Underwater optical communication LDPC coding processing method and communication system

Abstract

The invention relates to an underwater optical communication LDPC (Low Density Parity Check) coding processing method. The method comprises the following steps: determining a current channel quality grade based on a real-time communication distance and channel state information; dynamically selecting LDPC (Low Density Parity Check) coding parameters and modulation orders in combination with equipment mobile data and channel quality levels, and generating an adaptive parameter combination; performing error correction coding on the data according to the selected parameters and generating an optical communication signal; transmitting to a receiving end through an optical channel; and dynamically updating the coding parameter and the modulation order based on the feedback information of the receiving end. According to the method, the problem that a traditional fixed coding scheme is insufficient in error correction capability or wastes resources in a dynamic distance scene is solved by predicting the equipment moving trend and the channel state in real time and adaptively adjusting the coding parameters and the modulation mode, the error rate is remarkably reduced, the communication efficiency is improved, and the method is suitable for a complex and changeable underwater optical communication environment.

Description

Technical Field

[0001] The present invention relates to the field of coding processing, and in particular to an LDPC coding processing method and a communication system for underwater optical communication. Background Art

[0002] As a core technology in the fields of deep-sea exploration, underwater monitoring, etc., the reliability of underwater optical communication is limited by the absorption, scattering, and turbulence effects of the complex water environment on optical signals. In recent years, the low-density parity-check (LDPC) coding technology has been widely used to improve the anti-interference ability of underwater optical communication systems due to its excellent error correction performance. However, since underwater devices are often in a dynamic moving state, the real-time change of the communication distance causes the channel attenuation characteristics to fluctuate violently, and it is difficult for traditional fixed coding schemes to achieve a dynamic balance between transmission efficiency and error correction ability.

[0003] In the prior art, the static configuration of LDPC coding parameters has become a key bottleneck restricting system performance. For example, in a long-distance communication scenario, although fixed low code rate coding can improve error correction ability, it results in a significant decrease in transmission efficiency; while in short-distance communication, high code rate coding can reduce redundant overhead, but the error correction performance is insufficient, leading to a sharp increase in the bit error rate. In addition, the prior art lacks a cooperative prediction mechanism for channel state and device movement trend, and cannot predict the change of communication distance in advance and dynamically adjust coding parameters, resulting in parameter switching delay or misjudgment, seriously affecting communication continuity. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide an LDPC coding processing method for underwater optical communication that can adapt to dynamic channel characteristics in real time and balance error correction performance and transmission efficiency.

[0005] The purpose of the present invention is achieved by the following solutions:

[0006] In a first aspect, the present application provides an LDPC coding processing method for underwater optical communication, including the following steps:

[0007] S1: Determine the current channel quality level based on the real-time communication distance and channel state information;

[0008] S2: Dynamically select the LDPC coding parameters and modulation order based on the real-time position data, motion state data, historical communication distance sequence of the device, and the current channel quality level to generate an adapted parameter combination;

[0009] S3: Perform LDPC error correction coding processing on the input data based on the LDPC coding parameters in the adapted parameter combination to generate a coded codeword;

[0010] S4: Based on the modulation order in the adaptation parameter combination, perform pulse position modulation mapping on the encoded codewords to generate an optical communication signal and transmit it to the receiving end through an optical channel;

[0011] S5: Based on the demodulated optical pulse time slot sequence and the updated channel state information fed back by the receiving end, perform dynamic update processing on the LDPC coding parameters and modulation order to generate an updated adaptation parameter combination.

[0012] In one embodiment, step S2 includes:

[0013] Perform prediction processing on the real-time position data, motion state data, and historical communication distance sequence of the device based on the LSTM algorithm to generate a future distance change curve;

[0014] Based on the current channel quality level and the future distance change curve, perform joint decision-making processing on the code length, code rate, and modulation order of LDPC coding through a multi-objective optimization algorithm to generate a candidate parameter set;

[0015] Based on a preset distance threshold interval, select the optimal combination of code length, code rate, and modulation order from the candidate parameter set to generate an adaptation parameter combination.

[0016] In one embodiment, performing prediction processing on the real-time position data, motion state data, and historical communication distance sequence of the device based on the LSTM algorithm to generate a future distance change curve includes:

[0017] Based on the historical position data of the device, intercept the communication distance sequence through a sliding window sampling technique to generate a training data set;

[0018] Based on the training data set, perform feature extraction processing on the device motion trajectory through a temporal convolutional network to generate a spatio-temporal correlation feature vector;

[0019] Based on the spatio-temporal correlation feature vector, perform prediction processing on the future communication distance of the device through a Kalman filtering algorithm to generate a future distance change curve.

[0020] In one embodiment, step S4 includes:

[0021] Based on the modulation order, segment the encoded codewords through a symbol block segmentation algorithm to generate multiple modulation symbol blocks;

[0022] For each modulation symbol block, perform symbol mapping processing through a pulse position modulation coding table to generate an optical pulse time slot sequence;

[0023] Based on the current channel quality level, perform power calibration processing on the optical pulse time slot sequence through an optical power adaptive algorithm to generate an optical communication signal;

[0024] Transmit the optical communication signal to the receiving end through an optical channel.

[0025] In one embodiment, step S5 includes:

[0026] Perform error correction processing on the demodulated optical pulse time slot sequence fed back by the receiving end based on the soft decision decoding algorithm to generate an estimated bit error rate;

[0027] Based on the estimated bit error rate and the real-time updated channel state information, perform an update evaluation process on the current adapted parameter combination through a parameter switching decision model to generate an update decision instruction;

[0028] Based on the update decision instruction, perform seamless switching processing on the LDPC coding parameters and modulation order through the double buffer alternating loading technology to generate an updated adapted parameter combination.

[0029] In a second aspect, the present application provides an underwater optical communication LDPC coding processing system, which is configured with the following modules:

[0030] A channel quality evaluation module, which is used to determine the current channel quality level based on the real-time communication distance and channel state information;

[0031] A dynamic parameter selection module, which is used to perform dynamic selection processing on the LDPC coding parameters and modulation order based on the real-time position data, motion state data, historical communication distance sequence of the device, and the current channel quality level to generate an adapted parameter combination;

[0032] An LDPC coding processing module, which is used to perform LDPC error correction coding processing on the input data based on the LDPC coding parameters in the adapted parameter combination to generate a coded codeword;

[0033] A modulation mapping transmission module, which is used to perform pulse position modulation mapping processing on the coded codeword based on the modulation order in the adapted parameter combination to generate an optical communication signal and transmit it to the receiving end through an optical channel;

[0034] A feedback update processing module, which is used to perform dynamic update processing on the LDPC coding parameters and modulation order based on the demodulated optical pulse time slot sequence fed back by the receiving end and the updated channel state information to generate an updated adapted parameter combination.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned underwater optical communication LDPC coding processing method.

[0036] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned underwater optical communication LDPC coding processing method.

[0037] In summary, the underwater optical communication LDPC coding processing method provided by the present invention realizes the dynamic selection and update of LDPC coding parameters and modulation orders by real-time monitoring and analyzing various factors in the communication environment, greatly improving the flexibility and adaptability of the optical communication system. This adaptive mechanism can automatically optimize transmission parameters under different channel conditions to ensure the efficiency and reliability of data transmission. At the same time, by utilizing the strong error correction ability of LDPC coding and the anti-interference characteristics of PPM modulation, the performance of the system is further enhanced, the bit error rate is reduced, and the overall communication quality is improved, providing strong support for the wide application of optical communication technology in complex and changeable environments.

[0038] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flowchart of an underwater optical communication LDPC coding processing method provided by an embodiment of the present application;

[0040] Figure 2 It is a schematic flowchart of generating an adaptation parameter combination provided by an embodiment of the present application;

[0041] Figure 3 It is a schematic diagram of the result of an underwater optical communication LDPC coding processing communication system provided by another embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the understanding of the disclosure of the invention more thorough and comprehensive.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0044] In one embodiment, as Figure 1As shown, a method for LDPC coding processing in underwater optical communication is provided. In this embodiment, this method is exemplified by being applied to a terminal. It can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0045] S1: Determine the current channel quality level based on the real-time communication distance and channel state information.

[0046] Specifically, in an optical communication system, the real-time communication distance directly affects the attenuation degree of the optical signal, while the channel state information (CSI) reflects the propagation characteristics of the optical channel, including scattering, fading, multipath effects, etc. The system measures key indicators such as the signal-to-noise ratio (SNR) of the optical signal, and combines the real-time communication distance and CSI. Preferably, a pre-set channel quality indicator (CQI) standard can be used to determine the current channel quality level. For example, in a 5G network, the CQI value range is 0 to 15, and different CQI values correspond to different channel quality conditions, which can provide a basic basis for the selection of subsequent transmission parameters.

[0047] S2: Dynamically select the LDPC coding parameters and modulation order based on the device's real-time position data, motion state data, historical communication distance sequence, and the current channel quality level, and generate an adapted parameter combination.

[0048] Specifically, the device's real-time position data and motion state data can reflect its dynamic changes in space, while the historical communication distance sequence helps to analyze the stability trend of the communication link. The system combines these data with the current channel quality level to construct a multi-dimensional feature vector as input to drive a machine learning-based dynamic parameter selection model. Through learning and training on a large amount of historical communication data, this model can automatically predict the combination of LDPC coding parameters and modulation order that is most suitable for the current communication environment. For example, when the channel quality is good and the device is relatively stationary, the model may select a high-order modulation method and a higher coding rate to improve data transmission efficiency; while when the channel quality is poor or the device moves at a high speed, resulting in frequent channel changes, it tends to select a low-order modulation and a lower coding rate to ensure communication reliability.

[0049] S3: Perform LDPC error correction coding processing on the input data based on the LDPC coding parameters in the adapted parameter combination to generate a coded codeword.

[0050] Specifically, LDPC coding is an efficient forward error correction coding technique. Its core is to construct parity check codes with low density characteristics through sparse parity check matrices. In this process, first, according to the LDPC coding parameters in the adaptation parameter combination, such as code rate, block length, etc., the corresponding LDPC parity check matrix and generator matrix are generated. Then, the input data is subjected to data alignment and zero-padding operations to make its length adapt to the dimension requirements of the generator matrix. Finally, the zero-padded data is multiplied by the generator matrix to generate an encoded codeword containing the original information bits and parity bits. This coding method can effectively correct the errors introduced during the transmission at the receiving end and improve the reliability of data transmission.

[0051] S4: Based on the modulation order in the adaptation parameter combination, perform pulse position modulation mapping processing on the encoded codeword to generate an optical communication signal and transmit it to the receiving end through an optical channel.

[0052] Specifically, pulse position modulation (PPM) is a modulation method suitable for optical communication. Its basic principle is to map the encoded data to the positions of optical pulses. According to the modulation order in the adaptation parameter combination, the mapping rule of PPM is determined, that is, the number of positions of optical pulses within the time interval corresponding to each symbol. For example, in M-ary PPM, each symbol occupies M time intervals, and the appearance of an optical pulse in the k-th interval represents the value of k - 1. After converting the encoded codeword into the corresponding optical pulse sequence according to the PPM mapping rule, it is transmitted through the optical channel. During the transmission process, the optical signal may be affected by various factors, such as optical attenuation, scattering, noise, etc. However, the combination of LDPC coding and PPM modulation can resist these interferences to a certain extent and ensure the integrity and accuracy of the signal.

[0053] S5: Based on the demodulated optical pulse time slot sequence and the updated channel state information fed back by the receiving end, perform dynamic update processing on the LDPC coding parameters and modulation order to generate an updated adaptation parameter combination.

[0054] Specifically, after receiving the optical signal, the receiving end will perform demodulation processing to obtain the demodulated optical pulse time slot sequence, and based on this sequence and its own channel estimation results, feedback the updated channel state information to the transmitting end. After receiving this feedback data, the transmitting end combines it with the previous historical data to re-evaluate the current change in the communication environment. Based on the updated channel state information and the latest position and motion state data of the device, the LDPC coding parameters and modulation order are adjusted and optimized again through the dynamic parameter selection model to generate a new adaptation parameter combination to adapt to the changing communication conditions and achieve the adaptive optimization of the system.

[0055] In summary, the underwater optical communication LDPC coding processing method provided by the present invention realizes the dynamic selection and update of LDPC coding parameters and modulation orders by real-time monitoring and analyzing various factors in the communication environment, greatly improving the flexibility and adaptability of the optical communication system. This adaptive mechanism can automatically optimize transmission parameters under different channel conditions to ensure the efficiency and reliability of data transmission. At the same time, by utilizing the strong error correction ability of LDPC coding and the anti-interference characteristics of PPM modulation, the system performance is further enhanced, the bit error rate is reduced, and the overall communication quality is improved, providing strong support for the wide application of optical communication technology in complex and changing environments.

[0056] In one embodiment, as Figure 2 shown, step S2 includes:

[0057] S210. Perform prediction processing on the real-time position data, motion state data, and historical communication distance sequence of the device based on the LSTM algorithm to generate a future distance change curve.

[0058] Specifically, in an optical communication system, the real-time position data and motion state data of the device can reflect its dynamic changes in space, while the historical communication distance sequence helps analyze the stability trend of the communication link. Taking these data as inputs, a long short-term memory network (LSTM) is used for training and prediction. LSTM is a special type of recurrent neural network (RNN) that can capture long-term dependencies and is particularly suitable for processing time series data.

[0059] Preferably, as Figure 2 shown, the future distance change curve is generated through the following steps:

[0060] S211. Based on the historical position data of the device, intercept the communication distance sequence through the sliding window sampling technique to generate a training data set.

[0061] Specifically, in an optical communication system, the historical location data of a device can reflect its movement trajectory in space, while the communication distance sequence is a quantitative representation of the relative position change between devices. Taking these data as input, the sliding window sampling technique is used for processing. The sliding window sampling technique realizes continuous sampling of data by dividing the time series data into multiple windows of fixed length, and there is a certain overlap between each window. Specifically, first, the window size and overlap rate are determined. For example, the window size can be set to include a certain number of position data points, and the overlap rate can be set to half of the window length. Then, starting from the starting position of the historical location data, sliding window sampling is performed according to the set window size and overlap rate, sliding one window length each time until the entire time series data is traversed. The communication distance sequence within each obtained window is used as an independent sample, and multiple samples together constitute the training dataset, thereby retaining the continuity and local features of the time series data, while reducing the number of sampling points, lowering the computational complexity, and providing a high-quality data basis for subsequent feature extraction and model training.

[0062] S212. Based on the training dataset, the time convolutional network is used to extract features from the device movement trajectory to generate a spatio-temporal correlation feature vector.

[0063] Specifically, the Temporal Convolutional Network (TCN) is a deep learning model specifically designed for processing sequence data. It combines the parallel processing ability of the Convolutional Neural Network (CNN) and the long-term dependence modeling ability of the Recurrent Neural Network (RNN). The communication distance sequence samples in the training dataset are input into the TCN model. The convolutional kernels in the TCN slide on the input data and can capture local temporal features, such as short-term fluctuations and change patterns in the device movement trajectory. At the same time, through multiple layers of convolution and pooling operations, the TCN can further extract high-level spatio-temporal correlation features. These features not only contain the change trends in the time dimension but also integrate the position-related information in the space dimension. Finally, the TCN integrates the extracted spatio-temporal correlation features into a feature vector, which can comprehensively and compactly represent the features of the device movement trajectory and provide a rich feature basis for subsequent prediction processing.

[0064] S213. Based on the spatio-temporal correlation feature vector, the Kalman filter algorithm is used to predict the future communication distance of the device to generate a future distance change curve.

[0065] Specifically, the Kalman filter is a dynamic state estimation algorithm based on the mathematical model of a dynamic system and observed data. Its core idea is to use the existing system information and current observed data to predict and correct the system state. Taking the spatio-temporal correlation feature vector as the input observed data of the Kalman filter and combining it with the dynamic system model of the device movement, recursive state estimation is performed. Specifically, at each time step, first, based on the state estimate value at the previous time step and the system model, the device position and communication distance at the current time are predicted; then, the prediction value is corrected using the spatio-temporal correlation feature vector at the current time to obtain a more accurate state estimate value. By continuously iterating this process, the communication distances of the device at multiple future time steps can be predicted, and these predicted values are connected to form a future distance change curve. This prediction method can, to a certain extent, resist the noise interference in the observed data and provide a relatively smooth and accurate future communication distance prediction result.

[0066] S220. Based on the current channel quality level and the future distance change curve, perform a joint decision-making process on the code length, code rate, and modulation order of LDPC coding through a multi-objective optimization algorithm to generate a candidate parameter set.

[0067] Specifically, the current channel quality level reflects the propagation characteristics of the current optical channel, while the future distance change curve indicates the future state of the communication link. The system combines these two factors as the input conditions for the multi-objective optimization algorithm. The multi-objective optimization algorithm aims to optimize multiple objective functions simultaneously. For example, in this system, it may be necessary to minimize the bit error rate, maximize the spectral efficiency, and reduce the transmission delay, etc. By defining appropriate fitness functions and constraint conditions, the algorithm can search in the feasible solution space of the code length, code rate, and modulation order of LDPC coding to find a series of parameter combinations that meet the conditions, and these parameter combinations constitute the candidate parameter set. Preferably, an evolutionary algorithm such as the non-dominated sorting genetic algorithm (NSGA-II) can be used to perform this joint decision-making process, and through multiple generations of evolutionary iteration, gradually approach the Pareto optimal front.

[0068] S230. Based on the preset distance threshold interval, select the optimal combination of code length, code rate, and modulation order from the candidate parameter set to generate an adapted parameter combination.

[0069] Specifically, the preset distance threshold interval is determined according to the design requirements of the optical communication system and the actual application scenario, and is used to ensure the stability and reliability of the communication link. The future distance change curve is compared with this threshold interval, and candidate parameter combinations in which the communication distance is always within the threshold interval during the prediction time period are screened out. For these parameter combinations that meet the conditions, their performances on different performance metrics are further evaluated, such as bit error rate, spectral efficiency, etc. Finally, the combination of code length, code rate, and modulation order with the optimal comprehensive performance is selected as the adaptation parameter combination in the current communication environment to guide the subsequent communication transmission process.

[0070] In summary, an underwater optical communication LDPC coding processing method provided by the present invention realizes the dynamic and intelligent optimization of LDPC coding parameters and modulation order in an optical communication system by combining the prediction of future communication distance by the LSTM algorithm and the joint decision-making of transmission parameters by the multi-objective optimization algorithm. This mechanism can predict in advance the change trend of the communication link and adjust the transmission parameters in advance, so as to always maintain efficient and reliable communication performance under different channel conditions. At the same time, using the preset distance threshold interval for parameter screening further ensures the stability of the communication link, reduces the risk of communication interruption, and provides strong technical support for the application of optical communication technology in complex dynamic environments.

[0071] In one embodiment, step S4 includes:

[0072] S410. Based on the modulation order, the coded codeword is segmented by the symbol block segmentation algorithm to generate a plurality of modulation symbol blocks.

[0073] Specifically, in an optical communication system, the modulation order determines the amount of information that each symbol can carry. According to the selected modulation order, the coded codeword after LDPC error correction coding processing is segmented. The symbol block segmentation algorithm divides the coded codeword into a plurality of symbol blocks with a fixed length according to the number of bits corresponding to the modulation order. For example, in binary phase shift keying (BPSK) modulation, the modulation order is 2, and each symbol carries 1 bit of information. Therefore, the coded codeword is divided into one symbol block for every 1 bit; while in 16-order quadrature amplitude modulation (16QAM), the modulation order is 4, and each symbol carries 4 bits of information. At this time, the coded codeword is divided into one symbol block for every 4 bits. This segmentation processing can ensure that the length of each symbol block matches the modulation order and prepares for the subsequent symbol mapping processing.

[0074] S420. For each modulation symbol block, symbol mapping processing is performed through the pulse position modulation coding table to generate an optical pulse time slot sequence.

[0075] Specifically, the pulse position modulation (PPM) coding table defines the mapping relationship between symbols and the positions of optical pulses in time slots. For each modulation symbol block, the corresponding optical pulse position is looked up in the PPM coding table according to its value. For example, in M-order PPM, each symbol corresponds to M time slots, and the value k (0 ≤ k ≤ M - 1) indicates that an optical pulse is sent in the k-th time slot, and no optical pulse is sent in the remaining time slots. Through this mapping process, the modulation symbol block is converted into a specific optical pulse time slot sequence. This process can transform abstract symbol information into the actual optical signal time slot arrangement, laying the foundation for the generation of optical communication signals.

[0076] S430. Based on the current channel quality level, perform power calibration processing on the optical pulse time slot sequence through an optical power adaptive algorithm to generate an optical communication signal.

[0077] Specifically, the optical power adaptive algorithm dynamically adjusts the transmission power of optical pulses according to the current channel quality level. When the channel quality is good, the optical power is appropriately reduced to save energy; while when the channel quality is poor, the optical power is increased to improve the anti-interference ability of the signal. This algorithm accurately calibrates the signal power in each optical pulse time slot by real-time monitoring of the channel state and combining a pre-set power adjustment strategy. For example, a closed-loop power control method can be adopted, where the receiving end feeds back a power adjustment instruction to the sending end according to the received signal quality, and the sending end adjusts the optical power accordingly. The optical pulse time slot sequence after power calibration forms the final optical communication signal, which can better adapt to the current channel environment and improve the reliability and efficiency of communication.

[0078] S440. Transmit the optical communication signal to the receiving end through an optical channel.

[0079] Specifically, the optical communication signal is transmitted through an optical channel, and the optical channel can be different transmission media such as free space, optical fiber, etc. During the transmission process, the optical signal may be affected by various factors, such as optical attenuation, scattering, refraction, atmospheric turbulence, etc. These factors will cause changes in the intensity and phase of the optical signal, thereby affecting the communication quality. Preferably, the receiving end is equipped with corresponding optical detection devices, such as photodiodes, etc., which can convert the received optical signal into an electrical signal for subsequent demodulation and decoding processing. The receiving end will also optimize the received signal according to its own estimation of the channel state and signal processing ability to recover the original data as accurately as possible.

[0080] In summary, the underwater optical communication LDPC coding processing method provided by the present invention realizes the efficient conversion and optimized transmission from the coded codeword to the optical communication signal through the synergistic effect of the symbol block segmentation algorithm, the pulse position modulation coding table, and the optical power adaptive algorithm. The symbol block segmentation algorithm ensures the matching of the modulation symbol and the modulation order. The PPM coding table realizes the accurate mapping from the symbol to the optical pulse time slot. The optical power adaptive algorithm dynamically adjusts the optical signal power according to the channel condition, improving the adaptability and reliability of the communication.

[0081] In one embodiment, step S5 includes:

[0082] S510. Perform error correction processing on the demodulated optical pulse time slot sequence fed back by the receiving end based on the soft decision decoding algorithm to generate an estimated bit error rate.

[0083] Specifically, in the optical communication system, the demodulated optical pulse time slot sequence fed back by the receiving end may be incorrect due to factors such as channel interference. The soft decision decoding algorithm uses the soft information of the received signal (such as likelihood ratio, log-likelihood ratio, etc.) for decoding, and can more accurately recover the original information. Taking the LDPC code as an example, its decoding process is usually based on the belief propagation algorithm (such as the normalized min-sum decoding algorithm). By calculating the message passing between variable nodes and check nodes through multiple iterations, it gradually approaches the correct codeword. Specifically, first process the demodulated optical pulse time slot sequence to obtain the soft decision information of each bit; then, according to the parity-check matrix of the LDPC code, perform multiple iterations of decoding processing to correct the incorrect bits; finally, count the number of errors between the decoded bits and the original transmitted bits, and calculate the estimated bit error rate.

[0084] S520. Based on the estimated bit error rate and the real-time updated channel state information, perform an update evaluation process on the current adaptation parameter combination through the parameter switching decision model to generate an update decision instruction.

[0085] Specifically, the parameter switching decision model comprehensively considers the estimated bit error rate and the real-time channel state information to evaluate the performance of the current adaptation parameter combination and determine whether parameter update is required. Specifically, the model first sets a bit error rate threshold and a channel quality threshold. When the actual bit error rate exceeds the bit error rate threshold or the channel quality is lower than the channel quality threshold, the parameter update mechanism is triggered. Then, according to the predefined parameter adjustment strategy (such as increasing the code length, reducing the code rate, adjusting the modulation order, etc.), combined with the current bit error rate and channel state, evaluate the expected performance of different parameter combinations through a certain algorithm (such as rule-based decision algorithm, machine learning algorithm, etc.), select the optimal parameter combination as the update target, and generate the corresponding update decision instruction. For example, when the channel quality deteriorates and the bit error rate is high, the model may decide to reduce the modulation order to improve the anti-interference ability of the signal.

[0086] S530. Based on the update decision instruction, the LDPC coding parameters and modulation order are seamlessly switched through the dual-buffer alternating loading technology to generate an updated set of adapted parameters.

[0087] Specifically, the dual-buffer alternating loading technology can achieve seamless parameter switching without interrupting communication. In specific implementation, the system maintains two buffers, one for the LDPC coding parameters and modulation order used in the current communication process, and the other for loading the new set of parameters. When an update decision instruction is received, the new set of parameters is loaded into the spare buffer. At an appropriate time (such as at the end of the current communication frame), the system switches the roles of the two buffers to switch the new set of parameters to the currently used parameters, thus achieving seamless switching of the LDPC coding parameters and modulation order, ensuring the continuity and stability of communication. This technology avoids communication interruptions and data loss caused by parameter switching, improving the overall performance and user experience of the system.

[0088] In summary, an underwater optical communication LDPC coding processing method provided by the present invention realizes efficient processing of receiver feedback processing, parameter update evaluation, and parameter seamless switching in an optical communication system through the synergistic effect of the soft decision decoding algorithm, the parameter switching decision model, and the dual-buffer alternating loading technology. The soft decision decoding algorithm can effectively correct errors during transmission and improve the reliability of data; the parameter switching decision model dynamically evaluates and selects the optimal transmission parameters according to the actual communication situation to ensure the optimality of system performance; the dual-buffer alternating loading technology ensures smooth parameter switching and avoids communication interruptions. The entire process improves the adaptive ability, reliability, and stability of the optical communication system, enabling it to always maintain efficient and stable communication performance in a complex and changing channel environment, enhancing the user experience.

[0089] Preferably, as Figure 3 shown, the present application provides an underwater optical communication LDPC coding processing system 600, which is configured with the following modules:

[0090] A channel quality assessment module 610, which is used to determine the current channel quality level based on the real-time communication distance and channel state information;

[0091] A dynamic parameter selection module 620, which is used to dynamically select and process the LDPC coding parameters and modulation order based on the real-time position data, motion state data, historical communication distance sequence, and current channel quality level of the device to generate an adapted set of parameters;

[0092] An LDPC coding processing module 630, which is used to perform LDPC error correction coding processing on the input data based on the LDPC coding parameters in the adapted set of parameters to generate coded codewords;

[0093] A modulation mapping transmission module 640, configured to perform pulse position modulation mapping processing on the encoded codewords based on the modulation order in the adaptation parameter combination, generate an optical communication signal, and transmit it to the receiving end through an optical channel;

[0094] A feedback update processing module 650, configured to perform dynamic update processing on the LDPC coding parameters and modulation order based on the demodulated optical pulse time slot sequence and the updated channel state information fed back by the receiving end, and generate an updated adaptation parameter combination.

[0095] In summary, a communication system for LDPC coding processing in underwater optical communication provided by the present invention realizes a complete closed-loop optimization from channel quality assessment, dynamic selection of transmission parameters, data coding, signal modulation transmission to parameter feedback update in an optical communication system through the collaborative work of each unit. The channel quality assessment unit monitors the channel condition in real time and provides a basis for parameter selection; the dynamic parameter selection unit intelligently selects the optimal LDPC coding parameters and modulation order to adapt to different communication environments; the LDPC coding processing unit and the modulation mapping transmission unit are respectively responsible for error correction coding of data and modulation transmission of signals, ensuring the reliability and transmission efficiency of data; the feedback update processing unit dynamically updates the transmission parameters according to the feedback from the receiving end and channel changes, realizing the adaptive optimization of the system. The entire process fully considers the characteristics and requirements of the optical communication system, optimizes the generation, transmission and processing of signals, improves the adaptive ability, reliability and stability of the system, enables it to always maintain high-efficiency and stable communication performance in a complex and changeable channel environment, and enhances the user experience.

[0096] Preferably, the dynamic parameter selection module 620 is composed of the following components:

[0097] A spatio-temporal data prediction unit 621, configured to perform prediction processing on the real-time position data, motion state data and historical communication distance sequence of the device based on the LSTM algorithm, and generate a future distance change curve;

[0098] A data sampling sub-unit 6211, configured to intercept the communication distance sequence through a sliding window sampling technique based on the historical position data of the device, and generate a training data set;

[0099] A feature extraction sub-unit 6212, configured to perform feature extraction processing on the device motion trajectory through a temporal convolutional network based on the training data set, and generate a spatio-temporal correlation feature vector;

[0100] A distance prediction sub-unit 6213, configured to perform prediction processing on the future communication distance of the device through a Kalman filtering algorithm based on the spatio-temporal correlation feature vector, and generate a future distance change curve;

[0101] The multi-objective parameter optimization unit 622 is configured to perform joint decision-making processing on the code length, code rate, and modulation order of LDPC coding based on the current channel quality level and the future distance change curve through a multi-objective optimization algorithm, and generate a candidate parameter set;

[0102] The threshold-driven parameter selection unit 623 is configured to select the optimal combination of code length, code rate, and modulation order from the candidate parameter set based on a preset distance threshold range, and generate an adapted parameter combination.

[0103] Preferably, the modulation mapping transmission module 640 is configured with the following units:

[0104] The symbol block segmentation unit 641 is configured to segment the encoded codeword through a symbol block segmentation algorithm based on the modulation order, and generate a plurality of modulation symbol blocks;

[0105] The symbol mapping unit 642 is configured to perform symbol mapping processing on each modulation symbol block through a pulse position modulation coding table, and generate an optical pulse time slot sequence;

[0106] The power calibration unit 643 is configured to perform power calibration processing on the optical pulse time slot sequence through an optical power adaptive algorithm based on the current channel quality level, and generate an optical communication signal;

[0107] The signal transmission unit 644 is configured to transmit the optical communication signal to the receiving end through an optical channel.

[0108] Preferably, the feedback update processing module 650 is configured with the following units:

[0109] The bit error rate estimation unit 651 is configured to perform error correction processing on the demodulated optical pulse time slot sequence fed back by the receiving end through a soft decision decoding algorithm, and generate a bit error rate estimation value;

[0110] The parameter update decision unit 652 is configured to perform update evaluation processing on the current adapted parameter combination through a parameter switching decision model based on the bit error rate estimation value and the real-time updated channel state information, and generate an update decision instruction;

[0111] The parameter seamless switching unit 653 is configured to perform seamless switching processing on the LDPC coding parameters and the modulation order through a dual-buffer alternating loading technique based on the update decision instruction, and generate an updated adapted parameter combination.

[0112] In one embodiment, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the above-mentioned underwater optical communication LDPC coding processing method is implemented.

[0113] In one embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned underwater optical communication LDPC coding processing method is implemented.

[0114] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0115] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. Those of ordinary skill in the art can understand and implement it without creative work.

[0116] The above-described embodiments only represent several implementation manners of the embodiments of the present application, and their descriptions are relatively specific and detailed, but should not be construed as a limitation on the patent scope of the application embodiments. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A method for processing LDPC coding for underwater optical communication, characterized in that: The following steps are involved: S1: Determine the current channel quality level based on the real-time communication distance and channel status information; S2: Based on the real-time location data, motion state data, historical communication distance sequence and the current channel quality level of the device, dynamically select and process the LDPC coding parameters and modulation order to generate an adaptive parameter combination; S3: Based on the LDPC coding parameters in the adaptation parameter combination, perform LDPC error correction coding on the input data to generate a coding codeword; S4: Based on the modulation order in the adaptation parameter combination, the coded codeword is subjected to pulse position modulation mapping processing to generate an optical communication signal and transmit it to a receiving end through an optical channel; S5: Based on the demodulated optical pulse time slot sequence fed back by the receiving end and the updated channel state information, the LDPC coding parameters and the modulation order are dynamically updated to generate an updated adaptation parameter combination.

2. The method according to claim 1, characterized in that The step S2 comprises: Based on the LSTM algorithm, the real-time location data, motion status data and historical communication distance sequence of the device are predicted and processed to generate the future distance change curve; Based on the current channel quality level and the future distance change curve, a joint decision process is performed on the code length, code rate and modulation order of LDPC coding through a multi-objective optimization algorithm to generate a candidate parameter set; Based on a preset distance threshold interval, an optimal code length, code rate and modulation order combination is selected from the candidate parameter set to generate the adaptation parameter combination.

3. The method according to claim 2, characterized in that The LSTM algorithm is used to predict the real-time location data, motion state data and historical communication distance sequence of the device to generate a future distance change curve, including: Based on the historical location data of the device, the communication distance sequence is intercepted and processed through the sliding window sampling technology to generate a training data set; Based on the training data set, feature extraction processing is performed on the device motion trajectory through a temporal convolutional network to generate a spatiotemporal correlation feature vector; Based on the spatiotemporal correlation feature vector, the future communication distance of the device is predicted and processed by a Kalman filter algorithm to generate a future distance change curve.

4. The method according to claim 3, characterized in that The step S4 comprises: Based on the modulation order, segmenting the coded codeword by a symbol block segmentation algorithm to generate a plurality of modulation symbol blocks; For each of the modulation symbol blocks, a symbol mapping process is performed through a pulse position modulation coding table to generate an optical pulse time slot sequence; Based on the current channel quality level, power calibration processing is performed on the optical pulse time slot sequence by an optical power adaptive algorithm to generate the optical communication signal; The optical communication signal is transmitted to a receiving end through an optical channel.

5. The method according to claim 1, characterized in that: The step S5 comprises: Performing error correction processing on the demodulated optical pulse time slot sequence fed back by the receiving end based on a soft decision decoding algorithm to generate a bit error rate estimation value; Based on the bit error rate estimation value and the real-time updated channel state information, the current adaptation parameter combination is updated and evaluated through the parameter switching decision model to generate an update decision instruction; Based on the update decision instruction, the LDPC coding parameters and the modulation order are seamlessly switched through the double buffer alternating loading technology to generate an updated adaptation parameter combination.

6. A communication system for underwater optical communication LDPC coding processing, characterized in that: The system comprises: A channel quality assessment module is used to determine the current channel quality level based on real-time communication distance and channel status information; A dynamic parameter selection module is used to dynamically select LDPC coding parameters and modulation orders based on the real-time location data, motion state data, historical communication distance sequence and current channel quality level of the device to generate an adaptive parameter combination; An LDPC coding processing module, used to perform LDPC error correction coding processing on input data based on the LDPC coding parameters in the adaptation parameter combination to generate a coding codeword; A modulation mapping transmission module, configured to perform pulse position modulation mapping processing on the coded codeword based on the modulation order in the adaptation parameter combination, generate an optical communication signal and transmit it to a receiving end through an optical channel; The feedback update processing module is used to dynamically update the LDPC coding parameters and modulation order based on the demodulated optical pulse time slot sequence and updated channel state information fed back by the receiving end, and generate an updated adaptation parameter combination.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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