target mean values ​​and target standard deviations for each of the multiple levels in the eye diagram distribution histogram of the electrical signal; and determine a multipath interference deformation factor based on the target mean value, target standard deviation, and reference mean values ​​and reference standard deviations for each of the multiple levels in the eye diagram distribution histogram before signal transmission.

[0144] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0145] For each level in the eye diagram distribution histogram of the electrical signal, a first ratio between a reference mean and a reference standard deviation corresponding to the current level is obtained, and a second ratio between a target mean and a target standard deviation corresponding to the current level is obtained; the difference between the first ratio and the second ratio is used as a deformation factor corresponding to the current level; the deformation factors corresponding to multiple levels are summed, and the result obtained is used as a multipath interference deformation factor.

[0146] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0147] An initial signal-to-noise ratio is determined according to target means and target standard deviations of multiple levels in an eye diagram distribution histogram of the electrical signal. Error compensation is performed on the initial signal-to-noise ratio according to a multipath interference deformation factor to obtain an estimated signal-to-noise ratio value.

[0148] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0149] When the estimated signal-to-noise ratio value is lower than a preset threshold, it is determined that the performance monitoring result of the fronthaul network is that multipath interference exists.

[0150] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0151] Determine a mapping relationship between a multipath interference deformation factor and multipath interference intensity; determine the multipath interference intensity based on the mapping relationship and the multipath interference deformation factor; and generate performance monitoring alarm information for the fronthaul network based on the multipath interference intensity and performance monitoring results when the signal-to-noise ratio estimate is lower than a preset threshold.

[0152] 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0153] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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 various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0154] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, 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 application.

[0155] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for monitoring the performance of a fronthaul network, characterized in that: A performance monitoring optical module applied to a fronthaul network, the method comprising: Acquire an optical signal sent from a remote radio frequency unit and transmitted to a receiving unit of a performance monitoring optical module, and convert the optical signal into an electrical signal; Performing eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is used to characterize the distribution of different signal amplitudes in different intervals; determining a multipath interference deformation factor according to a deformation of an eye diagram distribution histogram of the electrical signal compared to an eye diagram distribution histogram before signal transmission; A signal-to-noise ratio estimation value is determined according to the multipath interference deformation factor, and a performance monitoring result of the fronthaul network is determined according to the signal-to-noise ratio estimation value.

2. The method according to claim 1, characterized in that The determining of the multipath interference deformation factor according to the deformation of the eye diagram distribution histogram of the electrical signal compared with the eye diagram distribution histogram before signal transmission includes: Obtaining a target mean value and a target standard deviation of each of a plurality of levels in an eye diagram distribution histogram of the electrical signal; A multipath interference deformation factor is determined according to the target mean, the target standard deviation, and reference mean values ​​and reference standard deviations of multiple levels in the eye diagram distribution histogram before signal transmission.

3. The method according to claim 2, characterized in that Determining the multipath interference deformation factor according to the target mean, the target standard deviation, and reference means and reference standard deviations of a plurality of levels in an eye diagram distribution histogram before signal transmission includes: For each level in the eye diagram distribution histogram of the electrical signal, obtaining a first ratio between a reference mean and a reference standard deviation corresponding to the current level, and obtaining a second ratio between a target mean and a target standard deviation corresponding to the current level; and using the difference between the first ratio and the second ratio as a deformation factor corresponding to the current level; The deformation factors corresponding to the multiple levels are summed up, and the result is used as the multipath interference deformation factor.

4. The method according to claim 1, wherein The determining, according to the multipath interference deformation factor, a signal-to-noise ratio estimation value includes: determining an initial signal-to-noise ratio according to target means and target standard deviations of a plurality of levels in an eye diagram distribution histogram of the electrical signal; Error compensation is performed on the initial signal-to-noise ratio according to the multipath interference deformation factor to obtain a signal-to-noise ratio estimation value.

5. The method according to claim 1, wherein Determining the performance monitoring result of the fronthaul network according to the signal-to-noise ratio estimation value includes: When the estimated signal-to-noise ratio value is lower than a preset threshold, it is determined that the performance monitoring result of the fronthaul network is that multipath interference exists.

6. The method according to claim 5, characterized in that The method further comprises: Determine a mapping relationship between a multipath interference deformation factor and a multipath interference intensity; Determining a multipath interference intensity according to the mapping relationship and the multipath interference deformation factor; When the estimated signal-to-noise ratio value is lower than a preset threshold, performance monitoring alarm information of the fronthaul network is generated according to the multipath interference intensity and the performance monitoring result.

7. A performance monitoring optical module for a fronthaul network, characterized in that: The optical module includes: a receiving unit, configured to obtain an optical signal sent from a remote radio frequency unit and transmitted to the receiving unit, and convert the optical signal into an electrical signal; a digital signal processing chip connected to the receiving unit and configured to perform eye diagram analysis on the electrical signal to generate an eye diagram distribution histogram; the eye diagram distribution histogram is configured to characterize the distribution of different signal amplitudes in different intervals; determine a multipath interference deformation factor based on a deformation of the eye diagram distribution histogram of the electrical signal compared to the eye diagram distribution histogram before signal transmission; and determine a signal-to-noise ratio estimation value based on the multipath interference deformation factor; A controller unit is connected to the digital signal processing chip and is used to determine a performance monitoring result of the fronthaul network according to the signal-to-noise ratio estimation value.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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

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