A reconfigurable optical add-drop multiplexer based on sparse representation and its down-wave enhancement method

Abstract

The present application relates to the technical field of wavelength division multiplexing, and particularly provides a down-wave enhancement method of a reconfigurable optical add-drop multiplexer based on sparse representation, which converts and processes a down-wave mixed optical signal to obtain a digitized electrical signal vector; wherein the down-wave mixed optical signal at least includes a target signal and an adjacent channel interference signal; the electrical signal vector is processed by sparse decomposition based on an overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary at least includes a target signal atom and an adjacent channel interference atom; a target coefficient corresponding to the target signal atom is identified and extracted from the sparse coefficient vector; and the target coefficient is combined with the target signal atom to obtain an enhanced target signal. The down-wave enhancement method of the reconfigurable optical add-drop multiplexer based on sparse representation can improve the accuracy of extracting the target signal in the ROADM down-wave, effectively suppress adjacent channel interference and amplified spontaneous emission noise, and thus improve performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wavelength division multiplexing technology, and in particular, to a down-wave enhancement method for a reconfigurable optical add-drop multiplexer based on sparse representation. BACKGROUND

[0002] In a DWDM (Dense Wavelength Division Multiplexing) system, the channel spacing is very small (e.g., 50 GHz or lower). The physical filter characteristics of the core device in a ROADM (Reconfigurable Optical Add-Drop Multiplexer), such as a WSS (Wavelength Selective Switch), are not ideal rectangular, and there is a certain roll-off factor and limited out-of-band suppression, resulting in a filter edge that is not steep enough. When the ROADM performs a drop operation on a target wavelength, due to the filter edge not being steep enough, the target wavelength signal will inevitably mix in the interference energy from the adjacent wavelength channels, i.e., ICI (Inter-Channel Interference). In addition, after long-distance transmission (especially in submarine systems using erbium-doped fiber amplifiers), the signal will accumulate a large amount of ASE (Amplified Spontaneous Emission) noise. These ICI and ASE noises will seriously deteriorate the quality of the dropped signal, reduce the BER (Bit Error Rate) performance of the receiver, and limit the system capacity and transmission distance.

[0003] Traditional ROADM down-wave post-processing usually relies on DSP (Digital Signal Processing) modules in a coherent receiver. Standard DSP algorithms, such as MMSE (Minimum Mean Square Error)-based digital equalizers, while they can compensate for linear impairments such as chromatic dispersion and polarization mode dispersion, their performance will decrease when dealing with strong ICI that is very close in frequency to the target signal and has a similar structure. They may have difficulty effectively distinguishing the target signal from the strong interference, especially under low OSNR (Optical Signal-to-Noise Ratio) conditions. SUMMARY

[0004] The down-wave enhancement method for a reconfigurable optical add-drop multiplexer based on sparse representation provided by the embodiments of the present application at least solves the problem of insufficient precision of the target signal extraction in the ROADM down-wave processing method in the related art.

[0005] To achieve the above object, the present application provides the following technical solutions.

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

[0007] Preferably, before converting the downwave mixed optical signal, the method comprises: receiving a wavelength division multiplexing optical signal by the reconfigurable optical add-drop multiplexer; selecting and outputting the downwave mixed optical signal from a physical layer of the wavelength division multiplexing optical signal to the local node by the reconfigurable optical add-drop multiplexer; wherein the downwave mixed optical signal includes a target signal, an adjacent channel interference signal and an amplified spontaneous emission noise signal.

[0008] Preferably, converting the downwave mixed optical signal to obtain a digitized electrical signal vector comprises: coherently receiving and optoelectronic converting the downwave mixed optical signal to obtain an analog mixed electrical signal; and digital-analog converting the analog mixed electrical signal to obtain a digitized electrical signal vector.

[0009] Preferably, before performing sparse decomposition on the electrical signal vector based on the overcomplete dictionary, the method comprises: constructing a target signal atom set including at least one target signal atom based on at least one parameter in modulation format, pulse shaping filter, carrier frequency offset range, and phase noise model; constructing an adjacent channel interference atom set including at least one adjacent channel 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 amplified spontaneous emission noise / distortion atom based on a standard orthogonal basis or an amplified spontaneous emission noise / distortion atom learned according to amplified spontaneous emission noise / distortion characteristics; and combining the target signal atom set, the adjacent channel interference atom set, and the damage atom set to obtain the overcomplete dictionary.

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

[0011] Preferably, the sparse decomposition processing of the electrical signal vector based on the overcomplete dictionary is performed to obtain a sparse coefficient vector satisfying an objective function, including: using a robust loss function to calculate a reconstruction residual of a 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; constructing a sparse regularization term based on individualized sparse constraints and / or group sparse constraints of the sparse coefficient vector; constructing an objective function by combining the fidelity term and the sparse regularization term; and projecting the electrical signal vector to the overcomplete dictionary for sparse decomposition processing to obtain a sparse coefficient vector satisfying the objective function.

[0012] Preferably, the sparse regularization term is constructed based on individualized sparse constraints or group sparse constraints of the sparse coefficient vector, including: applying different penalty weights to each coefficient in the sparse coefficient vector according to categories to form individualized sparse constraints and construct the sparse regularization term; wherein the categories include target coefficients corresponding to target signal atoms, adjacent channel interference coefficients corresponding to adjacent channel interference atoms, and noise coefficients corresponding to amplified spontaneous emission noise atoms; and / or applying different group penalty weights to each coefficient in the sparse coefficient vector according to groups to form group sparse constraints and construct the sparse regularization term; wherein adjacent channel interference coefficients corresponding to adjacent channel interference atoms representing the same adjacent channel interference source belong to a group.

[0013] Preferably, the target coefficients corresponding to the target signal atoms are identified and extracted from the sparse coefficient vector, including: applying a first penalty weight and a second penalty weight to the adjacent channel interference coefficients and the noise coefficients, respectively, and applying a third penalty weight to the target coefficients; 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 coefficients; and / or applying a first group penalty weight to adjacent channel interference coefficients corresponding to adjacent channel interference atoms representing adjacent signals of the target signal, and applying a second group penalty weight to target coefficients corresponding to target signal atoms representing the target signal; wherein the first group penalty weight is greater than the second group penalty weight, so as to identify and extract the target coefficients.

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

[0015] The second aspect of the application provides a reconfigurable optical add-drop multiplexer downwave enhancement device based on sparse representation, comprising: a reconfigurable optical add-drop multiplexer, configured to receive a wavelength division multiplexing optical signal and output a downwave mixed optical signal to a local node; the downwave mixed optical signal at least includes: a target signal and an adjacent channel interference signal; a coherent receiver, configured to receive the downwave mixed optical signal and convert the downwave 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, 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 at least includes: a target signal atom and an adjacent channel interference atom; identifying and extracting a target coefficient corresponding to the target signal atom from the sparse coefficient vector; combining the target coefficient with the target signal atom to obtain an enhanced target signal.

[0016] The above technical solutions of the application have the following beneficial effects compared with the prior art:

[0017] The downwave enhancement method of the reconfigurable optical add-drop multiplexer based on sparse representation provided by the embodiments of the application can significantly improve the extraction accuracy of the target signal, effectively suppress adjacent channel interference and amplified spontaneous emission noise, and thus improve performance, by performing sparse decomposition on the RODAM downwave mixed optical signal converted into a digitized electrical signal vector based on the sparse representation theory and using an overcomplete dictionary, extracting a target coefficient corresponding to a target signal atom in the overcomplete dictionary, and reconstructing the target signal by combining the target coefficient with the target signal atom. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other embodiments can also be obtained from these drawings without creative labor.

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

[0020] Figure 2 Figure 2 is a block diagram of a device for enhancing a drop of a reconfigurable optical add-drop multiplexer based on sparse representation according to an embodiment of the present invention.

[0021] Figure 3 Figure 3 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] Embodiments of the present invention will be described in more detail with reference to the drawings. Although certain embodiments of the present invention are shown in the drawings, it is understood that the present invention can be embodied in various forms and should not be interpreted in a limited sense provided by the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be more thoroughly understood and completely appreciated.

[0023] WDM (Wave Division Multiplexing) greatly improves the transmission capacity by transmitting multiple optical signals of different wavelengths in a single optical fiber. As a key node of the WDM network, the reconfigurable optical add-drop multiplexer (ROADM) allows flexible Add, Drop or Pass-through operations on any wavelength, significantly improving the flexibility and manageability of the network.

[0024] Sparse Representation or Sparse Coding theory is a research hotspot in the field of signal processing in recent years. The core idea is that many natural signals or artificial signals can be accurately or approximately represented by a small number of non-zero coefficients in a certain transform domain (defined by an overcomplete dictionary). In related technologies, sparse representation theory has been successfully applied to image denoising, compressive sensing, signal separation and other fields, but there is no precedent for successfully applying it to the digital domain processing of ROADM drop to improve the performance of ROADM drop.

[0025] As shown in Figure 1 To improve the accuracy of extracting target signals in the drop of the ROADM, embodiments of the present invention provide a method for enhancing a drop of a reconfigurable optical add-drop multiplexer based on sparse representation, which includes the following steps:

[0026] Step S1, the down-wave mixed optical signal is converted to obtain a digitized electrical signal vector; wherein the down-wave mixed optical signal is selected from the received wavelength division multiplexing optical signal by the reconfigurable optical add-drop multiplexer and output to the local node; the down-wave mixed optical signal at least includes: target signal and adjacent channel interference signal;

[0027] Step S2, the electrical signal vector is sparsely decomposed based on the overcomplete dictionary to obtain a sparse coefficient vector; wherein the overcomplete dictionary at least includes: target signal atom and adjacent channel interference atom;

[0028] Step S3, the target coefficient corresponding to the target signal atom is identified and extracted from the sparse coefficient vector;

[0029] Step S4, the target coefficient is combined with the target signal atom to obtain an enhanced target signal.

[0030] In step S2, the overcomplete dictionary is an important concept in the fields of signal processing, machine learning and image processing, which means that the number of elements (atoms) in the dictionary exceeds the minimum number required for signal representation in the dictionary, thereby having more flexible signal representation capability.

[0031] In the embodiment of the application, the electrical signal vector is sparsely decomposed based on the overcomplete dictionary, the redundancy and adaptive characteristics of the overcomplete dictionary are utilized, and each electrical signal in the electrical signal vector is represented as a linear combination of a small number of atoms in the overcomplete dictionary, thereby compressing the data amount while retaining the key features in the electrical signal vector, so as to facilitate subsequent separation of adjacent channel interference signals and amplified spontaneous emission noise signals and accurate extraction of target signals.

[0032] Further, the overcomplete dictionary provided by the embodiment of the application at least includes: target signal atom and adjacent channel interference atom, and can also include: amplified spontaneous emission noise atom and distortion atom.

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

[0034] Specifically, the down-wave mixed optical signal is input into the coherent receiver for coherent detection and photoelectric conversion, and digital-to-analog conversion is realized by a digital-to-analog converter to obtain an electrical signal vector, and then the DSP module is utilized to sparsely decompose the electrical signal vector and reconstruct the target signal, thereby realizing accurate extraction of the target signal, and then the reconstructed target signal is input into the DSP standard flow, for example, dispersion compensation (if not fully compensated), clock recovery, carrier phase recovery, channel equalization (may need to be fine-tuned or simplified again) and finally demodulation decision.

[0035] The embodiment of the present application provides a reconfigurable optical add-drop multiplexer based on a sparse representation of a down-wave enhancement method, based on the sparse representation theory, using an over-complete dictionary, sparse decomposition is performed on the RODAM down-wave mixed optical signal converted into a digital electrical signal vector, and target coefficients corresponding to target signal atoms in the over-complete dictionary are extracted, the target signal is reconstructed by combining the target coefficients with the target signal atoms, the extraction accuracy of the target signal can be significantly improved, adjacent channel interference and amplified spontaneous emission noise can be effectively suppressed, and therefore the performance is improved. The method provided by the embodiment of the present application is particularly suitable for long-distance and large-capacity optical transmission systems, such as submarine optical cable systems, which have high requirements for signal quality, prominent adjacent channel interference and amplified spontaneous emission noise, and can solve the problem of insufficient accuracy of the target signal extracted by the down-wave processing method in the related art.

[0036] Further optionally, before step S1 is performed, the method in the embodiment of the present application further comprises:

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

[0038] Step S012, selecting and outputting a down-wave mixed optical signal from a physical layer in the wavelength division multiplexing optical signal to a local node through the reconfigurable optical add-drop multiplexer; wherein the down-wave mixed optical signal comprises a target signal, an adjacent channel interference signal and an amplified spontaneous emission noise signal.

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

[0040] Further, the down-wave mixed optical signal can also include a distortion signal.

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

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

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

[0044] Specifically, in step S11, the lower wave mixed optical signal is sent into a coherent receiver to perform photoelectric conversion and homodyne / heterodyne detection to obtain an analog mixed electrical signal; wherein the analog mixed electrical signal at least includes: a target signal and an adjacent channel interference signal, and optionally includes an amplified spontaneous emission noise signal and a distortion signal.

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

[0046] Further optionally, before step S2 is performed, the method of the embodiment of the present application further includes:

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

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

[0049] Step S023, constructing a damage atom set including at least one amplified spontaneous emission noise / distortion atom through a standard orthogonal basis or an amplified spontaneous emission noise / distortion atom learned according to the amplified spontaneous emission noise / distortion characteristic;

[0050] Step S024, combining the target signal atom set, the adjacent channel interference atom set, and the damage atom set to obtain an over-complete dictionary.

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

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

[0053] The present invention constructs a target signal atom set, which essentially decomposes a digitized electrical signal vector into "atomic" units with specific characteristics. Each target signal atom can be regarded as a basic signal unit carrying a specific modulation format, undergoing specific pulse shaping, having a certain carrier offset, and being affected by specific phase noise. By combining these target signal atoms, complex target signals in actual communication scenarios can be simulated.

[0054] Preferably, the constructed target signal atom can be an ideal signal waveform segment at different frequencies / phase shifts.

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

[0056] The interference atom set constructed in this embodiment of the invention can generate interference atom sets covering multiple interference scenarios by combining different parameters, providing a foundation for subsequent interference suppression.

[0057] In step S023, the orthonormal basis includes: DCT (Discrete Cosine Transform), wavelet basis, etc. Learning noise / distortion atoms based on noise / distortion characteristics includes: using deep learning algorithms, utilizing the convolutional kernels of CNNs as "learnable atoms," and constructing a set of damaged atoms by training to capture spatial features of noise (such as the discriminator in GANs extracting distortion features) and / or distortion features.

[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, representing the total number of scan frequencies. The overcomplete dictionary D is carefully designed or learned so that its set of atoms can effectively represent the desired target signal components, the main adjacent channel interference components (such as ICI), and the amplified spontaneous emission noise components.

[0059] Specifically, an overcomplete dictionary D can be represented as: , Indicates the target signal atom, Indicates adjacent channel interfering atoms. This indicates amplified spontaneous emission noise atoms.

[0060] Further, the method of the embodiment of the present application can optionally comprise: periodically analyzing the reconstruction residual, and updating or adding the adjacent channel interference atoms and the amplified spontaneous emission noise atoms when the reconstruction residual exceeds a set value.

[0061] Specifically, the overcomplete dictionary D is either statically preset or adaptively learned online. The adaptive learning can be achieved by periodically updating the adjacent channel interference atoms, noise atoms and distortion atoms in the overcomplete dictionary D based on the statistical characteristics of the received electrical signal vector or the reconstruction residual (x) , to adapt to the changing adjacent channel interference environment and the amplified spontaneous emission noise characteristics.

[0062] Further, the step S2 comprises the following steps.

[0063] projecting the electrical signal vector into the overcomplete dictionary for sparse decomposition processing to obtain the sparse coefficient vector satisfying the objective function; wherein the objective function comprises: a fidelity term and a sparse 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] Further, the step S2 preferably comprises the following steps.

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

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

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

[0068] Step S24, projecting the electrical signal vector into the overcomplete dictionary for sparse decomposition processing to obtain the sparse coefficient vector satisfying the objective function.

[0069] In step S21, the robust loss function comprises: an L1 norm loss function, a Huber loss function or a weighted L2 norm loss function based on the statistical characteristics of the amplified spontaneous emission noise.

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

[0071] .

[0072] in, Indicates the fidelity item. Represents an electrical signal vector. This indicates a complete dictionary. This represents a sparse coefficient vector.

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

[0074] .

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

[0076] The fidelity term, constructed using a weighted L2 norm loss function based on the statistical characteristics of amplified spontaneous emission noise, can be expressed as follows:

[0077] .

[0078] in, It is a diagonal weighted matrix whose diagonal elements can be set based on an estimate of the variance of amplified spontaneous emission noise.

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

[0080] Step S221: Apply different penalty weights to each coefficient in the sparse coefficient vector according to its category to form personalized sparse constraints and construct a sparse regularization term; wherein, the categories include: target coefficients corresponding to target signal atoms, adjacent channel interference coefficients corresponding to adjacent channel interference atoms, and noise coefficients corresponding to amplified spontaneous emission noise atoms; and / or,

[0081] Step S222: Apply different group penalty weights to each coefficient in the sparse coefficient vector according to the group to form group sparsity constraints and construct sparse regularization terms; wherein, the neighboring interference coefficients corresponding to the neighboring interference atoms that represent the same neighboring interference source are in one group.

[0082] Furthermore, in step S221, the L1 norm can 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 sparse regularization terms. It is a diagonal matrix, whose diagonal elements is a penalty weight corresponding to the th atomic coefficient , and is the total number of atoms.

[0085] Embodiments of the present application set a larger weight for the coefficients corresponding to the adjacent channel interference atoms and the amplified spontaneous emission noise atoms than the coefficients corresponding to the target signal atoms in the diagonal matrix, so as to guide the optimization process to preferentially retain the target signal components while suppressing the adjacent channel interference and amplified spontaneous emission noise components.

[0086] In step S222, a sparse regularization term is constructed by group sparsity constraint, and is preferably expressed as follows:

[0087] .

[0088] wherein is a group set of atom indices, is a sparse coefficient sub-vector corresponding to the th group of atoms, is a group penalty weight.

[0089] The group penalty weight is applicable when an adjacent channel interference source (such as an adjacent channel) is jointly represented by a group of atoms in the dictionary, guiding the entire group of coefficients to tend to zero at the same time.

[0090] Further, the sparsity penalty weight (or ) can also be adaptively adjusted according to the real-time estimated signal-to-noise ratio (SNR) or adjacent channel interference level, for example, increasing the penalty weight for the amplified spontaneous emission noise coefficients when the SNR is low.

[0091] In step S23, a sparse decomposition process is performed on the digitized electrical signal vector based on a pre-designed or adaptively learned overcomplete dictionary . The goal is to find a sparse coefficient vector as sparse as possible, so that can be approximately represented by a linear combination of the overcomplete dictionary and the sparse coefficient vector .

[0092] The constructed objective function is preferably expressed as follows:

[0093] .

[0094] wherein is a received signal vector containing sampling points​ vector form).

[0095] Further, in step S24, the sparse decomposition method used includes but is not limited to: orthogonal matching pursuit (OMP), basis pursuit (BP), iterative shrinkage thresholding algorithm (ISTA) and its accelerated version (FISTA), or more general proximal gradient method (Proximal Gradient Methods) such as ADMM.

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

[0097] In step S31, a first penalty weight and a second penalty weight are respectively applied to the adjacent channel interference coefficient and the noise coefficient, and a third penalty weight is applied 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] In step S32, a first group of penalty weights is applied to the adjacent channel interference coefficients corresponding to the adjacent channel interference atoms representing the adjacent channel signals of the target signal; and a second group of penalty weights is applied to the target coefficients corresponding to the target signal atoms representing the target signal; wherein the first group of penalty weights is greater than the second group of penalty weights, so as to identify and extract the target coefficient.

[0099] Specifically, the greater the penalty weight applied, the easier it is for the corresponding sparse coefficient vector to be compressed to zero, and thus by applying a greater weight to the adjacent channel interference coefficient and the amplified spontaneous emission noise coefficient than to the target coefficient, the effect of suppressing adjacent channel interference and amplified spontaneous emission noise is achieved.

[0100] Applying group penalty weights can apply group penalty weights to the entire group of adjacent channel interference atoms representing adjacent channel interference, thereby guiding the entire group of coefficients to tend to zero at the same time, so that suppression can be performed on specific adjacent channel interference sources, and it is especially suitable for suppressing strong ICI.

[0101] Further, in step S4, the reconstructed enhanced target signal can be represented by the following expression:

[0102] .

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

[0104] As Figure 2The embodiment of the present application also provides a reconfigurable optical add-drop multiplexer based on sparse representation of a down-wave enhancement device, 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 a wavelength division multiplexing optical signal and outputting a down-wave mixed optical signal to a local node; the down-wave mixed optical signal at least comprises a target signal and an adjacent channel interference signal.

[0106] The coherent receiver is used for receiving the down-wave mixed optical signal and converting the down-wave mixed optical signal into an analog mixed electrical signal.

[0107] The digital-to-analog converter is used for converting the analog mixed electrical signal into a digitized electrical signal vector.

[0108] The digital signal processor is used for performing sparse decomposition processing on the electrical signal vector based on an over-complete dictionary to obtain a sparse coefficient vector; wherein the over-complete dictionary at least comprises a target signal atom and an adjacent channel interference atom; a target coefficient corresponding to the target signal atom is identified and extracted from the sparse coefficient vector; and the target coefficient is combined with the target signal atom to obtain an enhanced target signal.

[0109] Specifically, in the implementation of the device, the following settings are made:

[0110] Transmission link: an analog multi-section standard single-mode fiber (SSMF) and an EDFA cascaded submarine link.

[0111] WDM signal: containing a plurality of optical channels, and a channel spacing of 50 GHz.

[0112] Target channel: a center wavelength of , using a 100 Gbps polarization multiplexed quadrature phase shift keying (DP-QPSK) modulation format, and a symbol rate of about 32 Gbaud.

[0113] Main adjacent channel interference: from two adjacent channels with center wavelengths of and , using the same or different modulation formats and powers.

[0114] ROADM: an analog ROADM node with a typical WSS filtering characteristic is received at the receiving end, and is down-waved.

[0115] Receiver: a standard digital coherent receiver is used, and an ADC sampling rate is about 64 GSa / s.

[0116] The following is a specific example of implementing the downwave enhancement method and apparatus for a sparse representation-based reconfigurable optical add-drop multiplexer provided in the embodiments of the present invention.

[0117] First, ROADM downsampling and reception: WDM signals pass through ROADM, The signal is de-channeled, resulting in a mixed optical signal. This signal enters a coherent receiver, and after passing through a 90-degree mixer, a photodetector (PD), and an ADC, a digitized received signal sequence is obtained. (Including I-channel and Q-channel signals with X and Y polarizations, for simplicity, the processing flow will be represented by a single scalar sequence thereafter).

[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 The process includes the following steps.

[0120] (1) Construct the target signal atom set ( ) represents the target DP-QPSK signal. Each target signal atom is based on an ideal QPSK symbol (e.g., After root-raised cosine (RRC) pulse shaping (roll-off factor such as 0.1), and taking into account a certain carrier frequency offset (CFO) and phase-amplified spontaneous radiated noise (PN) range (e.g., coverage), Waveform segments generated by a MHz CFO and a PN sample path conforming to a specific linewidth model. To cover both polarizations, corresponding atom sets are required.

[0121] (2) Similarly, construct a channel representing two neighboring channels. and Atoms, constructing adjacent channel interference atom set ( It needs to be based on their relative to The frequency spacing, estimated leakage power level, and their modulation format are used to generate the signal.

[0122] (3) Select a set of Discrete Cosine Transform (DCT) basis or Short Time Fourier Transform (STFT) basis as amplified spontaneous emission noise atoms to fit the broadband ASE amplified spontaneous emission noise and construct the damage atom set ( ).

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

[0124] .

[0125] The L1 norm fidelity term is used in the above optimization problem to enhance the robustness to potential pulse adjacent channel interference, and a weighted L1 sparsity penalty is used. The weights are set (either empirically or tuned by simulation) as follows: , The FISTA algorithm efficiently solves the problem by iteratively updating the coefficients and an auxiliary variable, combining gradient descent and soft thresholding operators.

[0126] FISTA iteration step summary:

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

[0128] .

[0129] The non-smoothing term , preferably expressed as follows:

[0130] .

[0131] where , , represent the penalty weights corresponding to the target coefficient, adjacent channel interference coefficient and amplified spontaneous emission noise coefficient respectively, represents the target coefficient, and represent the adjacent channel interference coefficients of the two adjacent channels adjacent to the target signal, represents the amplified spontaneous emission noise coefficient.

[0132] Step 2: Initialization , , , , step size , where .

[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 operation (Soft Thresholding), different thresholds are used for different parts:

[0138] .

[0139] where, is a symbol function, represents that target, int, noise three regions use different weights .

[0140] Step 6: update momentum, as follows:

[0141] ;

[0142] .

[0143] Step 7: convergence judgment, when satisfying , then terminate iteration. Otherwise, let and jump to step 3 to continue iteration.

[0144] c, signal reconstruction: sparse coefficient is obtained after FISTA convergence . Extract part, calculate as follows:

[0145] .

[0146] The above sparse decomposition algorithm (especially iterative algorithm such as FISTA) has large amount of calculation. In a high-speed optical communication system, pipelining and parallelization design need to be carried out on a high-performance FPGA or a special ASIC to meet the requirements of real-time processing. Dictionary storage and matrix vector multiplication are key calculation bottlenecks, and hardware architecture needs to be optimized.

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

[0148] The reconfigurable optical add-drop multiplexer based on sparse representation provided by the embodiment of the application has obvious effects in the following aspects:

[0149] 1. Enhanced wavelength selectivity: the sparse representation provides resolution capability far exceeding physical filters in the digital domain, which can effectively separate closely adjacent wavelength signals and suppress ICI.

[0150] 2. Strong adjacent channel interference and amplified spontaneous emission noise suppression: by expressing the adjacent channel interference and amplified spontaneous emission noise as specific atoms and applying strong sparse constraints, the influence of the adjacent channel interference and amplified spontaneous emission noise on the target signal can be significantly suppressed, and the signal-to-noise ratio can be improved.

[0151] 3. Robustness: the use of a robust loss function and an adaptive mechanism makes the method have better adaptability to statistical changes of amplified spontaneous emission noise and changes in adjacent channel interference intensity.

[0152] 4. Flexibility: The dictionary and optimization parameters can be adjusted or adaptively learned according to system configuration and channel conditions, adapting to different scenarios.

[0153] 5. Compatibility: Mainly processed in the digital domain, with small changes to existing ROADM optical paths and coherent receiver hardware, easy to integrate.

[0154] 6. Improve system performance: Ultimately help to reduce the BER of the receiver, support denser channel spacing (improve spectral efficiency), or extend the transmission distance without electrical relay.

[0155] Embodiments of the present application also provide an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication. The above-mentioned memory stores a computer program capable of being executed by the above-mentioned at least one processor, and the above-mentioned computer program is used to make the electronic device execute the method of the embodiments of the present application when executed by the above-mentioned at least one processor.

[0156] Reference Figure 3 , which is an example of a hardware device that can be applied to various aspects of the present application, can be used as the structure block diagram of the electronic device of the server or the client of the embodiments of the present application. The electronic device is intended to represent various forms of digital electronic computing devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, 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 implementations of the present application described and / or claimed herein.

[0157] As shown in Figure 3 , the electronic device includes a computing unit 51 that can perform various appropriate actions and processes in accordance with 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. In the RAM 53, various programs and data required for the operation of the electronic device can also be stored. The computing unit 51, the ROM 52, and the RAM 53 are connected to each other through a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0158] A plurality of 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 that can input information to the electronic device, and can receive inputted digital or character information, as well as generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 57 can be any type of device that can present 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, an optical disk. The communication unit 59 allows the electronic device to exchange information / data with other devices through 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 various general and / or special purpose processing components having 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 special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 51 performs various methods and processes described above. For example, in some embodiments, the method embodiments of the present creation can be implemented as a computer program tangibly embodied in a machine-readable medium, such as the 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 appropriate means, such as by means of firmware.

[0160] The computer program for implementing the method embodiments of the present creation can be written in any combination of one or more programming languages. The computer program can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The computer program can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine or server, or entirely on a remote machine or server.

[0161] In the context of embodiments of the present invention, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared signals, or any suitable combination thereof. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a searchable electronic database or a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0162] It should be noted that the term "comprising" and variations thereof as used in the embodiments of the present invention are to be interpreted generically and do not exclude other steps. The term "based on" is to be interpreted as "based, at least in part, on". The term "one embodiment" means "at least one embodiment". The term "another embodiment" means "at least one additional embodiment". The term "some embodiments" means "at least some embodiments". The terms "a" or "an", as used in the context of the embodiments of the present invention, are to be interpreted as "one or more". Unless otherwise stated, the terms "one", "multiple", "a", "at least one", "one or more", "at least a plurality", and "plurality" mean "one or more than one of".

[0163] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data 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 related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

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

[0165] The word "implementation" in this description refers to the fact that the specific features, structures, or characteristics described in connection with an implementation can be included in at least one implementation of the invention. The occurrence of the phrase in various locations and repetitions of the phrase throughout the specification does not necessarily all refer to the same implementation, nor does it necessarily mean that other implementations are mutually exclusive or alternative. Each implementation described in this specification is described in a related manner, and the same or similar parts of each implementation refer to each other. In particular, for device, apparatus, system implementations, since they are basically similar to method implementations, the description is relatively simple, and the relevant parts refer to the part of the method implementation description.

[0166] The above-described implementations only express several implementation manners of the present invention, and the description is relatively specific and detailed, but it cannot be understood as a limitation on the protection scope. It should be noted that, for ordinary skilled persons in the art, under the premise of not departing from the inventive concept, a number of modifications and improvements can be made, which all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A method for reconfigurable optical add-drop multiplexer (ROADM) downwave enhancement based on sparse representation, characterized in that, The method comprises the following steps: Conducting conversion processing on a down-wave mixed optical signal to obtain a digitized electrical signal vector; wherein the down-wave mixed optical signal is a down-wave mixed optical signal selected by a reconfigurable optical add-drop multiplexer from a received wavelength division multiplexing optical signal and output to a local node; the down-wave mixed optical signal at least includes a target signal and an adjacent channel interference signal; Based on at least one parameter in a modulation format, a pulse shaping filter, a carrier frequency offset range, and a phase noise model, a target signal atom set including at least one target signal atom is constructed; Based on a parameter or an interference characteristic parameter of a neighboring channel of the target signal, an interference atom set including at least one adjacent channel interference atom is constructed; Based on a standard orthogonal basis or an amplified spontaneous emission noise / distortion atom learned according to an amplified spontaneous emission noise / distortion characteristic, a damage atom set including at least one amplified spontaneous emission noise / distortion atom is constructed; The target signal atom set, the adjacent channel interference atom set, and the damage atom set are combined to obtain an over-complete dictionary; wherein the over-complete dictionary at least includes target signal atoms and adjacent channel interference atoms; A robust loss function is used to calculate a reconstruction residual error of a reconstructed signal and the electrical signal vector, and a fidelity term is constructed; wherein the reconstructed signal is a linear combination of the electrical signal vector and a sparse coefficient vector; the fidelity term is used to measure the closeness of the reconstructed signal and the electrical signal vector; Based on individualized sparse constraints and / or group sparse constraints on the sparse coefficient vector, a sparse regularization term is constructed; the sparse regularization term is used to constrain the sparsity of the sparse coefficient vector; The fidelity term and the sparse regularization term are combined to construct an objective function; The electrical signal vector is projected to the over-complete dictionary for sparse decomposition processing, and a sparse coefficient vector satisfying the objective function is obtained; Target coefficients corresponding to the target signal atoms are identified and extracted from the sparse coefficient vector; The target coefficients and the target signal atoms are combined to obtain an enhanced target signal.

2. The method of claim 1, wherein the method is performed by a reconfigurable optical add / drop multiplexer based on sparse representation. Before the conversion processing on the down-wave mixed optical signal, the method comprises: Receiving a wavelength division multiplexing optical signal by a reconfigurable optical add-drop multiplexer; Selecting and outputting a down-wave mixed optical signal from the wavelength division multiplexing optical signal to a local node by a reconfigurable optical add-drop multiplexer; wherein the down-wave mixed optical signal includes a target signal, an adjacent channel interference signal, and an amplified spontaneous emission noise signal.

3. The method of claim 1, wherein the method is performed by a reconfigurable optical add / drop multiplexer based on sparse representation. Conducting conversion processing on the down-wave mixed optical signal to obtain a digitized electrical signal vector, comprising: Conducting coherent reception and photoelectric conversion processing on the down-wave mixed optical signal to obtain an analog mixed electrical signal; Conducting digital-to-analog conversion processing on the analog mixed electrical signal to obtain a digitized electrical signal vector.

4. The method of claim 1, wherein the method is performed by a reconfigurable optical add / drop multiplexer based on sparse representation. Based on individualized sparse constraints or group sparse constraints on the sparse coefficient vector, a sparse regularization term is constructed, comprising: Different penalty weights are applied to each coefficient in the sparse coefficient vector according to categories to form individualized sparse constraints, and a sparse regularization term is constructed; wherein the categories include target coefficients corresponding to target signal atoms, adjacent channel interference coefficients corresponding to adjacent channel interference atoms, and noise coefficients corresponding to amplified spontaneous emission noise atoms; and / or, Different group penalty weights are applied to each coefficient in the sparse coefficient vector according to groups to form group sparse constraints, and a sparse regularization term is constructed; wherein the adjacent channel interference coefficients corresponding to the adjacent channel interference atoms representing the same adjacent channel interference source are a group.

5. The method of claim 4, wherein the method is performed by a reconfigurable optical add / drop multiplexer based on sparse representation. The target coefficients corresponding to the target signal atoms are identified and extracted from the sparse coefficient vector, including: The first penalty weight and the second penalty weight are applied to the adjacent channel interference coefficients and the noise coefficients, respectively, and the third penalty weight is applied to the target coefficients; 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 coefficients; and / or, The first group penalty weight is applied to the adjacent channel interference coefficients corresponding to the adjacent channel interference atoms representing the adjacent channel signals of the target signal, and the second group penalty weight is applied to the target coefficients corresponding to the target signal atoms representing the target signal; wherein the first group penalty weight is greater than the second group penalty weight, so as to identify and extract the target coefficients.

6. The method of claim 4 or 5, wherein the method is performed by a reconfigurable optical add / drop multiplexer based on sparse representation. The method further includes: Periodic analysis is performed on the reconstruction residual, and when the reconstruction residual exceeds a set value, the adjacent channel interference atoms and the amplified spontaneous emission noise atoms are updated or added.

7. A sparse representation based reconfigurable optical add-drop multiplexer (ROADM) drop enhancement device, applied to the sparse representation based reconfigurable optical add-drop multiplexer (ROADM) drop enhancement method in any one of claims 1 to 6, characterized in that, It includes: A reconfigurable optical add-drop multiplexer is configured to receive a wavelength division multiplexed optical signal and output a downwave mixed optical signal to a local node; The downwave mixed optical signal includes at least a target signal and an adjacent channel interference signal; A coherent receiver is configured to receive the downwave mixed optical signal and convert the downwave mixed optical signal into an analog mixed electrical signal; A digital-to-analog converter is 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 adjacent channel interference atoms; target coefficients corresponding to the target signal atoms are identified and extracted from the sparse coefficient vector; and the target coefficients are combined with the target signal atoms to obtain an enhanced target signal.

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