A method for equalizing a channel of a coherent optical communication system based on independent component analysis

Abstract

The application discloses an independent component analysis-based channel equalization method for a coherent optical communication system, and belongs to the technical field of fiber communication.The application is realized by the following method: a target signal received by a receiving end of a multi-dimensional multiplexing coherent optical transmission system is collected, the target signal is first subjected to block processing to reduce the complexity of calculation, then data whitening operation is performed to eliminate the correlation between features in the data, and each data block is subjected to iteration of a weight matrix.After each iteration process is completed, a forgetting factor is adaptively adjusted, the forgetting factor is used to optimize parameters, the whitening matrix and the weight matrix are continuously converged, channel equalization is performed according to the optimal iteration effect, and original information is accurately recovered, that is, the independent component analysis-based channel equalization of the multi-dimensional multiplexing coherent optical transmission system is realized.The application can effectively suppress inter-channel crosstalk in the multi-dimensional multiplexing coherent optical transmission system, and improve the stability and quality of the fiber communication system.

Description

TECHNICAL FIELD

[0001] The application relates to a channel equalization method for a coherent optical transmission system based on independent component analysis, and belongs to the technical field of optical fiber communication. BACKGROUND

[0002] In a coherent optical transmission system, inter-channel crosstalk is the main bottleneck restricting the quality of the optical fiber communication system. In a coherent optical transmission system, random inter-channel crosstalk sometimes exists, which will cause interference components in the received signal, result in distortion of the waveform of the received signal, affect the signal quality, and make it difficult to recover accurate information. Therefore, exploring channel equalization technology has become one of the most in-depth research topics in recent years. Independent component analysis (ICA) is a calculation method based on blind source separation theory, and the observed data is regarded as the superposition of a plurality of statistically independent source signals. The goal is to separate these mixed signals into original and independent components by finding a suitable mathematical transformation and reasonable judgment basis (information entropy, natural gradient, matrix eigenvalue, etc.). ICA can not only process the external structure of data, but also deeply mine the independent features of data. So far, a plurality of different independent component analysis algorithms have been proved to be effective. For example, a multi-fork blind equalization and phase recovery scheme based on independent component analysis (ICA) can replace CMA and decision-directed least mean square (DD-LMS) algorithm to a certain extent. A mode demultiplexing technology based on robust independent component analysis (ICA) can effectively separate signals in a space division multiplexing system based on a multi-mode optical fiber (MMF), without significantly reducing the transmission quality.

[0003] However, the traditional ICA algorithm has a large processing delay and high computational complexity when facing a relatively complex system because the parameters are too fixed and all data are processed at one time. Moreover, when facing a time-varying channel, the existing independent component analysis algorithm is difficult to effectively equalize inter-channel crosstalk. SUMMARY

[0004] In order to solve the problem that random inter-channel crosstalk causes inaccurate received information, the main purpose of the application is to provide a channel equalization method for a coherent optical communication system based on independent component analysis. The method collects target signals received at the receiving end of a multi-dimensional multiplexing coherent optical transmission system, performs block processing on the target signals, reduces the complexity of calculation, then performs data whitening operation to eliminate the correlation between the characteristics in the data, and iterates the weight matrix for each data block. By using an adaptive forgetting factor to optimize the parameters, the whitening matrix and the weight matrix are constantly convergent, and according to the optimal iteration effect, channel equalization is performed, that is, multi-dimensional multiplexing coherent optical transmission system channel equalization based on independent component analysis is realized. The application can realize adaptive adjustment of parameters and block processing of data independent component analysis equalization.

[0005] The object of the present application is achieved by the following technical solutions:

[0006] The application discloses a coherent optical communication system channel equalization method based on independent component analysis, which comprises the following steps:

[0007] Step one, obtaining a target signal received by a receiving end of a multi-dimensional multiplexing coherent optical communication system, and performing block processing on the target signal to obtain a block signal; then performing data whitening on the block signal to obtain a whitened signal;

[0008] For the block signal with a block size of L, the iteration of the whitening matrix M is:

[0009]

[0010] Wherein n represents a data sequence, M n represents a whitening matrix corresponding to the data sequence n, l represents a time index, V l represents the data after whitening, λ l represents an adaptive forgetting factor at the lth iteration, and I represents a unit matrix;

[0011] Step two, iterating the whitening signal of step one by a weight matrix W:

[0012]

[0013] Wherein n represents a data sequence, W n represents a weight matrix corresponding to the data sequence n, y l represents a source signal corresponding to the data sequence n, and f(·) represents a nonlinear activation function, which is defined as any nonlinear function conducive to data processing;

[0014] Step three, after the end of each iteration process, the forgetting factor is adaptively adjusted to obtain a better whitening matrix and weight matrix;

[0015] The adaptive adjustment process of the forgetting factor is:

[0016]

[0017] Wherein N represents the number of iterations, λ N represents an adaptive forgetting factor after the Nth iteration, a represents an attenuation rate of the adaptive forgetting factor, and the greater the value, the faster the forgetting factor attenuates; β represents an upper limit factor, which is used to set an upper limit of the adaptive forgetting factor; if the adaptive forgetting factor is lower than the limit, the value will be used; ST N+1 represents a stability factor after the N+1th iteration; G(x) represents a nonlinear mapping relationship between the stability factor and the adaptive forgetting factor; wherein:

[0018]

[0019] In the formula, ST N represents the stability factor after the Nth iteration, b is the transition bandwidth of the adaptive forgetting factor, the transition band is the area where the forgetting factor transitions from the fast convergence stage to the adaptive stage, c is the center of the adaptive forgetting factor transition band, which represents the center position of the transition zone when the forgetting factor transitions from the fast convergence stage to the adaptive stage, ST min represents the stability index of the initial convergence, and epsilon represents a control factor that controls the transition of the forgetting factor from the fast convergence stage to the adaptive stage.

[0020] Step four, after reaching the preset number of iterations, stop iteration, and perform channel equalization according to the optimal whitening matrix obtained in step one and the optimal weight matrix obtained in step two, to alleviate the influence of inter-channel crosstalk on the signal during optical fiber transmission, and improve the stability and quality of the optical fiber communication system.

[0021] Advantages:

[0022] 1. The independent component analysis-based coherent optical communication system channel equalization method disclosed by the application reduces the complexity of calculation by collecting target signals received at the receiving end of a multi-dimensional multiplexing coherent optical transmission system, performing block processing on the target signals, then performing data whitening operation to eliminate the correlation between features in the data, and iteratively processing each data block to obtain a weight matrix, thereby realizing independent component analysis-based multi-dimensional multiplexing coherent optical transmission system channel equalization.

[0023] 2. The independent component analysis-based coherent optical communication system channel equalization method disclosed by the application uses an adaptive forgetting factor to optimize parameters, so that the whitening matrix and the weight matrix continuously converge, and channel equalization is performed according to the optimal iteration effect. The application is suitable for the field of optical fiber communication, and can effectively suppress inter-channel crosstalk in a multi-dimensional multiplexing coherent optical transmission system, thereby improving the stability and quality of the optical fiber communication system. DETAILED DESCRIPTION

[0024] Figure 1 The flowchart of the independent component analysis-based coherent optical communication system channel equalization method disclosed by the application is shown in the figure.

[0025] Figure 2 The simulation verification block diagram provided by the embodiment of the application is shown in the figure.

[0026] Figure 3 The whitening matrix update block diagram in the independent component analysis-based channel equalization method provided by the embodiment of the application is shown in the figure.

[0027] Figure 4The weight matrix updating block diagram in the channel equalization method based on independent component analysis provided by the embodiment of the present application is shown.

[0028] Figure 5 The crosstalk grayscale chart in the channel equalization technology provided by the embodiment of the present application is shown.

[0029] Figure 6 The relationship chart between the bit error rate and the signal-to-noise ratio in the channel equalization method based on independent component analysis of the multi-dimensional multiplexing coherent optical transmission system provided by the embodiment of the present application is shown, wherein chart (a) is the relationship chart between the bit error rate and the signal-to-noise ratio of signal one, chart (b) is the relationship chart between the bit error rate and the signal-to-noise ratio of signal two, chart (c) is the relationship chart between the bit error rate and the signal-to-noise ratio of signal three, and chart (d) is the relationship chart between the bit error rate and the signal-to-noise ratio of signal four. DETAILED DESCRIPTION

[0030] The present application will be described in detail below with reference to the accompanying drawings and embodiments. Meanwhile, the technical problems solved by the technical scheme of the present application and the beneficial effects are described, and it should be pointed out that the described embodiments are only intended to facilitate the understanding of the present application and do not limit the present application in any way.

[0031] As shown in Figure 1 The present embodiment discloses a channel equalization method of a coherent optical communication system based on independent component analysis, and the specific implementation steps are as follows:

[0032] Step one, obtaining a target signal received by a receiving end of a multi-dimensional multiplexing coherent optical communication system, performing block processing on the target signal, and performing data whitening;

[0033] Figure 2 The simulation verification block diagram provided by the embodiment of the present application is shown, and first, QPSK signals of 8 different modes are generated at the transmitting end, and are coupled into a 10km ring core fiber (RCF) according to mode division into two categories. At the receiving end, each light beam is coupled into a single mode fiber by using a collimator, and 8 different mode signals are processed by a digital signal processing (DSP) module through 8 optical 90° mixers and 8 analog-to-digital converters.

[0034] Figure 3 The whitening matrix updating block diagram in the channel equalization technology of a multi-dimensional multiplexing coherent optical transmission system based on independent component analysis is shown.

[0035] First, the mixed signal is divided into blocks according to the size L, and then the iteration of the whitening matrix is performed.

[0036]

[0037] Wherein n represents a data sequence, M n represents the whitening matrix corresponding to the data sequence n, l represents a time index, and Vl denotes the whitened data, λ l denotes the adaptive forgetting factor at the lth iteration, I denotes the identity matrix;

[0038] According to the iteration formula, the data blocks are whitened to eliminate the correlation between the features in the data.

[0039] Step two, the weight matrix is iterated for the signal after the data whitening in step one;

[0040] Figure 4 The weight matrix update block diagram in the channel equalization technology of the coherent optical transmission system based on independent component analysis is shown.

[0041] For the data block with a size of L after blocking, the iteration formula of the weight matrix is:

[0042]

[0043] where n denotes the data sequence, W n denotes the weight matrix corresponding to the data sequence n, y l represents the source signal corresponding to the data sequence n, f(·) denotes a nonlinear activation function, which is defined as any nonlinear function that is beneficial to data processing;

[0044] Step three, after the end of each iteration process, the forgetting factor is adaptively adjusted to obtain a better whitening matrix and weight matrix.

[0045] The adaptive adjustment process of the forgetting factor is:

[0046]

[0047] where N denotes the number of iterations, λ N denotes the adaptive forgetting factor after the Nth iteration, α denotes the decay rate of the adaptive forgetting factor, and the greater the value, the faster the forgetting factor decays; β denotes the upper limit factor, which is used to set the upper limit of the adaptive forgetting factor; if the adaptive forgetting factor is lower than this limit, the value will be used; ST N+1 denotes the stability factor after the (N+1)th iteration; G(x) denotes the nonlinear mapping relationship between the stability factor and the adaptive forgetting factor; wherein:

[0048]

[0049] In the formula, ST Nrepresents the stability factor after the Nth iteration, b is the transition bandwidth of the adaptive forgetting factor, the transition band is the area where the forgetting factor is transited from the fast convergence stage to the adaptive stage; c is the center of the adaptive forgetting factor transition band, which represents the center position of the transition area when the forgetting factor is transited from the fast convergence stage to the adaptive stage; ST min represents the stability index of initial convergence, and ε represents a control factor that controls the transition of the forgetting factor from the fast convergence stage to the adaptive stage.

[0050] Step four, according to the optimal whitening matrix and weight matrix after iteration, channel equalization is performed to alleviate the influence of inter-channel crosstalk on the signal in the process of optical fiber transmission, and the stability and quality of the optical fiber communication system are improved.

[0051] As shown in Figure 2 , after the channel equalization method based on independent component analysis, the received signal obtains the optimal whitening matrix and weight matrix, then sequentially passes through the carrier frequency recovery, clock phase recovery algorithm, and finally performs crosstalk judgment and bit error rate calculation.

[0052] Figure 5 The inter-channel crosstalk grayscale diagram of the channel equalization method based on independent component analysis is shown. In the communication system of the present application, signal 1 is most affected by signal 3 and least affected by signal 2 in the transmission process. The darker the block color, the greater the inter-channel crosstalk.

[0053] Figure 6 The relationship between the demodulation efficiency and the signal-to-noise ratio of the channel equalization method based on independent component analysis of the coherent optical transmission system is shown. Under different bit error rate conditions, the algorithm can make the bit error rate less than 0.05. When the signal-to-noise ratio is 23 dB, the bit error rate can be improved to the order of 10 -3 .

[0054] The above specific description further details the purpose, technical scheme and beneficial effects of the application. It should be understood that the above description is only a specific embodiment of the application and is not used to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A channel equalization method for a coherent optical communication system based on independent component analysis, characterized in that: Includes the following steps, Step 1: Obtain the target signal received by the receiver of the multidimensional multiplexed coherent optical communication system, and perform block processing on the target signal to obtain block signals; perform data whitening on the block signals to obtain whitened signals; For a block signal of size L, the iteration of the whitening matrix M is as follows: Where n represents the data sequence, M n V represents the whitening matrix corresponding to the data sequence n, l represents the time exponent, and V l λ represents the l-th whitened data. l Let I represent the adaptive forgetting factor in the l-th iteration; Step 2: Iterate the weight matrix W on the whitened signal from Step 1: Where n represents the data sequence, W n y represents the weight matrix corresponding to the data sequence n. l f(·) represents the source signal corresponding to the data sequence n, and f(·) represents the nonlinear activation function, which is defined as any nonlinear function that is beneficial to data processing. Step 3: After each iteration, adaptively adjust the forgetting factor to obtain a better whitening matrix and weight matrix; The adaptive adjustment process of the forgetting factor is as follows: Where N represents the number of iterations, λ N α represents the adaptive forgetting factor after the Nth iteration, and α represents the decay rate of the adaptive forgetting factor. The larger the value, the faster the forgetting factor decays. β represents the upper limit factor, which is used to set the upper limit of the adaptive forgetting factor; ST N+1 G(x) represents the stability factor after the (N+1)th iteration; G(x) represents the nonlinear mapping relationship between the stability factor and the adaptive forgetting factor. in: In the formula ST N Let represent the stability factor after the Nth iteration, b be the transition bandwidth of the adaptive forgetting factor, and the transition band is the region where the forgetting factor transitions from the fast convergence stage to the adaptive stage; c be the center of the transition band of the adaptive forgetting factor, indicating the center position of the transition region when the forgetting factor transitions from the fast convergence stage to the adaptive stage; ST min The initial convergence stability index is represented by ε, which represents the control factor that controls the transition of the forgetting factor from the rapid convergence phase to the adaptive phase. Step 4: After reaching the preset number of iterations, stop the iteration and perform channel equalization based on the optimal whitening matrix obtained in Step 1 and the optimal weight matrix obtained in Step 2. This will alleviate the influence of inter-channel crosstalk on the signal during optical fiber transmission and improve the stability and quality of the optical fiber communication system.

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