Multi-algorithm cooperative multi-channel optical module parallel debugging method, system and device

Abstract

This invention provides a method, system, and device for parallel debugging of multi-channel optical modules using a multi-algorithm collaborative approach. The method includes: in the system identification phase, applying orthogonal test sequences to multiple channels of the optical module in parallel and simultaneously acquiring responses, calculating the channel matrix characterizing the features and crosstalk of all channels in one operation; in the precoding optimization phase, calculating the precoding matrix for actively suppressing crosstalk based on the channel matrix; in the reinforcement learning optimization phase, treating each channel as an independent agent, and using a multi-objective collaborative optimization algorithm based on reinforcement learning to find the globally optimal solution for the operating parameters; finally, writing the optimized operating parameters into the optical module register, performing performance verification, and generating a debugging report. This invention achieves efficient and adaptive parallel debugging of multi-channel optical modules through the closed-loop collaboration of three algorithms: system identification, precoding optimization, and reinforcement learning optimization, improving debugging efficiency and overall system performance while reducing reliance on human experience.

Description

Technical Field

[0001] This invention relates to the field of optical communication technology, and in particular to a method, system and device for parallel debugging of multi-channel optical modules with multi-algorithm collaboration. Background Technology

[0002] With the rapid development of cloud computing, big data and artificial intelligence, the demand for network bandwidth in data centers has exploded. Pluggable high-speed optical modules have become the mainstream technology for data center interconnection. Among them, multi-channel PAM4 modulation technology is widely used due to its high spectral efficiency and relatively low cost.

[0003] Traditional multi-channel high-speed optical module debugging methods mainly include: serial debugging: parameter testing and optimization are performed channel by channel, with the next channel being debugged only after the previous one is completed; standard test instrument-based solutions: global parameter scanning is performed manually or semi-automatically using standard equipment such as sampling oscilloscopes and bit error rate testers; and simple parallel testing solutions: only the test measurements are parallelized, but parameter optimization is still performed serially. These traditional multi-channel high-speed optical module debugging methods also suffer from heavy reliance on personnel experience and low debugging efficiency.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] This invention provides a method, system, and device for parallel debugging of multi-channel optical modules with multi-algorithm collaboration. The main purpose of this invention is to solve the technical problems mentioned in the background art of the prior art.

[0006] The first aspect of this invention provides a parallel debugging method for multi-channel optical modules using multi-algorithm collaboration, comprising:

[0007] Based on the number of channels in the optical module and the system redundancy requirements, the sequence length is determined and Walsh-Hadamard orthogonal test sequences are recursively generated based on the Sylvester construction method.

[0008] The Walsh-Hadamard orthogonal test sequence is applied in parallel to all optical channels of the optical module, and the response signals of all optical channels are acquired synchronously.

[0009] Based on the response signals of all optical channels and using the MIMO system identification algorithm with Walsh-Hadamard transform, the channel matrix characterizing the properties and crosstalk of all optical channels is calculated in one step. H ;

[0010] The MIMO precoding algorithm based on the MMSE criterion is used for the channel matrix. HPreprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W ;

[0011] With the precoding matrix W Based on this, each optical channel of the optical module is regarded as an independent intelligent agent. A multi-objective collaborative optimization algorithm of distributed reinforcement learning is adopted to collaboratively optimize at least one operating parameter of the optical module according to the performance index, environmental parameters and system state of each optical channel, in order to find the global optimal solution.

[0012] The optimized operating parameters are written into the register of the optical module, performance verification is performed, and a debugging report is generated.

[0013] In an optional embodiment of the first aspect of the present invention, determining the sequence length based on the number of channels of the optical module and the system redundancy requirements, and recursively generating the Walsh-Hadamard orthogonal test sequence based on the Sylvester construction method, includes:

[0014] Based on the number of channels N and preset redundancy coefficient K Using formula L=2 ceil[log2(N+K)] Dynamically determine sequence length L ,in ceiling This is the floor function.

[0015] In an optional embodiment of the first aspect of the present invention, the channel matrix characterizing the properties and crosstalk of all optical channels is calculated at once based on the response signals of all optical channels and using a MIMO system identification algorithm with Walsh-Hadamard transform. H include:

[0016] By using a parallel correlator architecture, parallel correlation operations are performed on the response signal and the Walsh-Hadamard orthogonal test sequence to obtain the channel impulse response between the transmit channel and the receive channel in the optical module.

[0017] Key parameters are extracted from all the channel impulse responses to form a frequency domain channel matrix, and the validity of the frequency domain channel matrix is ​​verified.

[0018] When the ratio of the maximum singular value to the minimum singular value of the frequency domain channel matrix is ​​greater than a preset threshold, a remeasurement mechanism is triggered.

[0019] When the ratio of the maximum singular value to the minimum singular value of the frequency domain channel matrix is ​​less than or equal to the preset threshold, the frequency domain channel matrix undergoes abnormal channel monitoring and shielding, SVD noise reduction processing, and amplitude / phase error verification. Once the error verification is passed, the channel matrix is ​​output. H。

[0020] In an optional embodiment of the first aspect of the present invention, the MIMO precoding algorithm based on the MMSE criterion is applied to the channel matrix. H Preprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W include:

[0021] For the channel matrix H Channel analysis and preprocessing are performed, including matrix condition number analysis, noise power estimation, and regularization parameter calculation.

[0022] The preprocessed channel matrix H Perform SVD decomposition calculation, which includes matrix bidiagonalization, singular value iteration calculation, convergence judgment, and decomposition result recombination;

[0023] MMSE precoding is performed based on the SVD decomposition results. The MMSE precoding calculation includes regularization matrix construction, matrix inversion operation, matrix multiplication sequence, and initial precoding matrix generation.

[0024] The initial precoding matrix is ​​subjected to power constraint processing, which includes parallel computation of column norm, identification of maximum norm, power normalization processing, and margin management.

[0025] The power compliance of the normalized precoding matrix after power constraint processing is verified. The precoding matrix is ​​obtained after the verification is passed. W。

[0026] In an optional embodiment of the first aspect of the invention, the channel matrix is... H Channel analysis and preprocessing include:

[0027] Based on the channel matrix H The minimum singular value is used to estimate the noise power;

[0028] The regularization parameter λ is adaptively calculated based on the noise power and a preset empirical coefficient.

[0029] The channel matrix is ​​adjusted using the regularization parameter λ. H Regularization preprocessing is performed to improve the precoding matrix. W Numerical stability of the calculation.

[0030] In an optional embodiment of the first aspect of the present invention, the power constraint processing of the initial precoding matrix includes:

[0031] The column norm of each column of the initial precoding matrix is ​​calculated simultaneously using a parallel architecture;

[0032] Power allocation is performed based on the water-filling algorithm, and the water-filling power of each channel mode is calculated.

[0033] Determine the maximum norm, and calculate the scaling factor based on the maximum norm and the power margin factor;

[0034] Multiply the scaling factor by the initial precoding matrix to obtain the normalized precoding matrix.

[0035] In an optional embodiment of the first aspect of the present invention, the policy network of the agents adopts a deep fully connected structure, all agents store experience data in a shared experience replay pool, and update the policy network based on a near-end policy optimization algorithm; the performance indicators include the bit error rate, signal-to-noise ratio, error vector amplitude, eye diagram height and width, Q factor, and indicator sliding window statistics of the current channel and adjacent channels; the environmental parameters include the laser bias current, modulator drive voltage, transimpedance amplifier gain, received optical power, chip temperature, and ambient temperature of the current channel; the system state includes the channel weight norm, power amplifier output level, and phase error of the clock recovery phase-locked loop.

[0036] A second aspect of the present invention provides a multi-algorithm collaborative parallel debugging system for multi-channel optical modules, the multi-algorithm collaborative parallel debugging system for multi-channel optical modules comprising:

[0037] The orthogonal test sequence generation module is used to determine the sequence length based on the number of channels of the optical module and the system redundancy requirements, and recursively generate Walsh-Hadamard orthogonal test sequences based on the Sylvester construction method.

[0038] The parallel testing module is used to apply the Walsh-Hadamard orthogonal test sequence in parallel to all optical channels of the optical module and to synchronously acquire the response signals of all optical channels.

[0039] The channel matrix calculation module is used to calculate, in one step, the channel matrix characterizing the properties and crosstalk of all optical channels based on the response signals of all optical channels and using the MIMO system identification algorithm with Walsh-Hadamard transform. H ;

[0040] The precoding matrix calculation module is used to perform precoding on the channel matrix using a MIMO precoding algorithm based on the MMSE criterion. HPreprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W ;

[0041] The runtime parameter co-optimization module is used to optimize the precoding matrix. W Based on this, each optical channel of the optical module is regarded as an independent intelligent agent. A multi-objective collaborative optimization algorithm of distributed reinforcement learning is adopted to collaboratively optimize at least one operating parameter of the optical module according to the performance index, environmental parameters and system state of each optical channel, in order to find the global optimal solution.

[0042] The optimized performance verification module is used to write the optimized operating parameters into the register of the optical module, perform performance verification, and generate a debugging report.

[0043] A third aspect of the present invention provides a multi-algorithm collaborative multi-channel optical module parallel debugging device, the multi-algorithm collaborative multi-channel optical module parallel debugging device comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line;

[0044] The at least one processor invokes the instructions in the memory to cause the multi-algorithm collaborative multi-channel optical module parallel debugging device to execute the multi-algorithm collaborative multi-channel optical module parallel debugging method as described in any one of the first aspects of the present invention.

[0045] The fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the multi-algorithm collaborative multi-channel optical module parallel debugging method as described in any one of the first aspects of the present invention.

[0046] Beneficial Effects: This invention provides a method, system, and device for parallel debugging of multi-channel optical modules using a multi-algorithm collaborative approach. The method includes: in the system identification phase, applying orthogonal test sequences to multiple channels of the optical module in parallel and simultaneously acquiring responses, calculating the channel matrix characterizing the features and crosstalk of all channels in one operation; in the precoding optimization phase, calculating the precoding matrix for actively suppressing crosstalk based on the channel matrix; in the reinforcement learning optimization phase, treating each channel as an independent intelligent agent, and using a multi-objective collaborative optimization algorithm based on reinforcement learning to find the globally optimal solution for the operating parameters; finally, writing the optimized operating parameters into the optical module register, performing performance verification, and generating a debugging report. This invention achieves efficient and adaptive parallel debugging of multi-channel optical modules through the closed-loop collaboration of three algorithms: system identification, precoding optimization, and reinforcement learning optimization, improving debugging efficiency and overall system performance, and reducing reliance on human experience. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of an embodiment of the multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to the present invention;

[0048] Figure 2 This is a schematic diagram of an embodiment of the Walsh-Hadamard transform MIMO system identification algorithm flow of the present invention;

[0049] Figure 3 This is a schematic diagram of an embodiment of a MIMO precoding optimization algorithm based on the MMSE criterion of the present invention;

[0050] Figure 4 This is a schematic diagram of an embodiment of a multi-objective collaborative optimization algorithm for distributed reinforcement learning according to the present invention;

[0051] Figure 5 This is a schematic diagram of the main stages of a multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to the present invention;

[0052] Figure 6 This is a schematic diagram of an embodiment of the multi-algorithm collaborative multi-channel optical module parallel debugging system of the present invention;

[0053] Figure 7 This is a schematic diagram of an embodiment of a multi-channel optical module parallel debugging device with multi-algorithm collaboration according to the present invention. Detailed Implementation

[0054] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0055] For ease of understanding, the specific process of the embodiments of the present invention is described below. The first aspect of the present invention provides a multi-algorithm collaborative method for parallel debugging of multi-channel optical modules, including:

[0056] S100. Based on the number of channels in the optical module and the system redundancy requirements, determine the sequence length and recursively generate Walsh-Hadamard orthogonal test sequences using the Sylvester construction method. In this invention, the first step in system identification is to generate a set of Walsh-Hadamard orthogonal sequences with excellent correlation characteristics. These sequences have good autocorrelation and cross-correlation properties, laying the foundation for subsequent channel response extraction.

[0057] In this invention, the dynamic optimization of sequence parameters employs an intelligent length selection strategy. The algorithm is based on the number of channels. N To meet system redundancy requirements, the optimal sequence length is automatically selected. L The formula used is:

[0058] L=2 ceil[log2(N+K)]

[0059] in, ceiling The function is designed to round up, ensuring that the sequence length is a power of 2, which facilitates Hadamard matrix generation and hardware FFT (Fast Fourier Transform) / IFFT (Inverse Fast Fourier Transform) operations. N For the number of channels, K This is the redundancy factor, typically ranging from 4 to 8. K Value selection strategy: In initial debugging or low SNR (signal-to-noise ratio, <20dB) environments, select... K =8 to enhance noise immunity; in a stable high SNR (≥20dB) environment, to improve efficiency, take K =4. This strategy can be implemented using a lookup table. This formula ensures that the sequence length meets the orthogonality requirement while providing sufficient processing gain for noise suppression. That is, in an optional embodiment of the first aspect of the invention, determining the sequence length based on the number of channels of the optical module and the system redundancy requirements, and recursively generating the Walsh-Hadamard orthogonal test sequence based on the Sylvester construction method, includes: determining the sequence length based on the number of channels. N and preset redundancy coefficient K Using formula L=2 ceil[log2(N+K)] Dynamically determine sequence length L ,in ceiling This is the floor function.

[0060] In this invention, the Hadamard matrix recursive generation is based on the Sylvester construction method, recursively generating Walsh sequences through matrix tensors. The generation process is implemented on an FPGA via hardware logic, rather than pre-stored. The basic recursive formula is:

[0061] H 1= [1]

[0062] H 2 = [1, 1; 1, -1]

[0063] H 2n = H 2⊗ H n =[ H n , H n ; H n , -Hn ]

[0064] in, H 2 is the smallest Hadamard matrix. Using a recursive rule and the Kronecker tensor product, we can obtain the smallest Hadamard matrix. H 2. To construct a larger Hadamard matrix. The specific operation of the Kronecker product: using... H Multiply (scale) each element of 2 by the whole. H n Matrix, for H The 1 in 2, it retains H n The original value; for H -1 in 2, it makes the whole H n To take the negative, you can... H 2 to generate H 4. Similarly, it can be used H 2 and H 4 to generate H 8. And so on, this process can continue indefinitely. The process is related to the sequence length. L The generation process is synchronized, and this recursive generation method avoids the need to store large matrices, greatly saving hardware resources. Only the basic matrix needs to be saved to generate orthogonal sequences of arbitrary order in real time. Integer operations are used during the generation process to ensure numerical accuracy and avoid the accumulation of floating-point errors.

[0065] Sequence power adaptive control adjusts the sequence transmit power in real time based on the received signal strength indication (RSSI) monitored in real time. The power control algorithm is based on a closed-loop feedback mechanism:

[0066] P tx [ n ] = P base + α ·( SNRtarget – SNR estimated [ n -1])

[0067] in P base Based on the transmission power, α The power adjustment factor is (0.1~0.5). SNR target The target signal-to-noise ratio (SNR) is set to 25 dB. This mechanism ensures a sufficient SNR under different channel conditions while avoiding signal saturation.

[0068] If the RSSI is below the target threshold, the transmit power is increased in 1dB steps; if the RSSI is too high, causing the ADC to approach saturation, the power is reduced in 1dB steps. This process completes rapid convergence within 100μs, ensuring that the amplitude of the signal fed into the correlation calculation is stable at 70%-80% of the ADC range, in order to maximize the signal-to-noise ratio.

[0069] S200: The Walsh-Hadamard orthogonal test sequence is applied in parallel to all optical channels of the optical module, and the response signals of all optical channels are acquired synchronously. In this invention, the signal acquisition stage is responsible for acquiring the corresponding data of each channel with high quality, ensuring timing accuracy and signal integrity.

[0070] The high-precision synchronous acquisition mechanism employs a tree-structured clock distribution network. The global trigger signal generated by the clock management unit ensures that all channels begin acquisition synchronously through a carefully designed transmission path. The main controller issues software trigger commands, and the hardware platform generates multiple fully synchronized hardware trigger pulses via a fan-out buffer, distributing them to all ADC (Analog-to-Digital Converter) chips. Through PCB length equalization design and internal chip delay calibration, the clock offset between channels is ensured to be less than 5ps, achieved through the following techniques:

[0071] Phase alignment algorithm: Digital phase-locked loop corrects the clock phase difference of each channel in real time;

[0072] Timing tolerance control: Establish a detailed timing budget model, taking into account factors such as transmission delay and gate delay;

[0073] Adaptive calibration: dynamically adjusts the delay parameter based on measurement results;

[0074] Oversampling and filtering processes employ a 4x oversampling technique to improve signal quality. The sampling frequency satisfies:

[0075] f s = 4× f symbol

[0076] in, f symbol The symbol rate.

[0077] The anti-aliasing filter uses an 8th-order elliptic filter, whose transfer function is:

[0078]

[0079] in, R n It is an elliptic rational function; The ripple factor (usually 0.1~0.5) determines the magnitude of the passband ripple; a smaller value corresponds to a smaller passband ripple, and a larger value corresponds to a larger passband ripple. This is a selectivity parameter (commonly 1.5~3), which controls the steepness of the transition band. Larger values ​​correspond to steeper transition bands, and smaller values ​​correspond to gentler transition bands. f The input frequency of the filter. f c This is the passband cutoff frequency of the filter. The filter provides over 80dB of stopband attenuation, effectively suppressing high-frequency noise and aliasing components.

[0080] Data alignment is performed to compensate for inter-channel transmission delay using digital delay lines. A FIFO (First-In-First-Out) programmable delay unit provides a time resolution of 1 / 8 of a sampling period, and the alignment algorithm determines the two discrete-time signals by maximizing the cross-correlation function. x 1[ n ]and x 2[ n Optimal delay parameters between ] :

[0081]

[0082] in, >0 indicates x 2[ n Relative to x 1[ n Delayed A sample, otherwise indicating x 2[ n Leading x 1[ n ]; x 1[ n ] indicates the time index of the first discrete-time signal. n The sampled value at that location; x 2[ n-t ] indicates the second discrete-time signal at the time index n - t The value at that point, by introducing a delay t In fact,x 2[ n ] Shifted on the timeline t One sample, t >0 indicates x 2[ n-t ]for x 2[ n [The past value of ] is the past value, and the future value is the opposite.

[0083] This process is executed automatically, ensuring time consistency of multi-channel data.

[0084] S300. Based on the response signals of all optical channels and using the MIMO (Multiple-Input Multiple-Output) system identification algorithm with Walsh-Hadamard transform, calculate the channel matrix characterizing the properties and crosstalk of all optical channels in one operation. H In this invention, this step may include: performing parallel correlation operations on the response signal and the Walsh-Hadamard orthogonal test sequence using a parallel correlator architecture to obtain the channel impulse response between the transmit and receive channels in the optical module; extracting key parameters from all the channel impulse responses to form a frequency domain channel matrix, and verifying the validity of the frequency domain channel matrix; triggering a remeasurement mechanism when the ratio of the maximum singular value to the minimum singular value of the frequency domain channel matrix is ​​greater than a preset threshold; when the ratio of the maximum singular value to the minimum singular value of the frequency domain channel matrix is ​​less than or equal to the preset threshold, performing abnormal channel monitoring shielding and SVD (singular value decomposition) noise reduction processing on the frequency domain channel matrix, and verifying the amplitude / phase error; and outputting the channel matrix after the error verification is passed. H。

[0085] Specifically, in the correlator array calculation stage: the correlator array is the core calculation unit for system identification, responsible for extracting corresponding information from the received signal.

[0086] Parallel correlator architecture implementation N × N Several parallel correlation operations are performed, with each correlator calculating the impulse response for a specific transmit-receive channel pair. The mathematical expression for the correlation operation is:

[0087]

[0088] in, h ij [ k [Indicates from the launch channel] j to receiving channel i The corresponding channel impact, r i [ n [To receive signals] sj [ n [This refers to the transmission sequence.] This calculation is performed in parallel on dedicated hardware, completing in one clock cycle. N 2 This architecture achieves true O(1) time complexity.

[0089] The accumulator tree optimization design employs a Wallace tree structure to reduce critical path latency. Traditional linear accumulators have a latency of O(N), while for 48-bit accumulators, the Wallace tree reduces the latency to O(log N) through 3:2 and 4:2 compressors. Specifically, partial products are compressed through multiple levels:

[0090] First stage: The 3:2 compressor converts three partial products into two;

[0091] Second stage: 4:2 compressor further reduces data width;

[0092] Final stage: The fast adder completes the remaining accumulation.

[0093] A precision control mechanism ensures numerical accuracy during computation. The results utilize a 48-bit accumulator to provide sufficient dynamic range and prevent overflow, ultimately outputting a 32-bit fixed-point number. The bit width design is based on handling the worst-case accumulation gain (sequence length). L =64, 16-bit input data), the required bit width is 16 + log2(64) = 22 bits, and choosing 48 bits provides sufficient dynamic range margin. In the final output, through rounding and saturation processing, the high 32 bits are truncated as fixed-point output, which optimizes bus utilization while ensuring accuracy.

[0094] Channel matrix construction and verification phase: After obtaining the impulse response of each channel, a complete channel matrix is ​​constructed and its quality is verified.

[0095] Channel matrix construction involves extracting key parameters from the impulse response to form a frequency-domain channel matrix. For each channel response... h ij [ k Calculate its frequency response at the operating frequency:

[0096]

[0097] in, T s The frequency responses of all channel pairs form a complete channel matrix, with a sampling interval of [number]. H .

[0098] Matrix validity verification passed the matrix condition number test. k (H) Analyze and evaluate the quality of the matrix. The condition number is calculated using the following formula:

[0099]

[0100] in, s max (H) and s min (H) These are the maximum and minimum singular values ​​of the matrix, respectively. When k (H)>threshold 10 6 When the system determines that the matrix is ​​ill-conditioned, it triggers a remeasurement mechanism. If the condition number exceeds the limit, the system will automatically select another set of orthogonal bases (such as Walsh sequences in different orders) for remeasurement to eliminate ill-conditioning problems caused by the correlation between the test signal and a specific channel mode.

[0101] Anomaly channel monitoring identifies faulty channels based on principal component analysis (PCA). PCA is used to identify anomaly channels by analyzing the channel matrix. H PCA analysis is performed to calculate the projection of each channel's response onto the principal eigenvector. If the projection amplitude of a channel deviates from the mean by more than 3 standard deviations, it is identified as an abnormal channel. The system will mark this channel as faulty and set its corresponding row and column to zero in subsequent precoding calculations, thereby achieving automatic masking and preventing single-point failures from affecting global performance.

[0102] The noise suppression process employs a noise reduction algorithm based on singular value decomposition, applied to the measured channel matrix. H Perform SVD decomposition:

[0103]

[0104] in, This is a singular value matrix. Noise reduction is achieved through soft thresholding:

[0105]

[0106] threshold t Adaptive selection based on noise statistical characteristics:

[0107]

[0108] in s n This is the noise standard deviation estimate. The reconstructed channel matrix after denoising is:

[0109]

[0110] This operation can effectively filter out measurement noise and improve the robustness of subsequent precoding calculations.

[0111] Performance evaluation and output stage: The final stage is to comprehensively evaluate the identification results and output a qualified channel matrix.

[0112] Performance verification includes: amplitude error verification: comparing with reference measurement to ensure error <0.5%; phase consistency check: inter-channel phase error <1°; orthogonality test: verifying the matching degree between the identification matrix and the actual channel.

[0113] The data output and interface transmits the final channel matrix to the precoding processing unit via a high-speed interface. The output data contains complete channel characteristic information and quality indicators, providing reliable input for subsequent precoding optimization.

[0114] The adaptive learning mechanism records the parameter settings and performance results of this identification, and optimizes the parameter selection in subsequent identification processes through machine learning algorithms to achieve continuous performance improvement.

[0115] Through the precise coordination of the five stages described above, the system identification algorithm achieves rapid identification within 10ms in a 32-channel system and remains stable under a 15dB signal-to-noise ratio, providing accurate and reliable channel state information for the precoding system. The algorithm's innovation lies in combining traditional system identification theory with modern signal processing techniques, achieving an optimal balance between speed, accuracy, and robustness.

[0116] like Figure 2 As shown, the system identification algorithm of this invention, through parameter configuration, synchronous acquisition, and parallel correlation operations, until the channel matrix of a multi-channel system that accurately meets the accuracy requirements is obtained. The algorithm utilizes parallel excitation and parallel correlation operations of orthogonal sequences to achieve the results required by traditional methods. N 2 The number of measurements was reduced to one parallel measurement, which greatly improved efficiency and enabled efficient and accurate channel parameter extraction.

[0117] S400. A MIMO precoding algorithm based on the MMSE (Minimum Mean Square Error) criterion is used to precode the channel matrix. H Preprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W . Figure 3 This is a precoding optimization algorithm flow according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes channel analysis and preprocessing, MMSE precoding calculation, power constraint processing, real-time precoding execution, and system performance verification. The algorithm, based on SVD for MMSE precoding calculation and power constraint processing, ensures the numerical stability and power security of the precoding matrix. This algorithm achieves an optimal balance between suppressing crosstalk and noise, maximizing system capacity.

[0118] More specifically, step S400 of the present invention may include:

[0119] S401, regarding the channel matrix H Channel analysis and preprocessing are performed, including matrix condition number analysis, noise power estimation, and regularization parameter calculation; the channel matrix... H Channel analysis and preprocessing include: based on the channel matrix H The minimum singular value is used to estimate the noise power; based on the noise power and a preset empirical coefficient, a regularization parameter λ is adaptively calculated; and the channel matrix is ​​then adjusted using the regularization parameter λ. H Regularization preprocessing is performed to improve the precoding matrix. W Numerical stability of the calculation.

[0120] Specifically, in this invention, the channel matrix preprocessing process can be as follows:

[0121] Channel matrix condition number analysis: Calculate the matrix condition number and identify ill-conditioned matrices.

[0122]

[0123] Set threshold k thresh =10 6 ,when k (H)> k thresh When the system determines that the matrix is ​​ill-conditioned, it activates the regularization preprocessing mechanism.

[0124] Noise Power Estimation: Based on Matrix Minimum Singular Values s min (H) The formula for estimating noise power is:

[0125]

[0126] in, N t This refers to the number of launch channels.

[0127] Regularization parameter calculation: An adaptive strategy is used to calculate the regularization parameter. l Balancing signal distortion and noise suppression:

[0128]

[0129] in, α and β This is an empirical coefficient (1.2~1.5), dynamically adjusted according to channel conditions. For example, it is increased under low signal-to-noise ratio (SNR) conditions. α To enhance noise suppression.

[0130] S402, the preprocessed channel matrix H The SVD decomposition calculation is performed, which includes matrix bidiagonalization, singular value iteration calculation, convergence judgment, and decomposition result recombination.

[0131] Specifically, in this invention, the specific process of the singular value decomposition calculation stage can be as follows:

[0132] SVD decomposes the channel matrix into ,in U and V It is a unitary matrix. S This is a singular value matrix. This stage is a crucial computational step, and the steps are as follows:

[0133] 1) Matrix doubly diagonalization: using the householder transformation to... H Convert to a double diagonal matrix B .for m × n matrix H By left multiplying by the Householder matrix Q i And right multiplication P j Gradual elimination:

[0134]

[0135] After bidiagonalization, B Only the main diagonal and the previous diagonal are non-zero, reducing the complexity of subsequent calculations.

[0136] 2) Singular value iteration calculation:

[0137] Calculate using implicit QR iterative algorithm B The singular values. Each iteration updates the matrix using Givens rotations:

[0138]

[0139] in, G k and F k This is the Givens rotation matrix.

[0140] Convergence criterion: When the change in singular values ​​between adjacent iterations... (tolerance) t =10 -10 Stop iteration when ).

[0141] 3) Convergence acceleration technique: Applying the Wilkinson displacement strategy, the displacement amount... mAccelerate QR iteration convergence by calculating the eigenvalues ​​of the 2×2 submatrix at the tail of the matrix; dynamically adjust the maximum number of iterations. K max According to the residual norm The rate of change is adaptively set.

[0142] 4) Recombination and sorting: Combine the transformation matrices into... , , Sort by singular value in descending order s 1≥ s 2≥…≥ s r This ensures that the primary channel modes are processed first.

[0143] S403. Perform MMSE precoding calculation based on SVD decomposition results. The MMSE precoding calculation includes regularization matrix construction, matrix inversion operation, matrix multiplication sequence and initial precoding matrix generation.

[0144] Specifically, in this invention, the specific process of the MMSE precoding matrix calculation stage can be as follows:

[0145] The precoding matrix is ​​calculated based on the SVD results, and regularization is introduced to ensure numerical stability.

[0146] 1) Regularization matrix construction: using the singular value matrix obtained by SVD S Construct the regularization matrix:

[0147]

[0148] in, l These are the regularization parameters calculated during the preprocessing stage.

[0149] 2) Matrix inversion operation:

[0150] right Find the inverse. Because... Since it is a diagonal matrix, its inverse can be calculated directly:

[0151]

[0152] If the matrix is ​​close to singular (small singular values ​​exist), a truncation strategy is adopted: set a threshold. c ,like s i < c Then let .

[0153] 3) Precoding matrix calculation:

[0154] Based on the MMSE criterion, the precoding matrix is:

[0155]

[0156] Optimize the calculation order, calculate first (Diagonal matrix multiplication), then with V and U H Multiplying reduces computational complexity.

[0157] 4) Numerical stability monitoring:

[0158] Real-time monitoring of the condition number of matrix operations, if k (P)> k thresh When this happens, dynamic precision adjustment is triggered (e.g., switching to high-precision floating-point arithmetic); at the same time, outlier detection is implemented, and if the magnitude of a matrix element exceeds the set range, smoothing filtering is used.

[0159] S404. Perform power constraint processing on the initial precoding matrix. The power constraint processing includes parallel calculation of column norms, maximum norm identification, power normalization, and margin management. In this invention, this step may include: simultaneously calculating the column norms of each column of the initial precoding matrix using a parallel architecture; performing power allocation based on a water-filling algorithm and calculating the water-filling power for each channel mode; determining the maximum column norm and calculating a scaling factor based on the maximum column norm and a power margin factor; and multiplying the scaling factor by the initial precoding matrix to obtain a normalized precoding matrix.

[0160] Specifically, in this invention, the specific process of the power constraint processing stage can be as follows:

[0161] Ensure that the transmit power after precoding does not exceed the system hardware limit:

[0162] 1) Parallel computation of column norm: Parallel computation of precoding matrix P 2-norm of each column:

[0163]

[0164] Using a parallel architecture to compute all c j Improve efficiency.

[0165] 2) Intelligent power allocation is based on the water-filling algorithm to allocate power, calculating the water-filling power for each channel mode:

[0166]

[0167] in, m The water level constant is obtained by solving... , P total Total power constraint.

[0168] 3) Power normalization processing: finding the maximum column norm c max = max j c j ; Calculate the scaling factor ,in α =1.05 is the power margin factor; normalized precoding matrix .

[0169] S405. Perform power compliance verification on the normalized precoding matrix after power constraint processing. Obtain the precoding matrix after successful verification. W。

[0170] Specifically, in this invention, the power compliance verification process can be as follows: recalculate P norm Verify the norm of || P norm,j ||2<< P max If any channels exceed the limit, secondary normalization is initiated to further reduce the scaling factor until the constraints are met. Real-time precoding is executed using a deep pipelined architecture, processing one data vector per clock cycle; integrated saturation monitoring and overflow protection circuitry; real-time monitoring of output signal quality and automatic adjustment of precoding parameters.

[0171] S500, with the precoding matrix W Based on this, each optical channel of the optical module is treated as an independent intelligent agent. A distributed reinforcement learning multi-objective collaborative optimization algorithm is employed to collaboratively optimize at least one operating parameter of the optical module according to the performance indicators, environmental parameters, and system state of each optical channel, seeking the global optimum. In this invention, the policy network of the intelligent agents adopts a deep fully connected structure. All agents store experience data in a shared experience replay pool and update the policy network based on a near-end policy optimization algorithm. The performance indicators include the bit error rate, signal-to-noise ratio, error vector amplitude, eye diagram height and width, Q-factor (quality factor), and sliding window statistics of the indicators for this channel and adjacent channels. The environmental parameters include the laser bias current, modulator drive voltage, transimpedance amplifier gain, received optical power, chip temperature, and ambient temperature for this channel. The system state includes the channel weight norm, power amplifier output level, and phase error of the clock recovery phase-locked loop.

[0172] Figure 4 This is a reinforcement learning algorithm flow according to an embodiment of the present invention, such as... Figure 4As shown, the process includes a state observation system, a multi-agent system, an experience collection and training optimization engine. Through centralized training and distributed execution, the agents can collaboratively optimize the overall system performance. Experience replay and priority sampling improve learning efficiency.

[0173] Condition observation system:

[0174] 1) Multi-dimensional state vector: The state vector of each agent (corresponding to one optical channel) s _i Specifically, it includes the following 128 features: Performance metrics (80 dimensions in total): BER (Bit Error Rate), SNR (Signal-to-Noise Ratio), EVM (Error Vector Magnitude), eye diagram height and width, Q factor of this channel and adjacent channels, and the sliding window statistics (mean, variance) of these metrics over the most recent 10 time steps; Environmental parameters (32 dimensions in total): Laser bias current, modulator drive voltage, TIA (Transimpedance Amplifier) ​​gain, received optical power of this channel, as well as chip temperature and ambient temperature; System status (16 dimensions in total): Weight norm of the precoding matrix W corresponding to this channel, power amplifier output level, and phase error of the clock recovery phase-locked loop;

[0175] 2) Real-time feature extraction: For time-series performance metrics (such as BER), a length of [missing information] is used. L A sliding window with a value of 10 is used. At each time step, not only the mean and variance of the data within the window are calculated, but also its skewness (a measure of distribution asymmetry) and kurtosis (a measure of distribution sharpness). These four statistics together constitute an accurate description of the performance change trend and stability, providing the agent with richer decision-making information;

[0176] 3) Data normalization: Z-score standardization is used for each state feature. x Its standardized value is ( x - m ) / s .in, m and s These are the historical means and standard deviations of each feature, pre-calculated through extensive sampling during the initial system calibration phase. This operation transforms all features to a similar numerical range, accelerating the convergence of neural network training and preventing certain large numerical features from dominating the training process.

[0177] Multi-agent systems:

[0178] 1) Independent Policy Network: Each agent's policy network (Actor network) adopts a deep fully connected structure, specifically: Input layer (128 nodes) → Fully connected layer (256 nodes, ReLU) → Fully connected layer (128 nodes, ReLU) → Fully connected layer (64 nodes, ReLU) → Output layer (16 nodes, Tanh). The 16 nodes of the output layer correspond to the agent's 16-dimensional continuous action space, including: laser bias current fine-tuning, modulator drive voltage amplitude / phase fine-tuning, equalizer coefficient updates, etc. The Tanh activation function constrains the action values ​​within the range of [-1, +1], facilitating mapping to the adjustable range of actual hardware;

[0179] 2) Parameter sharing mechanism: This is the core of the CTDE (Centralized Training and Distributed Execution) framework. All agents share their collected experience tuples. s , a , r , s The data is stored in a shared experience replay pool. During training, each agent's Actor and Critic networks sample and learn from this shared pool. This allows each agent to learn from the experiences of all other agents, greatly improving sample utilization and learning efficiency, while maintaining their ability to make independent decisions based on their local state.

[0180] 3) Exploration strategy design: The Ornstein-Uhlenbeck (OU) process is used to generate time-dependent exploration noise. the_t Its update formula is: the_t = i ( m - or _{ t -1})+ σW ,in W This is a Wiener process. The advantage of the OU process is that the noise it generates has inertia, making it more suitable for continuous action space exploration in control systems (such as continuous fine-tuning of voltage and current). Compared to independent Gaussian noise, it can produce smoother and more physically feasible exploration behavior. Hyperparameters are set as follows: i =0.15, m =0, s =0.2.

[0181] Experience collection system:

[0182] 1) Priority Experience Replay: Calculate experience priority based on time series difference error, and prioritize the replay of important experiences;

[0183] 2) Maintaining Experience Diversity: In addition to priority-based sampling, coverage sampling is introduced. The system periodically clusters experiences based on the Euclidean distance of the state vectors and randomly samples a certain proportion of experiences from each cluster. This ensures that the training data covers all the different states the system may be in, avoiding the policy from getting trapped in local optima;

[0184] 3) Storage management optimization: A fixed-size circular buffer is used as the experience replay pool. When the buffer is full, new experiences will overwrite the oldest experiences. At the same time, the system will periodically clear those experiences with consistently low priority to make room for new and potentially more important experiences, thus achieving dynamic updates of experiences.

[0185] Training optimization engine:

[0186] 1) Distributed Training Architecture: A variant of the Ape-X architecture. Multiple experience collectors (one for each optical module) are deployed, interacting in parallel with the hardware environment to collect experience and store it in a central experience replay pool. One or more central trainers are dedicated to sampling from the pool and updating the model. This decoupled design ensures that the time-consuming training process does not affect the speed of data collection, greatly improving the overall data throughput and learning efficiency of the system.

[0187] 2) Policy Gradient Optimization: The Proximal Policy Optimization (PPO) algorithm is used to update the Actor network. PPO introduces a clipped surrogate objective, limiting the magnitude of each policy update and avoiding drastic oscillations during training, thus significantly improving training stability. Its objective function is:

[0188] L ^{ CLIP}(θ)= E _ t [ minutes ( r _ t ( i )× A _ t , clip ( r _ t ( i ),1- e ,1+ e )× A _ t )]

[0189] in r _ t ( i ) is the probability ratio. A _ t It is the dominance function.

[0190] 3) Target Network Synchronization: To further improve training stability, a target network is maintained for both the Actor and Critic networks. The parameters θ' of the target network are slowly updated to track the online network parameters using a soft update strategy. i : i '← tth +(1- t ) i '.in t It is a very small constant (e.g., 0.01), which means that the target network parameters are updated by only 1% in each training step, effectively stabilizing the training objective and preventing the divergence of value estimation.

[0191] S600. Write the optimized operating parameters into the register of the optical module, perform performance verification, and generate a debugging report. In summary, this invention provides a parallel debugging method for multi-channel optical modules. Through the collaborative work of multiple intelligent algorithms, it achieves efficient, intelligent, and adaptive multi-channel parallel testing. This invention adopts a layered intelligent architecture, and through the collaborative work of three core algorithms, it realizes a complete debugging process from physical layer measurement to system-level optimization.

[0192] See Figure 5 To better understand the technical solution of this invention, the overall workflow of the multi-algorithm collaborative multi-channel optical module parallel debugging method of this invention can be summarized as follows:

[0193] Initialization and System Identification Phase: After the user submits the debugging task, the system first performs hardware initialization and clock synchronization. Subsequently, the fast MIMO system identification algorithm based on the Walsh-Hadamard transform is initiated. The intelligent master control system generates orthogonal test sequences, which are applied to all optical channels through the parallel debugging hardware platform; simultaneously, the response signals of all channels are synchronously acquired and transmitted back. The system identification engine calculates the channel matrix representing the characteristics and crosstalk of all channels in one go through parallel correlation operations. H This process reduces the time complexity of system identification from the traditional O(N²) to O(1).

[0194] Precoding optimization stage: Obtaining the channel matrix H Then, the robust MIMO precoding algorithm based on the MMSE criterion begins execution. This algorithm first... H Preprocessing and SVD decomposition are performed, and then the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W This algorithm incorporates compensation for transmitter power constraints and receiver nonlinear impairments (such as TIA saturation) into the optimization model, and uses regularization techniques to ensure the robustness of the solution. The final calculated precoding matrix... W The precoded signal synthesizer is sent to the hardware platform.

[0195] Reinforcement Learning Optimization Phase: Building upon the good initial performance provided by the pre-encoding, a multi-objective collaborative optimization algorithm based on distributed reinforcement learning is initiated for more refined and adaptive optimization. In this framework, each channel is treated as an agent. The state space S includes the channel's BER, SNR, eye diagram height / width, drive current, received optical power, and temperature. The action space A includes fine-tuning of parameters such as drive voltage, bias current, and equalizer coefficients. Reward Function R It is a weighted sum with the goal of minimizing BER and power consumption while ensuring stable reward parameters. The agent uses the MADDPG (Multi-Agent Deep Deterministic Policy Gradient) algorithm for centralized training and distributed execution, achieving collaboration through a shared experience pool and Critic network, thereby finding the global optimum in a dynamic environment.

[0196] Parameter application and verification: The optimized parameters are written into the optical module register, and the system automatically performs a round of performance verification (such as a fast bit error rate test) and generates a debugging report.

[0197] In summary, the multi-algorithm collaborative parallel debugging method for multi-channel optical modules of this invention achieves the following beneficial effects through the aforementioned hardware architecture and algorithm design:

[0198] 1) Significantly improved debugging efficiency: The parallel debugging architecture reduces debugging time from the traditional O(N) time. 2 The debugging time for a 32-channel system is reduced from several days to several hours or even several minutes by reducing the calorific value to O(1).

[0199] 2) Comprehensive system performance optimization: Through active crosstalk control and nonlinear compensation, the system achieves globally optimal performance, with a performance margin improvement of 3~6dB;

[0200] 3) Significantly improved intelligence level: The system has self-learning and self-adaptive capabilities, reducing reliance on human experience and improving the consistency of debugging results;

[0201] 4) Enhanced system reliability: Multiple fault-tolerant mechanisms ensure stable system operation under abnormal conditions, and the fault self-recovery capability reduces maintenance requirements;

[0202] 5) Significant economic benefits: Improved debugging efficiency reduces production costs, performance optimization increases product yield, and intelligentization reduces manpower requirements.

[0203] See Figure 6 The second aspect of the present invention provides a multi-algorithm collaborative multi-channel optical module parallel debugging system, the multi-algorithm collaborative multi-channel optical module parallel debugging system comprising:

[0204] The orthogonal test sequence generation module 10 is used to determine the sequence length based on the number of channels of the optical module and the system redundancy requirements, and recursively generate the Walsh-Hadamard orthogonal test sequence based on the Sylvester construction method.

[0205] The parallel test module 20 is used to apply the Walsh-Hadamard orthogonal test sequence in parallel to all optical channels of the optical module and to synchronously acquire the response signals of all optical channels.

[0206] The channel matrix calculation module 30 is used to calculate, in one step, the channel matrix characterizing the properties and crosstalk of all the optical channels based on the response signals of all the optical channels and using the MIMO system identification algorithm with Walsh-Hadamard transform. H ;

[0207] Precoding matrix calculation module 40 is used to perform precoding on the channel matrix using a MIMO precoding algorithm based on the MMSE criterion. H Preprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W ;

[0208] Run parameter co-optimization module 50, used to optimize the precoding matrix W Based on this, each optical channel of the optical module is regarded as an independent intelligent agent. A multi-objective collaborative optimization algorithm of distributed reinforcement learning is adopted to collaboratively optimize at least one operating parameter of the optical module according to the performance index, environmental parameters and system state of each optical channel, in order to find the global optimal solution.

[0209] The optimized performance verification module 60 is used to write the optimized operating parameters into the register of the optical module, perform performance verification, and generate a debugging report.

[0210] In an optional embodiment of the second aspect of the present invention, the orthogonal test sequence generation module includes:

[0211] Sequence length determination unit, used to determine the number of channels N and preset redundancy coefficient K Using formula L=2 ceil[log2 (N+K)] Dynamically determine sequence length L ,in ceiling This is the floor function.

[0212] In an optional embodiment of the second aspect of the present invention, the channel matrix calculation module includes:

[0213] The impulse response acquisition unit is used to perform parallel correlation operations on the response signal and the Walsh-Hadamard orthogonal test sequence through a parallel correlator architecture to obtain the channel impulse response between the transmit channel and the receive channel in the optical module.

[0214] The frequency domain channel matrix construction and verification unit is used to extract key parameters from all the channel impulse responses, form a frequency domain channel matrix, and verify the validity of the frequency domain channel matrix.

[0215] The retest determination unit is used to trigger a retesting mechanism when the ratio of the maximum singular value to the minimum singular value of the frequency domain channel matrix is ​​greater than a preset threshold.

[0216] The evaluation output unit is used to perform abnormal channel monitoring and shielding, SVD noise reduction processing on the frequency domain channel matrix when the ratio of the maximum singular value to the minimum singular value is less than or equal to the preset threshold, and to perform amplitude / phase error verification. After the error verification passes, the output channel matrix is ​​generated. H。

[0217] In an optional embodiment of the second aspect of the present invention, the precoding matrix calculation module includes:

[0218] Preprocessing unit, used for processing the channel matrix H Channel analysis and preprocessing are performed, including matrix condition number analysis, noise power estimation, and regularization parameter calculation.

[0219] SVD decomposition unit, used for the preprocessed channel matrix H Perform SVD decomposition calculation, which includes matrix bidiagonalization, singular value iteration calculation, convergence judgment, and decomposition result recombination;

[0220] The precoding unit is used to perform MMSE precoding calculation based on the SVD decomposition results. The MMSE precoding calculation includes regularization matrix construction, matrix inversion operation, matrix multiplication sequence and initial precoding matrix generation.

[0221] The constraint processing unit is used to perform power constraint processing on the initial precoding matrix. The power constraint processing includes parallel computation of column norm, maximum norm identification, power normalization processing, and margin management.

[0222] The verification output unit is used to verify the power compliance of the normalized precoding matrix after power constraint processing. Upon successful verification, the precoding matrix is ​​obtained. W。

[0223] In an optional embodiment of a second aspect of the invention, the preprocessing unit includes:

[0224] Noise power estimation subunit, used for estimation based on the channel matrix H The minimum singular value is used to estimate the noise power;

[0225] The regularization parameter calculation subunit is used to adaptively calculate the regularization parameter λ based on the noise power and the preset empirical coefficient.

[0226] The regularization processing subunit is used to apply the regularization parameter λ to the channel matrix. H Regularization preprocessing is performed to improve the precoding matrix. W Numerical stability of the calculation.

[0227] In an optional embodiment of the second aspect of the present invention, the constraint processing unit includes:

[0228] The column norm calculation subunit is used to simultaneously calculate the column norm of each column of the initial precoding matrix using a parallel architecture;

[0229] The power allocation subunit is used to perform power allocation based on the water-filling algorithm and calculate the water-filling power for each channel mode.

[0230] The scaling factor calculation subunit is used to determine the maximum norm and calculate the scaling factor based on the maximum norm and the power margin factor.

[0231] The normalization processing subunit is used to multiply the scaling factor by the initial precoding matrix to obtain the normalized precoding matrix.

[0232] In an optional embodiment of the second aspect of the present invention, the policy network of the agents adopts a deep fully connected structure, all agents store experience data in a shared experience replay pool, and update the policy network based on a near-end policy optimization algorithm; the performance indicators include the bit error rate, signal-to-noise ratio, error vector amplitude, eye diagram height and width, Q factor, and indicator sliding window statistics of the current channel and adjacent channels; the environmental parameters include the laser bias current, modulator drive voltage, transimpedance amplifier gain, received optical power, chip temperature, and ambient temperature of the current channel; the system state includes the channel weight norm, power amplifier output level, and phase error of the clock recovery phase-locked loop.

[0233] This invention proposes a multi-algorithm collaborative parallel debugging system for multi-channel optical modules. The overall hardware architecture includes an intelligent main control system layer, a parallel debugging hardware platform layer, and a device interface layer. A dedicated hardware architecture is designed to match the computational characteristics of the algorithms, ensuring the entire system meets the real-time requirements of high-speed optical module parallel debugging. The algorithm collaborative architecture organically coordinates three different categories of intelligent algorithms (system identification, optimization theory, and autonomous learning) to construct a layered, closed-loop, and automated debugging system, fundamentally solving the bottleneck problem of traditional serial debugging methods. WHT (Walsh-Hadamard Transform) is used for fast system identification, enabling instantaneous, one-time global channel state acquisition, solving the problem of slow perception. MMSE precoding is used for model-based fast parameter calculation. Distributed RL is used for long-term, nonlinear, multi-objective adaptive optimization. Each of the three functions performs its own role, forming a complete closed loop. Whether it is the parallel channel estimation of WHT, the global matrix operation of MMSE, or the multi-agent cooperative decision-making of DRL, the core idea is to regard the channel as an interconnected system rather than an independent individual, thereby achieving true system-level performance optimization. The explicit inclusion of a nonlinear damage model in the MMSE model and the integration of multi-objective trade-offs and dynamic change penalties into the reward function of RL enable the system to not only handle ideal linear static problems, but also cope with complex real-world situations.

[0234] Figure 7 This is a schematic diagram of a multi-algorithm collaborative multi-channel optical module parallel debugging device provided by an embodiment of the present invention. This multi-algorithm collaborative multi-channel optical module parallel debugging device can vary significantly due to different configurations or performance. It may include one or more processors 70 (central processing units, CPUs) (e.g., one or more processors) and memory 80, and one or more storage media 90 (e.g., one or more mass storage devices) for storing application programs or data. The memory and storage media can be temporary or persistent storage. The program stored in the storage media may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the multi-algorithm collaborative multi-channel optical module parallel debugging device. Furthermore, the processor may be configured to communicate with the storage media to execute the series of instruction operations in the storage media on the multi-algorithm collaborative multi-channel optical module parallel debugging device.

[0235] The multi-algorithm collaborative multi-channel optical module parallel debugging device of the present invention may further include one or more power supplies 100, one or more wired or wireless network interfaces 110, one or more input / output interfaces 120, and / or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 7 The illustrated structure of the multi-algorithm collaborative multi-channel optical module parallel debugging device does not constitute a limitation on the multi-algorithm collaborative multi-channel optical module parallel debugging device. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0236] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the multi-algorithm collaborative multi-channel optical module parallel debugging method.

[0237] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system or system / unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0238] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0239] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A parallel debugging method for multi-channel optical modules using multi-algorithm collaboration, characterized in that, include: Based on the number of channels in the optical module and the system redundancy requirements, the sequence length is determined and Walsh-Hadamard orthogonal test sequences are recursively generated based on the Sylvester construction method. The Walsh-Hadamard orthogonal test sequence is applied in parallel to all optical channels of the optical module, and the response signals of all optical channels are acquired synchronously. Based on the response signals of all optical channels and using the MIMO system identification algorithm with Walsh-Hadamard transform, the channel matrix characterizing the properties and crosstalk of all optical channels is calculated in one step. H ; The MIMO precoding algorithm based on the MMSE criterion is used for the channel matrix. H Preprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W ; With the precoding matrix W Based on this, each optical channel of the optical module is regarded as an independent intelligent agent. A multi-objective collaborative optimization algorithm of distributed reinforcement learning is adopted to collaboratively optimize at least one operating parameter of the optical module according to the performance index, environmental parameters and system state of each optical channel, in order to find the global optimal solution. The optimized operating parameters are written into the register of the optical module, performance verification is performed, and a debugging report is generated.

2. The multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to claim 1, characterized in that, The process of determining the sequence length based on the number of channels in the optical module and system redundancy requirements, and recursively generating the Walsh-Hadamard orthogonal test sequence based on the Sylvester construction method, includes: Based on the number of channels N and preset redundancy coefficient K Using formula L=2 ceil[log2(N+K)] Dynamically determine sequence length L ,in ceil This is the floor function.

3. The multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to claim 1, characterized in that, The MIMO system identification algorithm, based on the response signals of all optical channels and using Walsh-Hadamard transform, calculates the channel matrix characterizing the properties and crosstalk of all optical channels in one step. H include: By using a parallel correlator architecture, parallel correlation operations are performed on the response signal and the Walsh-Hadamard orthogonal test sequence to obtain the channel impulse response between the transmit channel and the receive channel in the optical module. Key parameters are extracted from all the channel impulse responses to form a frequency domain channel matrix, and the validity of the frequency domain channel matrix is ​​verified. When the ratio of the maximum singular value to the minimum singular value of the frequency domain channel matrix is ​​greater than a preset threshold, a remeasurement mechanism is triggered. When the ratio of the maximum singular value to the minimum singular value of the frequency domain channel matrix is ​​less than or equal to the preset threshold, the frequency domain channel matrix undergoes abnormal channel monitoring and shielding, SVD noise reduction processing, and amplitude / phase error verification. Once the error verification is passed, the channel matrix is ​​output. H。 4. The multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to claim 1, characterized in that, The MIMO precoding algorithm based on the MMSE criterion applies the channel matrix. H Preprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W include: For the channel matrix H Channel analysis and preprocessing are performed, including matrix condition number analysis, noise power estimation, and regularization parameter calculation. The preprocessed channel matrix H Perform SVD decomposition calculation, which includes matrix bidiagonalization, singular value iteration calculation, convergence judgment, and decomposition result recombination; MMSE precoding is performed based on the SVD decomposition results. The MMSE precoding calculation includes regularization matrix construction, matrix inversion operation, matrix multiplication sequence, and initial precoding matrix generation. The initial precoding matrix is ​​subjected to power constraint processing, which includes parallel computation of column norm, identification of maximum norm, power normalization processing, and margin management. The power compliance of the normalized precoding matrix after power constraint processing is verified. The precoding matrix is ​​obtained after the verification is passed. W .

5. The multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to claim 4, characterized in that, The channel matrix H Channel analysis and preprocessing include: Based on the channel matrix H The minimum singular value is used to estimate the noise power; The regularization parameter λ is adaptively calculated based on the noise power and a preset empirical coefficient. The channel matrix is ​​adjusted using the regularization parameter λ. H Regularization preprocessing is performed to improve the precoding matrix. W Numerical stability of the calculation.

6. The multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to claim 4, characterized in that, The power constraint processing of the initial precoding matrix includes: The column norm of each column of the initial precoding matrix is ​​calculated simultaneously using a parallel architecture; Power allocation is performed based on the water-filling algorithm, and the water-filling power of each channel mode is calculated. Determine the maximum norm, and calculate the scaling factor based on the maximum norm and the power margin factor; Multiply the scaling factor by the initial precoding matrix to obtain the normalized precoding matrix.

7. The multi-algorithm collaborative parallel debugging method for multi-channel optical modules according to claim 1, characterized in that, The policy network of the agents adopts a deep fully connected structure. All agents store experience data in a shared experience replay pool and update the policy network based on the near-end policy optimization algorithm. The performance indicators include the bit error rate, signal-to-noise ratio, error vector amplitude, eye diagram height and width, Q factor, and indicator sliding window statistics for the current channel and adjacent channels. The environmental parameters include the laser bias current, modulator drive voltage, transimpedance amplifier gain, received optical power, chip temperature, and ambient temperature for the current channel. The system status includes the channel weight norm, power amplifier output level, and phase error of the clock recovery phase-locked loop.

8. A multi-algorithm collaborative parallel debugging system for multi-channel optical modules, characterized in that, The multi-algorithm collaborative multi-channel optical module parallel debugging system includes: The orthogonal test sequence generation module is used to determine the sequence length based on the number of channels of the optical module and the system redundancy requirements, and recursively generate Walsh-Hadamard orthogonal test sequences based on the Sylvester construction method. The parallel testing module is used to apply the Walsh-Hadamard orthogonal test sequence in parallel to all optical channels of the optical module and to synchronously acquire the response signals of all optical channels. The channel matrix calculation module is used to calculate, in one step, the channel matrix characterizing the properties and crosstalk of all optical channels based on the response signals of all optical channels and using the MIMO system identification algorithm with Walsh-Hadamard transform. H ; The precoding matrix calculation module is used to perform precoding on the channel matrix using a MIMO precoding algorithm based on the MMSE criterion. H Preprocessing and SVD decomposition are performed, and the precoding matrix is ​​calculated with the goal of balancing crosstalk suppression and noise enhancement. W ; The runtime parameter co-optimization module is used to optimize the precoding matrix. W Based on this, each optical channel of the optical module is regarded as an independent intelligent agent. A multi-objective collaborative optimization algorithm of distributed reinforcement learning is adopted to collaboratively optimize at least one operating parameter of the optical module according to the performance index, environmental parameters and system state of each optical channel, in order to find the global optimal solution. The optimized performance verification module is used to write the optimized operating parameters into the register of the optical module, perform performance verification, and generate a debugging report.

9. A multi-algorithm collaborative parallel debugging device for multi-channel optical modules, characterized in that, The multi-algorithm collaborative multi-channel optical module parallel debugging device includes: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor invokes the instructions in the memory to cause the multi-algorithm collaborative multi-channel optical module parallel debugging device to execute the multi-algorithm collaborative multi-channel optical module parallel debugging method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-algorithm collaborative multi-channel optical module parallel debugging method as described in any one of claims 1-7.

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