Downstream enhancement method of reconfigurable optical add-drop multiplexer based on sparse representation

Abstract

The invention relates to the technical field of wavelength division multiplexing, and particularly provides a sparse representation-based lower wave enhancement method of a reconfigurable optical add-drop multiplexer, which comprises the following steps of: performing conversion processing on a lower wave mixed optical signal to obtain a digital electric signal vector; wherein the lower wave mixed optical signal at least comprises a target signal and an interference signal; performing sparse decomposition processing on the electric signal vector based on the over-complete dictionary to obtain a sparse coefficient vector; wherein the over-complete dictionary at least comprises target signal atoms and interference atoms; identifying and extracting a target coefficient corresponding to the target signal atom from the sparse coefficient vector; and combining the target coefficient with the target signal atoms to obtain an enhanced target signal. According to the sparse representation-based lower wave enhancement method for the reconfigurable optical add drop multiplexer, provided by the invention, the accuracy of extracting a target signal in ROADM lower waves can be improved, and adjacent channel interference and noise are effectively inhibited, so that the performance is improved.

Description

Technical Field

[0001] The invention relates to the technical field of wavelength division multiplexing, and in particular to a wavelength drop enhancement method of a reconfigurable optical add / drop multiplexer based on sparse representation. Background Art

[0002] In DWDM (Dense Wavelength Division Multiplexing) systems, channel spacing is very small (e.g., 50 GHz or less). The physical filtering characteristics of core components in ROADMs (Reconfigurable Optical Add-Drop Multiplexers), such as WSSs (Wavelength Selective Switches), are not ideally rectangular. They exhibit a certain roll-off factor and limited out-of-band rejection, resulting in a lack of sharp filter edges. When a ROADM drops a target wavelength, the target wavelength signal inevitably mixes with interference energy from adjacent wavelength channels due to the lack of sharp filter edges, a phenomenon known as inter-channel interference (ICI). Furthermore, over long transmission distances (particularly in submarine systems using erbium-doped fiber amplifiers), the signal accumulates significant amounts of ASE (Amplified Spontaneous Emission) noise. This ICI and ASE noise can severely degrade the quality of the dropped signal, reducing the receiver's bit error rate (BER) performance and limiting system capacity and transmission distance.

[0003] Traditional ROADM drop post-processing typically relies on the DSP (Digital Signal Processing) module in coherent receivers. Standard DSP algorithms, such as MMSE (Minimum Mean Square Error)-based digital equalizers, can compensate for linear impairments such as chromatic dispersion and polarization mode dispersion. However, their performance degrades when dealing with strong ICI that is very close in frequency and structure to the target signal. These algorithms can struggle to effectively distinguish between the target signal and strong interference, especially under low Optical Signal-to-Noise Ratio (OSNR) conditions. Summary of the Invention

[0004] The present invention provides a method for enhancing the drop wavelength of a reconfigurable optical add / drop multiplexer based on sparse representation, which at least solves the problem of insufficient accuracy in extracting target signals in ROADM drop wavelength processing methods in related technologies.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a wavelength drop enhancement method for a reconfigurable optical add / drop multiplexer based on sparse representation, comprising the following steps: converting and processing a wavelength drop mixed optical signal to obtain a digitized electrical signal vector; wherein the wavelength drop mixed optical signal is a wavelength drop mixed optical signal selected from a received wavelength division multiplexed optical signal by a reconfigurable optical add / drop multiplexer and output to a local node; the wavelength drop mixed optical signal includes at least: a target signal and an interference signal; sparse decomposition processing is performed on the electrical signal vector based on an overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary includes at least: a target signal atom and an interference atom; identifying and extracting a target coefficient corresponding to the target signal atom from the sparse coefficient vector; and combining the target coefficient with the target signal atom to obtain an enhanced target signal.

[0007] Preferably, before converting and processing the down-wavelength mixed optical signal, the method includes: receiving a wavelength division multiplexed optical signal through a reconfigurable optical add / drop multiplexer; selecting and outputting the down-wavelength mixed optical signal from the physical layer in the wavelength division multiplexed optical signal through the reconfigurable optical add / drop multiplexer to a local node; wherein the down-wavelength mixed optical signal includes: a target signal, an interference signal, and a noise signal.

[0008] Preferably, the downlink mixed optical signal is converted to obtain a digitized electrical signal vector, including: coherently receiving and photoelectrically converting the downlink mixed optical signal to obtain an analog mixed electrical signal; and performing digital-to-analog conversion on the analog mixed electrical signal to obtain a digitized electrical signal vector.

[0009] Preferably, before performing sparse decomposition processing on the electrical signal vector based on an over-complete dictionary, the method includes: constructing a target signal atom set including at least one target signal atom based on at least one parameter of the modulation format, pulse shaping filter, carrier frequency offset range, and phase noise model; constructing an interference atom set including at least one interference atom based on parameters of adjacent channels of the target signal or interference characteristic parameters; constructing a damage atom set including at least one noise / distortion atom through a standard orthogonal basis or noise / distortion atoms obtained by learning according to noise / distortion characteristics; and combining the target signal atom set, the interference atom set, and the damage atom set to obtain an over-complete dictionary.

[0010] Preferably, the electrical signal vector is subjected to sparse decomposition processing based on an overcomplete dictionary to obtain a sparse coefficient vector, including: projecting the electrical signal vector onto an overcomplete dictionary for sparse decomposition processing, and solving to obtain a sparse coefficient vector that satisfies the objective function; wherein the objective function includes: a fidelity term and a sparse regularization term; the fidelity term is used to measure the degree of closeness between the reconstructed signal and the electrical signal vector; the reconstructed signal is a linear combination of the electrical signal vector and the sparse coefficient vector; the sparse regularization term is used to constrain the sparsity of the sparse coefficient vector.

[0011] Preferably, the electrical signal vector is projected onto an overcomplete dictionary for sparse decomposition processing, and a sparse coefficient vector that satisfies the objective function is obtained by solving the problem, including: using a robust loss function to calculate the reconstructed residual between the reconstructed signal and the electrical signal vector, and constructing a fidelity term; wherein the reconstructed signal is a linear combination of the electrical signal vector and the sparse coefficient vector; based on personalized sparse constraints and / or group sparse constraints on the sparse coefficient vector, a sparse regularization term is constructed; combining the fidelity term and the sparse regularization term to construct an objective function; projecting the electrical signal vector onto an overcomplete dictionary for sparse decomposition processing, and solving the problem to obtain a sparse coefficient vector that satisfies the objective function.

[0012] Preferably, based on the personalized sparse constraint or group sparse constraint on the sparse coefficient vector, a sparse regularization term is constructed, including: applying different penalty weights to each coefficient in the sparse coefficient vector according to category to form a personalized sparse constraint and constructing a sparse regularization term; wherein the categories include: target coefficients corresponding to target signal atoms, interference coefficients corresponding to interference atoms, and noise coefficients corresponding to noise atoms; and / or, applying different group penalty weights to each coefficient in the sparse coefficient vector according to groups to form a group sparse constraint and constructing a sparse regularization term; wherein, the interference coefficients corresponding to the interference atoms representing the same interference source are one group.

[0013] Preferably, identifying and extracting the target coefficient corresponding to the target signal atom from the sparse coefficient vector includes: applying a first penalty weight and a second penalty weight to the interference coefficient and the noise coefficient, respectively, and applying a third penalty weight to the target coefficient; wherein the first penalty weight and the second penalty weight are both greater than the third penalty weight, so as to identify and extract the target coefficient; and / or, applying a first set of penalty weights to the interference coefficient corresponding to the interference atom of the adjacent channel signal representing the target signal; applying a second set of penalty weights to the target coefficient corresponding to the target signal atom representing the target signal; wherein the first set of penalty weights is greater than the second set of penalty weights, so as to identify and extract the target coefficient.

[0014] Preferably, the method further comprises: performing periodic analysis on the reconstruction residual, and when the reconstruction residual exceeds a set value, updating or adding the interference atoms and noise atoms.

[0015] In a second aspect, the present invention provides a wavelength drop enhancement device of a reconfigurable optical add / drop multiplexer based on sparse representation, comprising: a reconfigurable optical add / drop multiplexer, for receiving a wavelength division multiplexed optical signal and outputting a wavelength drop mixed optical signal to a local node; the wavelength drop mixed optical signal comprises at least a target signal and an interference signal; a coherent receiver, for receiving the wavelength drop mixed optical signal and converting the wavelength drop mixed optical signal into an analog mixed electrical signal; a digital-to-analog converter, for converting the analog mixed electrical signal into a digitized electrical signal vector; a digital signal processor, for performing sparse decomposition processing on the electrical signal vector based on an overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary comprises at least a target signal atom and an interference atom; identifying and extracting a target coefficient corresponding to the target signal atom from the sparse coefficient vector; and combining the target coefficient with the target signal atom to obtain an enhanced target signal.

[0016] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0017] The present invention provides a method for enhancing the dropout frequency of a reconfigurable optical add / drop multiplexer based on sparse representation. Based on sparse representation theory, the method utilizes an overcomplete dictionary to perform sparse decomposition on the ROADM dropout mixed optical signal converted into a digitized electrical signal vector, and extracts the target coefficients corresponding to the target signal atoms in the overcomplete dictionary. By combining the target coefficients with the target signal atoms and reconstructing the target signal, the target signal can be significantly improved in terms of extraction accuracy, effectively suppressing adjacent channel interference and noise, and thus improving performance. The method provided by the embodiment of the present invention is particularly suitable for long-distance, large-capacity optical transmission systems with stringent signal quality requirements and prominent interference and noise problems, such as submarine optical cable systems. It can solve the problem of insufficient accuracy in extracting target signals in the ROADM dropout processing method in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without inventive effort.

[0019] Figure 1 The present invention is a flowchart of a method for enhancing the wavelength drop of a reconfigurable optical add / drop multiplexer based on sparse representation according to an embodiment of the present invention.

[0020] Figure 2 This is a structural block diagram of a wavelet drop enhancement device of a reconfigurable optical add / drop multiplexer based on sparse representation according to an embodiment of the present invention.

[0021] Figure 3 It is a structural schematic diagram of the electronic device created by the present invention. DETAILED DESCRIPTION

[0022] The following describes embodiments of the present invention in more detail with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0023] Wave division multiplexing (WDM) significantly increases transmission capacity by transmitting multiple optical signals of different wavelengths through a single optical fiber. Reconfigurable optical add / drop multiplexers (ROADMs), key nodes in WDM networks, allow for flexible addition, drop, or pass-through of any wavelength, significantly improving network flexibility and manageability.

[0024] Sparse representation, or sparse coding, theory has been a hot topic in signal processing research in recent years. Its core concept is that many natural or man-made signals can be accurately or approximately represented using a small number of nonzero coefficients in a transform domain (defined by an overcomplete dictionary). Sparse representation theory has been successfully applied to various fields, including image denoising, compressed sensing, and signal separation. However, its successful application to digital processing after ROADM drop to improve ROADM performance has yet to be demonstrated.

[0025] like Figure 1 As shown, in order to improve the accuracy of extracting the target signal from the ROADM drop wave, the embodiment of the present invention provides a drop wave enhancement method for a reconfigurable optical add / drop multiplexer based on sparse representation, comprising the following steps:

[0026] Step S1: Converting and processing the wavelength-division multiplexed optical signal to obtain a digitized electrical signal vector; wherein the wavelength-division multiplexed optical signal is selected from the received wavelength-division multiplexed optical signal by the reconfigurable optical add / drop multiplexer and output to the local node; the wavelength-division multiplexed optical signal includes at least a target signal and an interference signal;

[0027] Step S2, performing sparse decomposition processing on the electrical signal vector based on an overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary includes at least: target signal atoms and interference atoms;

[0028] Step S3, identifying and extracting target coefficients corresponding to the target signal atoms from the sparse coefficient vector;

[0029] Step S4: combining the target coefficient with the target signal atom to obtain an enhanced target signal.

[0030] In step S2, an overcomplete dictionary is an important concept in fields such as signal processing, machine learning, and image processing. It means that the number of elements (atoms) in the dictionary exceeds the minimum number required to represent the signal under the dictionary, thereby having more flexible signal representation capabilities.

[0031] In an embodiment of the present invention, the electrical signal vector is sparsely decomposed based on an over-complete dictionary. By utilizing the redundancy and adaptive characteristics of the over-complete dictionary, each electrical signal in the electrical signal vector is represented as a linear combination of a small number of atoms in the over-complete dictionary. While compressing the data volume, the key features in the electrical signal vector are retained to facilitate the subsequent separation of interference signals and noise signals and the accurate extraction of target signals.

[0032] Furthermore, the overcomplete dictionary provided by the embodiment of the present invention includes at least target signal atoms and interference atoms, and may also include noise atoms and distortion atoms.

[0033] Furthermore, steps S2-S4 provided in the embodiment of the present invention are preferably executed by a DSP module in a coherent receiver.

[0034] Specifically, the downlink mixed optical signal is input into a coherent receiver for coherent detection and optoelectronic conversion, and then converted from digital to analog using a digital-to-analog converter to obtain an electrical signal vector. The electrical signal vector is then sparsely decomposed using a DSP module, and the target signal is reconstructed to achieve accurate extraction of the target signal. The reconstructed target signal is then sent to the standard DSP process, for example, for dispersion compensation (if it has not yet been fully compensated), clock recovery, carrier phase recovery, channel equalization (which may require further fine-tuning or simplification), and final demodulation judgment.

[0035] The embodiments of the present invention provide a method for enhancing the dropout frequency of a reconfigurable optical add / drop multiplexer based on sparse representation. Based on sparse representation theory, the method utilizes an overcomplete dictionary to perform sparse decomposition on the ROADM dropout mixed optical signal converted into a digitized electrical signal vector, extracting target coefficients corresponding to the target signal atoms in the overcomplete dictionary. By combining the target coefficients with the target signal atoms and reconstructing the target signal, the target signal can be significantly improved in terms of extraction accuracy, effectively suppressing adjacent channel interference and noise, and thus improving performance. The method provided by the embodiments of the present invention is particularly suitable for long-distance, high-capacity optical transmission systems with stringent signal quality requirements and prominent interference and noise issues, such as submarine optical cable systems. It can address the problem of insufficient accuracy in extracting target signals in ROADM dropout processing methods in related technologies.

[0036] Further optionally, before executing step S1, the method in the embodiment of the present invention further includes:

[0037] Step S011, receiving a wavelength division multiplexed optical signal through a reconfigurable optical add / drop multiplexer;

[0038] Step S012: Selecting and outputting a wavelength-division multiplexed optical signal from the physical layer of the wavelength-division multiplexed optical signal to the local node through the reconfigurable optical add / drop multiplexer; wherein the wavelength-division multiplexed optical signal includes: a target signal, an interference signal, and a noise signal.

[0039] Specifically, the wavelength-dropped hybrid optical signal is a hybrid optical signal that is selected and dropped from the received WDM optical signal by the ROADM node according to the network management instruction through its internal wavelength selection device (such as WSS). The hybrid optical signal mainly contains the target wavelength. energy, but also mixed with adjacent wavelengths leakage energy and accumulated ASE noise, etc.

[0040] Furthermore, the dropped mixed optical signal may further include a distorted signal.

[0041] Further optionally, step S1 includes the following steps.

[0042] Step S11 , performing coherent reception and photoelectric conversion processing on the downlink mixed optical signal to obtain an analog mixed electrical signal.

[0043] Step S12: performing digital-to-analog conversion on the analog mixed electrical signal to obtain a digitized electrical signal vector.

[0044] Specifically, in step S11, the down-converted mixed optical signal is sent to a coherent receiver for photoelectric conversion and homodyne / heterodyne detection to obtain an analog mixed electrical signal; wherein the analog mixed electrical signal includes at least: a target signal and an interference signal, and optionally includes: an analog mixed electrical signal of a noise signal and a distorted signal.

[0045] In step S12, a digital-to-analog converter is used to perform high-speed sampling on the analog mixed electrical signal to obtain a digitized electrical signal vector.

[0046] Further optionally, before executing step S2, the method of the embodiment of the present invention further includes:

[0047] Step S021, constructing a target signal atom set including at least one target signal atom based on at least one parameter of a modulation format, a pulse shaping filter, a carrier frequency offset range, and a phase noise model;

[0048] Step S022: constructing an interference atom set including at least one interference atom based on parameters of adjacent channels of the target signal or interference characteristic parameters;

[0049] Step S023, constructing a damage atom set including at least one noise / distortion atom using a standard orthogonal basis or noise / distortion atoms learned according to noise / distortion characteristics;

[0050] Step S024 : combining the target signal atom set, the interference atom set, and the damage atom set to obtain an overcomplete dictionary.

[0051] It is worth noting that steps S021 to S024 are only marks for the corresponding step actions and are not to be understood as a restriction on the order of step execution. The order of steps S021 to S023 can be adjusted according to actual conditions and can be executed simultaneously or any one of them can be executed first.

[0052] Specifically, in step S021, the modulation format is preferably QPSK or 16QAM digital modulation. The pulse shaping filter is preferably a root-raised cosine (RRC) filter. The carrier frequency offset range can be selected based on the actual application scenario. For example, for indoor short-range communications, the range is ±500 Hz, and for cellular mobile communications (such as 4G / 5G), the range is ±10 kHz. The phase noise model can be a Wiener process model, an exponential model, an LTV (Linear Time-Varying) model, a Jakes model, or other models.

[0053] The target signal atom set constructed in this embodiment of the present invention essentially decomposes a digitized electrical signal vector into "atomic" units with specific characteristics. Each target signal atom can be considered a basic signal unit carrying a specific modulation format, undergoing specific pulse shaping, with a certain carrier offset, and subject to specific phase noise. By combining these target signal atoms, complex target signals found in actual communication scenarios can be simulated.

[0054] Preferably, the constructed target signal atoms may be ideal signal waveform segments at different frequency / phase offsets.

[0055] In step S022, adjacent channels of the target signal are correlated with the selected target signal, for example, one or two adjacent channels. Adjacent channel signal parameters include center frequency, estimated power level, modulation format, etc. Interference characteristic parameters include interference power spectral density, in-band interference power, and time-varying characteristics of the interference signal.

[0056] The interference atomic set constructed in the embodiment of the present invention can generate an interference atomic set covering various interference scenarios by combining different parameters, thereby providing a basis for subsequent interference suppression.

[0057] In step S023, standard orthogonal bases include DCT (Discrete Cosine Transform), wavelet bases, etc. Learning noise / distortion atoms based on noise / distortion characteristics includes: using a deep learning algorithm, using CNN convolution kernels as "learnable atoms," and training to capture spatial features of noise (such as the distortion features extracted by the discriminator in a GAN) and / or distortion features to construct a set of damage atoms.

[0058] In step S024, the constructed overcomplete dictionary D is a dimensional overcomplete dictionary matrix ( ), The column vectors of the overcomplete dictionary D are called atoms. The overcomplete dictionary D is carefully designed or learned so that its atom set can effectively represent the desired target signal component, the main interference component (such as ICI), and the noise component.

[0059] Specifically, the overcomplete dictionary D can be expressed as: , represents the target signal atom, represents the interfering atom, represents a noise atom.

[0060] Furthermore, the method of the embodiment of the present invention may optionally include: performing periodic analysis on the reconstruction residual, and when the reconstruction residual exceeds a set value, updating or adding the interference atoms and noise atoms.

[0061] Specifically, the overcomplete dictionary D can be statically preset or adaptively learned online. Adaptive learning can be done by analyzing the received electrical signal vector or reconstructed residuals ( , Represents the statistical characteristics of the sparse coefficient vector), and periodically updates the interference atoms, noise atoms, and distortion atoms in the overcomplete dictionary D to adapt to the changing interference environment and noise characteristics.

[0062] Further optionally, step S2 includes the following steps.

[0063] The electrical signal vector is projected onto an overcomplete dictionary for sparse decomposition, and a sparse coefficient vector that satisfies the objective function is obtained. The objective function includes a fidelity term and a sparsity regularization term. The fidelity term is used to measure the closeness between the reconstructed signal and the electrical signal vector. The reconstructed signal is a linear combination of the electrical signal vector and the sparse coefficient vector. The sparse regularization term is used to constrain the sparsity of the sparse coefficient vector.

[0064] Furthermore, step S2 preferably includes the following steps.

[0065] Step S21, using a robust loss function to calculate the reconstruction residual between the reconstructed signal and the electrical signal vector, and constructing a fidelity term; wherein the reconstructed signal is a linear combination of the electrical signal vector and the sparse coefficient vector;

[0066] Step S22, constructing a sparse regularization term based on the personalized sparse constraint and / or group sparse constraint on the sparse coefficient vector;

[0067] Step S23, combining the fidelity term and the sparse regularization term to construct an objective function;

[0068] Step S24 , projecting the electrical signal vector onto the overcomplete dictionary for sparse decomposition processing, and solving to obtain a sparse coefficient vector that satisfies the objective function.

[0069] Wherein, in step S21, the robust loss function includes: selected from: L1 norm loss function, Huber loss function or weighted L2 norm loss function based on noise statistical characteristics.

[0070] Specifically, the fidelity term constructed by the L1 norm loss function can be expressed as follows:

[0071] .

[0072] in, represents the fidelity term, represents the electric signal vector, D represents the overcomplete dictionary, represents a sparse coefficient vector.

[0073] The fidelity term constructed by the Huber loss function can be expressed as follows:

[0074] .

[0075] in, represents the reconstructed residual of the i-th electrical signal component, , Express about Huber function, Indicates preset parameters.

[0076] The fidelity term constructed by the weighted L2 norm loss function based on the statistical characteristics of noise can be expressed as follows:

[0077] .

[0078] in, is a diagonal weighting matrix whose diagonal elements can be set based on an estimate of the noise variance.

[0079] Furthermore, step S22 includes the following steps.

[0080] Step S221: applying different penalty weights to each coefficient in the sparse coefficient vector according to category to form a personalized sparse constraint and construct a sparse regularization term; wherein the categories include: target coefficients corresponding to target signal atoms, interference coefficients corresponding to interference atoms, and noise coefficients corresponding to noise atoms; and / or,

[0081] Step S222, applying different group penalty weights to each coefficient in the sparse coefficient vector according to the group, forming a group sparsity constraint, and constructing a sparse regularization term; wherein, the interference coefficients corresponding to the interference atoms representing the same interference source are one group.

[0082] Furthermore, in step S221, the L1 norm may be used to impose personalized coefficient constraints on the sparse coefficient vector, and the constructed sparse regularization term is preferably expressed as follows:

[0083] .

[0084] in, represents the sparse regularization term, is a diagonal matrix whose diagonal elements It corresponds to Atomic coefficients The penalty weight, is the total number of atoms.

[0085] The embodiment of the present invention is The diagonal matrix sets greater weights for the coefficients corresponding to interference atoms and noise atoms than those corresponding to target signal atoms, thereby guiding the optimization process to prioritize retaining the target signal components while suppressing the interference and noise components.

[0086] In step S222, a sparse regularization term is constructed by grouping sparse constraints, preferably the following expression:

[0087] .

[0088] in, is a grouped set of atomic indices, It corresponds to The sparse coefficient subvector of the group atoms, is the group penalty weight.

[0089] Group penalty weights are applied when an interferer (e.g., an adjacent channel) is jointly represented by a group of atoms in the dictionary, guiding the entire group of coefficients to zero simultaneously.

[0090] Furthermore, the sparsity penalty weights (or ) can also be adaptively adjusted based on the real-time estimated signal-to-noise ratio (SNR) or interference level, for example, increasing the penalty weight on the noise figure at low SNR.

[0091] In step S23, based on a pre-designed or adaptively learned over-complete dictionary , for the digitized electrical signal vector Perform sparse decomposition. The goal is to find a sparse coefficient vector that is as sparse as possible. , making Overcomplete dictionary and the sparse coefficient vector Linear combination of To approximate.

[0092] The constructed objective function is preferably expressed as the following expression:

[0093] .

[0094] in, is included The received signal vector of sampling points ( in vector form).

[0095] Furthermore, in step S24, the sparse decomposition method used includes but is not limited to: Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), Iterative Shrinkage Threshold Algorithm (ISTA) and its accelerated version (FISTA), or more general Proximal Gradient Methods (PGMs) such as ADMM.

[0096] Further optionally, step S3 includes the following steps.

[0097] Step S31, applying a first penalty weight and a second penalty weight to the interference coefficient and the noise coefficient respectively, and applying a third penalty weight to the target coefficient; wherein the first penalty weight and the second penalty weight are both greater than the third penalty weight, so as to identify and extract the target coefficient; and / or,

[0098] Step S32, applying a first set of penalty weights to the interference coefficients corresponding to the interference atoms of the adjacent channel signals representing the target signal; applying a second set of penalty weights to the target coefficients corresponding to the target signal atoms representing the target signal; wherein the first set of penalty weights is greater than the second set of penalty weights, so as to identify and extract the target coefficients.

[0099] Specifically, the greater the penalty weight applied, the easier it is for the corresponding sparse coefficient vector to be compressed to zero, thereby suppressing interference and noise by applying a larger weight to the interference coefficient and noise coefficient than the target coefficient.

[0100] Applying group penalty weights can apply group penalty weights to the entire group of interference atoms representing adjacent channel interference, thereby guiding the entire group of coefficients to tend to zero simultaneously. This allows for suppression of specific interference sources and is particularly suitable for suppressing strong ICI.

[0101] Furthermore, in step S4, the reconstructed enhanced target signal can be expressed as follows:

[0102] .

[0103] in, represents the enhanced target signal, represents the target signal atom, Represents the target coefficient.

[0104] like Figure 2 The embodiment of the present invention further provides a wavelet drop enhancement device for a reconfigurable optical add / drop multiplexer based on sparse representation, comprising: a reconfigurable optical add / drop multiplexer, a coherent receiver, a digital-to-analog converter, and a digital signal processor.

[0105] The reconfigurable optical add / drop multiplexer is used for receiving wavelength division multiplexed optical signals and outputting wavelength-division multiplexed mixed optical signals to local nodes; the wavelength-division multiplexed mixed optical signals at least include target signals and interference signals.

[0106] The coherent receiver is used to receive the down-wavelength mixed optical signal and convert the down-wavelength mixed optical signal into an analog mixed electrical signal.

[0107] A digital-to-analog converter is used to convert analog mixed electrical signals into digital electrical signal vectors.

[0108] A digital signal processor is used to perform sparse decomposition processing on an electrical signal vector based on an overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary includes at least: target signal atoms and interference atoms; identify and extract target coefficients corresponding to the target signal atoms from the sparse coefficient vector; and combine the target coefficients with the target signal atoms to obtain an enhanced target signal.

[0109] Specifically, when implementing this device, the following settings are performed:

[0110] Transmission link: simulates a submarine link consisting of multiple sections of standard single-mode optical fiber (SSMF) and EDFA cascaded together.

[0111] WDM signal: contains multiple optical channels with a channel spacing of 50 GHz.

[0112] Target channel: The central wavelength is , using 100 Gbps polarization-multiplexed quadrature phase-shift keying (DP-QPSK) modulation format with a symbol rate of approximately 32 Gbaud.

[0113] Main interference: from the central wavelength and Two adjacent channels use the same or different modulation formats and powers.

[0114] ROADM: Simulate a ROADM node with typical WSS filtering characteristics at the receiving end. Make the next wave.

[0115] Receiver: A standard digital coherent receiver is used, with an ADC sampling rate of approximately 64 GSa / s.

[0116] The following is a specific example of implementing the wavelength drop enhancement method and apparatus for the reconfigurable optical add / drop multiplexer based on sparse representation provided by the embodiments of the present invention to perform wavelength drop enhancement.

[0117] First, ROADM wavelet and reception: WDM signal passes through ROADM, The mixed optical signal is dropped and then sent to the coherent receiver. After passing through the 90-degree mixer, photodetector (PD) and ADC, the digital received signal sequence is obtained. (Including I and Q signals of two polarizations, X and Y. To simplify the description, the processing flow is subsequently represented by a single scalar sequence).

[0118] Secondly, the DSP unit processes: Digital signals Enter the DSP unit and perform the following steps:

[0119] a. Dictionary D construction (refer to Figure 3 ), including the following steps.

[0120] (1) Construct target signal atomic set ( ) represents the target DP-QPSK signal. Each target signal atom is based on an ideal QPSK symbol (such as ), after root raised cosine (RRC) pulse shaping (roll-off factor such as 0.1), and considering a certain carrier frequency offset (CFO) and phase noise (PN) range (for example, covering Waveform fragments generated by a CFO of 10 MHz and a PN sample path that conforms to a specific linewidth model. To cover both polarizations, corresponding sets of atoms are required.

[0121] (2) Similarly, construct two adjacent channels and atoms, construct the interference atom set ( ). They need to be compared with The estimated leakage power levels and their modulation formats are generated.

[0122] (3) Select a set of discrete cosine transform (DCT) basis or short-time Fourier transform (STFT) basis as noise atoms to fit the broadband ASE noise and construct the damage atom set ( ).

[0123] b. Sparse decomposition: The FISTA algorithm is used to solve the following optimization problem:

[0124] .

[0125] The above optimization problem uses the L1 norm fidelity term to enhance robustness to potential pulse interference, and uses a weighted L1 sparsity penalty. The weight setting (based on experience or simulation tuning) is as follows: , The FISTA algorithm updates the coefficients by iteration and an auxiliary variable, combining gradient descent and soft threshold shrinkage operators to solve efficiently.

[0126] Summary of FISTA iterative steps:

[0127] Step 1: Set the smoothing term , preferably expressed as the following expression:

[0128] .

[0129] Non-smooth terms , preferably expressed as the following expression:

[0130] .

[0131] in, 、 、 Represent the penalty weights corresponding to the target coefficient, interference coefficient and noise coefficient respectively, represents the target coefficient, and Respectively represent the interference coefficients of the two adjacent channels adjacent to the target signal, Represents the noise figure.

[0132] Step 2: Initialization , , , , step length ,in .

[0133] Step 3: Calculate the gradient as follows:

[0134] .

[0135] Step 4: Calculate the gradient descent amplitude as follows:

[0136] .

[0137] Step 5: Soft Thresholding, using different thresholds for different parts:

[0138] .

[0139] in, is a symbolic function, Indicates that target, int, and noise areas use different weights .

[0140] Step 6: Update the momentum as follows:

[0141] ;

[0142] .

[0143] Step 7: Convergence judgment, when satisfied When , the iteration is terminated. Otherwise, let And jump to step 3 to continue iteration.

[0144] c. Signal reconstruction: Sparse coefficients are obtained after FISTA converges .extract part, calculated as follows:

[0145] .

[0146] The aforementioned sparse decomposition algorithms (especially iterative algorithms like FISTA) are computationally intensive. In high-speed optical communication systems, pipeline and parallelization are required on high-performance FPGAs or dedicated ASICs to meet real-time processing requirements. Dictionary storage and matrix-vector multiplication are key computational bottlenecks, necessitating optimized hardware architecture.

[0147] After obtaining the reconstructed and increased target signal, The signal (including I / Q of X / Y polarization) is sent to the subsequent standard DP-QPSK processing module, including carrier recovery, timing recovery, equalization, demapping and BER calculation.

[0148] The method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation provided by the embodiment of the present invention has significant effects in the following aspects:

[0149] 1. Enhanced wavelength selectivity: Utilizing sparse representation in the digital domain, it provides a resolution far exceeding that of physical filters, effectively separating adjacent wavelength signals and suppressing ICI.

[0150] 2. Powerful interference and noise suppression: By representing interference and noise as specific atoms and imposing strong sparse constraints, its impact on the target signal can be significantly suppressed, thereby improving the signal-to-noise ratio.

[0151] 3. Robustness: The robust loss function and adaptive mechanism are used to make the method more adaptable to changes in noise statistics and interference intensity.

[0152] 4. Flexibility: The dictionary and optimization parameters can be adjusted or adaptively learned based on the system configuration and channel conditions to adapt to different scenarios.

[0153] 5. Compatibility: Processing is mainly performed in the digital domain, requiring minimal changes to existing ROADM optical paths and coherent receiver hardware, making it easy to integrate.

[0154] 6. Improved system performance: This ultimately helps reduce the receiver's BER, supports denser channel spacing (improving spectral efficiency), or extends transmission distance without electrical relays.

[0155] An embodiment of the present invention further provides an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program executable by the at least one processor, wherein the computer program, when executed by the at least one processor, causes the electronic device to perform the method of an embodiment of the present invention.

[0156] refer to Figure 3 , a structural block diagram of an electronic device that can be used as a server or client of an embodiment of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0157] like Figure 3 As shown, the electronic device includes a computing unit 51, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 52 or a computer program loaded from a storage unit 58 into a random access memory (RAM) 53. RAM 53 can also store various programs and data required for the operation of the electronic device. The computing unit 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0158] Multiple components in the electronic device are connected to the I / O interface 55, including: an input unit 56, an output unit 57, a storage unit 58, and a communication unit 59. The input unit 56 can be any type of device capable of inputting information into the electronic device. The input unit 56 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 57 can be any type of device capable of presenting information and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 58 can include, but is not limited to, a magnetic disk and an optical disk. The communication unit 59 allows the electronic device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0159] The computing unit 51 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 51 include, but are not limited to, a CPU, a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing units, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 51 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as a computer program tangibly embodied in a machine-readable medium, such as a storage unit 58. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 52 and / or the communication unit 59. In some embodiments, the computing unit 51 can be configured to perform the above-described methods by any other suitable means (e.g., via firmware).

[0160] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0161] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0162] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, that is, "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative and not restrictive. Those skilled in the art should understand that unless the context clearly indicates otherwise, they should be understood as "one or more".

[0163] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and corresponding operation entrances shall be provided for users to choose to authorize or refuse.

[0164] The various steps described in the method implementation methods provided by the embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0165] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments are referenced to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0166] The above-described embodiments merely represent several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that a person of ordinary skill in the art would be able to make various modifications and improvements without departing from the scope of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation, characterized in that: The following steps are involved: Converting and processing the wavelength-division multiplexed optical signal to obtain a digitized electrical signal vector; wherein the wavelength-division multiplexed optical signal is selected from the received wavelength-division multiplexed optical signal by the reconfigurable optical add / drop multiplexer and output to the local node; the wavelength-division multiplexed optical signal includes at least a target signal and an interference signal; Performing sparse decomposition processing on the electrical signal vector based on an overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary includes at least: target signal atoms and interference atoms; Identifying and extracting target coefficients corresponding to the target signal atoms from the sparse coefficient vector; The target coefficient is combined with the target signal atom to obtain an enhanced target signal.

2. The method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation according to claim 1, characterized in that: Before converting the downlink mixed optical signal, the method includes: receiving a wavelength division multiplexed optical signal through a reconfigurable optical add / drop multiplexer; A reconfigurable optical add / drop multiplexer selects and outputs a wavelength-division multiplexed optical signal from the physical layer of the wavelength-division multiplexed optical signal to a local node; wherein the wavelength-division multiplexed optical signal includes a target signal, an interference signal and a noise signal.

3. The method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation according to claim 1, characterized in that: Converting and processing the downlink mixed optical signal to obtain a digitized electrical signal vector includes: performing coherent reception and photoelectric conversion processing on the downlink mixed optical signal to obtain an analog mixed electrical signal; Performing digital-to-analog conversion on the analog mixed electrical signal to obtain a digitized electrical signal vector.

4. The method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation according to claim 1, wherein: Before performing sparse decomposition processing on the electrical signal vector based on the overcomplete dictionary, the method includes: Constructing a target signal atom set including at least one target signal atom based on at least one parameter of a modulation format, a pulse shaping filter, a carrier frequency offset range, and a phase noise model; constructing an interference atom set including at least one interference atom based on parameters of adjacent channels of the target signal or interference characteristic parameters; Constructing a damage atom set including at least one noise / distortion atom through a standard orthogonal basis or noise / distortion atoms learned according to noise / distortion characteristics; The target signal atom set, the interference atom set, and the damage atom set are combined to obtain an overcomplete dictionary.

5. The method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation according to claim 1, characterized in that: The electrical signal vector is subjected to sparse decomposition processing based on an overcomplete dictionary to obtain a sparse coefficient vector, including: The electrical signal vector is projected onto an overcomplete dictionary for sparse decomposition processing, and a sparse coefficient vector that satisfies the objective function is obtained; wherein the objective function includes: a fidelity term and a sparse regularization term; the fidelity term is used to measure the degree of closeness between the reconstructed signal and the electrical signal vector; the reconstructed signal is a linear combination of the electrical signal vector and the sparse coefficient vector; the sparse regularization term is used to constrain the sparsity of the sparse coefficient vector.

6. The method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation according to claim 5, characterized in that: Projecting the electrical signal vector onto an overcomplete dictionary for sparse decomposition processing to obtain a sparse coefficient vector that satisfies the objective function includes: A robust loss function is used to calculate the reconstructed residual between the reconstructed signal and the electrical signal vector to construct a fidelity term; wherein the reconstructed signal is a linear combination of the electrical signal vector and the sparse coefficient vector; constructing a sparse regularization term based on individualized sparsity constraints and / or group sparsity constraints on the sparse coefficient vector; Combining the fidelity term and the sparse regularization term to construct an objective function; The electrical signal vector is projected onto an overcomplete dictionary for sparse decomposition processing, and a sparse coefficient vector that satisfies the objective function is obtained.

7. The method for enhancing the drop wavelength of a reconfigurable optical add / drop multiplexer based on sparse representation according to claim 6, characterized in that: Based on the personalized sparse constraint or group sparse constraint on the sparse coefficient vector, a sparse regularization term is constructed, including: Apply different penalty weights to each coefficient in the sparse coefficient vector according to category to form personalized sparse constraints and construct a sparse regularization term; wherein the categories include: target coefficients corresponding to target signal atoms, interference coefficients corresponding to interference atoms, and noise coefficients corresponding to noise atoms; and / or, Different group penalty weights are applied to each coefficient in the sparse coefficient vector according to the group to form a group sparsity constraint and construct a sparse regularization term; among them, the interference coefficients corresponding to the interference atoms representing the same interference source are a group.

8. The method for enhancing the drop frequency of a reconfigurable optical add / drop multiplexer based on sparse representation according to claim 7, characterized in that: Identifying and extracting a target coefficient corresponding to the target signal atom from the sparse coefficient vector includes: applying a first penalty weight and a second penalty weight to the interference coefficient and the noise coefficient, respectively, and applying a third penalty weight to the target coefficient; wherein both the first penalty weight and the second penalty weight are greater than the third penalty weight, so as to identify and extract the target coefficient; and / or, A first set of penalty weights is applied to interference coefficients corresponding to interference atoms of adjacent channel signals representing the target signal; a second set of penalty weights is applied to target coefficients corresponding to target signal atoms representing the target signal; wherein the first set of penalty weights is greater than the second set of penalty weights, so as to identify and extract the target coefficients.

9. The method for enhancing the drop wavelength of a reconfigurable optical add / drop multiplexer based on sparse representation according to any one of claims 6 to 8, characterized in that: The method further comprises: The reconstruction residual is periodically analyzed, and when the reconstruction residual exceeds a set value, the interference atoms and noise atoms are updated or added.

10. A wavelet drop enhancement device for a reconfigurable optical add / drop multiplexer based on sparse representation, characterized in that: include: A reconfigurable optical add / drop multiplexer is used to receive wavelength division multiplexed optical signals and output wavelength-division multiplexed mixed optical signals to local nodes; The downlink mixed optical signal includes at least: a target signal and an interference signal; a coherent receiver, configured to receive the downlink mixed optical signal and convert the downlink mixed optical signal into an analog mixed electrical signal; A digital-to-analog converter, configured to convert the analog mixed electrical signal into a digitized electrical signal vector; A digital signal processor is configured to perform sparse decomposition processing on the electrical signal vector based on an overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary includes at least: target signal atoms and interference atoms; identify and extract target coefficients corresponding to the target signal atoms from the sparse coefficient vector; and combine the target coefficients with the target signal atoms to obtain an enhanced target signal.

Images