Adaptive cross-domain operation gateway underwater acoustic coding method and device

Abstract

The embodiment of the invention provides an adaptive cross-domain operation gateway underwater acoustic coding method and device, and the method comprises the steps: receiving a preset test signal transmitted by a transmitter before information transmission, and determining a corresponding channel state in a current time period according to the test signal and a preset least square method, time sequence analysis is carried out according to the current signal-to-noise ratio in the channel state of the current time period, the channel state of the next time period is obtained through prediction, and the channel quality of the channel state of the next time period is judged according to the numerical comparison relation between the predicted signal-to-noise ratio in the channel state of the next time period and a preset threshold value; if it is judged that the channel state in the next time period is poor, an error correction coding scheme with the low coding rate and the high error correction redundancy is selected from a preset coding scheme library, and if it is judged that the channel state in the next time period is good, an error correction coding scheme with the high coding rate is selected from the preset coding scheme library; according to the invention, the channel use efficiency can be optimized, and energy waste can be reduced.

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 of a gateway adapted for cross-domain operation. Background Art

[0002] Underwater acoustic communication is an important technology for information transmission in underwater environments. It has a wide range of applications in the fields of marine resource development, marine environment monitoring, underwater robot control, etc. However, due to the complexity and time-varying nature of underwater acoustic channels, underwater acoustic communication faces many challenges, among which the rapid change of channel state and signal attenuation are the main factors affecting communication quality. In order to meet these challenges, researchers have been exploring more efficient and reliable underwater acoustic communication coding methods.

[0003] Traditional underwater acoustic communication coding methods usually use fixed coding schemes, which are difficult to adapt to the complex and changeable underwater environment. This method may waste bandwidth resources when the channel state is good, and may not guarantee the reliability of communication when the channel state deteriorates. Therefore, developing an adaptive coding method that can dynamically adjust according to the channel state has become an important research direction in the field of underwater acoustic communication.

[0004] In recent years, with the development of signal processing technology and artificial intelligence algorithms, adaptive coding methods based on channel state prediction have gradually attracted the attention of researchers. This method achieves a better balance between communication reliability and transmission efficiency by dynamically selecting the most suitable coding scheme by real-time monitoring and prediction of channel states. However, how to accurately predict the rapidly changing underwater acoustic channel state and how to select the optimal coding scheme based on the prediction results are still the main challenges facing this field. Summary of the invention

[0005] In response to the problems in the prior art, the present application provides an adaptive cross-domain gateway underwater acoustic coding method and device, 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 adaptive cross-domain operation gateway underwater acoustic coding method, which is applied to a signal receiver, and the method includes:

[0008] Before transmitting information, a preset test signal sent by the transmitter is received, a corresponding current time period channel state is determined according to the test signal and a preset least square method, a time series analysis is performed according to a current signal-to-noise ratio in the current time period channel state, and a channel state of the next time period is predicted, and a channel quality of the channel state of the next time period is judged according to a numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and a preset threshold value;

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

[0012] Monitor the communication channel through the signal receiver, wait for the transmitter to send a preset test signal, use the radio frequency front-end module to receive and collect the test signal to perform analog-to-digital conversion, and cache the collected digital samples of the test signal into the memory of the receiver;

[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] Further, performing time series analysis according to the current signal-to-noise ratio in 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 of the current period, constructing a time series according to 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;

[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 a preset channel state prediction model to obtain the channel state of the next time period output by the channel state prediction model.

[0017] Further, judging the channel quality of the channel state in the next time period according to the numerical comparison relationship 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 it 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, 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.

[0020] Furthermore, 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, including:

[0021] Pre-establishing 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 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 bit and check bit 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 coded codeword, including:

[0024] 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 label is selected 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 triangle. 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] Further, the decoding operation is performed on the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result, including:

[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, used to receive a preset test signal sent by the transmitter before transmitting information, determine the corresponding current time period channel state according to the test signal and a preset least square method, perform time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predict the channel state of the next time period, and judge the channel quality of 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 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 select an error correction coding scheme with a higher coding rate from a preset coding scheme library if it is determined that the channel state in the next time period is good, and feed back 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, 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 coded 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, including 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 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 scheme 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 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; 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying 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 a 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 a flowchart of the method for adapting the cross-domain gateway underwater acoustic coding in the embodiment of the present application;

[0043] Figure 6 This is a 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 an underwater acoustic encoding device adapted for cross-domain operation gateway in an embodiment of the present application;

[0046] Fig. 9 It is a schematic diagram of the structure of an electronic device in an 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] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

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

[0051] In view of 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 a 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 according to the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and a 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 a 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 adaptive cross-domain gateway underwater acoustic coding method, which is applied to a signal receiver, see 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 current time period channel state according to the test signal and a preset least square method, performing time series analysis according to a current signal-to-noise ratio in the current time period channel state, predicting a next time period channel state, and judging a channel quality of the next time period channel state according to a numerical comparison relationship between the predicted signal-to-noise ratio in the next time period channel state and a preset threshold;

[0054] Optionally, in this embodiment, step S101 describes a complex channel state prediction and evaluation process, which is a crucial part of modern communication systems. The main purpose of this step is to predict and evaluate the quality of the communication channel before the actual information transmission, so as to provide a basis for the subsequent transmission strategy formulation. This process involves technologies in multiple fields such as signal processing, statistical analysis and machine learning, and aims to improve the reliability and efficiency of the communication system.

[0055] First, the system receives a preset test signal from the transmitter. This test signal is usually a known, standardized signal sequence, such as a pseudo-random bit sequence (PRBS) or a specific pilot signal. Selecting an appropriate test signal is critical to accurately assess the channel status. The test signal should be able to fully stimulate various characteristics of the channel, including frequency response, multipath effects, fading characteristics, etc.

[0056] After receiving the test signal, the system uses the 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, the least squares method is used to estimate the transfer function or impulse response of the channel. Specifically, the system may compare the transmitted test signal and the received signal to estimate the impact of the channel by minimizing the difference between them.

[0057] This process not only obtains the transmission characteristics of the channel, but also estimates the current signal-to-noise ratio (SNR). The signal-to-noise ratio is an important indicator of signal quality, which represents the ratio of useful signal power to noise power. A higher signal-to-noise ratio usually means better signal quality and more reliable communication.

[0058] Next, the system performs time series analysis based on the current SNR obtained 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 model (AR), moving average model (MA), autoregressive moving average model (ARMA), etc. 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 period, the system compares the predicted signal-to-noise ratio with a preset threshold to determine the quality of the channel status for the next period. This threshold may be set based on system performance requirements, communication protocol standards, or empirical values. If the predicted signal-to-noise ratio is higher than the threshold, the channel status for the next period is considered good; otherwise, the channel status is considered poor.

[0060] This step solves several key technical problems. First, it solves the problem of how to evaluate channel quality before actual communication. By using test signals and the least squares method, the system is able to quickly and accurately obtain an estimate of the current channel state. Second, it solves the problem of how to predict future channel states. Through time series analysis, the system is able to make reasonable predictions about future channel states based on current and historical data. Finally, it provides a way to objectively judge the quality of a channel, and by comparing the prediction results with a preset threshold, the system is able to make a clear judgment.

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

[0062] Let's use a specific example to illustrate the application of this step. Assume that in a wireless sensor network, a node needs to periodically send environmental monitoring data to a base station. To ensure the reliability and energy efficiency of communication, the system will execute 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 with a length of 1023. It has characteristics close to white noise in the frequency domain and can well stimulate various characteristics of the channel. After the node receives this test signal, it uses the least squares method to estimate the impulse response of the channel. Specifically, the node compares the received signal with the known transmission sequence and estimates the channel transfer function by minimizing the mean square error between them.

[0064] Through this process, the node obtains the channel state estimation 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 that the value obtained is 20dB.

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

[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 period is poor.

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

[0068] This embodiment shows how step S101 is applied in an actual wireless communication system. By predictively evaluating the channel state, the system can actively adapt to the changing communication environment and improve the reliability and efficiency of data transmission. This method is particularly suitable for resource-constrained scenarios, such as wireless sensor networks, IoT devices, etc., because it can help these systems achieve optimal communication performance under limited energy and bandwidth conditions.

[0069] In general, step S101 provides an advanced channel state prediction and evaluation method, which combines signal processing, statistical analysis and machine learning techniques to provide a powerful adaptability and efficiency improvement 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 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 coded codeword;

[0071] Optionally, in this embodiment, step S102 is an intelligent adaptive error correction coding scheme selection process, which is a key link to improve transmission reliability and efficiency in modern communication systems. This step dynamically selects the most suitable error correction coding scheme based on the prediction result of the channel state in the next time period in the previous step S101. This adaptive mechanism can balance transmission efficiency and reliability under different channel conditions, thereby optimizing the overall communication performance.

[0072] First, the system selects a suitable coding scheme based on the judgment result of the channel state in the next time period in step S101. If it is predicted that the channel state in the next time period is poor, the system will select an error correction coding scheme with a lower coding rate but higher error correction redundancy from the preset coding scheme library. This selection is based on the principle that when the channel conditions are poor, increasing redundant information can improve the anti-interference ability and error correction ability, although this will reduce the efficiency of information transmission. On the contrary, if it is predicted that the channel state in the next time period is good, the system will select an error correction coding scheme with a higher coding rate. This is because under good channel conditions, redundant information can be reduced and the transmission rate of actual information can be increased.

[0073] This selection process involves an important concept in coding theory: the trade-off between coding gain and coding efficiency. Coding gain reflects the extent to which the coding scheme improves the system's ability to resist noise, while coding efficiency indicates the proportion of actual information bits in the total transmitted bits. Under different channel conditions, the optimal balance between the two is different.

[0074] After selecting the appropriate coding scheme, the system will feedback this selection to the transmitter. This feedback mechanism is the 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 check matrix based on the selected error correction coding scheme. The check matrix is ​​an important part of the linear block code, which defines the rules of the encoding. The construction of the check matrix needs to take into account the code length, number of information bits, and number of check bits specified in the coding scheme. These parameters determine the redundancy and error correction capability of the code.

[0076] After constructing the check matrix, the transmitter uses the Gaussian elimination algorithm to convert the check matrix into a generator matrix. The purpose of this step is to obtain a matrix form that is more convenient for encoding operations. Gaussian elimination is a classic matrix transformation algorithm that converts a matrix into a ladder form or a simplified ladder form through a series of row operations. In this process, the original 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 matrix to encode and map the information to be transmitted to obtain the final encoded codeword. This process actually maps the original information into a higher-dimensional space through matrix multiplication, in which the distance between different valid codewords is increased, thereby improving the anti-interference and error correction capabilities.

[0078] This step solves several key technical problems. First, it solves the problem of 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 solves the problem 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 sacrificing efficiency. Finally, it provides a complete implementation method from coding scheme to actual coding operation, including the construction of the check matrix, the conversion of the generator matrix, and the final coding mapping.

[0079] This step achieves several important technical effects. First, it improves the adaptability and robustness of the communication system. The system can dynamically adjust the coding strategy according to the predicted channel conditions, so that it can maintain good performance under various channel conditions. Second, it optimizes resource utilization efficiency. Under good channel conditions, the system can use efficient coding schemes to increase the transmission rate; under poor conditions, it can use stronger error correction capabilities to ensure transmission reliability. Third, it provides a basis for the implementation of 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 instructions to a low-orbit satellite. Due to changes in satellite orbits and fluctuations in atmospheric conditions, channel conditions often change.

[0081] Based on the prediction of 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 the preset coding scheme library. Specifically, the system selected a (255,223) Reed-Solomon code. This coding scheme encodes every 223 information bits into a 255-bit codeword, providing strong error correction capabilities and can correct up to 16 symbol errors.

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

[0084] Next, the transmitter uses the Gaussian elimination algorithm 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 the Reed-Solomon code is defined based on this finite field.

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

[0086] Through this example, we can see how step S102 is applied in an actual communication system. The system can dynamically select a suitable coding scheme based on the predicted channel conditions and complete the entire process from coding scheme selection to actual coding operation. This adaptive mechanism enables the system to maintain reliable communication under changing channel conditions, which is particularly important for scenarios such as satellite communications that require high reliability.

[0087] In general, step S102 shows the implementation method 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 link in the communication system, which involves the receiving end decoding the transmitted codeword and finally recovering the original information. This step is directly related to the accuracy and reliability of communication and is the last link of the entire communication process.

[0090] The specific 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 be somewhat different from the codeword when it was sent. The receiver needs to process these interfered signals, including synchronization, equalization and other operations, to restore the original codewords as much as possible.

[0091] Next, the system will perform decoding operations based on the previously selected error correction coding scheme. Different coding schemes will have different decoding algorithms. For example, for block codes (such as BCH codes, Reed-Solomon codes, etc.), commonly used decoding methods include symptom decoding, maximum likelihood decoding, etc.; for convolutional codes, the Viterbi algorithm may be used for decoding; for more complex coding schemes such as Turbo codes or LDPC codes, it may be necessary to use an iterative decoding algorithm.

[0092] The core of the decoding process is to use the redundant information added during encoding to detect and correct possible errors. This process usually involves calculating symptoms (for block codes) or measuring path metrics (for convolutional codes), and then inferring the most likely original information sequence based on these calculation results. In some cases, if the detected errors exceed the error correction capabilities of the coding scheme, the system may request retransmission or take other error handling measures.

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

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

[0095] The technical effect achieved in step S103 is mainly reflected in improving the reliability and anti-interference ability of the communication system. Through effective decoding operations, the system can overcome the errors introduced by the channel to a certain extent and ensure the accurate transmission of information. At the same time, this step also provides adaptive capabilities for the communication system because it can flexibly adjust the decoding strategy according to different coding schemes.

[0096] From the above description, it can be seen that the adaptive cross-domain operation 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 according to 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 the cross-domain gateway underwater acoustic coding of the present application, see Figure 2 , and can also include the following:

[0098] Step S201: monitor the communication channel through the signal receiver, wait for the transmitter to send a preset test signal, use the RF front-end module to receive and collect the test signal to perform analog-to-digital conversion, and cache the collected digital samples of the test signal into the memory of the receiver;

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

[0100] Optionally, in this embodiment, steps S201 and S202 constitute a complete channel state estimation process, which plays a vital role in modern wireless communication systems. These two steps together realize the whole process from signal reception to channel state parameter estimation, providing an important basis for subsequent communication strategy adjustment. Let us discuss in detail the technical process, principles, problems solved and effects achieved in these two steps.

[0101] Step S201 first involves the signal receiver monitoring the communication channel. This process requires the receiver to remain active and continuously scan a predetermined frequency range to capture a preset test signal sent by the transmitter. The preset test signal is usually carefully designed with a specific structure and characteristics so that the receiver can easily identify it and perform subsequent processing. This test signal may be a series of known symbol sequences, such as a pseudo-random binary sequence (PRBS) or a specific pilot sequence.

[0102] When the preset test signal is detected, the RF front-end module starts working. The RF front-end module is a key component of the receiver, responsible for converting the received high-frequency analog signal into a baseband signal that can be processed by the digital processing system. This process usually includes several steps: first, the low-noise amplifier (LNA) amplifies the received weak signal; then, the mixer down-converts the RF signal to the intermediate frequency or directly to the baseband; finally, the analog-to-digital converter (ADC) converts the analog signal into a digital sample.

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

[0104] Step S202 then uses the cached test signal data to perform actual channel state estimation. The least squares estimation model is used here, which is a widely used parameter estimation method. The basic principle of the least squares method is to obtain the optimal parameter estimation by minimizing the sum of 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 sent, and y to represent the received signal, then the least squares estimation is to find an h that minimizes ||y-hx||^2, where represents the convolution operation.

[0106] This optimization problem can be solved efficiently through matrix operations. Usually, we construct a matrix consisting of known test signals and then obtain the estimated values ​​of the channel parameters through matrix inversion or pseudo-inversion operations. This process may need to consider the influence of noise, so some improved algorithms may be used in practical applications, such as weighted least squares or regularized least squares.

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

[0108] Together, these two steps solve several key technical problems. First, they address the problem of how to accurately obtain channel state information in a complex wireless environment. By using carefully designed test signals and advanced estimation algorithms, the system is able to accurately estimate channel characteristics in the presence of noise and interference. Second, this process provides a way to dynamically track channel changes. By performing these steps periodically, the system can update channel state information in real time and adapt to rapidly changing wireless environments. Finally, this process provides the necessary foundation for subsequent adaptive communication strategies, allowing the system to optimize transmission parameters based on current channel conditions.

[0109] These steps achieve several important technical effects. 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 mode, coding rate, and transmit power, thereby optimizing resource utilization while ensuring communication quality. Second, this process enhances the system's anti-interference ability. Accurate channel estimation enables the system to better cope with various interferences and fading, improving the robustness of communication. Third, this process provides the basis for the implementation of advanced MIMO (multiple-input, multiple-output) technology. In MIMO systems, accurate channel state information is critical for technologies such as spatial multiplexing and beamforming.

[0110] In one embodiment of the method for adapting the cross-domain gateway underwater acoustic 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 according to 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 realize the whole process from current channel state parameters to future channel state prediction, providing a key basis for adaptive adjustment and optimization of communication systems. Let us explore the technical process, principles, problems solved and effects achieved in these two steps.

[0114] Step S301 first involves extracting signal-to-noise ratio data from the channel state of the current period. Signal-to-noise ratio (SNR) is an important indicator for measuring communication quality, which directly affects the transmission rate and reliability of the system. Extracting signal-to-noise ratio data usually involves calculating the ratio of received signal power to noise power. In a practical system, this may require estimating the signal power using a pilot signal or a reference signal, and estimating the noise level by measuring the power during the signal-free period.

[0115] Once the signal-to-noise ratio data is extracted, the next step is to construct this data into a time series. This process involves arranging the signal-to-noise ratio data of multiple consecutive time points in chronological order to form a discrete data series. This time series reflects the changing trend of channel conditions over time and provides a basis for subsequent analysis and prediction.

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

[0117] When choosing an appropriate model, the system needs to consider the characteristics of the data, such as whether there is a trend, periodicity, or seasonality. For example, if the signal-to-noise data shows obvious periodic fluctuations (perhaps due to the user's daily activity pattern), then you may want to consider using a seasonal ARIMA (SARIMA) model. The model's parameter estimation is usually done using methods such as maximum likelihood or conditional least squares.

[0118] Once a suitable model is built, the system can use this model to predict the signal-to-noise ratio for the next period. This prediction process usually involves extrapolating the model forward one time step. The results of the prediction 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 prediction.

[0119] Step S302 further utilizes the signal-to-noise ratio of the next time period predicted in step S301, combined with other channel state parameters (such as signal attenuation and phase offset) of the current time period, 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), a random forest, or a neural network.

[0120] The design of this preset model needs to consider multiple factors. First, it needs to be able to handle multi-dimensional inputs, because in addition to the predicted SNR, other channel parameters need to be considered. Second, it should be able to capture the nonlinear relationship between these parameters, because the actual channel behavior is often complex and nonlinear. Finally, the model should have good generalization ability and be able to give reasonable predictions under a variety of different channel conditions.

[0121] Model training usually requires a large amount of historical data, which should cover a variety of possible channel conditions. The training process may involve cross-validation, regularization and other techniques to prevent overfitting and improve the generalization ability of the model.

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

[0123] Together, these two steps solve several key technical problems. First, they address how to predict future channel states in a dynamically changing wireless environment. By combining time series analysis and advanced machine learning techniques, the system is able to capture complex patterns of channel changes and make reasonable predictions about future states. Second, this process provides a way to make inferences 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-adapt to upcoming channel changes.

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

[0125] In one embodiment of the method for adapting the cross-domain gateway underwater acoustic 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 of the next time 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 constitute a concise and effective channel state assessment mechanism, which plays a vital role in modern wireless communication systems. These two steps together realize the channel state judgment based on the predicted signal-to-noise ratio, and provide a decision basis for the adaptive adjustment of the communication system. Let us explore the technical process, principles, problems solved and effects achieved of these two steps in depth.

[0129] Step S401 first involves obtaining the predicted signal-to-noise ratio for the next time period. This predicted signal-to-noise ratio is usually calculated by the previous steps (such as S301 and S302). The signal-to-noise ratio (SNR) is a key indicator for measuring communication quality, which reflects the ratio of useful signal power to background noise power. A higher signal-to-noise ratio generally means better communication quality because the useful signal is stronger relative to the noise.

[0130] After obtaining the predicted SNR, the system compares it numerically with the preset SNR threshold. This preset threshold is predetermined by the system based on specific communication requirements and environmental conditions. The selection of the threshold requires weighing multiple factors, including the required 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, while a lower threshold may be acceptable for simple text message transmission.

[0131] The numerical comparison process is a simple but critical operation. It involves comparing the predicted SNR value with a preset threshold. This comparison operation may seem simple, but it contains rich information because it converts the continuous SNR value into a discrete channel state judgment.

[0132] Step S402 determines the channel state based on the comparison result of S401. If the predicted signal-to-noise ratio is higher than the preset threshold, the system determines that the channel state in the next period is good. This means that the expected signal quality is sufficient to support the current communication needs. On the contrary, if the predicted signal-to-noise ratio is lower than the threshold, the system determines that the channel state is poor, which indicates that the expected signal quality may not meet the current communication requirements.

[0133] Although this binary judgment simplifies the complex channel conditions, it provides a clear decision-making benchmark, which helps the system to make adjustment decisions quickly. In actual applications, there may be more judgment levels, such as "very good", "good", "average", "poor", "very poor", etc., to provide a more detailed description of the channel status.

[0134] Together, these two steps solve several key technical problems. First, they provide a method to convert 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 a basis for adaptive adjustment of communication systems. By prejudging the channel state of the next period, the system can prepare corresponding adjustment strategies in advance, improving the system's response speed and efficiency.

[0135] These steps achieve several important technical effects. First, they improve the decision-making efficiency of the communication system. Through simplified binary judgments, the system can make adjustment decisions quickly, reducing the need for complex calculations and analysis. Second, this process enhances the robustness of the system. By predicting the channel state, the system can prepare response strategies in advance to reduce communication interruptions or quality degradation caused by sudden channel changes. Third, this process provides a basis for more intelligent resource allocation. Based on the judgment of the channel state, the system can allocate resources such as power and bandwidth more reasonably, improving the overall system efficiency.

[0136] In one embodiment of the method for adapting the cross-domain gateway underwater acoustic 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. These two steps together implement adaptive coding scheme selection based on channel status, providing the communication system with the ability to maintain reliable communication under different channel conditions. Let us explore the technical process, principles, problems solved, and effects achieved in these two steps.

[0140] Step S501 involves pre-establishing a coding scheme library containing different feature tag coding schemes. This coding scheme library is a pre-prepared data structure that contains 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 label reflects the information transmission efficiency of the coding scheme. A higher coding rate means that 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 usually associated with stronger error correction capability. The error correction redundancy label directly reflects the error correction capability of the coding scheme. A higher error correction redundancy means that the coding scheme is better able to resist transmission errors, but it will reduce the information transmission efficiency.

[0142] There are many factors to consider when building such a coding scheme library. First, you need to choose an appropriate coding type, such as convolutional code, Turbo code, LDPC code, etc. Second, for each coding type, you need to determine a series of parameter configurations, such as code rate, constraint length, generator polynomial, etc. Finally, you need to assign appropriate feature labels to each configuration. This process is usually based on theoretical analysis and a large amount of experimental data.

[0143] Step S502 selects an appropriate coding scheme from the coding scheme library based on the channel state judgment of the previous steps (such as S401 and S402). When the system determines that the channel state in the next period is poor, it tends to select an error correction coding scheme with a lower coding rate label and a higher error correction redundancy label. This choice reflects the system's strategy when facing adverse channel conditions: sacrificing part of the transmission efficiency in exchange for stronger anti-interference ability and more reliable communication.

[0144] This selection process may involve complex decision algorithms. The simplest approach may be to pre-configure fixed coding schemes for different channel states. More complex approaches may consider multiple factors, such as current quality of service requirements, system load, available resources, etc., and use multi-criteria decision algorithms to select the best coding scheme.

[0145] These two steps together solve several key technical problems. First, they provide a mechanism to flexibly cope with changing channel conditions. By preparing multiple coding schemes in advance and dynamically selecting them based on the channel status, the system can maintain reliable communication under different channel conditions. Second, this process achieves a dynamic balance between coding efficiency and reliability. Under good channel conditions, the system can select a highly efficient coding scheme; under poor 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 effects. First, they improve the adaptability and robustness of the system. By dynamically selecting coding schemes, the system can better cope with various channel conditions and reduce communication interruptions and errors. Second, this process optimizes the use of system resources. Under good channel conditions, the system can choose efficient coding schemes to improve data throughput; under poor conditions, it can choose more reliable schemes to ensure communication quality. Third, this process provides a basis for realizing smarter communication systems. By combining techniques such as machine learning, the system can continuously optimize the selection strategy of coding schemes to further improve performance.

[0147] In one embodiment of the method for adapting the cross-domain gateway underwater acoustic 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, repeats the elementary row transformation process until the check matrix is ​​simplified to an upper triangle. The upper triangular matrix obtained at this time is the generator matrix, and the coding sequence of the information to be transmitted is encoded and mapped through the generator matrix to obtain the encoded codeword.

[0150] Optionally, in this embodiment, steps S601 and S602 constitute the core part of an adaptive coding system, which can dynamically select the most suitable coding scheme according to the channel state and apply it to the actual data transmission process. These two steps together realize the complete process from coding scheme selection to specific coding implementation, providing the communication system with the ability to improve transmission efficiency under good channel conditions. Let us explore the technical process, principles, problems solved and effects achieved in these two steps.

[0151] Step S601 involves selecting an error correction coding scheme with a higher coding rate tag from a preset coding scheme library when it is determined that the channel state in the next time period is better. This step is based on the prediction results of the channel state in the previous steps (such as S401 and S402). When the system predicts that the channel state in the next time period is better, it will tend to select a scheme with a higher coding rate. This selection reflects the strategy of the system when facing good channel conditions: taking advantage of a favorable transmission environment and maximizing data throughput.

[0152] Choosing a high coding rate means that the system will reduce the redundant information used for error correction, thereby increasing the proportion of effective load. This approach is based on the assumption that under good channel conditions, the probability of transmission errors is low, so not much error correction capability is needed. By reducing redundant information, the system can transmit more effective data per unit time, improving overall transmission efficiency.

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

[0154] Step S602 describes in detail how to apply the selected coding scheme to actual data transmission. This step first feeds back the selected coding scheme to the transmitter, and then the transmitter constructs a check matrix according to the scheme, obtains a generator matrix through a series of matrix transformations, and finally uses the generator matrix to encode the information to be transmitted.

[0155] Constructing a check matrix is ​​the first step in the encoding process. The structure of the check matrix is ​​determined by the selected encoding scheme, which contains 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 of the original data, and the check bits are the extra bits added for error correction.

[0156] Next, the system transforms the check matrix into an upper triangular matrix through a series of elementary row transformations, which is essentially a variation of Gaussian elimination. The system traverses each column of the check matrix, finds the main element of the current column (usually the first non-zero element in the column), and then uses the row where the main element is located to eliminate the other rows. This process is repeated until the matrix is ​​simplified to an upper triangular matrix.

[0157] The resulting upper triangular matrix is ​​the so-called generator matrix. The characteristic of the generator matrix is ​​that it can map the original information to the coding space through simple matrix multiplication. Specifically, the information to be transmitted is regarded as a vector, and the encoded codeword can be obtained by multiplying this vector with the generator matrix.

[0158] Together, these two steps solve several key technical problems. First, they provide a mechanism to improve transmission efficiency under good channel conditions. By selecting a high coding rate scheme, the system can maximize data throughput when channel conditions permit. Second, this process implements the mapping of coding schemes to specific coding implementations. By constructing a check matrix and a generator matrix, the system converts the abstract coding scheme into a specific operation that can be directly applied to the data. Third, this process provides the system with a standardized coding implementation method, simplifying the design and implementation of the system.

[0159] These steps achieve several important technical effects. First, they improve the system's transmission efficiency under good channel conditions. By selecting a scheme with a high coding rate, the system can maximize data throughput while ensuring reliability. Second, this process optimizes the use of system resources. Under good channel conditions, the system can reduce redundant information used for error correction, thereby increasing the proportion of effective payload. Third, this process provides the basis 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 the cross-domain gateway underwater acoustic 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 of the receiving end of the communication system, which mainly involves the process of receiving, error correction and decoding. These two steps together realize the complete process from receiving the encoded codeword to finally outputting the correct information, providing a reliable data recovery capability for the communication system. Let us explore the technical process, principle, problem solved and effect achieved of these two steps in depth.

[0164] Step S701 first involves obtaining the encoded codeword transmitted by the transmitter. This process is usually completed by the front-end circuit of the receiver, including signal reception, amplification, filtering and sampling. The received signal may be distorted or erroneous due to various factors, such as channel noise, multipath effect, interference, etc. Therefore, the received codeword may be different from the codeword sent by the transmitter.

[0165] After obtaining the codeword, the system will perform error correction on the received codeword according to the parameters of the error correction coding scheme previously fed back to the transmitter. This process is usually called decoding. The specific method of decoding depends on the coding scheme used. Common methods include hard decision decoding and soft decision decoding. Hard decision decoding directly makes a 0 / 1 decision on the received bit, while soft decision decoding uses the amplitude information of the received signal to provide better error correction performance.

[0166] The principle of error correction is to use the 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 codes or Reed-Solomon codes), the system calculates the checksum of the received codeword and compares it with the expected checksum. If there is a difference, 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 usually involves iterative algorithms. These algorithms gradually improve the accuracy of the decoding through multiple iterations. For example, the decoding of LDPC codes usually uses a belief propagation algorithm, which exchanges information between variable nodes and check nodes, gradually updating the reliability estimate of each bit.

[0168] Step S702 is to output the result after decoding and error correction. This step seems simple, but it actually involves multiple key operations. First, the system needs to confirm whether the decoding process is successful. Most coding schemes have a mechanism to detect whether all errors have been corrected. For example, in the system code, 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 and removes the redundant bits added for error correction purposes. This process may also include reformatting the data for subsequent processing or application use.

[0170] If the decoding process fails to completely 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 and mark possible unreliable locations.

[0171] These two steps together solve several key technical problems. First, they provide a mechanism to recover the original information in the presence of transmission errors. By using an appropriate error correction coding scheme, the system can overcome the effects of channel noise and interference to a certain extent. Second, this process realizes the conversion from received signals to usable data. Through decoding and error correction, the system converts the signal of the physical layer into data that can be directly used by the application layer. Third, this process provides the system with a way to evaluate the transmission quality. By analyzing the process and results of error correction, the system can obtain valuable information about the channel conditions.

[0172] These steps achieve several important technical results. First, they improve the reliability of the communication system. By correcting errors during transmission, the system can maintain acceptable performance under poor channel conditions. Second, this process optimizes the transmission efficiency of the system. By using appropriate coding schemes, the system can minimize redundant information while ensuring reliability. Third, this process provides the basis for smarter and more 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 operation gateway underwater acoustic coding device for implementing all or part of the contents of the adaptive cross-domain operation 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 used to receive a preset test signal sent by the transmitter before transmitting information, determine the corresponding current time period channel state according to the test signal and a preset least square method, perform time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predict the channel state of the next time period, and judge the channel quality of 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 a preset threshold;

[0175] The adaptive coding module 20 is used 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 of 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 of the next time period is good, and feed back 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, 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 coded 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 according to 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] In order to further illustrate the present 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 contents:

[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 extracts channel state information based on the channel estimation result and bit error rate of the known test signal to evaluate the communication quality of the current period, and predicts the channel state of the next period based on the estimation result to select a suitable coding scheme. The corresponding coding scheme is used, and the information bits are processed and sent through the transmitter;

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

[0182] When the channel conditions are poor, a smaller coding rate is selected. When the channel conditions are good, a larger coding method is selected to maximize the transmission rate. In terms of threshold setting, if the threshold value is set too high, even if the channel quality is good, the system will still use a smaller coding rate, resulting in a low transmission rate of the entire communication link; conversely, if the threshold value is too low, it may cause an increase in the transmission error rate, thereby reducing the effectiveness of communication. Therefore, it is crucial to determine a reasonable threshold value. Based on reliability, the switching threshold of each coding scheme is determined based on a given system bit error rate.

[0183] For LDPC coding, let the code length be N, the information bit be K, the check bit be M=NK, the code rate be R=K / N, each row of the matrix H has ρ "1", each row of the matrix H has γ "1", then the check matrix H is recorded as (N, γ, ρ), and 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 the K-bit information sequence and the 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 codeword, called an information node (variable node) {v j ,j=1,2,...,N} are the information bits of the codeword, corresponding to the columns of the check matrix; there are M nodes on the top, each node represents a check set of the codeword, 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 a connection edge between 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 following figure shows the Tanner graph corresponding to a 5*10 check matrix:

[0194] The decoding process is also the probability judgment process. Let the received codeword set be {r}, 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 means the codeword sent complies with 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 satisfies the constraint of the check equation; 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 satisfies the constraint 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]

[0203] 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:

[0204]

[0205] From the hardware level, 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 contents of the adaptive cross-domain gateway underwater acoustic coding method, and the electronic device specifically includes the following contents:

[0206] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize the information transmission between the adaptation cross-domain operation gateway water 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 the present embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiment of the adaptation cross-domain operation gateway water acoustic coding method and the embodiment of the adaptation cross-domain operation gateway water acoustic coding device in the embodiment, and the contents are incorporated herein, and the repeated parts are not repeated.

[0207] 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.

[0208] In practical applications, the part of the method for adapting the cross-domain gateway water sound encoding 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.

[0209] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and 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, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0210] Fig. 9 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig. 9 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig. 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0211] In one embodiment, the function of adapting the cross-domain gateway underwater acoustic coding method can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:

[0212] Step S101: receiving a preset test signal sent by a transmitter before transmitting information, determining a corresponding current time period channel state according to the test signal and a preset least square method, performing time series analysis according to a current signal-to-noise ratio in the current time period channel state, predicting a next time period channel state, and judging a channel quality of the next time period channel state according to a numerical comparison relationship between the predicted signal-to-noise ratio in the next time period channel state and a preset threshold;

[0213] 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 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 coded codeword;

[0214] Step S103: Decoding the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result.

[0215] From the above description, it can be seen that the electronic device provided by 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 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; 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.

[0216] In another embodiment, the adaptive cross-domain operation gateway underwater acoustic coding device can be configured separately from the central processor 9100. For example, the adaptive cross-domain operation gateway underwater acoustic coding device can be configured as a chip connected to the central processor 9100, and the adaptive cross-domain operation gateway underwater acoustic coding method function is implemented through the control of the central processor.

[0217] like Fig. 9 As shown, the electronic device 9600 may also 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 Fig. 9 In addition, the electronic device 9600 may also include Fig. 9 For components not shown, reference may be made to the prior art.

[0218] like Fig. 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the electronic device 9600.

[0219] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0220] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0221] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. 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, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0222] 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 drivers 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.).

[0223] The communication module 9110 is a transmitter / receiver that sends 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.

[0224] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0225] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps in the method for adapting cross-domain operation gateway water sound coding 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 the steps of the method for adapting cross-domain operation gateway water sound coding in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0226] Step S101: receiving a preset test signal sent by a transmitter before transmitting information, determining a corresponding current time period channel state according to the test signal and a preset least square method, performing time series analysis according to a current signal-to-noise ratio in the current time period channel state, predicting a next time period channel state, and judging a channel quality of the next time period channel state according to a numerical comparison relationship between the predicted signal-to-noise ratio in the next time period channel state and a preset threshold;

[0227] 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 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 coded codeword;

[0228] Step S103: Decoding the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result.

[0229] From the above description, it can be seen that the computer-readable storage medium 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 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; 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.

[0230] The embodiments of the present application also provide a computer program product capable of implementing all the steps in the method for adapting cross-domain gateway water sound coding in the above embodiments, 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 water sound coding are implemented. For example, the computer program / instruction implements the following steps:

[0231] Step S101: receiving a preset test signal sent by a transmitter before transmitting information, determining a corresponding current time period channel state according to the test signal and a preset least square method, performing time series analysis according to a current signal-to-noise ratio in the current time period channel state, predicting a next time period channel state, and judging a channel quality of the next time period channel state according to a numerical comparison relationship between the predicted signal-to-noise ratio in the next time period channel state and a preset threshold;

[0232] 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 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 coded codeword;

[0233] Step S103: Decoding the codeword transmitted by the transmitter according to the error correction coding scheme, and outputting a final result.

[0234] From the above description, it can be seen that the computer program product provided by 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 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; 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.

[0235] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0236] 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 the 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 generate 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.

[0237] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0238] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0239] The present invention uses specific embodiments 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 idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on 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 comprises: Before transmitting information, a preset test signal sent by the transmitter is received, a corresponding current time period channel state is determined according to the test signal and a preset least square method, a time series analysis is performed according to a current signal-to-noise ratio in the current time period channel state, and a channel state of the next time period is predicted, and a channel quality of the channel state of the next time period is judged according to a numerical comparison relationship between the predicted signal-to-noise ratio in the channel state of the next time period and a preset threshold value; 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 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 coded 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 receiving a preset test signal sent by the transmitter before transmitting information, and determining the corresponding current time period channel state according to the test signal and a preset least square method, includes: Monitor the communication channel through the signal receiver, wait for the transmitter to send a preset test signal, use the radio frequency front-end module to receive and collect the test signal to perform analog-to-digital conversion, and cache the collected digital samples of the test signal into the memory of the receiver; 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.

3. The method for adapting cross-domain gateway underwater acoustic coding according to claim 2 is characterized in that: The performing time series analysis according to the current signal-to-noise ratio in the channel state in the current time period to predict the channel state in the next time period includes: Extracting signal-to-noise ratio data from the obtained channel state of the current period, constructing a time series according to 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; 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 a preset channel state prediction model to obtain the channel state of the next time period output by the channel state prediction model.

4. The method for adapting cross-domain gateway underwater acoustic coding according to claim 1 is characterized in that: The determining the quality of the channel state in the next time period according to the numerical comparison relationship between the predicted signal-to-noise ratio in the channel state in the next time period and a preset threshold value includes: Obtaining a predicted signal-to-noise ratio in the channel state for the next period and comparing it with a preset signal-to-noise ratio threshold; 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.

5. 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, an error correction coding scheme with a lower coding rate and a higher error correction redundancy is selected from a preset coding scheme library, including: Pre-establishing 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.

6. 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 the coded codeword, including: 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 label is selected 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 triangle. 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.

7. 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 comprises: 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.

8. An underwater acoustic coding device adapted for cross-domain operation gateway, characterized in that: The device comprises: A channel state prediction module, used to receive a preset test signal sent by the transmitter before transmitting information, determine the corresponding current time period channel state according to the test signal and a preset least square method, perform time series analysis according to the current signal-to-noise ratio in the channel state of the current time period, predict the channel state of the next time period, and judge the channel quality of 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 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 select an error correction coding scheme with a higher coding rate from a preset coding scheme library if it is determined that the channel state in the next time period is good, and feed back 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, 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 coded 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.

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

10. 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 7 are implemented.

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