Balancing method of adaptive optical fiber communication system

Abstract

The invention discloses a balancing method of a self-adaptive optical fiber communication system, and belongs to the technical field of optical fiber communication. Establishing an optical fiber communication platform, acquiring a signal of a transmitting end to obtain a modulation characteristic of the signal, and comparing the modulation characteristic with a channel characteristic threshold to obtain abnormal data; the change of the deviation signal of the abnormal data is removed, and a stable signal is extracted; and carrying out equalization processing on the stable signal by using an iterative algorithm to obtain an equalized signal. The overall transmission performance of the self-adaptive optical fiber communication system is improved, and the performance of the self-adaptive optical fiber communication system in different environments is kept optimal.

Description

Technical Field

[0001] The present invention belongs to the field of optical fiber communication technology, and in particular to an equalization method for an adaptive optical fiber communication system. Background Art

[0002] With the application of technical means such as error control coding, multi-order modulation formats, the use of coherent optical transmission and receiving equipment, and high-speed electronic signal processing equipment, the information transmission rate of optical communication systems has developed towards the Tb / s level.

[0003] In optical communication systems, the modulation formats used include coded modulation formats such as intensity modulation (IMDD), quadrature amplitude phase modulation (QAM), star quadrature amplitude phase modulation (SQAM), quadrature phase modulation (QPSK), differential phase shift keying (DQPSK), or modulation formats obtained by quantizing Gaussian sources, such as the format based on polar quantization (IPQ).

[0004] As the rate of information transmission increases, various influencing factors in optical communications, such as nonlinearity and polarization mode dispersion, seriously reduce the performance of the system and limit the utilization efficiency of the channel bandwidth. The optical transmission system based on optical fiber is affected by the nonlinearity and polarization mode dispersion of the fiber, which makes the noise and signal interact with each other. Simple models, such as Gaussian models, cannot be used to describe the performance of the fiber channel and estimate the system.

[0005] Therefore, the demand for adaptive equalization technology is proposed. The existing optical transmission system lacks adaptive equalization feedback and cannot adjust the coding and modulation format of the system transmitter in real time according to the channel conditions to ensure that the system performance is always in the optimal state.

[0006] Existing adaptive equalization technologies include zero forcing equalization, minimum mean square error equalization, decision feedback equalization, Volterra equalization, etc. These equalization technologies are designed to correct the inter-symbol interference problem caused by factors such as multipath delay and realize signal recovery and reconstruction.

[0007] However, the existing adaptive equalization technology has high complexity. In order to improve efficiency and reduce costs, technicians are committed to developing methods to reduce the complexity of equalization algorithms. Summary of the invention

[0008] In order to solve the problems in the related art, the present application provides an equalization method for an adaptive optical fiber communication system, comprising the following steps:

[0009] S1. Establish an optical fiber communication platform, collect the signal from the transmitting end to obtain the modulation characteristics of the signal, compare the modulation characteristics with the channel characteristic threshold, and obtain abnormal data;

[0010] S2, removing the change of the deviation signal of the abnormal data and extracting the stable signal;

[0011] S3. Perform equalization processing on the stable signal using an iterative algorithm to obtain an equalized signal.

[0012] In a preferred embodiment, in step S2, when there is a frequency deviation Δf in the abnormal data, the expression of the received nth segment signal S(n) is:

[0013] S(n)=exp[j(a n +2πΔfT s +θ n )]

[0014] Among them, a n is the original signal phase, T s For S n The time interval, θ n is the carrier angular frequency of the nth signal, 2πΔf is the angular frequency change caused by the frequency deviation;

[0015] The deviation signal f(n) corresponding to the frequency deviation Δf is:

[0016] f(n)=exp[j(2πΔfT s )]

[0017] The deviation signal f(n) is removed from the nth segment signal S(n) to obtain a stable signal d(n).

[0018] In a preferred embodiment, in step S3, an equalization error signal Y(n) is introduced to adjust the deviation between the stable signal and the equalizer output signal, and the stable signal is optimized. The expression of the output equalization signal H(n) is:

[0019] H(n)=d(n)-Y T (n)W(n)

[0020] Among them, d(n) is the stable signal, Y(n) is the equalization error signal, W(n) is the weight factor of the equalizer, and T is the transposed symbol.

[0021] In a preferred embodiment, the weight factor W(n) of the equalizer is adaptively adjusted through repeated iterations, and the channel error ε(n) of the stable signal is expressed as:

[0022] ε(n)=d 2 (n)+W T (n)RW(n)-2PT W(n)

[0023] Wherein, R is the autocorrelation matrix of the input stable signal, and P is the cross-correlation vector of the input stable signal and the channel impulse response signal;

[0024] The expression of the gradient value of the channel error is:

[0025]

[0026] In a preferred embodiment, an iterative algorithm is used to update the weights to obtain an updated weight factor W′(n) of the equalizer:

[0027]

[0028] Among them, μ is the iteration step size.

[0029] In a preferred embodiment, the method for solving the cross-correlation vector of the input stable signal and the channel impulse response signal is as follows:

[0030] Step A: Generate a training sequence and insert the training sequence into the header of a data frame of a stable signal;

[0031] Step B: receiving a data frame containing a training sequence, and calculating a channel impulse response signal C(n+τ) by a least square method;

[0032] Step C: Calculate the cross-correlation vector P between the input stable signal and the channel impulse response signal:

[0033] O = d(n)C*(n+τ).

[0034] Among them, C * is the complex conjugate of C, and τ is the delay of the channel impulse response signal.

[0035] In a preferred embodiment, step B specifically includes: the receiving end receives a signal containing a training sequence, converts the received signal into the frequency domain by means of a fast Fourier transform, compares the training sequence with the received signal in the frequency domain, calculates the channel impulse frequency signal by means of a least squares method, converts the channel impulse frequency signal back into the time domain by means of an inverse fast Fourier transform, and obtains a channel impulse response signal C(n+τ).

[0036] In a preferred embodiment, in step S2, characteristic parameters of abnormal data are predicted to obtain an interfering carrier signal;

[0037] The number of the carrier signal to be interfered with is estimated using carrier signal processing techniques:

[0038] a(O)

[0039] =[1,exp(j2nf1sin(θ1) / λ1),...exp(j2nnf i sin(θ n ) / λ n ),…,exp(j2π(M-1)f (M-1) sin(θ (M-1) ) / λ (M-1) )] T

[0040] Among them, a(θ n )Carrier vector matrix, θ n is the carrier angular frequency of the nth signal, f n is the frequency spacing of the carriers, λ n is the signal wavelength of the nth segment signal, M is the total number of segments of the carrier signal, n is the segment number of the carrier signal, n = [0, M-1];

[0041] Based on the carrier vector matrix a(θ n ) and find the sequence number n of the interfering carrier signal.

[0042] Compared with the prior art, the present invention has the following beneficial technical effects:

[0043] 1: The present invention can detect anomalies in signals in a timely manner, take measures in advance, avoid error accumulation in subsequent processing, ensure that the system works within a normal range through monitoring and comparison, and improve the reliability and stability of the system.

[0044] 2: Remove the changes in the deviation signal of the abnormal data, extract the stable signal, remove the noise and mutation in the abnormal data, and improve the purity of the signal; through filtering and signal processing, the signal-to-noise ratio of the signal is effectively improved, which reduces the complexity of subsequent processing and greatly improves the processing efficiency.

[0045] 3: Use an iterative algorithm to equalize the stable signal to obtain an equalized signal. Through equalization, the amplitude and phase distortion introduced by the channel are compensated, and the signal quality is improved. The equalized signal has a higher signal-to-noise ratio and a lower bit error rate, which improves the overall transmission performance of the system. The iterative algorithm can adaptively adjust the equalization parameters according to the changes in the channel, so that the system performance in different environments remains optimal, improving transmission efficiency while reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a flow chart of an equalization method for an adaptive optical fiber communication system of the present invention;

[0047] Figure 2 It is a schematic diagram of the structure of the optical fiber communication platform of the present invention;

[0048] Figure 3 It is a schematic diagram of the calculation flow of the cross-correlation vector of the input stable signal and the channel impulse response signal of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principles of the various components in the system, the connection relationship of the various parts in the device is shown, which only clearly distinguishes the relative position relationship between the various components, and cannot constitute a limitation on the signal transmission direction, connection sequence and structural size, size and shape of each part within the component or structure.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0052] Adaptive equalization is a technology used in communication systems that aims to counteract the impact of channel changes by continuously adjusting the parameters of the equalizer, thereby improving the quality of the received signal and improving the reliability and efficiency of data transmission.

[0053] like Figure 1 FIG. 1 is a flow chart of an equalization method for an adaptive optical fiber communication system of the present invention. The equalization method for an adaptive optical fiber communication system comprises the following steps:

[0054] S1. Establish an optical fiber communication platform, collect the signal at the transmitting end to obtain the modulation characteristics of the signal, compare the modulation characteristics with the channel characteristic threshold, and obtain abnormal data.

[0055] First, establish a fiber optic communication platform, such as Figure 2As shown in the figure, the structure of the optical fiber communication platform includes three parts: the transmitting end, the optical fiber transmission channel and the receiving end. The transmitting end is composed of a laser source and a signal modulator. The laser source emits a stable optical signal, and the signal modulator modulates the communication digital signal onto the optical carrier emitted by the laser source; the modulated optical signal is transmitted through the optical fiber transmission channel; at the receiving end, the received optical signal is subjected to a time series analysis of the sampled data set by the signal processor, and the modulation features such as amplitude, phase, frequency and other parameters are extracted by the feature extraction unit. The extracted modulation features are compared with the channel characteristic threshold to obtain abnormal data.

[0056] By setting the channel characteristic threshold in advance, the obtained modulation characteristics are divided into characteristic data that deviates from the normal range and stable data within the normal range; the channel characteristic threshold is determined by hypothesis testing or outlier analysis in statistical analysis, and the extracted modulation characteristics are compared with the channel characteristic threshold.

[0057] S2. Remove the changes in the deviation signal of the abnormal data and extract the stable signal.

[0058] After the transmitted signal passes through the optical fiber transmission channel of the adaptive optical fiber communication system, the signal will change or be interfered due to the influence of factors such as channel bandwidth and channel characteristics, resulting in a decrease in signal quality and affecting the performance of the communication system. Therefore, the received signal is adaptively equalized to weaken or offset the interference of the deviation frequency.

[0059] During channel transmission, optical signals will be affected by various external factors and produce frequency changes. The frequency changes can be removed to extract stable signals.

[0060] When the optical signal is affected by external factors, in order to eliminate the influence of frequency deviation on the optical signal, the frequency deviation estimation algorithm is used to process the abnormal signal in the optical signal. When there is a frequency deviation Δf in the abnormal data, the expression of the received nth segment signal S(n) is:

[0061] S(n)=exp[j(a n +2πΔfT s +θ n )]

[0062] Among them, a n is the original signal phase, T s For S n The time interval, θ n is the carrier angular frequency of the nth signal, 2πΔf is the angular frequency change caused by the frequency offset, and 2πΔf is the angular frequency change caused by the frequency offset. The signal will rotate around the origin at an angular velocity of 2πΔf in the complex plane, causing the phase of the optical signal to change with time, destroying the stability of the optical signal and increasing the probability of incorrect transmission of communication information in the signal.

[0063] The deviation signal f(n) corresponding to the frequency deviation Δf is:

[0064] f(n)=exp[j(2πΔfT s )]

[0065] Wherein, 1≤n≤N, N is the total number of signal segments.

[0066] The deviation signal f(n) corresponding to the frequency deviation Δf is removed from the received nth segment signal S(n) with frequency deviation, thereby obtaining a stable signal d(n) that is closer to the original signal in frequency.

[0067] S3. Perform equalization processing on the stable signal using an iterative algorithm to obtain an equalized signal.

[0068] Use an adaptive equalizer to process the stable signal and adjust the coefficients of the receiving equalizer to make the output signal as balanced as possible. Based on the received signal and the estimated channel impulse response, the coefficients of the equalizer are continuously monitored and updated to adapt to channel changes, thereby achieving adaptive channel equalization.

[0069] The equalization error signal Y(n) is introduced to adjust the deviation between the stable signal and the equalizer output signal, optimize the stable signal, and the expression of the output equalized signal H(n) is:

[0070] H(n)=d(n)-Y T (n)W(n)

[0071] Among them, d(n) is the stable signal, Y(n) is the equalization error signal, W(n) is the weight factor of the equalizer, the optimized output signal r(n), and T is the transposed symbol.

[0072] The selection of the weight factor W(n) of the equalizer is determined by the channel impulse response signal and the equalization algorithm, and the weight factor W(n) of the equalizer is adaptively adjusted through repeated iterations.

[0073] The channel error ε(n) of the stable signal is expressed as:

[0074] ε(n)=d 2 (n)+W T (n)RW(n)-2P T W(n)

[0075] Wherein, R is the autocorrelation matrix of the input stable signal, and P is the cross-correlation vector between the input stable signal and the channel impulse response signal.

[0076] The expression of the gradient value of the channel error is:

[0077]

[0078] The weights are updated using an iterative algorithm to obtain the updated weight factor W′(n) of the equalizer:

[0079]

[0080] Among them, μ is the iteration step size. The smaller the value of μ is, the more the cost error can converge to a smaller range until it converges to the global minimum, reducing the deviation between the equalizer output and the original signal under a specific metric, making the communication signal transmitted in the equalization method of the optical fiber communication system more robust.

[0081] Example 2

[0082] In step 3 of embodiment 1, the specific calculation method of the cross-correlation vector P of the input stable signal and the channel impulse response signal is as follows: Figure 3 As shown, the following steps are included:

[0083] Step A: Generate a training sequence and insert the training sequence into the header of a data frame of a stable signal.

[0084] First, a training sequence is generated, and the transmitter inserts a known training sequence.

[0085] Training sequence: used for frame synchronization and frequency synchronization. In the IEEE 802.11a standard, the training sequence consists of 10 periodically repeated symbols, and the interval between each symbol is 1 / 4 of the normal OFDM symbol interval.

[0086] The training sequence is inserted into the head of the data frame of the stable signal as a part of the preamble. For example, in the IEEE802.11a standard, the preamble consists of the training sequence with a total length of 16 microseconds.

[0087] Step B: Receive a signal containing a training sequence, and calculate a channel impulse response signal C(n+τ) by using a least squares method.

[0088] Received signal: The receiving end receives a signal containing a training sequence.

[0089] FFT transform: The received signal is converted into the frequency domain through Fast Fourier Transform (FFT).

[0090] Channel estimation: In the frequency domain, the known training sequence is compared with the received signal and the channel impulse frequency signal is calculated by the least squares method.

[0091] IFFT transform: The channel impulse frequency signal is converted back to the time domain through inverse fast Fourier transform (IFFT) to obtain the channel impulse response signal C(n+τ).

[0092] Step C: Calculate the cross-correlation vector P between the input stable signal and the channel impulse response signal:

[0093] P=d(n)C * (n+τ).

[0094] C * is the complex conjugate of C, and τ is the delay of the channel impulse response signal.

[0095] Example 3

[0096] In this embodiment, for step S2, further, characteristic parameters of abnormal data are predicted, and signal stability enhancement processing is performed on stable data according to the prediction result to obtain a carrier signal of potential interference; frequency mapping processing is performed on the carrier signal of potential interference to obtain a compensation signal.

[0097] Based on long-term monitoring data, data mining technology is used to analyze the fluctuation pattern of the signal, so as to identify abnormal signal patterns and record the characteristics and occurrence time of these events; specifically, association rule learning, cluster analysis or anomaly detection algorithms can be applied to identify several abnormal signal patterns. By comparing these abnormal signal patterns with external factors such as environmental variables and equipment status, further causal analysis is performed to determine the carrier signal of potential interference that causes signal anomalies.

[0098] The potential interfering carrier signal is frequency mapped to obtain the interfering carrier signal location information and interference strength information, the interfering carrier signal location information is used to perform interference elimination processing on the signal sampling data to obtain a compensation signal, and the interference strength information is used to perform interference compensation processing on the channel state information to obtain a compensation signal.

[0099] The number of the carrier signal to be interfered with is estimated using carrier signal processing techniques:

[0100] a(θ)

[0101] =[1,exp(j2πf1sin(θ1) / λ1),…exp(j2πnf n sin(θ n ) / λ n ),…,exp(j2π(M-1)f (M-1) sin(θ (M-1) ) / λ (M-1) )] T

[0102] Among them, a(θ n )Carrier vector matrix, θ n is the carrier angular frequency of the nth signal, f n is the frequency spacing of the carriers, λ nis the signal wavelength of the nth segment signal, M is the total number of segments of the carrier signal, n is the segment number of the carrier signal, n = [0, M-1];

[0103] In this step, the potential interfering carrier signal is determined through big data correlation analysis, the specific location of the interfering carrier signal is determined by the positioning algorithm, and the interference intensity information is obtained through the measurement tool; preferably, the frequency mapping process can be analyzed together with the data of multiple sensing sites to achieve more accurate positioning. Using this information, signal processing technology is used to eliminate interference from the signal sampling data to improve the reception quality of the signal. At the same time, the channel state information is compensated according to the interference intensity information, and the transmission power or signal processing parameters of the system are adjusted to obtain a compensated signal.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0105] The technical features of the above embodiments may be combined arbitrarily. 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 specification.

[0106] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An equalization method for an adaptive optical fiber communication system, characterized in that: The steps include: S1. Establish an optical fiber communication platform, collect the signal from the transmitting end to obtain the modulation characteristics of the signal, compare the modulation characteristics with the channel characteristic threshold, and obtain abnormal data; S2, removing the change of the deviation signal of the abnormal data and extracting the stable signal; S3. Perform equalization processing on the stable signal using an iterative algorithm to obtain an equalized signal.

2. The equalization method of the adaptive optical fiber communication system according to claim 1, characterized in that: In step S2, when there is a frequency deviation Δf in the abnormal data, the expression of the received nth segment signal S(n) is: S(n)=exp[j(a n +2πΔfT s +θ n )] Among them, a n is the original signal phase, T s For S n The time interval, θ n is the carrier angular frequency of the nth signal, 2πΔf is the angular frequency change caused by the frequency deviation; The deviation signal f(n) corresponding to the frequency deviation Δf is: f(n)=exp[j(2πΔfT s )] The deviation signal f(n) is removed from the nth segment signal S(n) to obtain a stable signal d(n).

3. The equalization method of the adaptive optical fiber communication system according to claim 2, characterized in that: In step S3, an equalization error signal Y(n) is introduced to adjust the deviation between the stable signal and the equalizer output signal, and the stable signal is optimized. The expression of the output equalization signal H(n) is: H(n)=d(n)-Y T (n)W(n) Among them, d(n) is the stable signal, Y(n) is the equalization error signal, W(n) is the weight factor of the equalizer, and T is the transposed symbol.

4. The equalization method of the adaptive optical fiber communication system according to claim 3, characterized in that: The weight factor W(n) of the equalizer is adaptively adjusted through repeated iterations, and the channel error ε(n) of the stable signal is expressed as: ε(n)=d 2 (n)+W T (n)RW(n)-2P T W(n) Wherein, R is the autocorrelation matrix of the input stable signal, and P is the cross-correlation vector of the input stable signal and the channel impulse response signal; The expression of the gradient value of the channel error is:

5. The equalization method of the adaptive optical fiber communication system according to claim 4, characterized in that: The weights are updated using an iterative algorithm to obtain the updated weight factor W′(n) of the equalizer: Among them, μ is the iteration step size.

6. The equalization method of the adaptive optical fiber communication system according to claim 4, characterized in that: The solution method for the cross-correlation vector of the input stable signal and the channel impulse response signal is as follows: Step A: Generate a training sequence and insert the training sequence into the header of a data frame of a stable signal; Step B: receiving a data frame containing a training sequence, and calculating a channel impulse response signal C(n+τ) by a least square method; Step C: Calculate the cross-correlation vector P between the input stable signal and the channel impulse response signal: P=d(n)C * (n+τ). Among them, C * is the complex conjugate of C, and τ is the delay of the channel impulse response signal.

7. The equalization method of the adaptive optical fiber communication system according to claim 6, characterized in that: Step B specifically includes: the receiving end receives a signal containing a training sequence, converts the received signal into the frequency domain through fast Fourier transform, compares the training sequence with the received signal in the frequency domain, calculates the channel impulse frequency signal through the least squares method, converts the channel impulse frequency signal back to the time domain through inverse fast Fourier transform, and obtains the channel impulse response signal C(n+τ).

8. The equalization method of the adaptive optical fiber communication system according to claim 1, characterized in that: In the step S2, the characteristic parameters of the abnormal data are predicted to obtain an interfering carrier signal; The number of the carrier signal to be interfered with is estimated using carrier signal processing techniques: a(θ)=[1,exp(j2πf1sin(θ1) / λ1),…exp(j2πnf n sin(θ n ) / min n ),…,exp(j2π(M-1)f (M-1) sin(θ (M-1) ) / min (M-1) )] 1 Among them, a(θ n )Carrier vector matrix, θ n is the carrier angular frequency of the nth signal, f n is the frequency spacing of the carriers, λ n is the signal wavelength of the nth segment signal, M is the total number of segments of the carrier signal, n is the segment number of the carrier signal, n = [0, M-1]; Based on the carrier vector matrix a(θ n ) and find the sequence number n of the interfering carrier signal.

Images