Learning-based nonlinear compensation using physical information neural networks for data access

Abstract

A system and computer-implemented method using a physical information neural network for data communication are provided. At a transmitter, the method is configured to obtain source data to be transmitted to encode the source data into code words based on a forward error correction code, map the code words to amplitude symbols, modify the mapped amplitude symbols into pre-equalized symbols including pre-distorted channel impairment symbols based on a predetermined physical model, and transmit the digital data of the pre-equalized symbols as channel data over a channel. At a receiver, the method is configured to receive and demodulate channel data from a channel to generate initial estimates of bits of the received channel data, mitigate channel noise and waveform distortion in the channel data based on the initial estimates, convert the channel data consisting of shaped non-uniform symbols as a non-uniform data sequence into an unshaped bit sequence, decode the unshaped bit sequence, correct residual errors in the converted channel data, and store the corrected channel data in a data sink.

Description

[Technical Field]

[0001] The present invention relates generally to the field of waveform equalization for data communications, and more particularly to a method and system for learning-based nonlinear compensation based on an artificial neural network employing a physical model. [Background technology]

[0002] In digital communication and data storage systems, transmitted signal waveforms are often distorted by many factors, including hardware defects, channel media, and signal processing imperfections. For example, optical fiber communications typically suffer from amplified spontaneous emission noise, shot noise, thermal noise, Kerr fiber nonlinearity, chromatic dispersion, polarization multiplexing, self-phase modulation, cross-phase modulation, Raman scattering, carrier offset, linewidth, and timing jitter. Many linear and nonlinear equalization or noise reduction methods have been proposed, such as decision feedback equalization, turbo equalization, and maximum likelihood sequence estimation. However, these methods largely rely on simple, mathematically tractable signal models, such as the linear dispersion model, autoregressive model, Volterra series, regular perturbation model, nonlinear Schrödinger equation, Maxwell's equations, Helmholtz equation, and delay, Doppler, and angular spectrum. Model-based methods have several drawbacks. i) constructing accurate signal models is numerically intractable or complex; ii) model mismatch is inevitable; iii) the interaction of unknown physical phenomena cannot be adequately modeled; and iv) the models are highly sensitive to uncertain physical parameters.

[0003] In recent years, data-driven learning-based methods, primarily based on deep neural networks (DNNs), have emerged as a promising alternative to model-based methods. For example, convolutional neural networks, recurrent neural networks, and other network architectures have been shown to achieve excellent performance for compensating signal distortions, such as nonlinear equalization. Generally, DNNs consist of a large number of trainable parameters, such as weights and biases for affine transformations, which are optimized using stochastic gradient methods based on training data. Such over-parameterized DNNs have a higher ability to approximate nearly arbitrary functions with higher accuracy. However, data-driven learning-based methods using over-parameterized DNNs inherently have several drawbacks and challenges: i) they require a sufficiently large amount of high-quality training data; ii) large-scale training data requires high computational power, resources, and storage; iii) the interpretability and certifiability of trained models are low; and iv) it is not clear how to find a suitable DNN architecture.

[0004] Therefore, there is a need and demand for an efficient solution to these problems regarding signal waveform equalization in data communication and storage systems. Summary of the Invention

[0005] Some embodiments of the present invention recognize that data communication and storage systems often suffer from signal distortions due to channel noise, device nonlinearities, and signal processing imperfections. Our invention provides a method for improving data quality by using physics-based neural networks. This invention overcomes some of the challenges of existing methods regarding efficient training and interpretability. In some embodiments, optical fiber physics, defined by the nonlinear Schrödinger equation, is modeled with Koopman operators, Volterra expansions, split-step Fourier methods, regular perturbations, and Runge-Kutta integrals. Model parameters such as Kerr nonlinearity, fiber attenuation, and dispersion are adaptively trained. To mitigate residual model mismatch errors, another embodiment uses an additional DNN in addition to the trained physical model. Another embodiment uses joint time / frequency / space domain physics. Imperfections are mitigated by designing transmitters and receivers with differentiable digital twin-based models, such as joint transmitter / receiver filtering, joint modulation / demodulation, and joint pre-distortion / post-distortion. The present invention enables the design of connections at intermediate points, e.g., programmable amplifiers, repeaters, switches, multiplexers, and routers. The physical information model includes implicit layers based on diffusion, equilibrium, quantum physics, and molecular physics.

[0006] The present invention recognizes that fully data-driven DNN models have difficult to interpret learned parameters, and that over-parameterized DNN models generally require large amounts of high-quality training data. Additionally, the present invention recognizes that signal distortion for communication and storage systems is potentially governed by physical models of the channel link (medium), such as Maxwell's equations and nonlinear Schrödinger equations for electromagnetics, optical elements, and electrical circuits. The present invention enables interpretable and efficiently trainable DNN models by integrating data-based methods with physical information models to compensate for signal waveform distortion in data communication and storage systems. Physical information DNN models can provide nontrivial benefits in terms of reducing the amount of redundant and unimportant parameters and interpretably tuning physically relevant parameters. At the same time, the present invention provides a method for adjusting the tradeoff between model accuracy and complexity through the hybrid use of data-driven DNN models and physical information mathematical models.

[0007] In some embodiments, optical fiber physics governed by the nonlinear Schrödinger equation is merged into DNN models, including, but not limited to, neural Koopman operators, neural Volterra expansions, neural split-step Fourier methods (SSFM), neural regular perturbations (RP), neural Runge-Kutta (RK) integrals, neural ordinary differential equations (ODEs), neural diffusion models, neural equilibrium models, collocation models, exponential integrators, etc. In some embodiments, model parameters such as Kerr nonlinearity coefficients, fiber attenuation, and dispersion coefficients can be trained to adapt to a given training dataset either online or offline. Additionally, incremental parameters such as phase corrections, gain corrections, and phase shifter corrections are provided to generalize the physical model.

[0008] In some embodiments, multiple physics information models are used simultaneously, for example, RP and SSFM are merged. Furthermore, a residual DNN model is used to correct residual errors due to model mismatch. Yet another embodiment uses joint time / frequency / space domain physics to perform the merge, for example, a time-domain neural kernel and a frequency-domain neural kernel are combined.

[0009] In some embodiments, signal impairments are mitigated by designing the transmitter and receiver ends together through a digital twin-based model that is discriminative to enable gradient-based optimization, e.g., joint transmitter / receiver filtering design, joint encoder / decoder design, joint modulation / demodulation design, joint pre-distortion / post-distortion design, etc. Some embodiments jointly design multiple intermediate points in addition to the transmitter / receiver ends, e.g., programmable amplifiers, repeaters, switches, multiplexers, routers, etc., through a discriminative computational graph in the system.

[0010] In some embodiments, the physics-based model includes implicit layers based on other differentiable computations such as diffusion, equilibrium, photon, quantum physics, and molecular physics with trainable parameters. In some embodiments, the integrated physics-based DNN model is applied to audio / visual signal processing, biosignal processing, data storage read / write, wireless communication, wired telecommunications, etc.

[0011] Accordingly, some embodiments disclose a method for reliably transferring data from a transmitter to a receiver over a communication channel. In some embodiments, the method and system include a transmitter having at least one computing processor, at least one memory bank, and a source for receiving source data to be transferred, an encoder for encoding the source data, and a front-end interface for transmitting the encoded codewords as modulated signals to the communication channel. In some embodiments, the method and system further include a communication channel comprising a wired medium channel, a wireless medium channel, an optical fiber channel, a vacuum free-space channel, a data storage medium, or variations thereof. In some embodiments, the method and system further include a receiver having a front-end interface for receiving an output signal from the communication channel, a decoder for decoding the output signal, and a sink for providing decoded codewords. In yet other embodiments, the method and system include a controller configured to specify (determine) parameters for pre-shaping codes, post-shaping codes, shaping mappers, and shaping demappers used in the encoder and decoder according to knowledge (type) of the channel link, the parameters further including codeword length, code rate, and overhead.

[0012] Thus, the method and system of the present invention uses inverse operations at the receiver to recover the shaped bits and original data through FEC decoding and a shaped demapper. Specifically, a computer-implemented method for recovering an encoded bit stream is executed by one or more computing processors at the receiver and includes the steps of accessing input data representing noisy codewords, demapping the input data to generate a soft-decision bit stream given noisy quadrature amplitude modulation symbols, decoding the bit stream according to a post-shaped error correcting code, mapping the decoded bit labels to amplitude modulation symbols, demapping the amplitude symbols according to a shaped demapper, and decoding the unshaped bit stream according to a pre-shaped error correcting code.

[0013] According to some embodiments of the present invention, there is provided a system for data communication over a channel link, the system being configured to include a communication module that accesses the channel link according to a set of specifications for data communication, and a controller including a physics-informed neural network (PINN), wherein the controller determines parameters represented by the set of specifications of the communication module to compensate for signal waveform distortion in the data communication according to a physical model of the channel link.

[0014] Additionally, some embodiments may provide a method, sometimes referred to as a computer-implemented method, for data communication over a channel link, including accessing the channel link according to a communication method based on a set of specifications for data communication, and modifying the set of specifications for data communication according to a Physical Information Neural Network (PINN)-based control method responsive to a physical model of the channel link. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a functional diagram illustrating a communication system and method for the transmission of digital data in accordance with an embodiment of the present invention. [Figure 2] FIG. 1 illustrates the structure of an LPR with parallelizable branches for real-time high-throughput DNN, in accordance with an embodiment of the present invention. [Figure 3] FIG. 1 illustrates how to improve the RP model with trainable phase shifter correction terms (complex-valued scalars) C={CO, Cl,...,CN} at the end of each branch, according to an embodiment of the present invention. [Figure 4] FIG. 1 illustrates an embodiment using a cascaded LRP model controlled by multiple staggered DNNs. [Figure 5A] FIG. 10 illustrates another embodiment using an external deep neural network (DNN) for residual model mismatch correction in additive aggregation, in accordance with an embodiment of the present invention. [Figure 5B] FIG. 10 illustrates another embodiment using an external deep neural network (DNN) for residual model mismatch correction in multiplicative aggregation, in accordance with an embodiment of the present invention. [Figure 5C] FIG. 10 illustrates another embodiment using an external deep neural network (DNN) for residual model mismatch correction in bilinear aggregation, in accordance with an embodiment of the present invention. [Figure 5D] FIG. 10 illustrates another embodiment using a parallel LRP model aggregated with an external deep neural network (DNN) for residual model mismatch correction, in accordance with an embodiment of the present invention. [Figure 5E] FIG. 10 illustrates another embodiment using a cascaded stack of multiple LRP models and multiple deep neural network (DNN) blocks for residual model mismatch correction, in accordance with an embodiment of the present invention. [Figure 5F] FIG. 10 illustrates another embodiment that uses multiple LRP models and multiple cascaded stacks of deep neural network (DNN) blocks with jumping paths for residual model mismatch correction, in accordance with an embodiment of the present invention. [Figure 6] FIG. 10 illustrates an exemplary performance gain for LRP-based PINN versus model-based SSFM digital backpropagation in an optical fiber channel plant, in accordance with an embodiment of the present invention. [Figure 7] FIG. 1 is an exemplary block diagram illustrating a system suitable for implementing various components of a transceiver for performing encoding, decoding, equalization, and de-shaping in accordance with an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] Various embodiments of the present invention will now be described with reference to the figures. It should be noted that the figures are not drawn to scale, and that elements having similar structure or function are represented by like reference numerals throughout the figures. It should also be noted that the figures are intended only to facilitate the description of particular embodiments of the present invention. They are not intended as an exhaustive description of the present invention or as limitations on the scope of the present invention. In addition, aspects described in connection with a particular embodiment of the present invention are not necessarily limited to that embodiment, but may be practiced in any other embodiment of the present invention.

[0017] Some embodiments of the present invention are based on the recognition that signal equalization uses physical information neural networks (PINNs), which integrate physical models into neural networks to improve the quality of data communication and storage systems. For example, some embodiments enable improved machine learning methods and systems based on inverse regular perturbation (RP) models to represent the physical phenomena underlying light wave propagation through optical fiber communications. Specifically, photonic physics related to fibers is typically governed by the nonlinear Schrodinger equation (NLSE) and its variants, such as the Manakov equation. The RP model numerically solves the NLSE using finite difference approximations in a parallel structure. Some embodiments generalize parallel RP branches by using a learned RP (LRP) model to jointly optimize trainable parameters such as step size, gain, and phase rotation. DNN models based on LRP can potentially outperform the corresponding trained digital back-propagation (DBP) method based on split-step Fourier method (SSFM) in standard single-mode fiber links. Thus, LRP also allows for reduced complexity through fractional step-per-span (SPS) modeling while maintaining better performance than 1-SPS SSFM-DBP.

[0018] Physical models of channel links to be embedded in PINNs include finite difference methods such as split-step Fourier methods, finite element methods such as regular perturbations, collocation models such as Runge-Kutta integrals, Volterra series expansions, Koopman operations, exponential integrators, ordinary differential equations, partial differential equations, nonlinear Schrodinger equations, finite impulse responses, infinite impulse responses, autoregressive models, diffusion processes, space-time-frequency dispersion models, Maxwell's equations, quantum dynamics, molecular dynamics, and their variants.

[0019] Over the past few decades, rapid advances in digital signal processing (DSP) technology have enabled significant improvements in the throughput and reliability of coherent optical fiber communication systems. For example, linear impairments such as chromatic dispersion (CD), polarization mode dispersion (PMD), and phase noise have been extensively studied and addressed in the digital domain. Therefore, optical fiber nonlinearities have become a major factor limiting the achievable maximum information rate and effective signal-to-noise ratio (SNR) of optical communication systems.

[0020] The Manakov equation has been used to model the propagation of dual-polarization (DP) signals through standard single-mode fiber (SSMF). It can be numerically solved using the split-step Fourier method (SSFM), which sequentially calculates nonlinear and linear operations in small finite-difference steps. Higher model accuracy can be achieved by reducing the step size. Alternatively, several models, including regular perturbation (RP), have been investigated. Digital backpropagation (DBP) is a state-of-the-art method for nonlinearity compensation (NLC) by inverting lightwave propagation through inverse SSFM calculations. This offers significantly improved performance compared to most other methods, but at the expense of higher computational complexity. In addition, it is highly sensitive to model parameters such as the Kerr nonlinearity coefficient and dispersion coefficient. Therefore, the main challenge in achieving practical NLC is finding the best balance between complexity and performance.

[0021] Recent advances in machine learning (ML) and deep neural networks (DNNs) have revolutionized a wide variety of scientific fields, including fiber optic communications and networking. Regarding NLC and equalization, several physics-informed DNNs for DBP have been proposed. While perturbation theory has also been used to improve trained DBPs, ML techniques have proven effective in improving nonlinear equalization based on symbol triplet interactions.

[0022] In some embodiments, the ML-assisted NLC approach based on the inverse RP physical model uses a parallel branch structure with generalized phase shifter correction parameters that can be tuned using low-complexity ML techniques to significantly improve accuracy. Furthermore, LRP in the fractional step-per-span (SPS) case, where each RP branch models multiple spans, can reduce computational complexity while maintaining higher performance than 1-SPS SSFM-DBP. PINN-assisted optical fiber communications

[0023] FIG. 1 illustrates a communication system for transmitting digital data from a transmitter 110 to a receiver 130 via a channel link 120, according to some embodiments. The channel link 120 is configured to provide data communication based on electromagnetic propagation, optical wave propagation, acoustic propagation, molecular propagation, or variations thereof. For example, the communication channel 120 may include, but is not limited to, air for wireless communication, copper cable for wired communication, solid-state drives for data storage read / write, vacuum for free-space satellite communication, and fiber cable for fiber optic communication. During communication to transfer digital data from the transmitter 110 to the receiver 130, the digital data may be corrupted by noise generated along the channel and data processing path between the data source 111 and the data sink 135. For example, noise may include, but is not limited to, thermal noise, spontaneous emission amplifier noise, reflection / refraction of inhomogeneous media, interference noise, and impulsive shot noise. The transmitter 110 achieves reliable data transmission using a concatenated encoder 112, a shaped mapper 113, and a PINN-based pre-equalizer 114. The receiver 130 recovers the original data from the noisy message transmitted by the transmitter 110 using a concatenated PINN-based post-equalizer 132, a shaped demapper 133, and a decoder 134. The encoder and decoder are based on forward error correction (FEC) codes such as low-density parity-check (LDPC), turbo, and polarity codes. Most communication systems use both the transmitter 110 and the receiver 130 as communication modules. Some communication systems use only the transmitter as a communication module, while others use only the receiver as a communication module.

[0024] In the transmitter 110, data to be transmitted is received from a source 111 configured to receive original data. The source may include, but is not limited to, a memory bank for reading data, an interface port for receiving data, and a device for generating data. For example, in some embodiments, the source includes a voice communication device that converts an input voice signal into digital data. The input data from the source 111 is encoded by an FEC encoder 112. The encoded data is sometimes referred to as a codeword. The pre-shaping FEC encoder adds a series of overhead parity bits to make the data robust against potential burst errors. The encoded data is mapped to a series of amplitude symbols according to a shaping mapper 113, which adds additional redundancy to match the probability mass function (PMF) of the amplitude occurrence to a target distribution. The shaped amplitude symbols are labeled into a series of shaped bits and further transformed by a PINN pre-equalizer 114. The PINN pre-equalizer modifies the shaped amplitude to pre-distort channel impairments based on a physical model such as RP. The pre-equalized symbols are fed to the channel medium via front-end circuitry 115, where various modulation formats are used, including, but not limited to, quadrature-amplitude modulation (QAM) with or without linear conversion such as orthogonal frequency-division multiplexing (OFDM). Front-end circuitry 115 may further include, but is not limited to, electro-optical circuits for optical communications and radio-frequency circuits for wireless communications. For example, the front-end circuitry may include digital-to-analog converters, power amplifiers, and signal preprocessing, such as bandpass filters, pulse shaping, precoding, power loading, pilot insertion, and predistortion. In some embodiments, the transmitter uses wavelength-division multiplexing (WDM) and spatial multiplexing for multimode / multicore fiber.

[0025] The channel 120 distorts the transmitted signal. For example, the channel adds additive white Gaussian noise (AWGN), co-channel interference, deep fading, impulsive noise, inter-symbol interference, Kerr-induced nonlinear interference, polarization crosstalk, and linear chromatic dispersion, as well as residual hardware imperfections such as quantization error, clock jitter, overflow, laser linewidth, and carrier phase noise.

[0026] The receiver 130 first converts the channel output into an electrical receive signal via front-end circuitry 131, which is typically complementary to the transmitter's front-end 115. For example, the front-end circuitry may include, but is not limited to, an analog-to-digital converter, linear equalization, nonlinear equalization, adaptive filtering, channel estimation, carrier phase recovery, synchronization, and polarization recovery. The front-end circuitry demodulates the received signal to mitigate channel noise and waveform distortion by generating initial estimates of the transmitted codeword bits via a PINN-based post-equalizer 132. For example, the demodulated signal represents a log-likelihood ratio (LLR) value as a soft input message to the FEC decoder.

[0027] The decoded message is then demapped by shaping demapper 133, which converts the shaped non-uniform symbols into a uniform data sequence by removing the overhead introduced by shaping mapper 113. To correct residual errors, the de-shaped bit sequence is further decoded by FEC decoder 134 before being provided to sink 135. Data sink 135 includes, but is not limited to, a memory bank for writing data, an output interface port for transmitting data, and a device for receiving data. For example, in some embodiments, the sink includes a voice communication device that converts the decoded data into an audio signal.

[0028] Some embodiments recognize that FEC code and shaping specifications are adaptively controlled by a controller 141 according to side information 140 of a communication channel 120. For example, parameters such as code rate (or overhead), codeword length, shaping rate, and shaping length are adaptively selected based on channel knowledge, such as signal-to-noise ratio (SNR), power delay profile, channel transfer function, and nonlinear statistics, including, but not limited to, mean, variance, kurtosis, and higher moments. The controller 141 is configured to determine transmitter and / or receiver specifications, such as best FEC code and modulation selection, given the channel knowledge for some embodiments using a lookup table or another PINN model. The specifications indicate parameters of pre-shaping codes, post-shaping codes, shaping mappers, and shaping de-mappers to be used in the encoder and decoder depending on knowledge (type) of the channel link. The parameters include codeword length, code rate, and overhead, and the best FEC code and modulation selection method for the channel link is used in data communications for some embodiments. The set of specifications for the communication module further includes sets of parameters for a channel encoder, a data compressor, a constellation shaper, a modulator, a pulse-shaping filter, a multiplexer, a pre-equalizer, an amplifier, a digital-to-analog converter, an oscillator, an antenna, a mixer, an analog-to-digital converter, an equalizer, a demultiplexer, a demodulator, a constellation de-shaper, a data decompressor, a channel decoder, etc. Thus, the set of parameters specified by the controller includes a generator matrix for channel coding, a shaping rate, a shaping method, a modulation format, a pulse-shaping filter length, a pre-equalizer method, an amplifier gain, a sampling rate and quantization bits of the digital-to-analog converter, an oscillator frequency, an antenna size, a sampling rate and quantization bits of the analog-to-digital converter, a pre-equalization method, a demodulation method, a constellation de-shaping method, a channel decoder method, etc.When controller 141 determines or modifies the specifications, the parameters are adaptively selected to best match the type of channel link used in the data communication system.

[0029] The transmitter 110 and / or the receiver 130 may be implemented using a hardware processor operatively connected to a memory. The transmitter 110 and the receiver 130 may each include one or more processors. For example, the memory of the receiver 130 may store information related to one or a combination of the following: the shaped mapper and demapper, the soft inputs and soft outputs of the decoder 133, the results and parameters of intermediate encoding and decoding calculations, and the adaptive coding policy. All components within the transmitter 110 and the receiver 130 may be implemented by hardware, one or more hardware processors, computer software (programs or program modules), or a combination of hardware and computer software. PR-based physical information model

[0030]

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[0031] This physics-based PDE model, based on the Manakov NLSE, is typically difficult to analytically derive a practical solution for arbitrary positions z and arbitrary initial conditions. To solve the physical equations, the waveform propagation is typically numerically identified using finite difference or finite element methods. In some embodiments, SSFM uses a finite difference step, then solves for linear and nonlinear effects. The linear step takes into account CD, fiber attenuation, and PMD in the Fourier domain to efficiently calculate the waveform transformation. The nonlinear step uses the time domain to modify the amplitude and phase of the lightwave according to its instantaneous power. ASE noise is also added to the amplifier.

[0032] In some embodiments, the RP model is used to numerically solve the Manakov NLSE by using finite difference steps to form parallel branches with linear and nonlinear operations. This is based on the recognition that optical signal propagation through SSMF can be approximated with the RP model, which solves the Manakov equation in the linear domain and bundles the nonlinear components into small additive terms. Similar to SSFM-DBP, the direction of the RP model can be inverted by reversing the direction of f(z) (i.e., switching attenuation to amplification) and changing the signs of the beta2 and gamma parameters. Thus, the inverted RP takes the received signal u(L,t) and reconstructs u(0,t) (i.e., the backpropagated signal) for the NLC. In some embodiments, the gamma parameter is adjusted using a gradient-based machine learning framework to achieve better results.

[0033]

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[0034]

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[0035] Similar to SPS in SSFM-DBP, the accuracy of RP increases with the number of branches and decreases with the optical emission power, i.e., higher fiber nonlinearity. Note that the model is designed to scale z so that the pre-linearity / post-linearity blocks cover the entire link length. n Note that we allow ={0, L}, which has been shown to improve performance when there are few branches.

[0036] While the RP model can provide a reasonably accurate approximation with a large number of branches, it suffers from significant problems when the number of branches is small. As shown in Figure 3, a traditional one-branch RP model takes a linear signal 31 and adds an amplitude-dependent signal 32 that is shifted by 90 degrees using a K-nonlinear operation to produce a nonlinear phase rotation. Adding these two vectors together can result in a phase shifter that tends toward the desired rotation 33. However, increasing nonlinearity inevitably leads to gain expansion outside the unit power circle 34. Thus, model accuracy degrades significantly in higher power regimes.

[0037]

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[0038] With this simple modification, we can collectively adjust the gain and rotation of each branch 35 to find a better phase shifter that is close to unit power 36. Therefore, the LRP model in this invention can more accurately approximate the nonlinear effect and effectively mitigate the undesired gain expansion.

[0039] In an exemplary implementation of the system, a single-carrier DP64-term quadrature amplitude modulation (QAM) signal with a symbol rate of 64 Gbaud is generated at the transmitter. The generated electrical signal is pulse-shaped using a root-raised cosine (RRC) filter with a roll-off factor of 0.1. This is then modulated onto an optical carrier using an in-phase / quadrature modulator. The carrier is provided by a laser with a center frequency of 1550 nm and zero frequency offset. The analog optical signal is upsampled to 8 sa / sym and propagated over 10 spans of an 80 km SSMF link. Each span is followed by an erbium-doped optical amplifier with 16 dB gain and a 4 dB noise figure. Nonlinear inter-channel interference can occur in a five-channel, 50 GHz-spaced wavelength division multiplexing (WDM) system. SSMF has a gamma Kerr coefficient of 1.2 W / km, an alpha loss coefficient of 0.2 dB / km, and a beta dispersion coefficient of 17 ps / nm / km. The received optical signal is downsampled to 2 sa / sym and passed through a PINN-based equalizer with linear CD compensation, SSFM-DBP, or LRP-DBP models to mitigate the channel impairments. The output is then shaped with a matched RRC filter. The received signal is then normalized and de-rotated with a one-tap equalizer and downsampled to 1 sa / sym.

[0040] In some embodiments, the LRP-based PINN equalizer is trained by an adaptive momentum (Adam) optimizer with a learning rate of 0.001 to find optimal values ​​for the phase shifter correction coefficient C and step size z. We use 200 randomly generated data frames, split 80% and 20% for training and testing, respectively. A single frame contains a snapshot of the coherently detected and digitally backpropagated signal as the target. Each snapshot has 8,192 samples per polarization with an oversampling of 2 sa / sym. The frames used for training are simulated (forward and backward propagated) with a 100-SPS Manakov SSFM. The forward-propagated signal is downsampled to 2 sa / sym. The raw nonlinear phase rotation is not compensated for before training and is included in the optimization, while one-tap equalization is used after the NLC. The signal is then passed to a 100-SPS DBP, and the corresponding nonlinearity-compensated output is saved as the second element of the snapshot. The detected signal is 2 sa / sym, while the forward propagation in the analog domain is 8 sa / sym. For some embodiments, we use a logarithmic step distribution across each fiber span and asymmetric SSFM, i.e., nonlinear steps at the segment boundaries.

[0041]

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[0042] The complex value C is adjusted by the gradient of the MSE loss function.

[0043] Proper initialization of trainable parameters is a key aspect for achieving higher performance and faster convergence. Some embodiments initialize z as a logarithmic step distribution over each span, while the phase correction C is initialized as a vector filled with ones on the real axis and zeros on the imaginary axis. For some embodiments, the model yields better convergence when Delta is decoupled from the initial values ​​of z and f(z).

[0044] In some embodiments, the ML-based adjustment of trainable parameters in the physical model is achieved by a deep neural network in an adaptive manner. Specifically, the DNN-based method and system uses additional trainable coefficients in addition to the RP model to correct the phase shifter. The DNN incorporates side information such as signal kurtosis and power of the instantaneous waveform at various z positions. Specifically, such a system uses various side information, including fiber plant information, to control the parameters of the LRP model via the DNN. In some embodiments, PINN-based parallel equalizers are cascaded in a pipeline sequence to adjust the parallel and pipeline coefficients.

[0045] Figure 6 shows the exemplary performance of an LRP-based PINN for a standard single-mode fiber plant. PINN-based nonlinearity compensation outperforms trained SSFM-based methods. Compared with 1-sps SSFM digital backpropagation, the 10-branch LRP achieves a 0.8 dB gain, but with comparable computational complexity. More importantly, the 5-branch LRP still performs 0.25 dB better than the 1-sps SSFM, despite its lower computational complexity. This is the benefit of trainable phase shifter correction. Compared with 3-sps SSFM, the 30-branch LRP still exhibits a 0.6 dB gain.

[0046] Another embodiment is based on the cascaded use of physical models such as LRPs controlled by additional DNNs. Figure 4 shows a cascaded model controlled by a staggered DNN. Here, the DNN uses intermediate signals to modify parameters of the LRP, such as the step size and nonlinear constant. The use of intermediate signals can reduce potential errors caused by model mismatch. In addition, the cascaded use of LRPs can further reduce the model mismatch per LRP.

[0047] Yet another embodiment uses additional DNN modules to mitigate model mismatch errors. Figure 5A illustrates an embodiment in which an external DNN, such as a convolutional neural network (CNN) and a multilayer perceptron (MLP), is used to additively compensate for the residual error of the LRP. Figure 5B illustrates an embodiment in which an additional DNN module is used multiplicatively. Figure 5C illustrates an embodiment in which an additional DNN module is used with a trainable physical model, such as the LRP, in a bilinear weighting manner. Here, the CNN is interpreted as a cascaded finite-impulse response (FIR) filter that generalizes the good linear behavior of the SSFM. In some embodiments, a recursive neural network (RNN) is used to represent the infinite impulse response (IIR) filter. In some embodiments, the bilinear aggregation is further generalized by additional DNN blocks. RP models have various variants, including log-domain RP and higher-order RP. Some embodiments use learnable parameterization according to a variation of the RP model.

[0048] Figure 5D shows another embodiment in which a parallel physical model is aggregated by a DNN block. Figure 5E shows another embodiment in which multiple LRPs and DNNs are stacked in a cascaded manner. Figure 5F shows another embodiment using jumping paths for intermediate signals to be fed to a DNN block at a subsequent stage. The embodiments of Figures 4 and 5A-5F are only examples of potential integration of physical models into DNN architectures through combinations such as cascaded stacking, parallel aggregation, jumping aggregation, and staggered control.

[0049]

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[0050] Some embodiments use different parameterizations according to these various types of SSFM physical models. Agrawal SSFM requires an iterative loop to propagate the waveform. Some embodiments use an implicit neural network to solve this iterative process. For example, neural ordinary differential equations (NODEs), deep equilibrium (DEQs), and neural diffusion models are used to implement a PINN-based equalizer. While linear operations are typically implemented in the frequency domain, time-domain convolution or filtering can also be used for some embodiments. In addition, both time-domain and frequency-domain operations are combined in the PINN architecture. For WDM and spatial multiplexing systems, some embodiments also merge spatial-domain processing into the PINN. Equalizers are also used as pre-equalizers or pre-distortion methods in transmitters, which reverse signal distortion before transmission over the channel in a similar manner for post-equalization at the receiver. Some embodiments use both pre-equalizers and post-equalizers simultaneously.

[0051] Besides RP and SSFM, there are various methods for solving NLSE. For example, the Runge-Kutta (RK) method, a collocation method, can numerically solve PDEs with higher accuracy. Some embodiments use an RK model integrated in a DNN architecture to solve the inverse NLSE. Another example of a numerical method is the exponential integrator (EI), in which a Krylov subspace approximation is used for the matrix exponential operator. For example, the Marcuse method and the Blow-Wood method are used as EI frameworks. Some embodiments use the EI method as a PINN-based equalization. Nonlinear Fourier Transform (NFT)

[0052] Another physical modeling framework exists for solving NLSEs, known as the nonlinear Fourier transform (NFT). The NFT transforms the waveform into an eigendomain where the NLSE dynamics act linearly. For example, the Koopman operator implements the NFT. To implement the Koopman-based NFT, an exemplary PINN uses an autoencoder (AE) that can be used to find a coordinate transformation that converts a nonlinear PDE to a linear PDE. Some embodiments of the present invention use a Koopman-AE DNN to model the behavior of the Manakov equation applied to nonlinear optical fibers. For long sequence transformations with high oversampling rates, some embodiments split the symbol sequence into smaller chunks and finally splice them back together. To address chromatic dispersion-induced channel memory effects, chunk duplication and edge discarding are used during DNN recovery. Specifically, a sliding window is applied to the DNN input tensor, discarding edge samples for each processed chunk. The PINN is trained to minimize the minimum squared error (MSE) function between the time-domain SSFM data and the generated predictions. In this case, the task of the Koopman DNN was to predict each step of a 100-step SSFM Manakov simulation across several spans of fiber. Other cost functions, such as cross-entropy loss, are alternatively used in some embodiments. Some embodiments use a reversible residual network to facilitate the optimization of forward and reverse NFTs. Digital twin-based end-to-end design

[0053] PINN-based equalization can provide the additional benefit of optimizing other system parameters, such as pulse-shaping filters, modulation schemes, constellation shaping, and FEC parameters, through gradient propagation across the system diagram. For example, once an LRP-based PINN model is trained to model a communication channel, the trained PINN model can be used to predict forward and reverse behavior as a digital twin model. In this manner, the PINN enables end-to-end design of transmitter and receiver parameters. In some embodiments, these parameters are also trained using reinforcement learning for gradient-free optimization. In some embodiments, the end-to-end design is extended to design intermediate subsystem components, such as parametric amplifiers, repeaters, relays, switches, and routers. For example, gain control and routing optimization in amplifiers are achieved through the digital twin PINN model. Other features

[0054] Other embodiments include quantum communication, where quantum physics is modeled with PINN-based equalization. Yet other embodiments include molecular communication, where molecular dynamics and chemical reaction physics are integrated as PINN-based equalization. Some other embodiments use neuronal communication, where neurophysics is modeled in PINN-based equalization.

[0055] 7 shows an example block diagram of a system suitable for implementing various components of a receiver for performing decoding, equalization, and unshaping according to some embodiments, and / or a transmitter for encoding, pre-equalizing, and shaping codewords according to some embodiments. The system 1700 may include one or a combination of a sensor 1710, an inertial measurement unit (IMU) 1730, a processor 1750, a memory 1760, a transceiver 1770, and a display / screen 1780, which may be operably coupled to other components via a connection 1720. The connection 1720 may include a bus, a line, a fiber, a link, or a combination thereof.

[0056] The transceiver 1770 may include, for example, a transmitter capable of transmitting one or more signals over one or more types of communication networks and a receiver for receiving one or more signals transmitted over one or more types of communication networks. The transceiver 1770 may enable communication over wired or wireless networks based on various technologies, including, but not limited to, femtocells, Wi-Fi networks or wireless local area networks (WLANs) that may be based on the IEEE 802.11 family of standards, wireless personal area networks (WPANS) such as Bluetooth®, near field communication (NFC), networks based on the IEEE 802.11 family of standards, and / or wireless wide area networks (WWANs) such as LTE, WiMAX, etc. The system may also include one or more ports for communicating over a wired network, such as fiber optic communication.

[0057] In some embodiments, the processor 1750 can also receive input from the IMU 1730. In other embodiments, the IMU 1730 can include a three-axis accelerometer, a three-axis gyroscope, and / or a magnetometer. The IMU 1730 can provide velocity, orientation, and / or other position-related information to the processor 1750. In some embodiments, the IMU 1730 can output measured information synchronized with the capture of each image frame by the sensor 1710. In some embodiments, the output of the IMU 1730 is used, in part, by the processor 1750 to fuse the sensor measurements and / or to further process the fused measurements.

[0058] System 1700 may also include a screen or display and speakers 1780 for rendering images, such as color and / or depth images. In some embodiments, display and speakers 1780 may be used to display live images captured by sensor 1710, fused images, augmented reality images, a graphical user interface (GUI), and other program output. In some embodiments, display 1780 may include and / or house a touchscreen that allows a user to input data through some combination of a virtual keyboard, icons, menus, or other GUI, user gestures, and / or input devices, such as a stylus and other writing instruments. In some embodiments, display 1780 may be implemented using a liquid crystal display or a light emitting diode (LED) display, such as an organic LED display. In other embodiments, display 1780 may be a wearable display.

[0059] Example system 1700 can also be modified in various ways in accordance with this disclosure, such as by adding, combining, or omitting one or more of the illustrated functional blocks. For example, in some configurations, system 1700 does not include IMU 1730 or sensors 1770. In some embodiments, portions of system 1700 take the form of one or more chipsets.

[0060] The processor 1750 may be implemented using a combination of hardware, firmware, and software. The processor 1750 may represent one or more circuits configurable to perform at least a portion of the computational procedures or processes associated with sensor fusion and / or methods for further processing the fused measurements. The processor 1750 retrieves instructions and / or data from memory 1760. The processor 1750 may be implemented using one or more application specific integrated circuits (ASICs), central processing units (CPUs), graphical processing units (GPUs), digital signal processors (DSPs), DSP devices, programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, embedded processor cores, quantum computing processors, electronic devices, quantum processors, molecular processors, other electronic units designed to perform the functions described herein, or combinations thereof.

[0061] Memory 1760 may be implemented within processor 1750 and / or external to processor 1750. As used herein, the term "memory" refers to any type of long-term, short-term, volatile, non-volatile, or other memory, and is not limited to any particular type or number of memories or the type of physical medium on which they are stored. In some embodiments, memory 1760 holds program code that facilitates equalization, pre-equalization, decoding, shaping, de-shaping, and encoding.

[0062] In some embodiments, additionally or alternatively, the processor 1750 can execute one or a combination of encoding, decoding, and shaping / deshaping applications 1755. For example, the output of the decoding can be used to decode a concatenated ECC formed from multiple component ECCs combined into a higher performance code. Another example is a system using iterative equalization and decoding, where soft-decision outputs from the decoder are sent back to a demodulator to iteratively refine the decoder input. Yet another example is acting on the decoded output, such as showing the output on a display and speaker 1780, storing the output in memory 1760, transmitting the output using a transceiver 1770, and / or performing actions based on the output and measurements from the sensor 1710.

[0063] Further, another embodiment of the present invention may provide an encoder circuit for generating coded digital data from input digital data. The encoder / shaping circuit is configured to generate coded / shaped digital data from a source bitstream as input digital data. The encoder / shaping circuit is configured to receive the source bitstream 111 and may include at least one memory bank configured to store a computer-executable encoding method (program / computer-executable instructions / or equivalent circuit) and one or more computing processors configured to generate codewords as coded digital data from the input digital data by executing steps of the computer-executable encoding method. In this case, the computer-executable encoding method is configured to cause the processor to perform the steps based on a code specification. In some embodiments, the generated digital data is modulated to be provided to a communication channel 120 via a front-end circuit 115.

[0064] All of the above-described embodiments of the present invention can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, firmware, software, or a combination thereof. If implemented in software, the software code may be executed on any suitable processor or collection of processors, whether provided on a single computer or distributed among multiple computers. Such a processor may be implemented as an integrated circuit, with one or more processors within an integrated circuit component. However, a processor may be implemented using circuitry in any suitable format.

[0065] Additionally, embodiments of the present invention may be implemented as a method, an example of which has been provided above. The operations performed as part of the method may be ordered in any suitable manner. Thus, embodiments may be constructed in which operations are performed in an order different from that illustrated, which may include performing some operations simultaneously although shown as sequential operations in the exemplary embodiment.

[0066] The use of ordinal terms such as "first" and "second" to modify claim elements in the claims does not, in itself, imply a priority, precedence, or order of one claim element over another, nor does it imply a chronological order in which method actions are performed; it is merely used as a label to distinguish one claim element having a particular name from another element having the same name (but using ordinal terms) to distinguish between claim elements.

[0067] Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the invention.

[0068] Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.

Claims

1. 1. A system for data communication over a channel link, comprising: a communication module for accessing said channel link in accordance with said set of specifications for data communication; a controller including a physics-based neural network; the controller determines parameters represented by the set of specifications of the communication module to compensate for signal waveform distortion in the data communication according to a physical model of the channel link; The system, wherein the physical information neural network is based on a cascaded use of regular perturbation models with phase shifter correction trained by a deep neural network that uses statistical information of the channel link to adaptively modify the set of specifications for the communication module.

2. The system of claim 1 , wherein the channel link is based on electromagnetic, optical, acoustic, or molecular propagation for the data communication.

3. the set of specifications for the communication module includes sets of parameters for a channel encoder, a data compressor, a constellation shaper, a modulator, a pulse shaping filter, a multiplexer, a pre-equalizer, an amplifier, a digital-to-analog converter, an oscillator, an antenna, a mixer, an analog-to-digital converter, an equalizer, a demultiplexer, a demodulator, a constellation de-shaper, a data decompressor, and a channel decoder; 2. The system of claim 1, wherein the set of parameters includes a generator matrix for channel coding, a shaping rate, a shaping method, a modulation format, a pulse-shaping filter length, a pre-equalizer method, an amplifier gain, a sampling rate and quantization bits of a digital-to-analog converter, an oscillator frequency, an antenna size, a sampling rate and quantization bits of an analog-to-digital converter, an equalizer method, a demodulator method, a constellation deshaper method, and a channel decoder method.

4. 2. The system of claim 1, wherein the physical model of the channel link is based on finite difference methods such as split-step Fourier methods, finite element methods such as regular perturbations, collocation models such as Runge-Kutta integrals, Volterra series expansions, Koopman operations, exponential integrators, ordinary differential equations, partial differential equations, nonlinear Schrodinger equations, finite impulse responses, infinite impulse responses, autoregressive models, diffusion processes, space-time-frequency dispersion models, Maxwell's equations, quantum dynamics, and molecular dynamics.

5. 2. The system of claim 1, wherein the physics-information neural network integrates the physical model of the channel link into a deep neural network having an additional set of trainable parameters to adjust for residual errors caused by model mismatch.

6. The system of claim 5 , wherein the deep neural network integrated with the physics-information neural network is based on a combination of parallel aggregation, cascaded stacking, staggered control, and jumping aggregation.

7. The system of claim 1 , wherein the communication module further modifies the set of specifications of the communication module, such as a pre-equalizer and an equalizer, using another physical information neural network modified by the controller.

8. The communication module includes: an encoder for encoding data into codewords based on a channel code; a shaped mapper that maps the codewords to amplitude symbols; a pre-equalizer that modifies the mapped amplitude symbols into pre-equalized symbols based on the physical model using a trained physical information neural network; a digital-to-analog converter for transmitting the digital data of the pre-equalized symbols as channel data via the channel link; an analog-to-digital converter that receives the channel data from the channel link and generates received symbols; a demodulator for modifying the received symbols as intermediate estimates of the digital data; a physical information neural network-based post-equalizer that reduces channel noise and waveform distortion caused by the channel link based on the intermediate estimate of the digital data to generate equalized data; a shaped demapper for modifying the equalized data into a shaped bit sequence as shaped data; a decoder that decodes the unshaped data using channel decoding to correct residual errors.

9. 1. A method for data communication over a channel link, comprising: a communication module accessing the channel link according to a communication method based on the set of specifications for data communication; and modifying the set of specifications for the data communication by the communication module according to a control method based on a physics-based neural network in accordance with a physical model of the channel link; The method, wherein the physical information neural network is based on a cascaded use of regular perturbation models with phase shifter correction trained by a deep neural network that uses statistical information of the channel link to adaptively modify the set of specifications for the communication module.

10. The method of claim 9 , wherein the channel link is based on electromagnetic, optical, acoustic, or molecular propagation for the data communication.

11. the set of specifications for the data communication includes sets of parameters for channel coding, data compression, constellation shaping, modulation, pulse-shaped filtering, multiplexing, pre-equalization, amplification, digital-to-analog conversion, oscillation, analog-to-digital conversion, equalization, demultiplexing, demodulation, constellation deshaping, data recovery, and channel decoding; 10. The method of claim 9, wherein the set of parameters includes: a generator matrix of channel coding, a shaping rate, a shaping method, a modulation format, a pulse-shaping filtering length, a pre-equalization method, an amplification gain, a sampling rate and a quantization bit of digital-to-analog conversion, an oscillation frequency, a sampling rate and a quantization bit of analog-to-digital conversion, an equalization method, a demodulation method, a constellation de-shaping method, and a channel decoding method.

12. 10. The method of claim 9, wherein the physical model of the channel link is based on finite difference methods such as split-step Fourier methods, finite element methods such as regular perturbations, collocation models such as Runge-Kutta integrals, Volterra series expansions, Koopman operations, exponential integrators, ordinary differential equations, partial differential equations, nonlinear Schrodinger equations, finite impulse responses, infinite impulse responses, autoregressive models, diffusion processes, space-time-frequency dispersion models, Maxwell's equations, quantum dynamics, and molecular dynamics.

13. the physics-information neural network integrates the physics model of the channel link into a deep neural network having an additional set of trainable parameters to adjust for residual errors caused by model mismatch; The method of claim 9 , wherein the deep neural network integrated with the physics-information neural network is based on a combination of parallel aggregation, cascaded stacking, staggered control, and jumping aggregation.

14. 10. The method of claim 9, wherein the communication module further modifies the set of specifications of the communication method, such as pre-equalization and equalization, using another physics-information neural network modified by the control method.

15. The communication method includes: obtaining source data to be transmitted via the data interface port for data communication; encoding the source data into codewords based on a channel code; mapping said codewords to amplitude symbols; modifying the mapped amplitude symbols into pre-equalized symbols based on the physical model using a trained physics-based neural network based pre-equalization; transmitting the digital data of the pre-equalized symbols as channel data over the channel link; receiving said channel data from said channel link to generate received symbols; demodulating the received symbols to generate an intermediate estimate of the digital data; mitigating channel noise and waveform distortion caused by the channel link based on the intermediate estimate of the digital data by using a physical information neural network based post-equalization to generate equalized data; converting the equalized data into an unshaped bit sequence as unshaped data using shaped demapping; decoding the unshaped data using channel decoding to correct for residual errors; and storing the corrected channel data in a data sink.

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