Method for adaptively relieving MPI noise in IMDD system and IMDD system

Abstract

The invention discloses a method for adaptively relieving MPI noise in an IMDD system and the IMDD system, and belongs to the technical field of optical communication. The method is applied to a receiving end and comprises the following steps: acquiring a received signal, and calculating probability density distribution of the received signal; according to the probability density distribution of the received signal, obtaining two quantization level values at each trough as a threshold level pair; dividing data points of the received signal into trustworthy points and stray points according to the threshold level pair; after the trustworthy point is judged, a recovery signal corresponding to the trustworthy point is obtained, and a judgment error is calculated; mPI noise estimation and MPI noise compensation are carried out on the spurious points according to the judgment errors of the trustworthy points, and recovery signals corresponding to the spurious points are obtained; and synthesizing the recovery signal corresponding to the reliable point and the recovery signal corresponding to the stray point to obtain a final output signal. The MPI noise of stray points in different intervals in the IMDD system link can be accurately compensated in a self-adaptive mode.

Description

Technical Field

[0001] The present invention belongs to the technical field of optical communications, and more specifically, relates to a method for adaptively alleviating MPI noise in an IMDD system and an IMDD system. Background Art

[0002] High-order modulation formats (such as PAM4, PAM8, etc.) are more sensitive to damage such as noise and signal jitter during transmission due to the reduction of level spacing. In the IMDD (Intensity modulation and direct detection) system, after the interference light caused by the reflection of the signal light between multiple dirty optical fiber connectors reaches the receiver, it will be detected by the PD (Photodetector) together with the signal light, thereby introducing MPI noise. MPI noise can cause large-scale (several thousand data points) and large-amplitude (a large number of data points approach or even exceed the decision level) jitter of the signal, which is difficult to be compensated by conventional FFE (Feed-Forward Equalizer), resulting in serious degradation of BER (Bit Error Ratio). MPI has become one of the main factors restricting the deployment of high-order formats in IMDD systems and further improvement of rates. However, the current construction is not standardized, resulting in common contamination of the fiber optic connector end faces. Problem connectors still require front-line employees to go on-site to locate and troubleshoot based on experience. The construction cycle is long and the efficiency is low. Therefore, it is crucial to alleviate the signal damage of MPI noise to high-order and high-speed IMDD transmission.

[0003] There are many studies on weakening the impact of MPI. For example, using external phase jitter to distribute interference noise out of band, using a scrambler to destroy polarization alignment conditions, or constructing an optical branch to simulate interference light for compensation, but these solutions require the introduction of additional optical devices and are incompatible with existing networks. Therefore, more economical DSP-based digital domain processing solutions have been proposed in recent years. However, the digital domain analog high-pass filter solution is highly complex and will also reduce transmission performance when the MPI noise is low; the hybrid coding filtering solution has high synchronization requirements and also requires additional spread spectrum bands; therefore, the low-complexity error signal reconstruction MPI noise method has shown advantages.

[0004] The current methods for reconstructing MPI noise based on error signals mainly include the ID-MPI-M (intensity-dependent MPI mitigation) scheme based on weight factors to compensate for level differences and the ADT (Adaptive Decision Threshold) scheme that dynamically adjusts the decision threshold voltage. ID-MPI-M restores the signal by reconstructing and removing MPI noise, while ADT adapts to MPI fluctuations by dynamically adjusting the threshold voltage. However, the ID-MPI-M scheme manually adjusts four weight factors and is not suitable for actual real-time transmission; the ADT scheme requires the initial convergence threshold voltage of the training sequence, which is difficult to achieve in actual short-distance high-speed transmission services.

[0005] Therefore, the existing methods for reconstructing MPI noise based on error signals in high-speed and high-order IMDD systems have the problems of being difficult to adapt, highly complex, and requiring additional devices. Summary of the invention

[0006] In view of the defects of the related art, the purpose of the present invention is to provide a method for adaptively mitigating MPI noise in an IMDD system and an IMDD system, aiming to solve the problems that the method of reconstructing MPI noise based on error signals in existing high-speed and high-order IMDD systems is difficult to adapt, has high complexity and requires additional devices.

[0007] To achieve the above object, in a first aspect, the present invention provides a method for adaptively mitigating MPI noise in an IMDD system, which is used at a receiving end and includes:

[0008] S1. Obtain a received signal and calculate its probability density distribution;

[0009] S2. According to the probability density distribution of the received signal, two quantization level values ​​are obtained at each trough as a threshold level pair;

[0010] S3, the interval between the threshold level pairs corresponding to each trough is a spurious interval, and the rest are reliable intervals; the data points in the received signal located in the reliable interval are classified as reliable points, and the data points located in the spurious interval are classified as spurious points;

[0011] S4. After performing a judgment operation on the trustworthy point, obtaining a recovery signal corresponding to the trustworthy point, and calculating a judgment error corresponding to the trustworthy point;

[0012] S5. Perform MPI noise estimation and MPI noise compensation on the stray point according to the decision error of the reliable point, perform a decision operation on the compensated stray point, and obtain a restored signal corresponding to the stray point;

[0013] S6. The restored signal corresponding to the reliable point and the restored signal corresponding to the stray point are integrated to obtain the final output signal.

[0014] Optionally, S1 specifically includes:

[0015] S11, the receiving end performs data acquisition to obtain the received signal data x(t) affected by the link MPI noise interference, and the received signal data is recorded as y(t); the transmitted signal data x(t) is the PAM-M transmitted signal data of the M-order level of the transmitting end in the IMDD optical communication system;

[0016] S12, according to the maximum value and the minimum value of y(t), the received signal data y(t) is uniformly quantized into n groups, the data points in y(t) are orderly divided into n equally spaced quantization intervals, and the midpoint of the quantization interval is taken as the quantization level of the group;

[0017] S13. According to the number of data points included in each group, a probability density distribution of the level distribution is obtained, and the probability density distribution is recorded as p(f).

[0018] Optionally, S2 specifically includes:

[0019] S21. The probability density distribution p(f) of the PAM-M signal presents M peaks and (M-1) troughs. On the left side of each trough, three quantization level values ​​near the trough are selected in order from the trough to the peak, and the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough is selected in order as the threshold level, which is recorded as the threshold level th on the m side of the trough level. m,upper ;

[0020] S22, on the right side of each trough, select three quantization level values ​​near the trough in order from the trough to the peak, and select in order the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough, and at the same time, the number of data points contained in the quantization level is greater than the threshold level th on the level m side. m,upper The number of data points is large, which is recorded as the threshold level th on the m+1 side of the valley level. m+1,lower .

[0021] Optionally, S3 specifically includes:

[0022] S31, the received signal y(t) belonging to the interval (th m,lower ,th m,upper ) is divided into trustworthy points of level m, where the trustworthy point intervals of level “1” and level “M” are (min(y(t)),th 1,upper ) and (th M,lower ,max(y(t)));

[0023] S32, will belong to the interval (th m,upper ,th m+1,lower ) is divided into stray points between level m and level m+1.

[0024] Optionally, the calculating and obtaining the decision error corresponding to the trustworthy point includes:

[0025] Subtract the restored signal of the trustworthy point from the trustworthy point to obtain the error corresponding to the trustworthy point.

[0026] Optionally, S5 specifically includes:

[0027] S51, estimating the MPI noise corresponding to each stray point; the MPI noise estimation of the stray point between level m and level m+1 is calculated by the mean of the error of the reliable point belonging to level m and the error of the reliable point belonging to level m+1 among the neighboring data points of the point;

[0028] S52, subtracting the MPI noise corresponding to each stray point one by one, to obtain the MPI noise compensation of the stray point;

[0029] S53: Perform PAM-M signal determination on the compensated stray points to obtain a restored transmission signal corresponding to the stray points.

[0030] Optionally, S6 specifically includes:

[0031] S61, replacing all the trusted points in the received data y(t) with the recovery signals corresponding to the trusted points;

[0032] S62, replace all the stray points in the received data y(t) with the restored signals corresponding to the stray points, and obtain the restored signal y with the MPI noise eliminated ′ (t), as the final output signal.

[0033] In a second aspect, the present invention further provides a receiving end of an IMDD system, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes any one of the methods described in the first aspect when executing the computer program.

[0034] In a third aspect, the present invention further provides an IMDD system, comprising:

[0035] A transmitting end, a transmission optical fiber, an optical fiber connector and a receiving end as described in the second aspect;

[0036] The transmitting end is used to generate and modulate a transmitting signal, and transmit the transmitting signal into a transmission optical fiber;

[0037] The transmission optical fiber is used to transmit the transmission signal to the receiving end for detection and reception;

[0038] The optical fiber connector is used to connect multiple transmission optical fibers;

[0039] The receiving end executes the method for adaptively mitigating MPI noise in an IMDD system as described in any one of the first aspects to obtain a final output signal.

[0040] Compared with the prior art, the above technical solution conceived by the present invention can achieve the following beneficial effects:

[0041] 1. The present invention provides a method for adaptively mitigating MPI noise in an IMDD system. The method is based on a digital domain processing solution of DSP, does not require additional optical devices, and is compatible with existing networks. According to the density distribution of a received signal, multiple threshold levels are selected to distinguish the data points of the received signal to obtain trust points and stray points. The received data is used to guide compensation of MPI noise, and after judging the trust points and stray points, the compensation obtains a restored signal, and no additional training sequence is required in this process. Among them, the calculation of the MPI noise estimation and compensation process only involves summation and averaging, and only requires dozens of adders and one divider, with low computational complexity. The method has the advantages of being applicable to actual transmission services, achieving adaptability, low complexity, and low cost, and provides a new idea for MPI noise mitigation in optical communications.

[0042] 2. The present invention provides a method for adaptively mitigating MPI noise in an IMDD system, which formulates a threshold level establishment rule, extracts the probability density distribution of the received signal about the quantization level in the receiving end DSP, and adaptively determines 2 (M-1) threshold levels according to the proposed threshold level establishment rule. The signal data point division method based on probability density distribution is adopted to retain the accuracy of the reliable points and accurately compensate for the MPI noise of stray points in different intervals, and adaptively achieves the mitigation of MPI noise in the IMDD system link.

[0043] 3. The IMDD system provided by the present invention is based on a digital domain processing solution of DSP, does not require additional optical devices, and the calculation of the MPI noise estimation and compensation process only involves summation and averaging, requiring only dozens of adders and one divider, thus simplifying the complexity of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of a method for adaptively alleviating MPI noise in an IMDD system provided by an embodiment of the present invention;

[0045] Figure 2Schematic diagram of a typical application scenario of an embodiment of the present invention; wherein (a) is a diagram of an actual transmission link structure of IMDD in a centralized access network mobile fronthaul, and (b) is a schematic diagram of optical MPI generation in a typical scenario;

[0046] Figure 3 is a schematic diagram of a threshold level establishment rule provided by an embodiment of the present invention;

[0047] Figure 4 is a schematic diagram of a data point classification method provided by an embodiment of the present invention;

[0048] Figure 5 It is a principle diagram of a method for adaptively alleviating MPI noise in an IMDD system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0050] The contents involved in the above embodiment are described below in conjunction with a preferred embodiment.

[0051] like Figure 1 As shown, the present invention provides a method for adaptively alleviating MPI noise in an IMDD system, which is used at a receiving end and includes:

[0052] S1. Obtain a received signal and calculate its probability density distribution;

[0053] S2. According to the probability density distribution of the received signal, two quantization level values ​​are obtained at each trough as a threshold level pair;

[0054] S3, the interval between the threshold level pairs corresponding to each trough is a spurious interval, and the rest are reliable intervals; the data points in the received signal located in the reliable interval are classified as reliable points, and the data points located in the spurious interval are classified as spurious points;

[0055] S4. After performing a judgment operation on the trustworthy point, obtaining a recovery signal corresponding to the trustworthy point, and calculating a judgment error corresponding to the trustworthy point;

[0056] S5. Perform MPI noise estimation and MPI noise compensation on the stray point according to the decision error of the reliable point, perform a decision operation on the compensated stray point, and obtain a restored signal corresponding to the stray point;

[0057] S6. The restored signal corresponding to the reliable point and the restored signal corresponding to the stray point are integrated to obtain the final output signal.

[0058] The embodiment of the present invention provides a method for adaptively alleviating the MPI (Multipath Interference) noise introduced by the dirty optical fiber connector in the IMDD (Intensity modulation and direct detection) system link in the receiving end DSP (Digital Signal Processing). According to the input PAM-M (M-order Pulse Amplitude Modulation) signal damaged by MPI, the probability density distribution of the received signal about the quantization level is extracted in the receiving end DSP, and 2 (M-1) threshold levels are determined according to the proposed threshold level establishment rule. According to the threshold level pairs, the received signal data points are divided into reliable points near the transmission level and stray points that are easily misjudged near the decision level. Specifically, the interval between the threshold level pairs corresponding to each trough is the stray interval, and the rest is the reliable interval. The reliable point directly restores the original signal through judgment, and the corresponding error can be obtained at the same time. The stray point first compensates for the MPI noise, and then judges to restore the original signal. The MPI noise corresponding to each spurious point is obtained by averaging the errors of the reliable points of its two adjacent transmission levels. By processing the reliable points and spurious points comprehensively, the entire input signal can be restored in the digital signal processor at the receiving end.

[0059] Optionally, S1 specifically includes:

[0060] S11, the receiving end performs data acquisition to obtain the received signal data x(t) affected by the link MPI noise interference, and the received signal data is recorded as y(t); the transmitted signal data x(t) is the PAM-M transmitted signal data of the M-order level of the transmitting end in the IMDD optical communication system;

[0061] S12, according to the maximum value and the minimum value of y(t), the received signal data y(t) is uniformly quantized into n groups, the data points in y(t) are orderly divided into n equally spaced quantization intervals, and the midpoint of the quantization interval is taken as the quantization level of the group;

[0062] S13. According to the number of data points included in each group, a probability density distribution of the level distribution is obtained, and the probability density distribution is recorded as p(f).

[0063] Optionally, S2 specifically includes:

[0064] S21. The probability density distribution p(f) of the PAM-M signal presents M peaks and (M-1) troughs. On the left side of each trough, three quantization level values ​​near the trough are selected in order from the trough to the peak, and the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough is selected in order as the threshold level, which is recorded as the threshold level th on the m side of the trough level. m,upper ;

[0065] S22, on the right side of each trough, select three quantization level values ​​near the trough in order from the trough to the peak, and select in order the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough, and at the same time, the number of data points contained in the quantization level is greater than the threshold level th on the level m side. m,upper The number of data points is large, which is recorded as the threshold level th on the m+1 side of the valley level. m+1,lower .

[0066] Reliable points refer to data points in the received data that are distributed near M transmission levels and are not easily misjudged and can be considered correct. Spurious points refer to signal points in the received data that are distributed near (M-1) decision levels and are easily misjudged due to data fluctuations caused by MPI noise. Reliable points and spurious points together constitute the received signal y(t).

[0067] The probability density distribution p(f) of the PAM-M signal presents M peaks and (M-1) troughs; each trough can determine two threshold levels on its left and right sides, which serve as the upper boundary th of the reliable point of level m. m,upper and the trustworthy point th of level m+1 m+1,lower (Ignore the lower boundary of the lowest level "1" and the upper boundary of the highest level "M"). m,lower ,th m,upper ) is a reliable point of level m (where the reliable point interval between level “1” and level “M” is (min(y(t)),th 1,upper ) and (th M,lower ,max(y(t)))), and in the interval (th m,upper ,th m+1,lower ) is a stray point between level m and level m+1. m,upper , consider selecting three nearby quantization level values ​​from the left side of the trough from the mth trough to the mth peak in the order from the trough to the peak; and select the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough in order as the threshold level th m,upper For the threshold level th m,lower, consider selecting three nearby quantization level values ​​from the right side of the trough from the m-1th trough to the mth peak side in the order from the trough to the peak; select the quantization level that satisfies the following two conditions as the threshold level th m,lower , (1) sequentially select the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough, (2) the number of data points in the selected quantization level is greater than th m-1,upper Contains a lot.

[0068] Optionally, S3 specifically includes:

[0069] S31, the received signal y(t) belonging to the interval (th m,lower ,th m,upper ) is divided into trustworthy points of level m, where the trustworthy point intervals of level “1” and level “M” are (min(y(t)),th 1,upper ) and (th M,lower ,max(y(t)));

[0070] S32, will belong to the interval (th m,upper ,th m+1,lower ) is divided into stray points between level m and level m+1.

[0071] Based on the probability density distribution p(f), according to the threshold level establishment rule in the specific step of S2 above, 2(M-1) threshold levels are determined, and the combination of 2(M-1) threshold levels is expressed as {th 1,upper ,th 2,lower ,th 2,upper ,…,th m,upper ,th m+1,lower ,…,th M,lower}, which is used to divide the reliable points and stray points in the received data y(t), where m = 1, 2, ..., M, ignoring th 1,lower and M,upper .

[0072] Optionally, the calculating and obtaining the decision error corresponding to the trustworthy point includes:

[0073] Subtract the restored signal of the trustworthy point from the trustworthy point to obtain the error corresponding to the trustworthy point.

[0074] For the trusted point, the PAM-M signal judgment is performed to obtain the restored original signal and the corresponding error corresponding to the trusted point. The error of the trusted point is obtained by subtracting the original signal restored from the trusted point from the trusted point.

[0075] Optionally, S5 specifically includes:

[0076] S51, estimating the MPI noise corresponding to each stray point; the MPI noise estimation of the stray point between level m and level m+1 is calculated by the mean of the error of the reliable point belonging to level m and the error of the reliable point belonging to level m+1 among the neighboring data points of the point;

[0077] S52, subtracting the MPI noise corresponding to each stray point one by one, to obtain the MPI noise compensation of the stray point;

[0078] S53: Perform PAM-M signal determination on the compensated stray points to obtain a restored transmission signal corresponding to the stray points.

[0079] First, the MPI noise corresponding to each stray point is estimated. The MPI noise of the stray point between level m and level m+1 is obtained by the average of the errors of the reliable points belonging to level m and the errors of the reliable points belonging to level m+1 in its neighboring data points. Then, the stray points are subtracted from the calculated corresponding error mean values ​​one by one, thereby realizing the MPI noise compensation of the point. Finally, the compensated stray points are judged to obtain the restored original signal corresponding to the stray points.

[0080] Optionally, S6 specifically includes:

[0081] S61, replacing all the trusted points in the received data y(t) with the recovery signals corresponding to the trusted points;

[0082] S62, replace all the stray points in the received data y(t) with the restored signals corresponding to the stray points, and obtain the restored signal y with the MPI noise eliminated ′ (t), as the final output signal.

[0083] The reliable points and stray points are combined to recover the signal and obtain the final output signal. The entire input signal can be recovered in the digital signal processor at the receiving end. The output signal is the restored transmission signal after removing the MPI noise. The MPI noise is alleviated by the above method.

[0084] In a specific embodiment, for ease of understanding, Figure 2 As shown, a typical application scenario of the present invention is given. The actual transmission link structure of IMDD in the mobile fronthaul of the centralized access network is as follows Figure 2 As shown in (a), the signal light starts from the transmitting end laser, passes through a series of link nodes connected by optical fiber jumpers with different lengths of active connectors or fused fiber connectors, and finally reaches the receiver and is received by the internal detector. The optical MPI generation process is as follows: Figure 2As shown in (b), when the signal light passes through the contaminated optical fiber connector, part of its energy will be reflected by the optical fiber connector and become interference light. After an even number of reflections, the interference light will be transmitted in the same direction as the signal light. The multiple interference lights formed by reflections between different connectors in the link overlap with the signal light and are detected by the receiver together.

[0085] In this embodiment, a 56Gbps PAM4 signal is first generated. After 2x upsampling, a root raised cosine filter with a roll-off factor of 0.1 is used to pulse shape the signal to generate a transmit signal. The transmit signal is then loaded into an arbitrary waveform generator with a sampling rate of 64GSa / s. The output of the arbitrary waveform generator is enhanced by an electronic amplifier and then drives the Mach-Zehnder modulator. A continuous wave laser is used as a laser source to inject the modulated signal light into a fiber link with a length of 15.5 kilometers and connected by three contaminated connectors. The optical signal with the signal light and the interference light superimposed is transmitted to the receiving end photodetector by the optical fiber, and finally sampled by a digital sampling oscilloscope at 100GSa / s for offline signal processing. The subsequent adaptive MPI noise mitigation process of the received signal is completed in the receiving end DSP.

[0086] The received PAM4 signal y(t) interfered by MPI is sent to the DSP. According to the maximum and minimum values ​​of y(t), it is evenly divided into 120 quantization levels, so that the y(t) signal data points are divided into 120 groups according to the quantization level. According to the number of data points in each quantization level group, the probability density distribution of y(t) with respect to the quantization level can be obtained, which is recorded as p(f).

[0087] The embodiment of the present invention provides a preferred threshold level establishment rule, the specific method is as follows Figure 3 According to the above threshold level establishment rules, six threshold levels {th 1,upper ,th 2,lower ,th 2,upper ,th 3,lower ,th 3,upper ,th 4,lower}. The three quantization levels on the left and right sides of each of the three troughs of p(f) are taken as potential values ​​of the threshold level; secondly, for the threshold level th m,upper , in the order from trough to peak, select the potential value whose number of data points in the first group is greater than 125% of the number of data points at the trough as the threshold level th m,upper , in order to determine {th 1,upper ,th 2,upper ,th 3,upper}; Finally, for the threshold level th m,lower, in the order from trough to peak, select the first group whose number of data points is greater than 125% of the number of data points at the trough and whose number of data points is greater than that for th m-1,upper The potential value of the number of data points contained in it is used as the threshold level th m,lower , in order to determine {th 2,lower ,th 3,lower ,th 4,lower}, and finally get six threshold levels. Among them, PAM4 contains four transmission levels -3, -1, 1 and 3, so M = 4. At this time, the mth level described corresponds to the transmission levels -3, -1, 1 and 3 when m = 1, 2, 3, 4 respectively.

[0088] According to Figure 4 The input signal point classification method shown in the figure classifies the received signal y(t) belonging to the interval (min(y(t)), 1,upper )、(th 2,lower ,th 2,upper )、(th 3,lower ,th 3,upper )、(th 4,lower , max(y(t))) are classified as reliable points at levels -3, -1, 1, and 3; and those belonging to the interval (th 1,upper ,th 2,lower )、(th 2,upper ,th 3,lower )、(th 3,upper ,th 4,lower ) are divided into stray points between level-3 and level-1, between level-1 and level 1, and between level 1 and level 3.

[0089] The classified data points are Figure 5 The MPI noise mitigation process shown in FIG. 4 is used for processing. The reliable point is directly judged to obtain the restored original signal corresponding to the reliable point, and the restored original signal of the reliable point is subtracted from the reliable point to obtain the error corresponding to the reliable point.

[0090] Specifically, for the stray point between level-3 and level-1, the estimated MPI noise is obtained by the average value of the errors of the reliable points near the point belonging to level-3 and level-1; for the stray point between level-1 and level 1, the estimated MPI noise is obtained by the average value of the errors of the reliable points near the point belonging to level-1 and level 1; for the stray point between level 3 and level 1, the estimated MPI noise is obtained by the average value of the errors of the reliable points near the point belonging to level 3 and level 1. The MPI noise corresponding to each stray point is first subtracted, and then judged to obtain the restored original signal corresponding to the stray point.

[0091] The embodiment of the present invention processes the received signal through the DSP at the receiving end, selects multiple threshold levels according to the density distribution of the received signal, distinguishes the data points of the received signal, and obtains the trust points and stray points; uses the received data to guide the compensation of MPI noise, and after judging the trust points and stray points, the compensation obtains the recovery signal, and no additional training sequence is required in this process; wherein, the calculation of the MPI noise estimation and compensation process only involves summation and averaging, and only requires dozens of adders and a divider, and the calculation complexity is low. The method solves the problems of difficulty in adaptability, high complexity and need for additional devices in the existing high-speed and high-order IMDD system in reconstructing MPI noise based on the error signal. The accurate compensation of MPI noise of stray points in different intervals in the IMDD system link is adaptively realized, and the beneficial effects of wide application range, adaptability, low complexity and low cost are achieved.

[0092] Based on the above embodiments, the present invention further provides a receiving end of an IMDD system, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor executes any one of the methods in the above embodiments when executing the computer program.

[0093] Based on the above embodiments, Figure 2 As shown, the present invention also provides an IMDD system, comprising:

[0094] The transmitting end, the transmission optical fiber, the optical fiber connector and the receiving end described in the above embodiments;

[0095] The transmitting end is used to generate and modulate a transmitting signal, and transmit the transmitting signal into a transmission optical fiber;

[0096] The transmission optical fiber is used to transmit the transmission signal to the receiving end for detection and reception;

[0097] The optical fiber connector is used to connect multiple transmission optical fibers;

[0098] The receiving end executes the method for adaptively alleviating MPI noise in an IMDD system as described in the above embodiment to obtain a final output signal.

[0099] A system for adaptively alleviating MPI noise in an IMDD system provided by an embodiment of the present invention is used to execute a method for adaptively alleviating MPI noise in an IMDD system provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects.

[0100] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptively mitigating MPI noise in an IMDD system, characterized in that: For the receiving end, including: S1. Obtain a received signal and calculate its probability density distribution; S2. According to the probability density distribution of the received signal, two quantization level values ​​are obtained at each trough as a threshold level pair; S3, the interval between the threshold level pairs corresponding to each trough is a spurious interval, and the rest are reliable intervals; the data points in the received signal located in the reliable interval are classified as reliable points, and the data points located in the spurious interval are classified as spurious points; S4. After performing a judgment operation on the trustworthy point, obtaining a recovery signal corresponding to the trustworthy point, and calculating a judgment error corresponding to the trustworthy point; S5. Perform MPI noise estimation and MPI noise compensation on the stray point according to the decision error of the reliable point, perform a decision operation on the compensated stray point, and obtain a restored signal corresponding to the stray point; S6. The restored signal corresponding to the reliable point and the restored signal corresponding to the stray point are integrated to obtain the final output signal.

2. The method according to claim 1, characterized in that S1 specifically includes: S11, the receiving end performs data acquisition to obtain the received signal data x(t) affected by the link MPI noise interference, and the received signal data is recorded as y(t); the transmitted signal data x(t) is the PAM-M transmitted signal data of the M-order level of the transmitting end in the IMDD optical communication system; S12, according to the maximum value and the minimum value of y(t), the received signal data y(t) is uniformly quantized into n groups, the data points in y(t) are orderly divided into n equally spaced quantization intervals, and the midpoint of the quantization interval is taken as the quantization level of the group; S13. According to the number of data points included in each group, a probability density distribution of the level distribution is obtained, and the probability density distribution is recorded as p(f).

3. The method according to claim 1, characterized in that S2 specifically includes: S21. The probability density distribution p(f) of the PAM-M signal presents M peaks and (M-1) troughs. On the left side of each trough, three quantization level values ​​near the trough are selected in order from the trough to the peak, and the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough is selected in order as the threshold level, which is recorded as the threshold level th on the m side of the trough level. m,upper ; S22, on the right side of each trough, select three quantization level values ​​near the trough in order from the trough to the peak, and select in order the quantization level whose number of data points in the first group is greater than 125% of the number of data points at the trough, and at the same time, the number of data points contained in the quantization level is greater than the threshold level th on the level m side. m,upper The number of data points is large, which is recorded as the threshold level th on the m+1 side of the valley level. m+1,lower .

4. The method according to claim 3, characterized in that S3 specifically includes: S31, the received signal y(t) belonging to the interval (th m,lower ,th m,upper ) is divided into trustworthy points of level m, where the trustworthy point intervals of level "1" and level "M" are (min(y(t)),th 1,upper ) and (th M,lower ,max(y(t))); S32, will belong to the interval (th m,upper ,th m+1,lower ) is divided into stray points between level m and level m+1.

5. The method according to claim 1, characterized in that The calculating and obtaining the decision error corresponding to the trustworthy point includes: Subtract the restored signal of the trustworthy point from the trustworthy point to obtain the error corresponding to the trustworthy point.

6. The method according to claim 5, characterized in that S5 specifically includes: S51, estimating the MPI noise corresponding to each stray point; the MPI noise estimation of the stray point between level m and level m+1 is calculated by the mean of the error of the reliable point belonging to level m and the error of the reliable point belonging to level m+1 among the neighboring data points of the point; S52, subtracting the MPI noise corresponding to each stray point one by one, to obtain the MPI noise compensation of the stray point; S53: Perform PAM-M signal determination on the compensated stray points to obtain a restored transmission signal corresponding to the stray points.

7. The method according to claim 6, characterized in that S6 specifically includes: S61, replacing all the trusted points in the received data y(t) with the recovery signals corresponding to the trusted points; S62, replace all the stray points in the received data y(t) with the restored signals corresponding to the stray points, and obtain the restored signal y with the MPI noise eliminated ′ (t), as the final output signal.

8. A receiving end of an IMDD system, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor executes the method according to any one of claims 1 to 7 when executing the computer program.

9. An IMDD system, characterized in that: include: A transmitting end, a transmission optical fiber, an optical fiber connector and a receiving end as claimed in claim 8; The transmitting end is used to generate and modulate a transmitting signal, and transmit the transmitting signal into a transmission optical fiber; The transmission optical fiber is used to transmit the transmission signal to the receiving end for detection and reception; The optical fiber connector is used to connect multiple transmission optical fibers; The receiving end executes the method for adaptively mitigating MPI noise in an IMDD system as described in any one of claims 1 to 7 to obtain a final output signal.

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