Coherent channel pre-equalization optimization method and device based on double-real-value input Volterra series

Abstract

A coherent channel pre-equalization optimization method, apparatus and device based on double real value input Volterra series, and a computer readable storage medium, comprising: separating an output signal obtained by processing an input signal by an obtained target channel to obtain a real part corresponding to an I path and an imaginary part corresponding to a Q path in the output signal, the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values; inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer so as to optimize the preset equalizer, and obtaining an optimized equalizer; and inputting the input signal into the optimized equalizer, and obtaining a target output signal output by the optimized equalizer so as to complete pre-equalization of the transmitting end, thereby solving the technical problem that a traditional single-input real value equalization scheme in related technologies is not accurate enough in signal distortion compensation, and improving the accuracy of channel transmission.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a coherent channel pre-equalization optimization method, apparatus, device, and computer-readable storage medium based on a double-real-valued input Volterra series. Background Art

[0002] During communications, signals are subject to various channel influences, such as noise and multipath propagation, which can cause signal distortion and intersymbol interference (ISI). To compensate for this distortion, equalization technology has emerged. Traditional single-input real-valued equalization schemes have long been widely used in communications systems. Based on real-valued signal processing theory, they attempt to eliminate ISI by processing the received signal.

[0003] However, modern communication systems increasingly employ complex signal modulation schemes, such as quadrature amplitude modulation (QAM) and phase-shift keying (PSK). In these modulation schemes, the signal is split into an I-path (in-phase component) and a Q-path (quadrature component) for transmission. These two components carry different information and have a specific relationship with each other. For example, in QAM modulation, the combination of the I-path and Q-path amplitudes determines the position of the signal point on the constellation diagram, thereby carrying the digital information.

[0004] However, traditional single-input real-valued equalization schemes treat such complex signals solely as real-valued signals, completely ignoring the close relationship between the I and Q paths. This approach has significant limitations. Because the correlation between the I and Q paths is not considered, the full information carried by the signals cannot be fully utilized in complex channel environments, resulting in inaccurate compensation for signal distortion. For example, in a multipath fading channel, the fading characteristics of the I and Q path signals may differ, but traditional single-input real-valued equalization schemes cannot effectively address these differences, resulting in a high bit error rate in the equalized signal, severely impacting the overall performance of the communication system and limiting further increases in data rates and improvements in communication quality. With the diversification of communication services, applications with extremely high communication quality requirements, such as high-definition video transmission and real-time online gaming, are constantly emerging. Traditional single-input real-valued equalization schemes are no longer able to meet practical needs. A new equalization scheme that fully considers the relationship between the I and Q paths is urgently needed to improve the performance of communication systems. Summary of the Invention

[0005] The present application provides a coherent channel pre-equalization optimization method, apparatus, device and computer-readable storage medium based on a dual-real-valued input Volterra series, which can solve the technical problem in the prior art that the traditional single-input real-valued equalization scheme has insufficient accuracy in compensating for signal distortion.

[0006] In a first aspect, an embodiment of the present application provides a coherent channel pre-equalization optimization method based on a double real-valued input Volterra series, characterized by comprising:

[0007] Separating the output signal after the obtained target channel processes the input signal to obtain a real part corresponding to the I path and an imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values;

[0008] Inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain an optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is used to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is used to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path;

[0009] The input signal is input into the optimized equalizer, and a target output signal output by the optimized equalizer is obtained, so as to complete pre-equalization at the transmitting end.

[0010] In combination with the first aspect, in one embodiment, inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain the optimized equalizer includes:

[0011] Inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer;

[0012] Reconstructing the real part corresponding to the I path and the imaginary part corresponding to the Q path according to the construction module to obtain a reconstructed sequence;

[0013] Performing iterative calculation according to the iterative module based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path to obtain a convergence coefficient;

[0014] The preset equalizer is optimized using the convergence coefficient to obtain an optimized equalizer.

[0015] In combination with the first aspect, in one embodiment, reconstructing the real part corresponding to the I path and the imaginary part corresponding to the Q path according to the construction module to obtain a reconstructed sequence includes:

[0016] Obtaining, by the construction module, a delay index variable of a multi-order polynomial term based on the real part corresponding to the I path, the imaginary part corresponding to the Q path, and a first preset formula;

[0017] Obtaining sequences of different orders based on the delay index variable of the multi-order polynomial term, the preset matrix and the second preset formula through the construction module;

[0018] A reconstructed sequence is obtained by the construction module based on the sequences of different orders and a third preset formula.

[0019] In combination with the first aspect, in one embodiment, the iterative calculation performed by the iterative module based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path to obtain a convergence coefficient includes:

[0020] Obtaining a first iteration result based on a preset starting coefficient and the reconstructed sequence by the iteration module, wherein the iteration module includes an I-path iteration module and a Q-path iteration module;

[0021] Obtaining a first convergence coefficient according to the I-path iterative module, the first iteration result, the reconstructed sequence, the real part corresponding to the I-path, and a preset learning rate;

[0022] A second convergence coefficient is obtained according to the Q-path iteration module, the first iteration result, the reconstructed sequence, the imaginary part corresponding to the Q-path and a preset learning rate.

[0023] In combination with the first aspect, in one embodiment, obtaining a first convergence coefficient according to the I-path iteration module, the first iteration result, the reconstructed sequence, the real part corresponding to the I-path, and a preset learning rate includes:

[0024] Obtaining a first error value by the I-path iteration module based on the first iteration result, the real part corresponding to the I-path, and a fourth preset formula;

[0025] A first convergence coefficient is obtained by the I-way iterative module based on the first error value, the reconstructed series, the preset starting coefficient, the preset learning rate and a fifth preset formula, wherein the fifth preset formula includes a penalty factor.

[0026] In combination with the first aspect, in one embodiment, obtaining a second convergence coefficient according to the Q-path iteration module, the first iteration result, the reconstructed sequence, the imaginary part corresponding to the Q-path, and the preset learning rate includes:

[0027] Obtaining, by the Q-path iteration module, a second error value based on the first iteration result, the imaginary part corresponding to the Q-path, and a sixth preset formula;

[0028] A second convergence coefficient is obtained by the Q-path iteration module based on the second error value, the reconstructed series, the preset starting coefficient, the preset learning rate and a seventh preset formula.

[0029] In combination with the first aspect, in one embodiment, before optimizing the preset equalizer using the convergence coefficient and obtaining the optimized equalizer, the method further includes:

[0030] determining, according to the obtained number of iterations of the preset equalizer, whether the first convergence coefficient and the second convergence coefficient are target convergence numbers;

[0031] If it is determined that the number of iterations of the preset equalizer is greater than or equal to the preset number of iterations, determining the first convergence coefficient and the second convergence coefficient as target convergence coefficients;

[0032] Or, determining whether the first convergence coefficient and the second convergence coefficient are target convergence times according to the obtained error value;

[0033] If it is determined that the error value is less than or equal to the preset error value, the first convergence coefficient and the second convergence coefficient are determined to be target convergence coefficients.

[0034] In a second aspect, an embodiment of the present application provides a coherent channel pre-equalization optimization device based on a double real-valued input Volterra series, wherein the coherent channel pre-equalization optimization device based on a double real-valued input Volterra series includes:

[0035] A separation and acquisition module is used to separate the output signal after the acquired target channel processes the input signal, and obtain the real part corresponding to the I path and the imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values;

[0036] an optimization module, configured to input the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain an optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is configured to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is configured to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path;

[0037] The acquisition module is used to input the input signal into the optimized equalizer and obtain the target output signal output by the optimized equalizer to complete the pre-equalization of the transmitting end.

[0038] In a third aspect, an embodiment of the present application provides a coherent channel pre-equalization optimization device based on a double real-valued input Volterra series, wherein the coherent channel pre-equalization optimization device based on a double real-valued input Volterra series includes a processor, a memory, and a coherent channel pre-equalization optimization program based on a double real-valued input Volterra series stored on the memory and executable by the processor, wherein when the coherent channel pre-equalization optimization program based on a double real-valued input Volterra series is executed by the processor, the steps of the coherent channel pre-equalization optimization method based on a double real-valued input Volterra series as described above are implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a coherent channel pre-equalization optimization program based on a double real-valued input Volterra series. When the coherent channel pre-equalization optimization program based on a double real-valued input Volterra series is executed by a processor, the steps of the coherent channel pre-equalization optimization method based on a double real-valued input Volterra series as described above are implemented.

[0040] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0041] The output signal after the acquired target channel processes the input signal is separated to obtain the real part corresponding to the I path and the imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values; the real part corresponding to the I path and the imaginary part corresponding to the Q path are input into a preset equalizer to optimize the preset equalizer and obtain the optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is used to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is used to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path and the imaginary part corresponding to the Q path; the input signal is input into the optimized equalizer to obtain the target output signal output by the optimized equalizer to complete the pre-equalization of the transmitting end, thereby solving the technical problem in the related art that the traditional single-input real-valued equalization scheme is not accurate enough in compensating for signal distortion, thereby improving the accuracy of channel transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of the first embodiment of the coherent channel pre-equalization optimization method based on double real-valued input Volterra series of the present application;

[0043] Figure 2 This is a functional module diagram of an embodiment of a coherent channel pre-equalization optimization device based on a double real-valued input Volterra series according to the present application;

[0044] Figure 3 Schematic diagram of the hardware structure of the coherent channel pre-equalization optimization device based on double real-valued input Volterra series involved in the embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] First, some technical terms in this application are explained to facilitate those skilled in the art to understand this application.

[0047] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0048] In a first aspect, an embodiment of the present application provides a coherent channel pre-equalization optimization method based on a double real-valued input Volterra series.

[0049] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the coherent channel pre-equalization optimization method based on double real-valued input Volterra series of this application. Figure 1 As shown, the coherent channel pre-equalization optimization method based on double real-valued input Volterra series includes:

[0050] Step S10: Separating the output signal obtained by processing the input signal through the acquired target channel to obtain a real part corresponding to the I path and an imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values;

[0051] For example, the input signal is input into the target channel to obtain the output signal after the target channel processes the input signal. The target channel is a channel without any equalization. The obtained output signal is then separated to obtain the real part corresponding to the I path and the imaginary part corresponding to the Q path in the output signal. For example, the input signal x(n) = (x1, x2, ~ x n ) is input into the target channel, and the output signal y(n)=(y1,y2,~y n ). Then the output signal y(n)=(y1,y2,~y n) to separate and obtain the real part y corresponding to the I path in the output signal I (n)=(y I 1,y I 2,~y I n ) and the imaginary part y corresponding to the Q path Q (n)=(y Q 1,y Q 2,~y Q n ), the real part y corresponding to path I I (n)=(y I 1,y I 2,~y I n ) and the imaginary part y corresponding to the Q path Q (n)=(y Q 1,y Q 2,~y Q n ) is a double real value.

[0052] Step S20: Inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain an optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is used to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is used to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path;

[0053] For example, the real part y corresponding to the I path I (n)=(y I 1,y I 2,~y I n ) and the imaginary part y corresponding to the Q path Q (n)=(y Q 1,y Q 2,~y Q n ) input preset equalizer, through the construction module based on the real part y corresponding to I I (n)=(y I 1,y I 2,~y I n ) and the imaginary part y corresponding to the Q path Q (n)=(y Q 1,y Q 2,~y Q n) to reconstruct the sequence to obtain the reconstructed sequence, perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path and the imaginary part corresponding to the Q path to obtain the convergence coefficient, and optimize the matrix coefficients in the preset equalizer through the convergence coefficient to obtain the optimized equalizer.

[0054] Specifically, the real part corresponding to the I path and the imaginary part corresponding to the Q path are input into the preset equalizer to optimize the preset equalizer and obtain the optimized equalizer, including: inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into the preset equalizer; performing sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path according to the construction module to obtain a reconstructed sequence; performing iterative calculation based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path according to the iteration module to obtain a convergence coefficient; optimizing the preset equalizer through the convergence coefficient to obtain the optimized equalizer.

[0055] Exemplarily, by constructing a module based on the real part y corresponding to I I (n)=(y I 1,y I 2,~y I n ), the imaginary part y corresponding to the Q path Q (n)=(y Q 1,y Q 2,~y Q n ) and the first preset formula y ki =[y I (n-ki)y Q (n-ki)] T , get the delay index variable y of the multi-order polynomial term ki , where T is the matrix transpose, n is the current moment of the signal sequence, i∈[1,p]. The delay index variable y is constructed based on the multi-order polynomial term ki , preset matrix and second preset formula W P (n)=[y k1 ⊙y k2 ⊙y k3 ...y kP ] T , get sequences W of different orders p (n), the preset matrix ⊙ is [AB] T ⊙[CD] T =[AB AC BC BD] T , kp is a variable specifically referring to the delay index of the p-th order polynomial term, kp=0,1,2,3,...L P -1. By constructing modules based on sequences W of different ordersp (n) and the third preset formula W(n)=[W 1 (n),W 2 (n),W 3 (n),...,W p (n)] T , and obtain the reconstructed sequence W(n).

[0056] According to the iterative module based on the reconstructed sequence W(n), the real part y corresponding to the I path I (n)=(y I 1,y I 2,~y I n ) and the imaginary part y corresponding to the Q path Q (n)=(y Q 1,y Q 2,~y Q n ) performs iterative calculation to obtain the convergence coefficient. In the embodiment, the iterative module is based on the preset starting coefficient h I (n)=[h1,h2...h n ] and the reconstructed sequence W(n), and the first iteration result y is obtained n (n), wherein the iteration module includes an I-way iteration module and a Q-way iteration module, for example, obtaining a preset iteration formula y n (n) = h n (n)W(n), get the first iteration result y n (n). According to the I-way iterative module, the first iteration result, the reconstructed sequence, the real part corresponding to the I-way and the preset learning rate, the first convergence coefficient h is obtained. I (n+1). For example, based on the first iteration result y through the I-way iteration module n (n), the real part y corresponding to path I I (n)=(y I 1,y I 2,~y I n ) and the fourth preset formula Get the first error value E(n); through the I-way iterative module based on the first error value E(n), the reconstructed series W(n), the preset starting coefficient h I (n)=[h1,h2...h n ], preset learning rate r and fifth preset formula h I (n+1)=h I (n)+rE(n)W(n)-λsgn[h I (n)], and obtain the first convergence coefficient h I (n+1), wherein the fifth preset formula includes a penalty factor λ,

[0057] According to the Q-path iteration module, the first iteration result, the reconstructed sequence, the imaginary part corresponding to the Q-path and the preset learning rate, the second convergence coefficient is obtained. For example, the Q-path iteration module is based on the first iteration result y n (n), the imaginary part y corresponding to the Q path Q (n)=(y Q 1,y Q 2,~y Q n ) and the sixth preset formula Get the second error value e(n); through the Q-path iteration module based on the second error value e(n), the reconstructed series W(n), the preset starting coefficient h I (n)=[h1,h2...h n ], preset learning rate r and seventh preset formula H I (n+1)=h I (n)+re(n)W(n)-λsgn[h I (n)], and obtain the second convergence coefficient H I (n+1), wherein the seventh preset formula includes a penalty factor λ,

[0058] After obtaining the first convergence coefficient h I (n+1) and the second convergence coefficient H I (n+1) Optimize the coefficient matrix in the preset equalizer to obtain the optimized equalizer. For example, the first convergence coefficient h I (n+1) and the second convergence coefficient H I (n+1) is substituted into the coefficient matrix y(n)=h I (n+1)W x (n)+j*H I (n+1)W x (n), and get the optimized equalizer.

[0059] Specifically, the optimization of the preset equalizer through the convergence coefficient, before obtaining the optimized equalizer, also includes: determining whether the first convergence coefficient and the second convergence coefficient are the target convergence number according to the obtained number of iterations of the preset equalizer; if it is determined that the number of iterations of the preset equalizer is greater than or equal to the preset number of iterations, determining that the first convergence coefficient and the second convergence coefficient are the target convergence coefficients; or, determining whether the first convergence coefficient and the second convergence coefficient are the target convergence number according to the obtained error value; if it is determined that the error value is less than or equal to the preset error value, determining that the first convergence coefficient and the second convergence coefficient are the target convergence coefficients.

[0060] Exemplarily, the number of iterations of the preset equalizer is recorded. When the number of iterations is greater than or equal to the preset number of iterations, the convergence coefficient calculated for the last iteration is obtained, and the convergence coefficient is used as the first convergence coefficient and the second convergence coefficient, i.e., the target convergence coefficient. Alternatively, an error value is calculated for each iteration, and the error value is compared with a preset error value. If the current error value is less than or equal to the preset error value, the convergence coefficient calculated based on the current error value is used as the first convergence coefficient and the second convergence coefficient, i.e., the target convergence coefficient.

[0061] Step S30: inputting the input signal into the optimized equalizer, obtaining a target output signal output by the optimized equalizer, and completing pre-equalization at the transmitting end.

[0062] Exemplarily, an input signal is input into the optimized equalizer, and a target output signal output by the optimized equalizer is obtained to complete pre-equalization at the transmitting end.

[0063] In this embodiment, the output signal after the acquired target channel processes the input signal is separated to obtain the real part corresponding to the I path and the imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values; the real part corresponding to the I path and the imaginary part corresponding to the Q path are input into a preset equalizer to optimize the preset equalizer and obtain an optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is used to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is used to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path and the imaginary part corresponding to the Q path; the input signal is input into the optimized equalizer to obtain the target output signal output by the optimized equalizer to complete the pre-equalization of the transmitting end, thereby solving the technical problem in the related art that the traditional single-input real-valued equalization scheme is not accurate enough in compensating for signal distortion, thereby improving the accuracy of channel transmission.

[0064] In a second aspect, an embodiment of the present application further provides a coherent channel pre-equalization optimization device based on a double real-valued input Volterra series.

[0065] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of an embodiment of a coherent channel pre-equalization optimization device based on a double real-valued input Volterra series. Figure 2 As shown, the coherent channel pre-equalization optimization device based on double real-valued input Volterra series includes:

[0066] The separation and acquisition module 10 is used to separate the output signal after the acquired target channel processes the input signal, and obtain the real part corresponding to the I path and the imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values;

[0067] an optimization module 20, configured to input the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain an optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is configured to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is configured to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path;

[0068] The acquisition module 30 is configured to input the input signal into the optimized equalizer and acquire a target output signal output by the optimized equalizer to complete pre-equalization at the transmitting end.

[0069] Furthermore, in one embodiment, the optimization module 20 is configured to:

[0070] Inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer;

[0071] Reconstructing the real part corresponding to the I path and the imaginary part corresponding to the Q path according to the construction module to obtain a reconstructed sequence;

[0072] Performing iterative calculation according to the iterative module based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path to obtain a convergence coefficient;

[0073] The preset equalizer is optimized using the convergence coefficient to obtain an optimized equalizer.

[0074] Furthermore, in one embodiment, the coherent channel pre-equalization optimization apparatus based on a double real-valued input Volterra series further includes a new module for:

[0075] Obtaining, by the construction module, a delay index variable of a multi-order polynomial term based on the real part corresponding to the I path, the imaginary part corresponding to the Q path, and a first preset formula;

[0076] Obtaining sequences of different orders based on the delay index variable of the multi-order polynomial term, the preset matrix and the second preset formula through the construction module;

[0077] A reconstructed sequence is obtained by the construction module based on the sequences of different orders and a third preset formula.

[0078] Furthermore, in one embodiment, the coherent channel pre-equalization optimization apparatus based on a double real-valued input Volterra series further includes a new module for:

[0079] Obtaining a first iteration result based on a preset starting coefficient and the reconstructed sequence by the iteration module, wherein the iteration module includes an I-path iteration module and a Q-path iteration module;

[0080] Obtaining a first convergence coefficient according to the I-path iterative module, the first iteration result, the reconstructed sequence, the real part corresponding to the I-path, and a preset learning rate;

[0081] A second convergence coefficient is obtained according to the Q-path iteration module, the first iteration result, the reconstructed sequence, the imaginary part corresponding to the Q-path and a preset learning rate.

[0082] Furthermore, in one embodiment, the coherent channel pre-equalization optimization apparatus based on a double real-valued input Volterra series further includes a new module for:

[0083] Obtaining a first error value by the I-path iteration module based on the first iteration result, the real part corresponding to the I-path, and a fourth preset formula;

[0084] A first convergence coefficient is obtained by the I-way iterative module based on the first error value, the reconstructed series, the preset starting coefficient, the preset learning rate and a fifth preset formula, wherein the fifth preset formula includes a penalty factor.

[0085] Furthermore, in one embodiment, the coherent channel pre-equalization optimization apparatus based on a double real-valued input Volterra series further includes a new module for:

[0086] Obtaining, by the Q-path iteration module, a second error value based on the first iteration result, the imaginary part corresponding to the Q-path, and a sixth preset formula;

[0087] A second convergence coefficient is obtained by the Q-path iteration module based on the second error value, the reconstructed series, the preset starting coefficient, the preset learning rate and a seventh preset formula.

[0088] Furthermore, in one embodiment, the coherent channel pre-equalization optimization apparatus based on a double real-valued input Volterra series further includes a new module for:

[0089] determining, according to the obtained number of iterations of the preset equalizer, whether the first convergence coefficient and the second convergence coefficient are target convergence numbers;

[0090] If it is determined that the number of iterations of the preset equalizer is greater than or equal to the preset number of iterations, determining the first convergence coefficient and the second convergence coefficient as target convergence coefficients;

[0091] Or, determining whether the first convergence coefficient and the second convergence coefficient are target convergence times according to the obtained error value;

[0092] If it is determined that the error value is less than or equal to the preset error value, the first convergence coefficient and the second convergence coefficient are determined to be target convergence coefficients.

[0093] Among them, the functional implementation of each module in the above-mentioned coherent channel pre-equalization optimization device based on double real-valued input Volterra series corresponds to the various steps in the above-mentioned coherent channel pre-equalization optimization method embodiment based on double real-valued input Volterra series, and their functions and implementation processes are not repeated here one by one.

[0094] In a third aspect, an embodiment of the present application provides a coherent channel pre-equalization optimization device based on a double real-valued input Volterra series. The coherent channel pre-equalization optimization device based on a double real-valued input Volterra series can be a personal computer (PC), a laptop, a server, or other device with data processing capabilities.

[0095] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of a coherent channel pre-equalization optimization device based on a double real-valued input Volterra series involved in an embodiment of the present application. In the embodiment of the present application, the coherent channel pre-equalization optimization device based on a double real-valued input Volterra series may include a processor, a memory, a communication interface, and a communication bus.

[0096] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0097] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces, used to interconnect components within the coherent channel pre-equalization optimization device based on a dual-real-valued input Volterra series, as well as interfaces used to interconnect the coherent channel pre-equalization optimization device based on a dual-real-valued input Volterra series with other devices (e.g., other computing devices or user equipment). Physical interfaces can include Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can include displays, keyboards, etc.

[0098] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0099] The processor may be a general-purpose processor that can call a coherent channel pre-equalization optimization program based on a dual-real-valued input Volterra series stored in a memory and execute the coherent channel pre-equalization optimization method based on a dual-real-valued input Volterra series provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the coherent channel pre-equalization optimization program based on a dual-real-valued input Volterra series is called can refer to the various embodiments of the coherent channel pre-equalization optimization method based on a dual-real-valued input Volterra series of the present application, and will not be repeated here.

[0100] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0101] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0102] The computer-readable storage medium of the present application stores a coherent channel pre-equalization optimization program based on a double real-valued input Volterra series, wherein when the coherent channel pre-equalization optimization program based on a double real-valued input Volterra series is executed by a processor, the steps of the coherent channel pre-equalization optimization method based on a double real-valued input Volterra series as described above are implemented.

[0103] Among them, the method implemented when the coherent channel pre-equalization optimization program based on double real-valued input Volterra series is executed can refer to the various embodiments of the coherent channel pre-equalization optimization method based on double real-valued input Volterra series of this application, and will not be repeated here.

[0104] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0105] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0106] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0107] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0108] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0109] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present application.

[0110] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A coherent channel pre-equalization optimization method based on double real-valued input Volterra series, characterized in that: include: Separating the output signal after the obtained target channel processes the input signal to obtain a real part corresponding to the I path and an imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values; Inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain an optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is used to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is used to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path; The input signal is input into the optimized equalizer, and a target output signal output by the optimized equalizer is obtained, so as to complete pre-equalization at the transmitting end.

2. The coherent channel pre-equalization optimization method based on double real-valued input Volterra series according to claim 1, characterized in that: Inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain the optimized equalizer includes: Inputting the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer; Reconstructing the real part corresponding to the I path and the imaginary part corresponding to the Q path according to the construction module to obtain a reconstructed sequence; Performing iterative calculations according to the iterative module based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path to obtain a convergence coefficient; The preset equalizer is optimized using the convergence coefficient to obtain an optimized equalizer.

3. The coherent channel pre-equalization optimization method based on double real-valued input Volterra series according to claim 2, characterized in that: The step of reconstructing the real part corresponding to the I path and the imaginary part corresponding to the Q path according to the construction module to obtain a reconstructed sequence includes: Obtaining, by the construction module, a delay index variable of a multi-order polynomial term based on the real part corresponding to the I path, the imaginary part corresponding to the Q path, and a first preset formula; Obtaining sequences of different orders based on the delay index variable of the multi-order polynomial term, the preset matrix and the second preset formula through the construction module; A reconstructed sequence is obtained by the construction module based on the sequences of different orders and a third preset formula.

4. The coherent channel pre-equalization optimization method based on double real-valued input Volterra series according to claim 2, characterized in that: The iterative calculation by the iterative module based on the reconstructed sequence, the real part corresponding to the I path and the imaginary part corresponding to the Q path to obtain a convergence coefficient includes: Obtaining a first iteration result based on a preset starting coefficient and the reconstructed sequence by the iteration module, wherein the iteration module includes an I-path iteration module and a Q-path iteration module; Obtaining a first convergence coefficient according to the I-path iterative module, the first iteration result, the reconstructed sequence, the real part corresponding to the I-path, and a preset learning rate; A second convergence coefficient is obtained according to the Q-path iteration module, the first iteration result, the reconstructed sequence, the imaginary part corresponding to the Q-path and a preset learning rate.

5. The coherent channel pre-equalization optimization method based on double real-valued input Volterra series according to claim 4, characterized in that: Obtaining a first convergence coefficient according to the I-path iterative module, the first iteration result, the reconstructed sequence, the real part corresponding to the I-path, and a preset learning rate includes: Obtaining a first error value by the I-path iteration module based on the first iteration result, the real part corresponding to the I-path, and a fourth preset formula; A first convergence coefficient is obtained by the I-way iterative module based on the first error value, the reconstructed series, the preset starting coefficient, the preset learning rate and a fifth preset formula, wherein the fifth preset formula includes a penalty factor.

6. The coherent channel pre-equalization optimization method based on double real-valued input Volterra series according to claim 4, characterized in that: Obtaining a second convergence coefficient according to the Q-path iteration module, the first iteration result, the reconstructed sequence, the imaginary part corresponding to the Q-path, and a preset learning rate includes: Obtaining, by the Q-path iteration module, a second error value based on the first iteration result, the imaginary part corresponding to the Q-path, and a sixth preset formula; A second convergence coefficient is obtained by the Q-path iteration module based on the second error value, the reconstructed series, the preset starting coefficient, the preset learning rate and a seventh preset formula.

7. The coherent channel pre-equalization optimization method based on double real-valued input Volterra series according to claim 4, characterized in that: Before optimizing the preset equalizer by using the convergence coefficient and obtaining the optimized equalizer, the method further includes: determining, according to the obtained number of iterations of the preset equalizer, whether the first convergence coefficient and the second convergence coefficient are target convergence numbers; If it is determined that the number of iterations of the preset equalizer is greater than or equal to the preset number of iterations, determining the first convergence coefficient and the second convergence coefficient as target convergence coefficients; Or, determining whether the first convergence coefficient and the second convergence coefficient are target convergence times according to the obtained error value; If it is determined that the error value is less than or equal to the preset error value, the first convergence coefficient and the second convergence coefficient are determined to be target convergence coefficients.

8. A coherent channel pre-equalization optimization device based on double real-valued input Volterra series, characterized in that: The coherent channel pre-equalization optimization device based on double real-valued input Volterra series includes: A separation and acquisition module is used to separate the output signal after the acquired target channel processes the input signal, and obtain the real part corresponding to the I path and the imaginary part corresponding to the Q path in the output signal, wherein the real part corresponding to the I path and the imaginary part corresponding to the Q path are double real values; an optimization module, configured to input the real part corresponding to the I path and the imaginary part corresponding to the Q path into a preset equalizer to optimize the preset equalizer and obtain an optimized equalizer, wherein the preset equalizer includes a construction module and an iteration module, the construction module is configured to perform sequence reconstruction on the real part corresponding to the I path and the imaginary part corresponding to the Q path, and the iteration module is configured to perform iterative calculation based on the reconstructed sequence, the real part corresponding to the I path, and the imaginary part corresponding to the Q path; The acquisition module is used to input the input signal into the optimized equalizer and obtain the target output signal output by the optimized equalizer to complete the pre-equalization of the transmitting end.

9. A coherent channel pre-equalization optimization device based on a double real-valued input Volterra series, characterized in that: The coherent channel pre-equalization optimization device based on double real-valued input Volterra series includes a processor, a memory, and a coherent channel pre-equalization optimization program based on double real-valued input Volterra series stored on the memory and executable by the processor, wherein when the coherent channel pre-equalization optimization program based on double real-valued input Volterra series is executed by the processor, the steps of the coherent channel pre-equalization optimization method based on double real-valued input Volterra series according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a coherent channel pre-equalization optimization program based on a double real-valued input Volterra series, wherein when the coherent channel pre-equalization optimization program based on a double real-valued input Volterra series is executed by a processor, the steps of the coherent channel pre-equalization optimization method based on a double real-valued input Volterra series are implemented as described in any one of claims 1 to 7.

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