Adaptive cross-domain gateway underwater acoustic coding method, device, electronic device and medium

Abstract

An embodiment of the present application provides an underwater acoustic coding method and device for an adaptive cross-domain gateway operation, the method comprising: receiving a preset test signal sent by a transmitter before transmitting information, determining the corresponding current time period channel state according to the test signal and a preset least squares method, performing time series analysis according to the current signal-to-noise ratio in the current time period channel state, predicting the channel state for the next time period, and judging the channel quality of the channel state for the next time period based on a numerical comparison relationship between the predicted signal-to-noise ratio in the channel state for the next time period and a preset threshold; if it is determined that the channel state for the next time period is poor, selecting an error correction coding scheme with a lower coding rate and a higher error correction redundancy from a preset coding scheme library; if it is determined that the channel state for the next time period is good, selecting an error correction coding scheme with a higher coding rate from the preset coding scheme library; the present application can optimize channel utilization efficiency and reduce energy waste.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method and device for underwater acoustic coding adapted for cross-domain gateway operation. Background Art

[0002] Underwater acoustic communication is an important technology for transmitting information underwater, with widespread applications in marine resource development, marine environmental monitoring, and underwater robot control. However, due to the complexity and time-varying nature of underwater acoustic channels, underwater acoustic communication faces numerous challenges, with rapid changes in channel states and signal attenuation being the primary factors affecting communication quality. To address these challenges, researchers have been exploring more efficient and reliable underwater acoustic communication coding methods.

[0003] Traditional underwater acoustic communication coding methods typically use fixed coding schemes, making them difficult to adapt to the complex and changing underwater environment. This approach can waste bandwidth resources when the channel is in good condition, while failing to guarantee communication reliability when the channel deteriorates. Therefore, developing adaptive coding methods that can dynamically adjust to channel conditions has become an important research direction in underwater acoustic communication.

[0004] In recent years, with the advancement of signal processing technology and artificial intelligence algorithms, adaptive coding methods based on channel state prediction have gradually attracted the attention of researchers. This approach achieves a better balance between communication reliability and transmission efficiency by dynamically selecting the most appropriate coding scheme through real-time monitoring and prediction of channel conditions. However, accurately predicting rapidly changing underwater acoustic channel conditions and selecting the optimal coding scheme based on the predictions remain major challenges in this field. Summary of the Invention

[0005] In response to the problems in the prior art, the present application provides a method and device for underwater acoustic coding adapted for cross-domain gateway operation, which can optimize channel utilization efficiency and reduce energy waste.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an underwater acoustic coding method for an adaptive cross-domain gateway operation, which is applied to a signal receiver, and the method includes:

[0008] Before transmitting information, receiving a preset test signal sent by the transmitter, determining a corresponding channel state for a current period based on the test signal and a preset least squares method, performing a time series analysis based on a current signal-to-noise ratio in the channel state for the current period to predict a channel state for a next period, and judging the quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio in the channel state for the next period and a preset threshold;

[0009] If it is determined that the channel state in the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from a preset coding scheme library; if it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix according to the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted through the generator matrix to obtain an encoded codeword;

[0010] The codeword transmitted by the transmitter is decoded according to the error correction coding scheme, and a final result is output.

[0011] Furthermore, the receiving of a preset test signal sent by the transmitter before transmitting information, and determining the corresponding channel state in the current period according to the test signal and a preset least squares method, includes:

[0012] The signal receiver monitors the communication channel, waits for the transmitter to send a preset test signal, receives and samples the test signal using the radio frequency front-end module, performs analog-to-digital conversion, and caches the collected digital samples of the test signal in the receiver's memory;

[0013] The buffered test signal data is input into a pre-designed least squares estimation model, and the channel state parameters of the current period are obtained using the least squares principle.

[0014] Furthermore, performing time series analysis based on the current signal-to-noise ratio of the channel state in the current time period to predict the channel state in the next time period includes:

[0015] Extracting signal-to-noise ratio data from the obtained channel state for the current period, constructing a time series based on the signal-to-noise ratio data, and using a time series analysis method to model and predict the time series to obtain the signal-to-noise ratio for the next period;

[0016] The predicted signal-to-noise ratio of the next time period is used as the key indicator of the channel state, combined with the signal attenuation and phase offset indicators in the channel state parameters of the current time period, and input into the preset channel state prediction model to obtain the channel state of the next time period output by the channel state prediction model.

[0017] Furthermore, judging the quality of the channel state in the next time period according to a numerical comparison between the predicted signal-to-noise ratio in the channel state in the next time period and a preset threshold value includes:

[0018] Obtaining a predicted signal-to-noise ratio in the channel state for the next period and comparing the value with a preset signal-to-noise ratio threshold;

[0019] If the predicted signal-to-noise ratio is higher than the signal-to-noise ratio threshold, the channel state is determined to be good; if the predicted signal-to-noise ratio is lower than the signal-to-noise ratio threshold, the channel state is determined to be poor.

[0020] Furthermore, if it is determined that the channel state in the next time period is poor, selecting an error correction coding scheme with a lower coding rate and a higher error correction redundancy from a preset coding scheme library includes:

[0021] Pre-establish a coding scheme library containing different feature label coding schemes;

[0022] If it is determined that the channel state in the next time period is poor, an error correction coding scheme having a lower coding rate label and a higher error correction redundancy label is selected from a preset coding scheme library.

[0023] Furthermore, if it is determined that the channel state in the next time period is better, an error correction coding scheme with a higher coding rate is selected from a preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix according to the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs coding mapping on the coding sequence of the information to be transmitted through the generator matrix to obtain the encoded codeword, including:

[0024] If it is determined that the channel state in the next time period is better, selecting an error correction coding scheme with a higher coding rate label from a preset coding scheme library;

[0025] The selected error correction coding scheme is fed back to the transmitter so that the transmitter constructs a check matrix according to the code length, information bits and check bits in the error correction coding scheme, traverses each column of the check matrix, finds the row where the main element of the current column is located, performs elementary row transformation on the row where the main element is located and the current iteration row, and repeats the elementary row transformation process until the check matrix is ​​simplified to an upper triangular matrix. The upper triangular matrix obtained at this time is the generator matrix, and the coding sequence of the information to be transmitted is coded and mapped through the generator matrix to obtain the coded codeword.

[0026] Furthermore, the decoding operation on the codeword transmitted by the transmitter according to the error correction coding scheme and outputting a final result includes:

[0027] Obtaining the encoded codeword transmitted by the transmitter, and performing error correction on the received codeword according to the parameters of the error correction coding scheme fed back to the transmitter;

[0028] Output the transmission result after decoding and error correction.

[0029] In a second aspect, the present application provides an underwater acoustic coding device adapted for cross-domain operation gateway, comprising:

[0030] a channel state prediction module, configured to receive a preset test signal sent by the transmitter before transmitting information, determine the corresponding channel state for the current period based on the test signal and a preset least squares method, perform time series analysis based on the current signal-to-noise ratio of the channel state for the current period, predict the channel state for the next period, and determine the quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio of the channel state for the next period and a preset threshold;

[0031] an adaptive coding module, configured to select an error correction coding scheme with a lower coding rate and a higher error correction redundancy from a preset coding scheme library if it is determined that the channel state in the next time period is poor; and to select an error correction coding scheme with a higher coding rate from the preset coding scheme library if it is determined that the channel state in the next time period is good, and to feed back the selected error correction coding scheme to the transmitter, so that the transmitter constructs a check matrix based on the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix using a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted using the generator matrix to obtain an encoded codeword;

[0032] A decoding module is used to perform a decoding operation on the codeword transmitted by the transmitter according to the error correction coding scheme and output a final result.

[0033] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for adapting the cross-domain gateway underwater acoustic coding are implemented.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for adapting the cross-domain gateway underwater acoustic coding.

[0035] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for adapting the cross-domain gateway underwater acoustic coding.

[0036] It can be seen from the above technical solution that the present application provides an underwater acoustic coding method and device for an adaptive cross-domain gateway operation, which receives a preset test signal sent by a transmitter before transmitting information, determines the corresponding current time period channel state according to the test signal and the preset least squares method, performs time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predicts the channel state of the next time period, and judges the channel quality of the channel state of the next time period based on the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and the preset threshold; if it is determined that the channel state of the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from the preset coding scheme library; if it is determined that the channel state of the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, thereby optimizing channel utilization efficiency and reducing energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 This is one of the flow charts of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0039] Figure 2 This is the second flow chart of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0040] Figure 3 This is the third flow chart of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0041] Figure 4 This is the fourth flow chart of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0042] Figure 5 This is the fifth flow chart of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0043] Figure 6 This is the sixth flow chart of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0044] Figure 7 This is the seventh flow chart of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0045] Figure 8This is a structural diagram of the underwater acoustic coding device adapted for cross-domain operation gateway in an embodiment of the present application;

[0046] Figure 9 Schematic diagram of the structure of the electronic device in the embodiment of the present application.

[0047] Reference numerals:

[0048] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0049] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0051] Taking into account the problems existing in the prior art, the present application provides an underwater acoustic coding method and device for an adaptive cross-domain gateway operation, which receives a preset test signal sent by a transmitter before transmitting information, determines the corresponding current time period channel state according to the test signal and the preset least squares method, performs time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predicts the channel state of the next time period, and judges the channel quality of the channel state of the next time period based on the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and the preset threshold; if it is determined that the channel state of the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from the preset coding scheme library; if it is determined that the channel state of the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, thereby optimizing channel utilization efficiency and reducing energy waste.

[0052] In order to optimize channel utilization efficiency and reduce energy waste, the present application provides an embodiment of an underwater acoustic coding method adapted for a cross-domain gateway, which is applied to a signal receiver. Figure 1 The method for adapting the cross-domain gateway underwater acoustic coding specifically includes the following contents:

[0053] Step S101: receiving a preset test signal sent by a transmitter before transmitting information, determining a corresponding channel state for a current period based on the test signal and a preset least squares method, performing a time series analysis based on a current signal-to-noise ratio in the channel state for the current period, predicting a channel state for a next period, and determining the quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio in the channel state for the next period and a preset threshold;

[0054] Optionally, in this embodiment, step S101 describes a complex channel state prediction and assessment process, a crucial component of modern communication systems. This step primarily aims to predict and assess the quality of the communication channel before actual information transmission, thereby providing a basis for subsequent transmission strategy formulation. This process involves technologies from multiple fields, including signal processing, statistical analysis, and machine learning, and aims to improve the reliability and efficiency of communication systems.

[0055] First, the system receives a preset test signal from the transmitter. This test signal is typically a known, standardized signal sequence, such as a pseudo-random bit sequence (PRBS) or a specific pilot signal. Choosing an appropriate test signal is crucial for accurately assessing channel conditions. The test signal should adequately stimulate various channel characteristics, including frequency response, multipath effects, and fading characteristics.

[0056] After receiving the test signal, the system uses a preset least squares method to determine the channel status for the current period. Least squares is a common mathematical optimization technique that finds the best function match for the data by minimizing the sum of squared errors. In this context, least squares is used to estimate the channel's transfer function or impulse response. Specifically, the system may compare the transmitted test signal with the received signal and estimate the channel's impact by minimizing the difference between them.

[0057] This process not only determines the channel's transmission characteristics but also estimates the current signal-to-noise ratio (SNR). The SNR is an important indicator of signal quality, representing the ratio of the useful signal power to the noise power. A higher SNR generally indicates better signal quality and more reliable communications.

[0058] Next, the system performs time series analysis based on the current SNR to predict the channel status for the next period. Time series analysis is a statistical method used to analyze data points arranged in chronological order. In this scenario, it is used to predict the future trend of the SNR. Common time series analysis methods include autoregressive models (AR), moving average models (MA), and autoregressive moving average models (ARMA). The system may train these models based on historical data and then use the current SNR data to predict the future SNR.

[0059] After predicting the channel status for the next time period, the system compares the predicted signal-to-noise ratio (SNR) with a preset threshold to determine the quality of the channel status for the next time period. This threshold may be set based on system performance requirements, communication protocol standards, or empirical data. If the predicted SNR is higher than the threshold, the channel status for the next time period is considered good; otherwise, the channel status is considered poor.

[0060] This step addresses several key technical issues. First, it solves the problem of how to assess channel quality before actual communication begins. By using test signals and the least squares method, the system can quickly and accurately estimate the current channel state. Second, it addresses the problem of how to predict future channel conditions. Through time series analysis, the system can make reasonable predictions of future channel conditions based on current and historical data. Finally, it provides a method for objectively assessing channel quality. By comparing the predicted results with preset thresholds, the system can make a clear judgment.

[0061] This step achieves several important technical benefits. First, it improves the adaptability of the communication system. By predicting channel conditions, the system can take appropriate measures before channel conditions deteriorate, such as adjusting transmit power or switching to a more stable frequency band. Second, it improves resource utilization efficiency. The system can decide whether to transmit based on the predicted channel conditions, avoiding wasting energy and bandwidth when channel conditions are poor. Third, it provides a foundation for subsequent transmission strategy optimization. Based on the prediction of future channel conditions, the system can adjust parameters such as the coding scheme and modulation method in advance to adapt to the expected channel conditions.

[0062] Let's illustrate the application of this step through a specific example. Assume that in a wireless sensor network, nodes need to periodically send environmental monitoring data to a base station. To ensure communication reliability and energy efficiency, the system executes step S101 before each data transmission.

[0063] First, the base station sends a preset test signal to the node. This test signal is a pseudo-random bit sequence of length 1023. It has characteristics close to white noise in the frequency domain, which effectively stimulates various channel characteristics. After receiving this test signal, the node uses the least squares method to estimate the channel impulse response. Specifically, the node compares the received signal with the known transmitted sequence and estimates the channel transfer function by minimizing the mean squared error between them.

[0064] Through this process, the node obtains the channel state estimate for the current period, including parameters such as channel gain and phase response. At the same time, the node also calculates the current signal-to-noise ratio, assuming the obtained value is 20dB.

[0065] Next, the node uses an autoregressive integrated moving average (ARIMA) model to predict the SNR for the next period. This ARIMA model is trained based on SNR data collected over the past few weeks. The node inputs the current SNR of 20dB into the ARIMA model, and the model predicts that the SNR will drop to 15dB in the next period (assuming it's 10 minutes from now).

[0066] The node then compares the predicted 15dB SNR with the preset threshold of 18dB. Since 15dB is less than 18dB, the node determines that the channel state in the next time period is poor.

[0067] Based on this judgment, the node can take a variety of measures to cope with the expected degradation in channel quality. For example, it might decide to send its currently accumulated data in advance to avoid transmission when channel conditions deteriorate. Alternatively, it might notify the base station to request the use of stronger forward error correction coding in the next period to offset the expected degradation in signal-to-noise ratio.

[0068] This embodiment demonstrates how step S101 can be applied in a practical wireless communication system. By predictively assessing channel conditions, the system can proactively adapt to changing communication environments, improving the reliability and efficiency of data transmission. This approach is particularly suitable for resource-constrained scenarios, such as wireless sensor networks and IoT devices, as it helps these systems achieve optimal communication performance within limited energy and bandwidth constraints.

[0069] In summary, step S101 provides an advanced channel state prediction and assessment method that combines signal processing, statistical analysis, and machine learning techniques to provide a powerful adaptability and efficiency-enhancing tool for modern communication systems.

[0070] Step S102: If it is determined that the channel state in the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from a preset coding scheme library. If it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix based on the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted through the generator matrix to obtain an encoded codeword;

[0071] Optionally, in this embodiment, step S102 involves an intelligent, adaptive error correction coding scheme selection process, a key step in improving transmission reliability and efficiency in modern communication systems. This step dynamically selects the most appropriate error correction coding scheme based on the prediction of the channel state for the next time period in step S101. This adaptive mechanism balances transmission efficiency and reliability under varying channel conditions, thereby optimizing overall communication performance.

[0072] First, the system selects an appropriate coding scheme based on the channel state determination for the next time period in step S101. If the channel state is predicted to be poor for the next time period, the system selects an error correction coding scheme from a preset library of coding schemes that has a lower coding rate but higher error correction redundancy. This selection is based on the principle that, when channel conditions are poor, increasing redundant information can improve interference immunity and error correction capabilities, although this may reduce information transmission efficiency. Conversely, if the channel state is predicted to be good for the next time period, the system selects an error correction coding scheme with a higher coding rate. This is because, under good channel conditions, redundant information can be reduced, increasing the actual information transmission rate.

[0073] This selection process involves a key concept in coding theory: the trade-off between coding gain and coding efficiency. Coding gain reflects the degree to which a coding scheme improves a system's noise immunity, while coding efficiency indicates the proportion of actual information bits in the total transmitted bits. The optimal balance between these two factors varies under different channel conditions.

[0074] After selecting an appropriate coding scheme, the system provides feedback to the transmitter. This feedback mechanism is key to implementing adaptive coding. It enables the transmitter to adjust its coding strategy based on current and predicted channel conditions, rather than using a fixed coding scheme.

[0075] After receiving the feedback, the transmitter constructs a parity check matrix based on the selected error correction coding scheme. The parity check matrix is ​​a crucial component of linear block codes, defining the encoding rules. Constructing the parity check matrix takes into account the code length, number of information bits, and number of parity bits specified in the coding scheme. These parameters determine the code's redundancy and error correction capabilities.

[0076] After constructing the parity check matrix, the transmitter uses Gaussian elimination to convert it into a generator matrix. This step aims to obtain a matrix form that is more suitable for encoding operations. Gaussian elimination is a classic matrix transformation algorithm that converts a matrix into an echelon form or a reduced echelon form through a series of row operations. In this process, the original parity check matrix is ​​converted into an equivalent generator matrix, but the latter is more suitable for encoding operations.

[0077] Finally, the transmitter uses the generated generator matrix to encode and map the information to be transmitted, obtaining the final encoded codeword. This process actually maps the original information into a higher-dimensional space through matrix multiplication. In this space, the distance between different valid codewords is increased, thereby improving anti-interference and error correction capabilities.

[0078] This step addresses several key technical issues. First, it addresses how to dynamically adjust the coding strategy under changing channel conditions. By selecting different coding schemes based on the predicted channel state, the system can maintain good performance under different conditions. Second, it addresses the issue of how to strike a balance between coding gain and transmission efficiency. Under different channel conditions, the system can select appropriate coding parameters to ensure reliability without excessively sacrificing efficiency. Finally, it provides a complete implementation method from coding scheme to actual coding operation, including the construction of the check matrix, the transformation of the generator matrix, and the final code mapping.

[0079] This step achieves several important technical benefits. First, it improves the adaptability and robustness of the communication system. The system can dynamically adjust the coding strategy based on predicted channel conditions, maintaining good performance under various channel conditions. Second, it optimizes resource utilization. Under favorable channel conditions, the system can use efficient coding schemes to increase transmission rates; under adverse conditions, it can utilize stronger error correction capabilities to ensure transmission reliability. Third, it provides a foundation for implementing software-defined adaptive communication systems. By dynamically selecting and implementing different coding schemes, the system demonstrates a high degree of flexibility and programmability.

[0080] Let's use a specific example to illustrate the application of this step. Assume that in a satellite communication system, a ground station needs to send control commands to a low-orbit satellite. Due to changes in the satellite's orbit and fluctuations in atmospheric conditions, channel conditions often change.

[0081] Based on the prediction in step S101, the system determines that the channel condition will deteriorate in the next 10 minutes, and the signal-to-noise ratio is expected to drop from the current 15dB to 8dB. Based on this prediction, step S102 determines that the channel condition in the next period is poor.

[0082] Therefore, the system selected a low-rate, high-redundancy coding scheme from a library of pre-defined coding schemes. Specifically, the system chose a (255,223) Reed-Solomon code. This coding scheme encodes every 223 information bits into a 255-bit codeword, providing strong error correction capabilities, capable of correcting up to 16 symbol errors.

[0083] The system feeds back the selected coding scheme to the transmitter. After receiving this information, the transmitter begins constructing the corresponding check matrix. For the (255,223) Reed-Solomon code, the check matrix is ​​a 32x255 matrix.

[0084] Next, the transmitter uses Gaussian elimination to convert the 32x255 check matrix into an equivalent 223x255 generator matrix. This process involves matrix operations on the finite field GF(2^8), because Reed-Solomon codes are defined based on this finite field.

[0085] Finally, when a control command needs to be sent, the transmitter multiplies the original 223-bit information sequence with the generator matrix to obtain a 255-bit encoded codeword. This encoding process adds 32 bits of redundant information, greatly improving the information's ability to resist interference during transmission.

[0086] This example illustrates how step S102 is applied in a real-world communication system. The system dynamically selects an appropriate coding scheme based on predicted channel conditions, completing the entire process from coding scheme selection to actual coding. This adaptive mechanism enables the system to maintain reliable communication despite changing channel conditions, which is particularly important for high-reliability scenarios such as satellite communications.

[0087] In general, step S102 illustrates the implementation of adaptive error correction coding in modern communication systems. It combines channel prediction, coding theory, and matrix algebra to provide powerful anti-interference and error correction capabilities for communication systems.

[0088] Step S103: Decoding the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result.

[0089] Optionally, in this embodiment, step S103 is a key step in the communication system, involving the receiving end decoding the transmitted codeword and ultimately recovering the original information. This step is directly related to the accuracy and reliability of communication and is the last link in the entire communication process.

[0090] The technical implementation process first involves receiving the transmitted codewords. In actual communication systems, these codewords may have been affected by channel noise, interference, or fading, so the received signal may differ from the transmitted codeword. The receiver needs to process these interfered signals, including synchronization and equalization, to recover the original codewords as closely as possible.

[0091] Next, the system performs decoding based on the previously selected error correction coding scheme. Different coding schemes have different decoding algorithms. For example, for block codes (such as BCH codes and Reed-Solomon codes), common decoding methods include symptomatic decoding and maximum likelihood decoding. For convolutional codes, the Viterbi algorithm may be used for decoding. For more complex coding schemes such as Turbo codes or LDPC codes, iterative decoding algorithms may be required.

[0092] The core of the decoding process is to exploit the redundant information added during encoding to detect and correct possible errors. This process typically involves calculating symptoms (for block codes) or path metrics (for convolutional codes), and then using these calculations to infer the most likely original information sequence. In some cases, if the number of detected errors exceeds the error correction capabilities of the encoding scheme, the system may request a retransmission or take other error handling measures.

[0093] Finally, the decoder outputs the final decoded result, which is usually the recovered original information sequence. In some applications, the system may also provide additional information, such as the confidence level of the decoding or the number of errors detected.

[0094] This step solves the technical problem of accurately recovering the original information from a received signal that may contain errors. It fully utilizes coding theory and signal processing technology, and through intelligent algorithm design, it can reliably recover the original information even in the presence of a certain degree of noise and interference.

[0095] The technical effect achieved in step S103 is primarily reflected in improving the reliability and anti-interference capabilities of the communication system. Through effective decoding operations, the system can, to a certain extent, overcome channel-induced errors and ensure accurate information transmission. Furthermore, this step provides the communication system with adaptability, as it can flexibly adjust decoding strategies based on different coding schemes.

[0096] From the above description, it can be seen that the adaptive cross-domain gateway underwater acoustic coding method provided in the embodiment of the present application can determine the corresponding current time period channel state according to the test signal and the preset least squares method by receiving a preset test signal sent by the transmitter before transmitting information, and perform time series analysis based on the current signal-to-noise ratio in the channel state of the current time period to predict the channel state of the next time period. According to the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and the preset threshold, the channel quality of the channel state of the next time period is judged; if it is determined that the channel state of the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from the preset coding scheme library; if it is determined that the channel state of the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, thereby optimizing channel utilization efficiency and reducing energy waste.

[0097] In one embodiment of the method for adapting cross-domain gateway underwater sound coding of the present application, see Figure 2 , and can also include the following:

[0098] Step S201: monitoring a communication channel through a signal receiver, waiting for a transmitter to send a preset test signal, receiving and sampling the test signal using a radio frequency front-end module, performing analog-to-digital conversion, and buffering the sampled digital samples of the test signal into a memory of the receiver;

[0099] Step S202: input the buffered test signal data into a pre-designed least squares estimation model, and use the least squares method principle to obtain the channel state parameters of the current period.

[0100] Optionally, in this embodiment, steps S201 and S202 constitute a complete channel state estimation process, which plays a crucial role in modern wireless communication systems. These two steps together implement the entire process from signal reception to channel state parameter estimation, providing an important basis for subsequent communication strategy adjustments. Let's discuss the technical process, principles, problems solved, and results achieved by these two steps in detail.

[0101] Step S201 first involves the signal receiver monitoring the communication channel. This process requires the receiver to remain active, continuously scanning a predetermined frequency range to capture a preset test signal from the transmitter. Preset test signals are typically carefully designed, with specific structures and characteristics that enable the receiver to easily identify and process them. This test signal may be a known symbol sequence, such as a pseudo-random binary sequence (PRBS) or a specific pilot sequence.

[0102] When a preset test signal is detected, the RF front-end module (RFFEM) begins operation. A key component of the receiver, the RFFEM is responsible for converting the received high-frequency analog signal into a baseband signal that can be processed by the digital processing system. This process typically involves several steps: First, a low-noise amplifier (LNA) amplifies the weak received signal; then, a mixer downconverts the RF signal to an intermediate frequency (IF) or directly to baseband; and finally, an analog-to-digital converter (ADC) converts the analog signal into digital samples.

[0103] Analog-to-digital conversion is a critical step that determines the accuracy and effectiveness of subsequent digital signal processing. Modern communications systems typically use high-speed, high-resolution ADCs to capture detailed signal characteristics. The converted digital samples are temporarily stored in the receiver's memory to prepare for subsequent channel estimation. This buffering process allows the system to collect sufficient data samples to improve estimation accuracy.

[0104] Step S202 then uses the cached test signal data to perform actual channel state estimation. This employs the least squares estimation model, a widely used parameter estimation method. The basic principle of the least squares method is to obtain the optimal parameter estimate by minimizing the sum of the squares of the errors between the estimated value and the actual observed value.

[0105] In the context of channel estimation, the least squares method attempts to find a set of channel parameters that minimizes the difference between the theoretical received signal calculated based on this set of parameters and the actual received signal. Specifically, if we use h to represent the impulse response of the channel, x to represent the known test signal transmitted, and y to represent the received signal, then the least squares estimation is to find h such that ||y-hx||^2 is minimized, where represents the convolution operation.

[0106] This optimization problem can be solved efficiently using matrix operations. Typically, a matrix consisting of known test signals is constructed, and then channel parameter estimates are obtained through matrix inversion or pseudo-inversion. This process may need to account for the effects of noise, so in practice, improved algorithms such as weighted least squares or regularized least squares may be used.

[0107] The channel state parameters obtained by least squares estimation usually include channel gain, phase offset, multipath delay, etc. These parameters comprehensively describe the channel characteristics of the current period and provide an important basis for subsequent communication strategy adjustments.

[0108] These two steps together address several key technical challenges. First, they solve the problem of accurately acquiring channel state information in complex wireless environments. By using carefully designed test signals and advanced estimation algorithms, the system can accurately estimate channel characteristics in the presence of noise and interference. Second, this process provides a method for dynamically tracking channel changes. By periodically executing these steps, the system can update channel state information in real time, adapting to rapidly changing wireless environments. Finally, this process provides the necessary foundation for subsequent adaptive communication strategies, enabling the system to optimize transmission parameters based on current channel conditions.

[0109] These steps achieve several important technical benefits. First, they improve the reliability and efficiency of the communication system. By accurately estimating the channel state, the system can select the most appropriate modulation method, coding rate, and transmit power, thereby optimizing resource utilization while ensuring communication quality. Second, this process enhances the system's anti-interference capabilities. Accurate channel estimation enables the system to better cope with various interference and fading, improving communication robustness. Third, this process provides the foundation for implementing advanced MIMO (Multiple Input Multiple Output) technology. In MIMO systems, accurate channel state information is crucial for technologies such as spatial multiplexing and beamforming.

[0110] In one embodiment of the method for adapting cross-domain gateway underwater sound coding of the present application, see Figure 3 , and can also include the following:

[0111] Step S301: extracting signal-to-noise ratio data from the obtained channel state of the current period, constructing a time series based on the signal-to-noise ratio data, and using a time series analysis method to model and predict the time series to obtain the signal-to-noise ratio of the next period;

[0112] Step S302: The predicted signal-to-noise ratio of the next time period is used as a key indicator of the channel state, combined with the signal attenuation and phase offset indicators in the channel state parameters of the current time period, and input into a preset channel state prediction model to obtain the channel state of the next time period output by the channel state prediction model.

[0113] Optionally, in this embodiment, steps S301 and S302 constitute a complete channel state prediction process, which is of great significance in modern wireless communication systems. These two steps together implement the entire process from current channel state parameters to future channel state prediction, providing a key basis for adaptive adjustment and optimization of communication systems. Let's delve deeper into the technical process, principles, problems solved, and results achieved by these two steps.

[0114] Step S301 first involves extracting signal-to-noise ratio data from the channel status during the current period. The signal-to-noise ratio (SNR) is an important indicator of communication quality, directly impacting the system's transmission rate and reliability. Extracting SNR data typically involves calculating the ratio of received signal power to noise power. In practical systems, this may require estimating signal power using a pilot signal or reference signal, and estimating the noise level by measuring power during periods of no signal.

[0115] Once the SNR data has been extracted, the next step is to construct it into a time series. This process involves arranging the SNR data at multiple consecutive time points in chronological order to form a discrete data series. This time series reflects the changing trends of channel conditions over time, providing a foundation for subsequent analysis and prediction.

[0116] Next, the system uses time series analysis to model and forecast this series. Time series analysis is a powerful statistical tool that assumes that there is a temporal dependency between data points and attempts to capture this relationship for future forecasting. Commonly used time series models include autoregressive (AR) models, moving average (MA) models, autoregressive moving average (ARMA) models, and the more complex autoregressive integrated moving average (ARIMA) model.

[0117] When selecting an appropriate model, the system needs to consider the characteristics of the data, such as whether there is trend, cyclical, or seasonal variation. For example, if the signal-to-noise ratio data shows significant cyclical fluctuations (perhaps due to the user's daily activity patterns), then a seasonal ARIMA (SARIMA) model may be considered. Model parameter estimation typically uses methods such as maximum likelihood or conditional least squares.

[0118] Once a suitable model is established, the system can use it to predict the signal-to-noise ratio for the next time period. This prediction process typically involves extrapolating the model forward one time step. The forecast results include not only a point estimate (i.e., the most likely signal-to-noise ratio value) but also a prediction interval, which provides a range of uncertainty in the forecast.

[0119] Step S302 further utilizes the SNR for the next time period predicted in step S301, combined with other channel state parameters for the current time period (such as signal attenuation and phase offset), and inputs them into a preset channel state prediction model. This preset model may be a more complex machine learning model, such as a support vector machine (SVM), random forest, or neural network.

[0120] The design of this pre-defined model requires consideration of several factors. First, it needs to be able to handle multidimensional inputs, as other channel parameters must be considered in addition to the predicted signal-to-noise ratio. Second, it should be able to capture the nonlinear relationships between these parameters, as actual channel behavior is often complex and nonlinear. Finally, the model should generalize well, providing reasonable predictions under a wide range of channel conditions.

[0121] Model training typically requires a large amount of historical data that covers a variety of possible channel conditions. The training process may involve techniques such as cross-validation and regularization to prevent overfitting and improve the model's generalization ability.

[0122] Ultimately, this pre-set model outputs a complete channel state prediction for the next period of time, which includes not only the signal-to-noise ratio (SNR) but also other important channel characteristics such as multipath delay, Doppler shift, and channel correlation.

[0123] Together, these two steps address several key technical challenges. First, they address how to predict future channel states in dynamically changing wireless environments. By combining time series analysis and advanced machine learning techniques, the system is able to capture complex patterns in channel changes and make reasonable predictions about future states. Second, this process provides a method for inferring based on limited observations. By using only current and historical channel state information, the system is able to make predictions about the future, which is particularly useful in scenarios where resources are limited or real-time measurements are not possible. Finally, this process provides the necessary information for proactive adjustments to the communication system, allowing the system to pre-emptively adapt to upcoming channel changes.

[0124] These steps achieve several important technical benefits. First, they improve the performance and efficiency of communication systems. By accurately predicting future channel conditions, the system can proactively adjust transmission parameters such as modulation, coding rate, and transmit power, thereby optimizing resource utilization and improving transmission quality. Second, this process enhances system stability and reliability. Predictive adjustments can reduce communication interruptions or degradation caused by sudden channel changes. Third, this process provides a foundation for more advanced communication technologies. For example, in cognitive radio systems, accurate channel predictions can help the system better perform spectrum sensing and dynamic spectrum access.

[0125] In one embodiment of the method for adapting cross-domain gateway underwater sound coding of the present application, see Figure 4 , and can also include the following:

[0126] Step S401: obtaining a predicted signal-to-noise ratio in the channel state for the next period and comparing the predicted signal-to-noise ratio with a preset signal-to-noise ratio threshold;

[0127] Step S402: If the predicted signal-to-noise ratio is higher than the signal-to-noise ratio threshold, it is determined that the channel state is good; if the predicted signal-to-noise ratio is lower than the signal-to-noise ratio threshold, it is determined that the channel state is poor.

[0128] Optionally, in this embodiment, steps S401 and S402 form a concise and effective channel state assessment mechanism, which plays a crucial role in modern wireless communication systems. Together, these two steps enable channel state assessment based on the predicted signal-to-noise ratio, providing a decision-making basis for adaptive adjustments in the communication system. Let's delve deeper into the technical process, principles, problems solved, and results achieved by these two steps.

[0129] Step S401 first involves obtaining the predicted signal-to-noise ratio for the next time period. This predicted signal-to-noise ratio is typically calculated through previous steps (e.g., S301 and S302). The signal-to-noise ratio (SNR) is a key metric for measuring communication quality, reflecting the ratio of the useful signal power to the background noise power. A higher SNR generally indicates better communication quality because the useful signal is stronger relative to the noise.

[0130] After obtaining the predicted SNR, the system compares it with a preset SNR threshold. This threshold is predetermined by the system based on specific communication requirements and environmental conditions. Choosing the threshold requires weighing multiple factors, including the desired communication quality, the system's fault tolerance, and the needs of the specific application. For example, for high-definition video streaming, a higher SNR threshold may be required to ensure adequate transmission quality; whereas for simple text messaging, a lower threshold may be acceptable.

[0131] The numerical comparison process is a simple yet crucial operation. It involves comparing the predicted signal-to-noise ratio value with a preset threshold. This comparison operation may seem simple, but it contains a wealth of information because it converts the continuous signal-to-noise ratio value into a discrete channel state judgment.

[0132] Step S402 determines the channel status based on the comparison results of S401. If the predicted signal-to-noise ratio is higher than the preset threshold, the system determines that the channel status for the next time period is good. This means that the expected signal quality is sufficient to support current communication needs. Conversely, if the predicted signal-to-noise ratio is lower than the threshold, the system determines that the channel status is poor, indicating that the expected signal quality may not meet current communication requirements.

[0133] While this binary judgment simplifies complex channel conditions, it provides a clear decision-making benchmark, helping the system make quick adjustments. In actual applications, there may be more judgment levels, such as "excellent," "good," "fair," "poor," and "very poor," to provide a more detailed description of the channel status.

[0134] These two steps together address several key technical challenges. First, they provide a method for converting continuous signal-to-noise ratio predictions into discrete channel state judgments. This conversion simplifies the subsequent decision-making process, enabling the system to adjust communication parameters based on clear judgment results. Second, this process introduces the concept of thresholds, allowing the system to flexibly adjust judgment criteria based on specific needs. By adjusting the thresholds, the system can find a balance between communication quality and resource utilization. Third, this process provides the foundation for adaptive adjustment of the communication system. By pre-determining the channel state for the next time period, the system can prepare corresponding adjustment strategies in advance, improving system response speed and efficiency.

[0135] These steps achieve several important technical benefits. First, they improve the decision-making efficiency of the communication system. By simplifying binary judgments, the system can make rapid adjustments, reducing the need for complex calculations and analysis. Second, this process enhances system robustness. By predicting channel conditions, the system can prepare response strategies in advance, reducing communication interruptions or quality degradation caused by sudden channel changes. Third, this process provides the foundation for more intelligent resource allocation. Based on channel status predictions, the system can more rationally allocate resources such as power and bandwidth, improving overall system efficiency.

[0136] In one embodiment of the method for adapting cross-domain gateway underwater sound coding of the present application, see Figure 5 , and can also include the following:

[0137] Step S501: pre-establishing a coding scheme library containing different feature label coding schemes;

[0138] Step S502: If it is determined that the channel state in the next time period is poor, an error correction coding scheme having a lower coding rate label and a higher error correction redundancy label is selected from a preset coding scheme library.

[0139] Optionally, in this embodiment, steps S501 and S502 constitute a flexible and efficient coding scheme selection mechanism, which plays a key role in modern wireless communication systems. Together, these two steps enable adaptive coding scheme selection based on channel conditions, enabling the communication system to maintain reliable communication under varying channel conditions. Let's delve deeper into the technical process, principles, problems solved, and results achieved by these two steps.

[0140] Step S501 involves pre-establishing a coding scheme library containing different feature tag encoding schemes. This coding scheme library is a pre-prepared data structure containing a variety of coding schemes suitable for different scenarios. Each coding scheme is associated with a set of feature tags that describe the key characteristics of the scheme. Common feature tags may include coding rate, error correction capability, complexity, delay, etc.

[0141] The coding rate tag reflects the information transmission efficiency of the coding scheme. A higher coding rate means more effective information can be transmitted per unit time, but usually at the expense of lower error correction capability. Conversely, a lower coding rate is generally associated with stronger error correction capability. The error correction redundancy tag directly reflects the error correction capability of the coding scheme. A higher error correction redundancy means that the coding scheme is more resistant to transmission errors, but it reduces information transmission efficiency.

[0142] Building such a coding scheme library requires considering multiple factors. First, it's necessary to select an appropriate coding type, such as convolutional codes, turbo codes, or LDPC codes. Second, for each coding type, a series of parameter configurations must be determined, such as the code rate, constraint length, and generator polynomial. Finally, each configuration must be assigned an appropriate feature label. This process is typically based on theoretical analysis and extensive experimental data.

[0143] Step S502 selects an appropriate coding scheme from a library of coding schemes based on the channel state determined in the previous steps (e.g., S401 and S402). If the system determines that the channel state is poor for the next period, it will tend to select an error-correcting coding scheme with a lower coding rate and a higher error correction redundancy. This selection reflects the system's strategy when faced with adverse channel conditions: sacrificing some transmission efficiency in exchange for stronger interference immunity and more reliable communication.

[0144] This selection process can involve complex decision-making algorithms. The simplest approach might be to pre-configure fixed coding schemes for different channel conditions. More complex approaches might consider multiple factors, such as current quality of service requirements, system load, and available resources, using a multi-criteria decision-making algorithm to select the optimal coding scheme.

[0145] These two steps together address several key technical issues. First, they provide a mechanism for flexibly responding to changing channel conditions. By preparing multiple coding schemes in advance and dynamically selecting based on channel conditions, the system can maintain reliable communication under varying channel conditions. Second, this process achieves a dynamic balance between coding efficiency and reliability. Under favorable channel conditions, the system can select a highly efficient coding scheme; under adverse conditions, it can switch to a more reliable scheme. Third, this process provides the system with a standardized coding scheme management and selection mechanism, simplifying system design and implementation.

[0146] These steps achieve several important technical benefits. First, they improve the system's adaptability and robustness. By dynamically selecting coding schemes, the system can better cope with various channel conditions, reducing communication interruptions and errors. Second, this process optimizes the utilization of system resources. Under favorable channel conditions, the system can select an efficient coding scheme, thereby improving data throughput; under adverse conditions, a more reliable scheme can be selected to ensure communication quality. Third, this process provides the foundation for a more intelligent communication system. By incorporating techniques such as machine learning, the system can continuously optimize its coding scheme selection strategy, further improving performance.

[0147] In one embodiment of the method for adapting cross-domain gateway underwater sound coding of the present application, see Figure 6 , and can also include the following:

[0148] Step S601: If it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate label is selected from a preset coding scheme library;

[0149] Step S602: Feedback the selected error correction coding scheme to the transmitter, so that the transmitter constructs a check matrix according to the code length, information bits and check bits in the error correction coding scheme, traverses each column of the check matrix, finds the row where the main element of the current column is located, performs elementary row transformation on the row where the main element is located and the current iteration row, and repeats the elementary row transformation process until the check matrix is ​​simplified to an upper triangular matrix. The upper triangular matrix obtained at this time is the generator matrix, and the coding sequence of the information to be transmitted is coded and mapped through the generator matrix to obtain the coded codeword.

[0150] Optionally, in this embodiment, steps S601 and S602 constitute the core of an adaptive coding system that dynamically selects the most appropriate coding scheme based on channel conditions and applies it to actual data transmission. Together, these two steps complete the entire process from coding scheme selection to specific coding implementation, enabling the communication system to improve transmission efficiency under favorable channel conditions. Let's delve deeper into the technical process, principles, problems solved, and results achieved by these two steps.

[0151] Step S601 involves selecting an error correction coding scheme with a higher coding rate tag from a library of preset coding schemes, if the channel conditions are determined to be favorable for the next time period. This step is based on the channel condition predictions made in previous steps (e.g., S401 and S402). When the system predicts favorable channel conditions for the next time period, it will tend to select a scheme with a higher coding rate. This selection reflects the system's strategy when faced with favorable channel conditions: to maximize data throughput by leveraging favorable transmission conditions.

[0152] Choosing a high coding rate means the system reduces redundant information used for error correction, thereby increasing the effective payload ratio. This approach is based on the assumption that under good channel conditions, the probability of transmission errors is low, thus requiring less error correction capability. By reducing redundant information, the system can transmit more valid data per unit time, improving overall transmission efficiency.

[0153] The selection of a coding rate typically takes into account multiple factors, such as the predicted channel state, current quality of service requirements, and system load. The system may use a scoring or ranking algorithm to select the most appropriate coding scheme from a library of coding schemes. For example, the system might calculate a composite score for each candidate, which could be a weighted sum of factors such as the coding rate, expected bit error rate, and complexity.

[0154] Step S602 details how the selected coding scheme is applied to actual data transmission. This step first feeds the selected coding scheme back to the transmitter, which then constructs a parity check matrix based on the scheme and, through a series of matrix transformations, generates a generator matrix. Finally, this generator matrix is ​​used to encode the information to be transmitted.

[0155] Constructing a parity check matrix is ​​the first step in the encoding process. The structure of the parity check matrix is ​​determined by the selected encoding scheme and includes information about the code length, information bits, and check bits. The code length refers to the total number of bits after encoding, the information bits are the number of bits in the original data, and the check bits are additional bits added for error correction.

[0156] Next, the system converts the parity check matrix into an upper triangular matrix through a series of elementary row transformations, essentially a variation of Gaussian elimination. The system iterates through each column of the parity check matrix, finds the principal element of the column (usually the first non-zero element in the column), and then uses the row containing this principal element to eliminate the remaining rows. This process is repeated until the matrix is ​​reduced to an upper triangular form.

[0157] The resulting upper triangular matrix is ​​known as the generator matrix. The characteristic of the generator matrix is ​​that it can map the original information into the encoding space through simple matrix multiplication. Specifically, the information to be transmitted is treated as a vector, and multiplying this vector by the generator matrix yields the encoded codeword.

[0158] These two steps together address several key technical issues. First, they provide a mechanism for improving transmission efficiency under favorable channel conditions. By selecting a high-rate coding scheme, the system can maximize data throughput when channel conditions permit. Second, this process maps coding schemes to specific coding implementations. By constructing a check matrix and a generator matrix, the system transforms the abstract coding scheme into concrete operations that can be directly applied to the data. Third, this process provides the system with a standardized coding implementation, simplifying system design and implementation.

[0159] These steps achieve several important technical benefits. First, they improve the system's transmission efficiency under favorable channel conditions. By selecting a high-rate coding scheme, the system maximizes data throughput while ensuring reliability. Second, this process optimizes system resource utilization. Under favorable channel conditions, the system can reduce redundant information used for error correction, thereby increasing the effective payload ratio. Third, this process provides the foundation for a more flexible and efficient communication system. By dynamically selecting and applying coding schemes, the system can better adapt to changing network environments.

[0160] In one embodiment of the method for adapting cross-domain gateway underwater sound coding of the present application, see Figure 7 , and can also include the following:

[0161] Step S701: Obtain the encoded codeword transmitted by the transmitter, and perform error correction on the received codeword according to the parameters of the error correction coding scheme fed back to the transmitter;

[0162] Step S702: Output the transmission result after decoding and error correction.

[0163] Optionally, in this embodiment, steps S701 and S702 constitute the core processing flow at the receiving end of the communication system, primarily involving reception, error correction, and decoding. Together, these two steps implement the complete flow from receiving the encoded codeword to ultimately outputting the correct information, providing the communication system with reliable data recovery capabilities. Let's delve deeper into the technical process, principles, problems solved, and results achieved by these two steps.

[0164] Step S701 first involves acquiring the encoded codeword transmitted by the transmitter. This process is typically performed by the receiver's front-end circuitry and includes operations such as signal reception, amplification, filtering, and sampling. The received signal may be distorted or erroneous due to various factors, such as channel noise, multipath effects, and interference. Therefore, the received codeword may differ from the codeword sent by the transmitter.

[0165] After receiving the codeword, the system performs error correction based on the parameters of the error-correction coding scheme previously fed back to the transmitter. This process is commonly referred to as decoding. The specific decoding method depends on the coding scheme used. Common methods include hard-decision decoding and soft-decision decoding. Hard-decision decoding directly determines whether the received bit is 0 or 1, while soft-decision decoding utilizes the amplitude information of the received signal, providing better error correction performance.

[0166] Error correction works by exploiting redundant information added during encoding to detect and correct errors that may occur during transmission. For example, when using linear block codes (such as BCH or Reed-Solomon codes), the system calculates the checksum of the received codeword and compares it with the expected checksum. If there is a discrepancy, the system attempts to locate and correct the error.

[0167] For more complex coding schemes, such as LDPC codes or Turbo codes, the decoding process typically involves iterative algorithms. These algorithms gradually improve the decoding accuracy over multiple iterations. For example, LDPC code decoding often uses a belief propagation algorithm, which exchanges information between variable nodes and check nodes, gradually updating the reliability estimate for each bit.

[0168] Step S702 outputs the decoded and error-corrected result. This step may seem simple, but it actually involves several key operations. First, the system needs to confirm the success of the decoding process. Most coding schemes have a mechanism to detect whether all errors have been corrected. For example, in systematic codes, the decoding result can be verified by recalculating the check bit and comparing it with the received check bit.

[0169] If the decoding is successful, the system extracts the original information bits, removing any redundant bits added for error correction. This process may also include reformatting the data for subsequent processing or application use.

[0170] If the decoding process fails to fully correct all errors, the system may adopt different strategies. For some applications, the system may request a retransmission. For applications with high real-time requirements, the system may choose to output the best estimate while marking any unreliable locations.

[0171] These two steps together address several key technical issues. First, they provide a mechanism for recovering the original information in the presence of transmission errors. By using appropriate error-correction coding schemes, the system can, to a certain extent, overcome the effects of channel noise and interference. Second, this process transforms the received signal into usable data. Through decoding and error correction, the system converts the physical layer signal into data that can be directly used by the application layer. Third, this process provides a method for evaluating transmission quality. By analyzing the error correction process and results, the system can obtain valuable information about channel conditions.

[0172] These steps achieve several important technical benefits. First, they improve the reliability of the communication system. By correcting errors during transmission, the system can maintain acceptable performance even under poor channel conditions. Second, this process optimizes the system's transmission efficiency. By using appropriate coding schemes, the system can minimize redundant information while ensuring reliability. Third, this process provides the foundation for more intelligent and adaptive communication systems. By analyzing the results of error correction, the system can dynamically adjust its transmission strategy.

[0173] In order to optimize channel utilization efficiency and reduce energy waste, the present application provides an embodiment of an adaptive cross-domain gateway underwater acoustic coding device for implementing all or part of the content of the adaptive cross-domain gateway underwater acoustic coding method, see Figure 8 The adapted cross-domain gateway underwater acoustic coding device specifically includes the following contents:

[0174] The channel state prediction module 10 is configured to receive a preset test signal sent by the transmitter before transmitting information, determine the corresponding channel state for the current period based on the test signal and a preset least squares method, perform time series analysis based on the current signal-to-noise ratio of the channel state for the current period, predict the channel state for the next period, and determine the quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio of the channel state for the next period and a preset threshold;

[0175] The adaptive coding module 20 is configured to select an error correction coding scheme with a lower coding rate and a higher error correction redundancy from a preset coding scheme library if it is determined that the channel state in the next time period is poor; and select an error correction coding scheme with a higher coding rate from the preset coding scheme library if it is determined that the channel state in the next time period is good, and feed the selected error correction coding scheme back to the transmitter, so that the transmitter constructs a check matrix based on the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix using a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted using the generator matrix to obtain an encoded codeword;

[0176] The decoding module 30 is used to perform a decoding operation on the codeword transmitted by the transmitter according to the error correction coding scheme and output a final result.

[0177] From the above description, it can be seen that the adaptive cross-domain operation gateway underwater acoustic coding device provided in the embodiment of the present application can determine the corresponding current time period channel state according to the test signal and the preset least squares method by receiving a preset test signal sent by the transmitter before transmitting information, and perform time series analysis based on the current signal-to-noise ratio in the channel state of the current time period to predict the channel state of the next time period. According to the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and the preset threshold, the channel quality of the channel state of the next time period is judged; if it is determined that the channel state of the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from the preset coding scheme library; if it is determined that the channel state of the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, thereby optimizing channel utilization efficiency and reducing energy waste.

[0178] To further illustrate this solution, the present application also provides a specific application example of using the above-mentioned adaptive cross-domain operation gateway underwater acoustic coding device to implement the adaptive cross-domain operation gateway underwater acoustic coding method, which specifically includes the following content:

[0179] (1) Before transmitting information, the transmitter first sends a known test signal. The receiver uses the least squares method combined with the test signal to estimate the channel state information of the current period and feeds it back to the transmitting end. The estimated result is used as the input of the predictor to predict the channel state of the next period. Based on the predicted channel state information, the corresponding coding scheme is selected and sent to the transmitting end through the feedback link.

[0180] (2) The information to be sent is evaluated based on the channel estimation result and bit error rate of the known test signal to extract the channel state information to evaluate the communication quality of the current period, and the channel state of the next period is predicted based on the estimation result to select the appropriate coding scheme. The corresponding coding scheme is used, and the information bits are processed and sent through the transmitter;

[0181] (3) In an adaptive coding system, the receiver selects the appropriate decoding scheme based on the predicted channel state information and the set switching threshold. Adaptive channel coding involves selecting different coding techniques and adding redundancy of varying lengths based on the channel state. The quality of the underwater acoustic channel is dynamic and time-varying. How to dynamically select the physical channel coding method based on the dynamic and time-varying quality of the underwater acoustic channel is the key to adaptive coding.

[0182] When channel conditions are poor, a lower coding rate is selected. When channel conditions are good, a higher coding rate is selected, maximizing the transmission rate. Regarding threshold settings, if the threshold is too high, the system will use a lower coding rate even when channel quality is good, resulting in a lower transmission rate for the entire communication link. Conversely, if the threshold is too low, the transmission error rate may increase, reducing communication efficiency. Therefore, determining an appropriate threshold is crucial. Prioritize reliability and determine the switching threshold for each coding scheme based on a given system bit error rate.

[0183] For LDPC coding, let the code length be N, the information bits be K, the check bits be M=NK, the code rate be R=K / N, and each row of the matrix H has ρ "1"s and each row of the matrix H has γ "1"s. Then the check matrix H is denoted as (N, γ, ρ). The matrix H can be written as:

[0184] H=[I (N-K)*(N-K) ,P (N-K)*K ](1)

[0185] After Gaussian elimination, we can get:

[0186]

[0187] The generator matrix G is:

[0188]

[0189] Assume that the information sequence to be encoded is u=(u0,u1,...,u k-1 ), mapped to codeword c by generating matrix G, at this time codeword c satisfies cG T = 0. The codeword c consists of a K-bit information sequence and an M-bit check bit sequence:

[0190] c=(u1,u2,...,u k-1 ,r0,r1,...,r m-1 )(4)

[0191]

[0192] The check matrix of the LDPC code should be represented by a bidirectional graph. There are N nodes at the bottom of the graph. Each node represents the information bit of the code word, which is called an information node (variable node). j ,j=1,2,...,N} is the information bit of the code word, corresponding to each column of the check matrix; there are M nodes on it, each node represents a check set of the code word, called a check node {c j ,j=1,2,...,M}, represents the check equation, corresponding to each row of the check matrix.

[0193] There is an edge connecting the left and right nodes corresponding to the "1" element in the check matrix. The number of edges connected to each node is called the degree of the node. The figure below shows the Tanner graph corresponding to a 5*10 check matrix:

[0194] The decoding process is also the probability judgment process. The received codeword set is recorded as {r}, and the codeword X = {x1, x2, ..., x N} represents a set of information nodes {x j ,j=1,2,...,N}, Z={z1,z2,...,z m} represents a set of check nodes {z j :i=1,2,...,M}, S represents the codeword sent to meet the set of j check equations,

[0195] P r (x j =0|{r},S) represents the probability that the codeword is judged to be 0 when the received data is r and the check equation constraints are met; P r (x j =1|{r},S) represents the probability that the codeword is judged to be 1 when the received data is r and meets the constraints of the check equation; if

[0196]

[0197] If true, then accept the symbol r i Valuation is 0, otherwise;

[0198] The least squares method used by the receiver is briefly introduced below:

[0199] Assume the communication system model is:

[0200] M=NH+Z(7)

[0201] Where M represents the received information, N represents the sent information, H represents the actual channel response, and Z represents the noise. The goal of the least squares method is to minimize the cost function, that is, to find the minimum value of the following function:

[0202] Represents the estimated channel response. Taking the partial derivative of the above function, the estimated channel response corresponding to the minimum value of the function can be obtained as follows:

[0203]

[0204] From a hardware perspective, in order to optimize channel utilization efficiency and reduce energy waste, the present application provides an embodiment of an electronic device for implementing all or part of the content of the adaptive cross-domain gateway underwater acoustic coding method, and the electronic device specifically includes the following content:

[0205] Processor (processor), memory (memory), communication interface (Communications Interface) and bus; wherein, the processor, memory, and communication interface complete communication with each other through the bus; the communication interface is used to realize the information transmission between the adaptation cross-domain operation gateway underwater acoustic coding device and the core business system, user terminal and related databases and other related equipment; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., and this embodiment is not limited to this. In this embodiment, the logic controller can refer to the embodiment of the adaptation cross-domain operation gateway underwater acoustic coding method and the embodiment of the adaptation cross-domain operation gateway underwater acoustic coding device in the embodiment for implementation, and the contents are merged here, and the repeated parts are not repeated.

[0206] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0207] In practical applications, the part of the underwater acoustic encoding method adapted for the cross-domain gateway operation can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0208] The client device may include a communication module (i.e., a communication unit) that can establish a communication connection with a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0209] Figure 9 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 9 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0210] In one embodiment, the function of adapting the cross-domain gateway underwater acoustic coding method can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0211] Step S101: receiving a preset test signal sent by a transmitter before transmitting information, determining a corresponding channel state for a current period based on the test signal and a preset least squares method, performing a time series analysis based on a current signal-to-noise ratio in the channel state for the current period, predicting a channel state for a next period, and determining the quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio in the channel state for the next period and a preset threshold;

[0212] Step S102: If it is determined that the channel state in the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from a preset coding scheme library. If it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix based on the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted through the generator matrix to obtain an encoded codeword;

[0213] Step S103: Decoding the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result.

[0214] From the above description, it can be seen that the electronic device provided in the embodiment of the present application receives a preset test signal sent by the transmitter before transmitting information, determines the corresponding current time period channel state according to the test signal and the preset least squares method, performs time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predicts the channel state of the next time period, and judges the channel quality of the channel state of the next time period based on the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and the preset threshold; if it is determined that the channel state of the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from the preset coding scheme library; if it is determined that the channel state of the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, thereby optimizing channel utilization efficiency and reducing energy waste.

[0215] In another embodiment, the adaptive cross-domain operation gateway underwater acoustic coding device can be configured separately from the central processing unit 9100. For example, the adaptive cross-domain operation gateway underwater acoustic coding device can be configured as a chip connected to the central processing unit 9100, and the adaptive cross-domain operation gateway underwater acoustic coding method function is realized through the control of the central processing unit.

[0216] like Figure 9 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 9 In addition, the electronic device 9600 may also include all components shown in Figure 9 For components not shown, reference may be made to the prior art.

[0217] like Figure 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0218] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0219] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0220] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), or a SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 by the central processing unit 9100.

[0221] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0222] The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0223] Based on different communication technologies, multiple communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing typical telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0224] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the method for underwater sound coding of a gateway adapted for cross-domain operation in the above-mentioned embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all steps of the method for underwater sound coding of a gateway adapted for cross-domain operation in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0225] Step S101: receiving a preset test signal sent by a transmitter before transmitting information, determining a corresponding channel state for a current period based on the test signal and a preset least squares method, performing a time series analysis based on a current signal-to-noise ratio in the channel state for the current period, predicting a channel state for a next period, and determining the quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio in the channel state for the next period and a preset threshold;

[0226] Step S102: If it is determined that the channel state in the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from a preset coding scheme library. If it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix based on the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted through the generator matrix to obtain an encoded codeword;

[0227] Step S103: Decoding the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result.

[0228] From the above description, it can be seen that the computer-readable storage medium provided in the embodiment of the present application determines the corresponding current time period channel state according to the test signal and the preset least squares method by receiving a preset test signal sent by the transmitter before transmitting information, performs time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predicts the channel state of the next time period, and judges the channel quality of the channel state of the next time period based on the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and the preset threshold; if it is determined that the channel state of the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from the preset coding scheme library; if it is determined that the channel state of the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, thereby optimizing channel utilization efficiency and reducing energy waste.

[0229] The embodiments of the present application also provide a computer program product capable of implementing all steps of the method for adapting cross-domain gateway underwater sound coding in the above embodiment, where the execution subject is a server or a client. When the computer program / instruction is executed by a processor, the steps of the method for adapting cross-domain gateway underwater sound coding are implemented. For example, the computer program / instruction implements the following steps:

[0230] Step S101: receiving a preset test signal sent by a transmitter before transmitting information, determining a corresponding channel state for a current period based on the test signal and a preset least squares method, performing a time series analysis based on a current signal-to-noise ratio in the channel state for the current period, predicting a channel state for a next period, and determining the quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio in the channel state for the next period and a preset threshold;

[0231] Step S102: If it is determined that the channel state in the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from a preset coding scheme library. If it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix based on the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted through the generator matrix to obtain an encoded codeword;

[0232] Step S103: Decoding the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result.

[0233] As can be seen from the above description, the computer program product provided in the embodiment of the present application determines the corresponding channel state of the current time period according to the test signal and the preset least squares method by receiving a preset test signal sent by the transmitter before transmitting information, performs time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predicts the channel state of the next time period, and judges the channel quality of the channel state of the next time period based on the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and the preset threshold; if it is determined that the channel state of the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from the preset coding scheme library; if it is determined that the channel state of the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, thereby optimizing channel utilization efficiency and reducing energy waste.

[0234] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0235] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0236] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0237] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0238] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for adapting cross-domain gateway underwater acoustic coding, characterized in that: Applied to a signal receiver, the method includes: The communication channel is monitored by a signal receiver, and a preset test signal is sent by a transmitter. The test signal is received and collected by a radio frequency front-end module for analog-to-digital conversion, and the collected digital samples of the test signal are cached in the memory of the receiver; the cached test signal data is input into a pre-designed least squares estimation model, and the channel state parameters of the current period are obtained by using the least squares method principle, and the signal-to-noise ratio data is extracted from the obtained channel state of the current period, a time series is constructed based on the signal-to-noise ratio data, and the time series is modeled and predicted using a time series analysis method to calculate the signal-to-noise ratio of the next period; the predicted signal-to-noise ratio of the next period is used as a key indicator of the channel state, combined with the signal attenuation and phase offset indicators in the channel state parameters of the current period, and input into a preset channel state prediction model to obtain the channel state of the next period output by the channel state prediction model, and the channel quality of the channel state of the next period is judged based on the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next period and a preset threshold; If it is determined that the channel state in the next time period is poor, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from a preset coding scheme library; if it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate is selected from the preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix according to the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted through the generator matrix to obtain an encoded codeword; The codeword transmitted by the transmitter is decoded according to the error correction coding scheme, and a final result is output.

2. The method for adapting cross-domain gateway underwater acoustic coding according to claim 1 is characterized in that: The determining, based on a numerical comparison between a predicted signal-to-noise ratio in the channel state for the next time period and a preset threshold, of the channel quality of the channel state for the next time period includes: Obtaining a predicted signal-to-noise ratio in the channel state for the next period and comparing the value with a preset signal-to-noise ratio threshold; If the predicted signal-to-noise ratio is higher than the signal-to-noise ratio threshold, the channel state is determined to be good; if the predicted signal-to-noise ratio is lower than the signal-to-noise ratio threshold, the channel state is determined to be poor.

3. The method for adapting cross-domain gateway underwater acoustic coding according to claim 1 is characterized in that: If it is determined that the channel state in the next time period is poor, selecting an error correction coding scheme with a lower coding rate and a higher error correction redundancy from a preset coding scheme library, including: Pre-establish a coding scheme library containing different feature label coding schemes; If it is determined that the channel state in the next time period is poor, an error correction coding scheme having a lower coding rate label and a higher error correction redundancy label is selected from a preset coding scheme library.

4. The method for adapting cross-domain gateway underwater acoustic coding according to claim 1 is characterized in that: If it is determined that the channel state in the next time period is good, an error correction coding scheme with a higher coding rate is selected from a preset coding scheme library, and the selected error correction coding scheme is fed back to the transmitter, so that the transmitter constructs a check matrix according to the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix through a Gaussian elimination algorithm, and performs coding mapping on the coding sequence of the information to be transmitted through the generator matrix to obtain an encoded codeword, including: If it is determined that the channel state in the next time period is better, selecting an error correction coding scheme with a higher coding rate label from a preset coding scheme library; The selected error correction coding scheme is fed back to the transmitter so that the transmitter constructs a check matrix according to the code length, information bits and check bits in the error correction coding scheme, traverses each column of the check matrix, finds the row where the main element of the current column is located, performs elementary row transformation on the row where the main element is located and the current iteration row, and repeats the elementary row transformation process until the check matrix is ​​simplified to an upper triangular matrix. The upper triangular matrix obtained at this time is the generator matrix, and the coding sequence of the information to be transmitted is coded and mapped through the generator matrix to obtain the coded codeword.

5. The method for adapting cross-domain gateway underwater acoustic coding according to claim 1 is characterized in that: The decoding operation on the codeword transmitted by the transmitter according to the error correction coding scheme and outputting a final result includes: Obtaining the encoded codeword transmitted by the transmitter, and performing error correction on the received codeword according to the parameters of the error correction coding scheme fed back to the transmitter; Output the transmission result after decoding and error correction.

6. An underwater acoustic coding device adapted for cross-domain gateway operation, characterized in that: The device comprises: A channel state prediction module is configured to monitor a communication channel through a signal receiver, wait for a transmitter to send a preset test signal, receive and acquire the test signal using a radio frequency front-end module for analog-to-digital conversion, and cache the acquired digital samples of the test signal in the receiver's memory; input the cached test signal data into a pre-designed least squares estimation model, and utilize the least squares method to obtain the channel state parameters for the current period; extract signal-to-noise ratio data from the obtained channel state for the current period; construct a time series based on the signal-to-noise ratio data; and utilize a time series analysis method to model and predict the time series to obtain the signal-to-noise ratio for the next period; input the predicted signal-to-noise ratio for the next period as a key indicator of the channel state, combined with the signal attenuation and phase offset indicators in the channel state parameters for the current period, into a preset channel state prediction model to obtain the channel state for the next period output by the channel state prediction model; and determine the channel quality of the channel state for the next period based on a numerical comparison between the predicted signal-to-noise ratio in the channel state for the next period and a preset threshold; an adaptive coding module, configured to select an error correction coding scheme with a lower coding rate and a higher error correction redundancy from a preset coding scheme library if it is determined that the channel state in the next time period is poor; and to select an error correction coding scheme with a higher coding rate from the preset coding scheme library if it is determined that the channel state in the next time period is good, and to feed back the selected error correction coding scheme to the transmitter, so that the transmitter constructs a check matrix based on the code length, information bits, and check bits in the error correction coding scheme, converts the check matrix into a generator matrix using a Gaussian elimination algorithm, and performs code mapping on the code sequence of the information to be transmitted using the generator matrix to obtain an encoded codeword; A decoding module is used to perform a decoding operation on the codeword transmitted by the transmitter according to the error correction coding scheme and output a final result.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for adapting the cross-domain gateway underwater acoustic coding as described in any one of claims 1 to 5 are 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 steps of the method for adapting cross-domain gateway underwater acoustic coding as described in any one of claims 1 to 5 are implemented.

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