Coherent optical communication system channel equalization method based on independent component analysis

Abstract

The invention discloses a coherent optical communication system channel equalization method based on independent component analysis, and belongs to the technical field of optical fiber communication. The implementation method comprises the following steps: collecting a target signal received by a receiving end of a multi-dimensional multiplexing coherent optical transmission system, firstly carrying out block processing on the target signal to reduce the calculation complexity, then carrying out data whitening operation to eliminate the correlation among characteristics in data, and carrying out weight matrix iteration on each data block. After each iteration process is finished, a forgetting factor is adaptively adjusted, parameters are optimized by using the adaptive forgetting factor, so that a whitening matrix and a weight matrix are continuously converged, channel equalization is performed according to an optimal iteration effect, and original information is accurately recovered. Therefore, the channel equalization of the multi-dimensional multiplexing coherent optical transmission system based on independent component analysis is realized. According to the invention, the inter-channel crosstalk in the multi-dimensional multiplexing coherent optical transmission system can be effectively suppressed, and the stability and quality of the optical fiber communication system are improved.

Description

Technical Field

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

[0002] In coherent optical transmission systems, crosstalk between channels is the main bottleneck restricting the quality of optical fiber communication systems. Sometimes there is random crosstalk between channels in coherent optical transmission systems, which will cause interference components in the received signal, resulting in distortion or distortion of the waveform of the received signal, affecting the signal quality and making 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 computational method based on blind source separation theory, which regards the observed data as the superposition of several statistically independent source signals. The goal is to separate these mixed signals into original, independent components by finding a suitable mathematical transformation and a reasonable judgment basis (information entropy, natural gradient, matrix eigenvalue, etc.). ICA can not only process the external structure of the data, but also deeply explore the independent features inside the data. So far, a variety of different independent component analysis algorithms have been proven to be effective. For example, multi-bifurcation blind equalization and phase recovery schemes based on independent component analysis (ICA) can replace CMA and decision-directed least mean square (DD-LMS) algorithms to a certain extent. Mode demultiplexing technology based on robust independent component analysis (ICA) can effectively separate signals in space-division multiplexing systems based on multimode fiber (MMF) without significantly reducing the transmission quality.

[0003] However, when faced with more complex systems, traditional ICA algorithms have too fixed parameters and process all data at once, which will cause large processing delays and increase computational complexity. In addition, when faced with time-varying channels, existing independent component analysis algorithms are difficult to effectively balance crosstalk between channels. Summary of the invention

[0004] In order to solve the problem of inaccurate received information caused by random crosstalk between channels, the main purpose of the present invention is to provide a channel equalization method for a coherent optical communication system based on independent component analysis. The method collects the target signal received by the receiving end of the multi-dimensional multiplexed coherent optical transmission system, processes the target signal in blocks, reduces the complexity of the calculation, and then performs a data whitening operation to eliminate the correlation between the features 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 continue to converge, and according to the optimal iterative effect, channel equalization is performed, that is, channel equalization of a multi-dimensional multiplexed coherent optical transmission system based on independent component analysis is realized. The present invention can realize independent component analysis equalization that adaptively adjusts parameters and processes data in blocks.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] The present invention discloses a coherent optical communication system channel equalization method based on independent component analysis, comprising the following steps:

[0007] Step 1: 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 a block signal with a block size of L, the iteration of the whitening matrix M is:

[0009]

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

[0011] Step 2: Iterate the weight matrix W of the whitened signal in step 1:

[0012]

[0013] Where n represents the data sequence, W n Represents the weight matrix corresponding to the data sequence n, y l 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;

[0014] Step 3: After each iteration, 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] Where N is the number of iterations, λ N Represents the adaptive forgetting factor after the Nth iteration, α represents the decay rate of the adaptive forgetting factor, and 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; if the adaptive forgetting factor is lower than this limit, this value will be used; ST N+1 represents the stability factor after the N+1th iteration; G(x) represents the nonlinear mapping relationship between the stability factor and the adaptive forgetting factor; where:

[0018]

[0019] Where ST N represents the stability factor after the Nth iteration, b is the transition bandwidth of the adaptive forgetting factor, and 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 transition band of the adaptive forgetting factor, which represents the center position of the transition zone when the forgetting factor transfers from the fast convergence stage to the adaptive stage; ST min represents the stability index of the initial convergence, ε represents the control factor, which controls the transition of the forgetting factor from the fast convergence stage to the adaptive stage;

[0020] Step 4: After reaching the preset number of iterations, stop the iteration and perform channel equalization according to the optimal whitening matrix obtained in step 1 and the optimal weight matrix obtained in step 2 to alleviate the impact of inter-channel crosstalk on the signal during optical fiber transmission and improve the stability and quality of the optical fiber communication system.

[0021] Beneficial effects:

[0022] 1. The present invention discloses a method for channel equalization of a coherent optical communication system based on independent component analysis. The method collects the target signal received by the receiving end of the multi-dimensional multiplexed coherent optical transmission system, processes the target signal in blocks, reduces the complexity of calculation, and then performs data whitening operation to eliminate the correlation between features in the data. The method also iterates the weight matrix for each data block to achieve channel equalization of the multi-dimensional multiplexed coherent optical transmission system based on independent component analysis.

[0023] 2. The present invention discloses a coherent optical communication system channel equalization method based on independent component analysis, which optimizes parameters by using an adaptive forgetting factor so that the whitening matrix and the weight matrix continue to converge, and performs channel equalization according to the optimal iterative effect. The present invention is applicable to the field of optical fiber communication, and can effectively suppress the crosstalk between channels in a multi-dimensional multiplexed coherent optical transmission system, and improve the stability and quality of the optical fiber communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a coherent optical communication system channel equalization method based on independent component analysis disclosed in the present invention;

[0025] Figure 2 A simulation verification block diagram provided for an embodiment of the present invention;

[0026] Figure 3 A block diagram of updating a whitening matrix in a channel equalization method based on independent component analysis provided in an embodiment of the present invention;

[0027] Figure 4A block diagram of weight matrix update in a channel equalization method based on independent component analysis provided in an embodiment of the present invention.

[0028] Figure 5 A grayscale diagram of inter-channel crosstalk of the channel equalization technology provided by an embodiment of the present invention;

[0029] Figure 6 A relationship diagram between the bit error rate and the signal-to-noise ratio of the channel equalization method of a multidimensional multiplexed coherent optical transmission system based on independent component analysis provided in an embodiment of the present invention, wherein Figure (a) is a relationship diagram between the bit error rate and the signal-to-noise ratio of signal one; Figure (b) is a relationship diagram between the bit error rate and the signal-to-noise ratio of signal two, Figure (c) is a relationship diagram between the bit error rate and the signal-to-noise ratio of signal three, and Figure (d) is a relationship diagram between the bit error rate and the signal-to-noise ratio of signal four. DETAILED DESCRIPTION

[0030] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. The technical problems solved by the technical solution of the present invention and the beneficial effects are also described. It should be noted that the described embodiments are only intended to facilitate the understanding of the present invention and do not have any limiting effect on it.

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

[0032] Step 1: 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 As shown in the simulation verification block diagram provided for the embodiment of the present invention, 8 different modes of QPSK signals are first generated at the transmitting end, divided into two categories according to the mode and multiplexed and coupled into a 10km ring core fiber (RCF). At the receiving end, each light beam is coupled into a single-mode fiber by using a collimator, and the 8 different modes of signals are processed by a digital signal processing (DSP) module by relying on 8 optical 90° hybrids and 8 analog-to-digital converters.

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

[0035] First, the mixed signal is divided into blocks of size L, and then the whitening matrix is ​​iterated.

[0036]

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

[0038] According to the iterative formula, each data block is whitened to eliminate the correlation between features in the data.

[0039] Step 2, iterating the weight matrix of the signal after data whitening in step 1;

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

[0041] For a data block of size L after block division, the iterative formula of the weight matrix is:

[0042]

[0043] Where n represents the data sequence, W n Represents the weight matrix corresponding to the data sequence n, y l 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;

[0044] Step 3: After each iteration, 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 is the number of iterations, λ N Represents the adaptive forgetting factor after the Nth iteration, α represents the decay rate of the adaptive forgetting factor, and 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; if the adaptive forgetting factor is lower than this limit, this value will be used; ST N+1 represents the stability factor after the N+1th iteration; G(x) represents the nonlinear mapping relationship between the stability factor and the adaptive forgetting factor; where:

[0048]

[0049] Where ST Nrepresents the stability factor after the Nth iteration, b is the transition bandwidth of the adaptive forgetting factor, and 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 transition band of the adaptive forgetting factor, which represents the center position of the transition zone when the forgetting factor transfers from the fast convergence stage to the adaptive stage; ST min represents the stability index of the initial convergence, ε represents the control factor, which controls the transition of the forgetting factor from the fast convergence stage to the adaptive stage;

[0050] Step 4: Perform channel equalization based on the optimal whitening matrix and weight matrix after iteration to alleviate the impact of inter-channel crosstalk on the signal during optical fiber transmission and improve the stability and quality of the optical fiber communication system.

[0051] like Figure 2 As shown in , after the receiving end signal passes through the channel equalization method based on independent component analysis, the optimal whitening matrix and weight matrix are obtained, and then the carrier frequency recovery and clock phase recovery algorithms are successively passed, and finally the crosstalk judgment and bit error rate calculation are performed.

[0052] Figure 5 The grayscale image of the inter-channel crosstalk of the channel equalization method based on independent component analysis is shown. In the communication system of the present invention example, during the transmission of signal 1, signal 3 has the greatest impact on it, and signal 2 has the least impact on it. 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 for coherent optical transmission systems based on independent component analysis is shown. Under different bit error rate conditions, the algorithm can make the bit error rate lower than 0.05. When the signal-to-noise ratio is 23dB, the bit error rate can be improved to 10 -3 order of magnitude.

[0054] The specific description above further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A channel equalization method for a coherent optical communication system based on independent component analysis, characterized in that: The following steps are included: Step 1: 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; performing data whitening on the block signal to obtain a whitened signal; For a block signal with a block size of L, the iteration of the whitening matrix M is: Where n represents the data sequence, M n represents the whitening matrix corresponding to the data sequence n, l represents the time index, V l represents the lth whitened data, λ l represents the adaptive forgetting factor at the lth iteration, and I represents the identity matrix; Step 2: Iterate the weight matrix W of the whitened signal in step 1: Where n represents the data sequence, W n Represents the weight matrix corresponding to the data sequence n, y l 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, the forgetting factor is adaptively adjusted to obtain a better whitening matrix and weight matrix; The adaptive adjustment process of the forgetting factor is: Where N is the number of iterations, λ N Represents the adaptive forgetting factor after the Nth iteration, α represents the decay rate of the adaptive forgetting factor, and 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; If the adaptive forgetting factor is below this limit, this value will be used; ST N+1 represents the stability factor after the N+1th iteration; G(x) represents the nonlinear mapping relationship between the stability factor and the adaptive forgetting factor; in: Where ST N represents the stability factor after the Nth iteration, b is the transition bandwidth of the adaptive forgetting factor, and 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 transition band of the adaptive forgetting factor, which represents the center position of the transition zone when the forgetting factor transfers from the fast convergence stage to the adaptive stage; ST min represents the stability index of the initial convergence, ε represents the control factor, which controls the transition of the forgetting factor from the fast convergence stage to the adaptive stage; Step 4: After reaching the preset number of iterations, stop the iteration and perform channel equalization according to the optimal whitening matrix obtained in step 1 and the optimal weight matrix obtained in step 2 to alleviate the impact 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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