A Nonlinear Compensation Method, Device and Storage Medium for an Optical Communication System

Abstract

The present invention discloses a method, device and storage medium for non - linear compensation in an optical communication system. The method includes: obtaining the output of an objective function; obtaining the values of a non - zero real number ω and M tap coefficients according to the output of the objective function; obtaining the signal before equalization, and obtaining a first signal set according to the signal before equalization and the non - zero real number ω, where the first signal set is composed of output signals of more than one trigonometric function; obtaining a second signal set according to the signal before equalization and the first signal set, where the second signal set is composed of the constant 1, the signal before equalization and its delayed signals, and the signals in the first signal set and their delayed signals; obtaining the signal after equalization according to the values of the M tap coefficients and the second signal set. While effectively compensating for non - linear impairments, the present invention reduces the complexity and improves the convergence speed compared with traditional algorithms. The present invention can be widely applied to the field of optical communication technology.

Description

Technical Field

[0001] The present invention relates to the field of optical communication technologies, and in particular, to a method, device, and storage medium for nonlinear compensation in an optical communication system. Background Art

[0002] Optical communication is the backbone of various information technology infrastructures in modern society. With the development of technologies such as the Internet of Things and 6G, the demand for data traffic is increasing day by day, driving the improvement of the capacity of optical communication systems to meet the urgent need for future high-speed connections. In the fields of visible light communication based on direct detection, optical interconnection, and optical access, low-cost devices such as LEDs, PDs, and the interaction between dispersion and square detection will all cause nonlinear effects. Nonlinearity is the main impairment that limits the performance of optical communication systems, restricting the achievable transmission distance and transmission rate. Currently, many scholars have carried out a lot of research on nonlinear compensation technologies in optical communication systems. The existing first literature uses a decision feedback equalizer with a second-order Volterra polynomial model as the feedforward to compensate for the nonlinearity in the link, but the computational complexity is relatively high. The second literature uses Chebyshev polynomials as the basis to learn the nonlinearity of LEDs to achieve adaptive predistortion, but the compensation effect is limited. The third literature proposes a minimum symbol error rate equalizer based on the reproducing kernel Hilbert space. This method can reduce the computational cost under the condition of similar performance compared with the Volterra decision feedback equalizer, but its computational complexity is still relatively high. The fourth literature proposes an adaptive nonlinear equalizer based on sparse Bayesian learning and Kalman filtering. This equalizer screens and extracts the Volterra kernel through the sparse Bayesian method. However, since the initial number of taps of the Volterra equalizer is very large, the screening time is long, and the number of taps after simplification is still relatively large. Summary of the Invention

[0003] To solve at least one of the technical problems existing in the prior art to a certain extent, an object of the present invention is to provide a method, device, and storage medium for nonlinear compensation in an optical communication system, which can reduce the complexity and improve the convergence speed while effectively suppressing the nonlinear effects in the optical communication system.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A method for nonlinear compensation in an optical communication system includes the following steps:

[0006] Obtain the output of the objective function, where the independent variables of the objective function include non-zero real numbers ω and M tap coefficients; the objective of the objective function is to make the output value of the objective function approach the maximum or minimum value of the objective function;

[0007] Obtain the non-zero real number ω and the values of M tap coefficients according to the output of the objective function, where the obtained non-zero real number ω and the values of M tap coefficients make the output value of the objective function approach the maximum or minimum value of the objective function;

[0008] Obtain the signal before equalization, and obtain the first signal set according to the signal before equalization and the non-zero real number ω, where the first signal set is composed of output signals of more than one trigonometric function, where the inputs of the trigonometric functions are all based on the signal before equalization as the independent variable, and where at least one input of the trigonometric function takes the non-zero real number ω as a parameter;

[0009] Obtain the second signal set according to the signal before equalization and the first signal set, where the second signal set is composed of the constant 1, the signal before equalization and its delayed signals, the signals in the first signal set and their delayed signals, and the number of signals in the second signal set is M;

[0010] Obtain the equalized signal according to the values of the M tap coefficients and the second signal set.

[0011] The method of the present invention can be used at the transmitting end, the receiving end or both at the transmitting end and the receiving end. In addition, this method can be used in the case where the values of the M tap coefficients and the non-zero real number ω remain unchanged after obtaining the optimized values of the M tap coefficients and the non-zero real number ω, and can also be used in the case where the values of the M tap coefficients and the non-zero real number ω are continuously updated during the equalization process. For the latter case, the method of the present invention can be understood as the steps of one update iteration in the update-equalization iteration process.

[0012] In the present invention, first obtain the non-zero real number ω and the values of M tap coefficients according to the output of the objective function, where the obtained non-zero real number ω and the values of M tap coefficients make the output value of the objective function approach the maximum or minimum value of the objective function. The output of the objective function can be performance evaluation indicators such as mean square error, bit error rate, generalized mutual information, etc. When using mean square error and bit error rate, the smaller the output value of the desired objective function is, the better. When using generalized mutual information, the larger the output value of the desired objective function is, the better. The step of obtaining relevant parameters can be carried out through a training sequence in the initial establishment stage of the equalizer, or in the parameter update stage during operation. In implementation, algorithms such as LMS algorithm and RLS algorithm can be used, and specific limitations are not made.

[0013] Then, the relevant parameters are used for equalization. First, a first signal set is obtained based on the signal y(n) before equalization. The signals in the first signal set are all trigonometric functions of y(n), such as sin(ω·y(n)), cos(ω·y(n)), sin(2ω·y(n)), cos(2ω·y(n)), etc. The non-zero real number ω is a parameter in the trigonometric functions. Other trigonometric functions also include tan(ω·y(n)), cot(ω·y(n)), sec(ω·y(n)), etc. The output of the trigonometric functions can be obtained by looking up a table or by using other algorithms such as the CORDIC algorithm. The present invention does not make any restrictions. Then, a second signal set is obtained. The signals in the second signal set are composed of the constant 1, the input signal, the signals in the first signal set, and their delayed signals, such as 1, y(n), y(n - 1), y(n - 2)…sin(ω·y(n)), sin(ω·y(n - 1)), sin(ω·y(n - 2))…, cos(ω·y(n)), cos(ω·y(n - 1)), cos(ω·y(n - 2))…. It should be noted that not all signals with time delays less than a certain delay length are within the second signal set. The present invention will selectively select M signals to reduce the complexity. These M signals have the greatest impact on the effect of non-linear equalization. The selection of the M signals can be determined in advance through a training sequence using methods such as the sparse Bayesian algorithm. For example, the selected M signals are y(n), y(n - 2)…sin(ω·y(n - 1)), sin(ω·y(n - 2))…, cos(ω·y(n)), cos(ω·y(n - 3))…. The M signals in the second signal set are respectively multiplied by M tap coefficients to obtain the equalized signal.

[0014] Furthermore, the obtained first signal set includes output signals of at least one of the sine function and the cosine function.

[0015] The present invention limits the trigonometric functions to include at least one of the sine function and the cosine function, such as including sin(αω·y(n)), cos(αω·y(n)), sin(2αω·×y(n)), cos(2αω·×y(n)), sin(αω·y 2 (n)), cos(αω·y 2 (n)), sin(αω·y 3 (n)), cos(αω·y 3 (n))…, where α is an arbitrary non-zero constant.

[0016] Furthermore, the inputs of the trigonometric functions in the obtained first signal set are all proportional to the signal before equalization and are proportional or inversely proportional to the non-zero real number ω.

[0017] The input of the trigonometric functions adopted by the present invention is proportional to the signal before equalization, and is proportional or inversely proportional to the non-zero real number ω, such as including sin(αω·y(n)), cos(αω·y(n)), sin(2αω·×y(n)), cos(2αω·×y(n)), etc., where α is an arbitrary non-zero constant.

[0018] Further, the output signal of the trigonometric functions is obtained by at least one of the look-up table method and the CORDIC algorithm.

[0019] The output of the trigonometric functions can be obtained by establishing a look-up table in advance and then looking up the table according to the signal before equalization of the input to obtain the output signal. In addition, it can also be obtained by methods such as the CORDIC algorithm.

[0020] Further, the output of the objective function is one or a combination of mean square error, cross entropy, bit error rate, symbol error rate, mutual information, generalized mutual information, and normalized generalized mutual information.

[0021] The output of the objective function can be one or a combination of mean square error, cross entropy, bit error rate, symbol error rate, mutual information, generalized mutual information, and normalized generalized mutual information. When using mean square error, bit error rate, cross entropy, and symbol error rate, the values of the non-zero real number ω and the M tap coefficients obtained should make the output value of the objective function as small as possible. When using mutual information, generalized mutual information, and normalized generalized mutual information, the values of the non-zero real number ω and the M tap coefficients obtained should make the output value of the objective function as large as possible.

[0022] Further, the functional relationship between the M signals in the second signal set and the signal before equalization is obtained by at least one of the sparse Bayesian algorithm, the matching pursuit algorithm, the orthogonal matching pursuit algorithm, and the regularized orthogonal matching pursuit algorithm.

[0023] In implementation, a certain maximum time delay L can be preset in advance, and then M signals are selected from the constant 1, the input signal, the signals in the first signal set, and all the delayed signals of the input signal and the signals in the first signal set with a delay not greater than L to form the second signal set, so as to reduce the number of M and thus reduce the complexity. These M signals have the greatest influence on the effect of non-linear equalization, and their selection can be determined by training sequences and using methods such as the sparse Bayesian algorithm. For example, the selected M signals are y(n), y(n - 2)…sin(ω·y(n - 1)), sin(ω·y(n - 2))…, cos(ω·y(n)), cos(ω·y(n - 3))…

[0024] Furthermore, at least one of the values of the non-zero real number ω and the M tap coefficients is obtained through a training sequence.

[0025] The values of the non-zero real number ω and the M tap coefficients can obtain the output of the objective function through training data, and then be obtained according to the output of the objective function.

[0026] Furthermore, at least one of the values of the non-zero real number ω and the M tap coefficients is obtained through the LMS, RLS algorithm, and Kalman algorithm.

[0027] The values of the non-zero real number ω and the M tap coefficients are updated through the LMS, RLS algorithm, and Kalman algorithm.

[0028] Another technical solution adopted by the present invention is:

[0029] An optical communication system nonlinear compensation device, comprising:

[0030] At least one processor;

[0031] At least one memory for storing at least one program;

[0032] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0033] Another technical solution adopted by the present invention is:

[0034] A computer-readable storage medium, in which a processor-executable program is stored, and the processor-executable program is used to execute the above method when executed by a processor.

[0035] The beneficial effects of the present invention are: while effectively compensating for nonlinear damage, the present invention reduces the complexity and improves the convergence speed compared with traditional algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings in the following introduction are only for conveniently and clearly presenting some embodiments of the technical solutions in the present invention. For those skilled in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0037] Figure 1 It is a flowchart of a nonlinear compensation method based on trigonometric functions in an embodiment of the present invention;

[0038] Figure 2 It is a block diagram of an optical communication system in an embodiment of the present invention;

[0039] Figure 3 is the structural diagram of a non - linear equalizer based on trigonometric functions in an embodiment of the present invention;

[0040] Figure 4 is the structural diagram of another non - linear equalizer based on trigonometric functions in an embodiment of the present invention;

[0041] Figure 5 is the convergence curve diagram of trigonometric function non - linear equalization, feed - forward equalization and Volterra non - linear equalization in an embodiment of the present invention;

[0042] Figure 6 is the curve diagram of the bit error rate varying with the number of multiplications of the Volterra non - linear equalization simplified by the orthogonal matching pursuit algorithm and the trigonometric non - linear equalization simplified by the orthogonal matching pursuit algorithm in an embodiment of the present invention;

[0043] Figure 7 is the curve diagram of the bit error rate varying with the peak - to - peak value of the input electrical signal of the feed - forward equalization, the Volterra non - linear equalization simplified by the orthogonal matching pursuit algorithm, and the trigonometric non - linear equalization simplified by the orthogonal matching pursuit algorithm in an embodiment of the present invention. Detailed implementation manners

[0044] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation of the present invention. For the step numbers in the following embodiments, they are only set for the convenience of description and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adjusted adaptively according to the understanding of those skilled in the art.

[0045] In the description of the present invention, it should be understood that for the orientation description, such as the orientation or positional relationship indicated by up, down, front, back, left, right, etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0046] In the description of the present invention, the meaning of several is one or more, the meaning of multiple is two or more. Understandings such as greater than, less than, exceeding, etc. do not include the present number, and understandings such as above, below, within, etc. include the present number. If there is a description of first and second, it is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or the sequence of the indicated technical features.

[0047] In the description of the present invention, unless otherwise clearly defined, terms such as "setting", "installation", "connection", etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific content of the technical solution.

[0048] As Figure 1 shown, this embodiment provides a method for compensating the nonlinearity of a communication system based on trigonometric functions, including the following steps:

[0049] Step 1: Obtain the output of the objective function, where the independent variables of the objective function include non-zero real numbers ω and M tap coefficients;

[0050] Step 2: Obtain the values of the non-zero real number ω and M tap coefficients according to the output of the objective function, where the obtained values of the non-zero real number ω and M tap coefficients make the output value of the objective function approach the maximum or minimum value of the objective function;

[0051] Step 3: Obtain the signal before equalization, and obtain a first signal set according to the signal before equalization and the non-zero real number ω, where the first signal set is composed of output signals of one or more trigonometric functions, where the inputs of the trigonometric functions are all the signal before equalization as the independent variable, and at least one of the inputs of the trigonometric functions takes the non-zero real number ω as a parameter;

[0052] Step 4: Obtain a second signal set according to the signal before equalization and the first signal set, where the second signal set is composed of the constant 1, the signal before equalization and its delayed signals, and the signals in the first signal set and their delayed signals, and the number of signals in the second signal set is M;

[0053] Step 5: Obtain the equalized signal according to the values of the M tap coefficients and the second signal set.

[0054] As Figure 2 shown, a binary information sequence is generated at the transmitting end, pulse amplitude modulation is performed to obtain the signal x(n), and then the digital signal x(n) is upsampled by a factor of two and shaped by a filter, and the filtered signal is input to an arbitrary waveform generator to generate an analog electrical signal. The analog signal is used to modulate the optical transmitter after bias voltage and amplification.

[0055] At the receiving end, the received signal is sampled and digital signal preprocessing is performed to obtain the preprocessed signal, where the digital signal preprocessing includes steps such as resampling, power normalization, clock synchronization, and symbol synchronization.

[0056] A non-linear equalizer for an optical communication system based on trigonometric functions proposed in an embodiment of the present invention is as Figure 3 shown.

[0057] First, determine the maximum delay length L according to the channel characteristics, and then use methods such as the sparse Bayesian algorithm to select M taps of the equalizer that have a greater impact on the equalization effect to reduce complexity. For simplicity of explanation, Figure 3 in this case, the screening algorithm is not adopted. Only the delay length of the linear signal component in the equalization is determined as L1, and the delay length of the nonlinear signal component is determined as L2, where L2 ≤ L1 ≤ L.

[0058] Obtain non-zero real number ω and the values of M tap coefficients according to the output of the objective function. The implementation method of this step will be introduced later, Figure 3 in this case, it is assumed that the above values have been obtained.

[0059] Obtain the signal y(n) before equalization. According to the signal y(n) before equalization and the non-zero real number ω, obtain the first signal set, where the first signal set is composed of the output signals of more than one trigonometric function. The input of the trigonometric function takes the signal before equalization as the independent variable, and this input is proportional to the signal y(n) before equalization and is proportional to or inversely proportional to the non-zero real number ω. In this embodiment, the trigonometric functions to be used are determined as the sine function and the cosine function, and the input of the sine function and the cosine function is determined as ωy(n). The first signal set is as follows:

[0060] [sin(ωy(n)), cos(ωy(n))]

[0061] The step of obtaining sin(ωy(n)) and cos(ωy(n)) according to y(n) can adopt the method of look-up table.

[0062] Then, according to the signal y(n) before equalization and the first signal set, obtain the second signal set, where the second signal set is composed of the constant 1, the signal before equalization and its delayed signals, and the signals in the first signal set and their delayed signals. In this embodiment, the signals in the second set are divided into linear signals and nonlinear signals. The delay length of the linear signal is determined as L1, and the delay length of the nonlinear signal is determined as L2, where L2 ≤ L1 ≤ L. Denote the second signal vector as g(n), and g(n) is as follows:

[0063] g(n) = [1, y(n), y(n - 1), …, y(n - L1 + 1),

[0064] sin(ωy(n)), sin(ωy(n - 1)) …, sin(ωy(n - L2 + 1))

[0065] cos(ωy(n)), cos(ωy(n - 1)) …, cos(ωy(n - L2 + 1))] T

[0066] Among them, the number of signals M in the second signal set is M = 1 + L1 + 2×L2.

[0067] Multiply each signal in the second signal set by the corresponding tap coefficient, and add all the products to obtain the equalized signal. Denote the tap coefficient vector as w, and the equalized signal is:

[0068]

[0069] In the above, the number of taps of the equalizer is M = 1 + L1 + 2×L2. In practice, signals that have a greater impact on the non - linear equalization effect can be selected through algorithms such as the sparse Bayesian algorithm, the matching pursuit algorithm, the orthogonal matching pursuit algorithm, and the regularized orthogonal matching pursuit algorithm to reduce the value of M, thereby reducing the computational complexity. It should be noted that this process is completed before equalization, that is, first evaluate which signal components in g(n) have the greatest impact on equalization through the training sequence. During the equalization process, only obtain the signals of the components that have a greater impact on equalization based on the signal before equalization, and then multiply by the corresponding coefficients to obtain the equalized signal. Taking the orthogonal matching pursuit algorithm as an example, calculate the correlation between the signal components in g(n) and the target signal by calculating the inner product, and select the signal with the greatest correlation as the matching signal. After taking out this matching signal from g(n), recalculate the correlation between the remaining signal components in g(n) and the target signal, and also select the signal with the greatest correlation. Repeat this process until M signal components are selected. These M signals are the M signals that have been selected and have a greater impact on the non - linear equalization effect. During each calculation of the second signal set in the equalization process, only the selected M signals need to be calculated.

[0070] Next, introduce the process of obtaining the values of the tap coefficient w and the non - zero real number ω according to the objective function, as Figure 4 shown. This process can be completed in the initial establishment stage of equalization or can be updated iteratively during the equalization process. Here, take the training sequence as an example to introduce the process of obtaining the optimized tap coefficient w and the non - zero real number ω. The output of the objective function is one or a combination of several of the mean square error, cross - entropy, bit error rate, symbol error rate, mutual information, generalized mutual information, and normalized generalized mutual information. In this embodiment, the output of the objective function is determined to be the mean square error, and its independent variables include the non - zero real number ω and M tap coefficients. The smaller the mean square error, the better. Here, define the error signal e(n) as follows:

[0071]

[0072] Among them, d(n) is the expected signal output of the nth training symbol, is the equalized signal of the nth training symbol.

[0073] The objective function is e 2 (n). To minimize the objective function, calculate the derivatives of the objective function with respect to w and ω respectively, and obtain the update formulas for the tap coefficient w and the non-zero real number ω under the LMS algorithm:

[0074] w(n + 1) = w(n) + μ1×g(n)×e(n)

[0075]

[0076] where μ1 is the update step size of the tap coefficient w, μ2 is the update step size of the non-zero real number ω, y2(n) represents the set of input signals of all sine functions in the second signal set after screening, w2 represents the tap coefficient corresponding to the output signal of the sine function, y3(n) represents the set of input signals of all cosine functions in the second signal set after screening, w3 represents the tap coefficient corresponding to the output signal of the cosine function. ⊙ represents the Hadamard product of matrices, that is, the dot product of matrices.

[0077] The tap coefficient w and the non-zero real number ω are continuously iteratively updated according to the above update formulas until the objective function converges to the minimum value. At this time, the optimal values of the tap coefficient w and the non-zero real number ω are obtained.

[0078] Multiply the signals in the second signal set after screening by the corresponding values of the M tap coefficients that have been obtained, and add the products to obtain the equalized signal.

[0079] Next, test the method of the present invention in the 185 Mbit / s PAM8 visible light communication system experiment, and compare it with other methods to highlight the superiority of this method. In the experiment, the LED bandwidth is about 15 MHz, the bias voltage is 3 V, the signal Vpp input to the LED is 2.5 V, the total length of the transmitted signal is 13000 symbols, and the length of the training sequence is 5000 symbols. The non-linear equalization method described in the present invention is hereinafter written as Trigonometric Nonlinear Equalization (TNLE). Use feed-forward equalization (FFE), Volterra non-linear equalization (VNLE), trigonometric non-linear equalization method, Volterra non-linear equalization simplified by the orthogonal matching pursuit algorithm (VNLE+OMP), and trigonometric non-linear equalization simplified by the orthogonal matching pursuit algorithm (TNLE+OMP) to perform non-linear compensation on the received signal, and use the bit error rate of the equalized signal to measure the performance of the method.

[0080] Next, compare the results of the three methods from three perspectives to illustrate the advantages of the method of the present invention.

[0081] Appendix Figure 5The convergence curves of triangular nonlinear equalization, feedforward equalization, and Volterra nonlinear equalization are given. In this figure, L1 = 13 and L2 = 9 are adopted, and the orthogonal matching pursuit algorithm is not used to streamline the number of taps. It can be seen that the triangular nonlinear equalization has converged after 1000 iterations, and the convergence speed is faster than the other two methods.

[0082] Appendix Figure 6 The bit error rate curves of the Volterra nonlinear equalization simplified by the orthogonal matching pursuit algorithm and the triangular nonlinear equalization simplified by the orthogonal matching pursuit algorithm under the same number of multiplications are given. Under the condition of the same number of multiplications, the equalization effect of the method of the present invention is better than that of the traditional Volterra equalization method. Table 1 gives the comparison of the number of taps, the number of real-valued multiplications, and the bit error rate of the four methods. The delay lengths of the Volterra nonlinear equalization and the triangular nonlinear equalization are both L1 = L2 = 15. The orthogonal matching pursuit algorithm fixes the number of taps of both the Volterra equalization method and the triangular nonlinear equalization method to 40. It can be seen that the bit error rates of the Volterra nonlinear equalization, the triangular nonlinear equalization, and the triangular nonlinear equalization simplified by the orthogonal matching pursuit algorithm are almost the same. At this time, the number of taps and the number of real-valued multiplications of the triangular nonlinear equalization are much smaller than those of the Volterra nonlinear equalization, and the orthogonal matching pursuit algorithm has almost no performance damage while reducing the complexity of the triangular nonlinear equalization. On the contrary, the equalization effect of the Volterra equalization simplified by the orthogonal matching pursuit algorithm has been reduced a lot.

[0083] Table 1

[0084] VNLE TNLE VNLE + OMP TNLE + OMP Number of taps 136 46 40 40 Number of multiplications 256 76 73 72 Bit error rate 2.5e-04 2.5e-04 1.6e-03 4.6e-04

[0085] Note: Table 1 is a comparison table of the number of taps, the number of multiplications, and the bit error rate of the Volterra nonlinear equalization, the trigonometric function nonlinear equalization, the Volterra nonlinear equalization simplified by the orthogonal matching pursuit algorithm, and the trigonometric function nonlinear equalization simplified by the orthogonal matching pursuit algorithm under the optimal bit error rate.

[0086] Figure 7 The curves of the bit error rate varying with the signal voltage (peak-to-peak value) of the input LED for the three equalization methods are given. The number of taps of both the Volterra equalization method and the triangular nonlinear equalization method is fixed to 40. The effect of the triangular nonlinear equalization simplified by the orthogonal matching pursuit algorithm is significantly better than that of the Volterra nonlinear equalization simplified by the orthogonal matching pursuit algorithm.

[0087] In summary, the present invention proposes a nonlinear compensation method for a visible light communication system based on trigonometric functions, which has the characteristics of simple structure and strong equalization effect. The present invention introduces the specific implementation manner of using this method to equalize the input signal, and compares and analyzes the method of the present invention with the traditional algorithm. Under the same communication conditions, the equalization effect of the method of the present invention is the same as that of the traditional algorithm, but the computational complexity is significantly lower than the latter.

[0088] This embodiment also provides a non - linear compensation device for an optical communication system, including:

[0089] At least one processor;

[0090] At least one memory for storing at least one program;

[0091] When the at least one program is executed by the at least one processor, the at least one processor is caused to implement Figure 1 The method shown.

[0092] A non - linear compensation device for an optical communication system according to this embodiment can execute a non - linear compensation method for an optical communication system based on trigonometric functions provided by the method embodiment of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0093] This application embodiment also discloses a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer - readable storage medium. The processor of the computer device can read the computer instructions from the computer - readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0094] This embodiment also provides a storage medium storing instructions or a program that can execute a non - linear compensation method based on trigonometric functions provided by the method embodiment of the present invention. When the instructions or the program is run, any combination of implementation steps of the method embodiment can be executed, and it has the corresponding functions and beneficial effects of the method.

[0095] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown can actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are expected, in which the order of various operations is changed and the sub - operations described as part of a larger operation are executed independently.

[0096] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features described may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of such modules would be understood within the ordinary skill of an engineer. Thus, those of ordinary skill in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the particular concepts disclosed are merely illustrative and not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0097] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a predefined sequence of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0099] More specific examples (nonexhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0100] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0101] In the above description of this specification, the descriptions referring to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0102] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0103] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for compensating non - linearity in an optical communication system, characterized in that, It includes the following steps: Obtain the output of the objective function, where the independent variables of the objective function include non-zero real numbers ω and M tap coefficients; Obtain the values of the non-zero real number ω and M tap coefficients according to the output of the objective function, where the obtained values of the non-zero real number ω and M tap coefficients make the output value of the objective function approach the maximum or minimum value of the objective function; Obtain the signal before equalization, and obtain a first signal set according to the signal before equalization and the non-zero real number ω, where the first signal set is composed of output signals of more than one trigonometric function, where the inputs of the trigonometric functions are all the signal before equalization as the independent variable, and where the input of at least one trigonometric function takes the non-zero real number ω as a parameter; Obtain a second signal set according to the signal before equalization and the first signal set, where the second signal set is composed of the constant 1, the signal before equalization and its delayed signals, and the signals in the first signal set and their delayed signals, where the number of signals in the second signal set is M; Obtain the equalized signal according to the values of the M tap coefficients and the second signal set.

2. The non - linear compensation method for an optical communication system according to claim 1, characterized in that, The obtained first signal set includes output signals of at least one of the sine function and the cosine function.

3. A method for compensating non-linearity of an optical communication system according to claim 1, characterized in that, The inputs of the trigonometric functions in the obtained first signal set are all proportional to the signal before equalization, and are proportional or inversely proportional to the non-zero real number ω.

4. A method for compensating non-linearity of an optical communication system according to claim 1, characterized in that, The output signals of the trigonometric functions are obtained by at least one of the look-up table method and the CORDIC algorithm.

5. A method for non - linear compensation of an optical communication system according to claim 1, characterized in that, The output of the objective function is a combination of one or more of mean square error, cross entropy, bit error rate, symbol error rate, mutual information, generalized mutual information, and normalized generalized mutual information.

6. The non - linear compensation method for an optical communication system according to claim 1, characterized in that, The functional relationship between the M signals in the second signal set and the signal before equalization is obtained by at least one of the sparse Bayesian algorithm, the matching pursuit algorithm, the orthogonal matching pursuit algorithm, and the regularized orthogonal matching pursuit algorithm.

7. A method for non - linear compensation of an optical communication system according to claim 1, characterized in that, At least one of the values of the non-zero real number ω and M tap coefficients is obtained through a training sequence.

8. A method for nonlinear compensation of an optical communication system according to claim 1, characterized in that At least one of the values of the non-zero real number ω and M tap coefficients is obtained through the LMS, RLS algorithm, and Kalman algorithm.

9. A non - linear compensation device for an optical communication system, characterized in that, It includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method described in any one of claims 1-8.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that, The program executable by the processor is used to execute the method described in any one of claims 1-8 when executed by the processor.

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