Underwater information transmission method adaptive to air-sea cross-domain communication gateway

Abstract

The invention relates to the technical field of wireless communication, and discloses an underwater information transmission method adaptive to an air-sea cross-domain communication gateway, which is based on a communication signal modulation identification technology of multiple modulation modes, performs accurate identification on multiple modulation modes such as OFDM (Orthogonal Frequency Division Multiplexing), MASK (Multi-Amplitude Shift Keying), MQAM (Multi-Quadrature Amplitude Modulation), MPSK (Multi-Point Shift Keying) and the like through extraction of multiple characteristic parameters such as high-order cumulant, cyclic spectrum characteristics and instantaneous parameters and the like. And efficient classification is realized in combination with a decision tree classifier. The air-sea cross-domain communication gateway can dynamically adjust a self-modulation mode according to an identification result, adapts to different underwater nodes, and improves communication reliability and transmission efficiency. The method has the advantages of high reliability, low energy consumption, high adaptability and the like, can adapt to a complex dynamic marine environment, optimizes resource utilization, prolongs the service life of underwater nodes, remarkably improves the cooperative capability and stability of air-sea cross-domain communication, and provides powerful technical support for the fields of marine exploration, resource development and the like.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to an underwater information transmission method adapted to an air-sea cross-domain communication gateway. Background Technology

[0002] Air-sea cross-domain communication gateways integrate air, surface, and underwater communication networks, providing crucial support for marine resource development and marine exploration. However, underwater information transmission technology faces numerous challenges. Underwater communication primarily relies on acoustic signals, but these have low bandwidth, large propagation delays, and are susceptible to noise interference. Optical and radio signals have limited propagation distances underwater, making it difficult to meet long-distance transmission requirements. Simultaneously, the complex and dynamic marine environment, such as waves, currents, and random node movement, increases the reliability and stability challenges of cross-domain network connections. The limited energy of underwater nodes further necessitates communication technologies that are highly efficient and energy-saving. Furthermore, as a vital supporting technology for marine resource development, air-sea cross-domain communication systems need to achieve high efficiency and reliability in data transmission.

[0003] Therefore, in order to address the above problems, there is an urgent need for an underwater information transmission method that can take into account the characteristics of efficiency, reliability and low energy consumption, so as to adapt to the air and sea cross-domain communication gateway, realize the efficient collaboration and stable operation of multi-media heterogeneous networks, and provide strong technical support for applications in related fields. Summary of the Invention

[0004] To address the technical problems of modulation mismatch, low transmission success rate in low signal-to-noise ratio environments, and high energy consumption in underwater information transmission by current air-sea cross-domain communication gateways, this invention provides an underwater information transmission method adapted to air-sea cross-domain communication gateways. This invention identifies the modulation scheme of the underwater acoustic communication signal received by the air-sea cross-domain communication gateway and adjusts the gateway's modulation scheme based on the relevant information it carries. This reduces losses caused by using a communication signal modulation scheme that is incompatible with the current underwater environment. This invention aims to provide effective and reliable underwater information transmission for nodes such as air-sea cross-domain communication gateways and underwater sensors.

[0005] The underwater information transmission method adapted to air-sea cross-domain communication gateways provided by this invention includes the following steps:

[0006] S1. Receive underwater acoustic communication signals sent by the underwater node;

[0007] S2. Identify the modulation method of the underwater acoustic communication signal;

[0008] S3. Adjust the modulation scheme of the air-sea cross-domain communication gateway according to the identified modulation scheme;

[0009] S4. Use the adjusted modulation method to communicate with the corresponding underwater node.

[0010] Preferably, the modulation scheme identification algorithm includes:

[0011] a. Signal preprocessing, including separating the modulated signal from the mixed signal and filtering and noise reduction of the modulated signal;

[0012] b. Feature extraction: By analyzing the time-domain features and spectral characteristics of the signal, suitable feature parameters are constructed and thresholds are determined for use in the classifier.

[0013] c. Classifier selection: The type of signal is determined by comparing the feature parameters of the input signal with a threshold.

[0014] Preferably, the feature extraction includes:

[0015] a. Extract higher-order cumulants to suppress Gaussian white noise;

[0016] b. Extract instantaneous parameters for signal identification in low signal-to-noise ratio environments;

[0017] c. Extract cyclic spectrum parameters to improve noise immunity.

[0018] Preferably, the classifier is a decision tree classifier because it is easy to understand and implement, and has good real-time performance.

[0019] Preferably, the modulation method based on higher-order cumulants and cyclic spectrum parameters is identified as follows:

[0020] a. For MASK, MQAM, MPSK, MFSK and DSSS modulation schemes, they are distinguished by second- and fourth-order statistics and autocorrelation functions;

[0021] b. For OFDM modulation, identification and extraction are performed by combining fourth-order cumulants and cyclic spectrum features.

[0022] Preferably, the method for identifying the OFDM modulation scheme includes:

[0023] a. Calculate the normalized generalized fourth-order cumulant estimate to separate multicarrier OFDM from single-carrier signals;

[0024] b. Calculate parameters based on instantaneous characteristics, and classify single-carrier signals into three categories based on these parameters: 2ASK, 4ASK and 16QAM, and BPSK and 4FSK;

[0025] c. Calculate characteristic parameters based on the cyclic spectrum, and use these parameters to distinguish between 4ASK and 16QAM.

[0026] Compared with related technologies, the underwater information transmission method adapted to air-sea cross-domain communication gateways provided by this invention has the following beneficial effects:

[0027] This invention significantly improves the reliability and adaptability of underwater communication by dynamically identifying the modulation mode of underwater acoustic signals and adjusting the modulation mode of the air-sea cross-domain communication gateway in real time. Furthermore, by utilizing various feature parameters such as high-order cumulants, cyclic spectrum features, and instantaneous parameters, combined with a decision tree classifier, it achieves accurate identification of various modulation modes such as OFDM, MASK, and MQAM, thereby improving the success rate of information transmission in low signal-to-noise ratio environments.

[0028] This method has advantages such as strong anti-interference ability, high reliability and low energy consumption. It can adapt to complex and dynamic marine environments, optimize resource utilization and extend node life, effectively improve the effectiveness and stability of air-sea cross-domain communication, and provide reliable technical support for marine exploration, resource development and other activities. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating the identification process in this invention, which combines generalized fourth-order cumulants, instantaneous features, and cyclic spectra.

[0030] Figure 2 The time-frequency diagrams of the 2ASK modulated signal and the 4ASK modulated signal in this invention are shown.

[0031] Figure 3 The time-frequency diagrams of the 2FSK modulated signal and the 4FSK modulated signal in this invention are shown.

[0032] Figure 4 This is a diagram illustrating the modulation and demodulation process of MPSK in this invention;

[0033] Figure 5 This is a diagram illustrating the modulation and demodulation process of MQAM in this invention. Detailed Implementation

[0034] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0035] The underwater information transmission method adapted to air-sea cross-domain communication gateways proposed in this invention mainly includes a communication signal modulation identification technology supporting multiple modulation modes. The communication signal modulation identification technology supporting multiple modulation modes is specifically implemented through the following methods:

[0036] The fundamental starting point of this invention is to address the complex and variable characteristics of underwater acoustic channels. The research focuses on underwater acoustic communication devices that support high-speed short-range (OFDM) and various basic modulation signal deep-sea longitudinal active communication modes. Signal identification is achieved through a feature extraction-based debugging and recognition method. After receiving underwater acoustic communication signals from underwater nodes, the air-sea cross-domain communication gateway identifies the modulation mode of the signal and switches its own communication modulation mode accordingly to communicate with the corresponding underwater nodes.

[0037] The modulation scheme identification algorithm proposed in this invention generally includes three aspects: signal preprocessing, feature parameter selection and extraction, and classifier selection.

[0038] Signal preprocessing includes separating the modulated signal from the mixed signal, filtering the modulated signal, and performing noise reduction. This step is fundamental for subsequent feature extraction and classification. Chapter 3 of this paper studies how to efficiently extract modulated signal segments from mixed signal segments and perform simple noise reduction, which is a part of signal preprocessing.

[0039] Feature extraction: By analyzing the time-domain and spectral characteristics of the signal, suitable feature parameters are constructed and thresholds are determined for classifier classification. This part is the key to the recognition algorithm; the suitability of the selected features directly affects the algorithm's recognition performance and is the focus of this chapter. Among these, higher-order cumulant features are easy to extract and can effectively suppress Gaussian white noise, but they cannot classify some signals; instantaneous feature parameters have low complexity and are simple to implement, but their recognition performance is poor in low signal-to-noise ratio environments; cyclic spectrum feature parameters have good noise resistance, but their computational complexity is high.

[0040] Classifier selection: The signal type is determined by comparing the feature parameters of the input signal with a threshold. Decision trees are easy to understand and implement, and offer good real-time performance. Support vector machines and neural networks have good recognition capabilities and are adaptive, but require prior training. This invention chooses a decision tree classifier.

[0041] The specific modulation methods of the communication signals applicable to this invention are: multi-level amplitude shift keying (MASK), multi-level frequency shift keying (MFSK), multi-level phase shift keying (MPSK), multi-level quadrature amplitude modulation (MQAM), and OFDM modulation.

[0042] Combination Figure 2 Explain Multi-level Amplitude Shift Keying (MASK). A MASK signal represents the information carried by a signal through amplitude shifts of a carrier wave; its frequency and initial phase are fixed. The baseband signal of a MASK signal can be considered as consisting of signals with the same carrier frequency but different amplitudes and different timings. It is formed by superimposing two MASK signals. The formula for a MASK signal is as follows:

[0043]

[0044] In the formula, This indicates the amplitude of the amplitude-modulated signal. , It is a baseband pulse waveform. It is the code width. and These are the carrier frequency and initial phase of the modulated signal, respectively.

[0045] Combination Figure 3 Explain Multi-level Frequency Shift Keying (MFSK). An MFSK signal represents information carried by a signal through changes in the frequency state of the carrier wave. The carrier frequency of a 2FSK signal is... and The variation between two frequency points means that a 2FSK signal can be represented as the sum of two 2ASK signals with different carrier frequencies, expressed as:

[0046]

[0047] The carrier frequency of MFSK is at The variation between different frequencies represents different information, which is manifested as different carrier frequencies. If two adjacent symbols have different frequencies, the formula for MFSK can be derived as follows:

[0048]

[0049] Combination Figure 4 This section explains Multi-Level Phase Shift Keying (MPSK). An MPSK signal represents information carried by a carrier wave through changes in its initial phase, while the signal amplitude and frequency remain constant. In a 2PSK signal, the information "0" and "1" correspond to initial carrier phases of π and 0, respectively. The generation function for a 2PSK signal is as follows:

[0050]

[0051] In the formula , When the value is 1 or -1, the binary representation of the transmitted baseband signal is "0". Take 0 for the phase; otherwise, take -1, with a phase of π.

[0052] Then the formula for MPSK can be derived as follows:

[0053]

[0054] In the formula , is the initial phase of the modulated signal.

[0055] Combination Figure 5 This section explains Quadrature Amplitude Modulation (MQAM). In a binary ASK system, the bandwidth utilization is 1 bit / s·Hz. Using quadrature carrier modulation (QAM) to transmit ASK signals can double the bandwidth utilization. Combining QAM with other techniques can further improve bandwidth utilization. The technique capable of this is called Quadrature Amplitude Modulation (QAM). It utilizes quadrature carriers to perform double-sideband suppressed carrier amplitude modulation on two separate signals. Common types include binary QAM, quaternary QAM (16QAM), octal QAM (64QAM), and so on. A simulation diagram of the specific modulation and demodulation process of MQAM is shown below. Figure 5 As shown.

[0056] The modulation identification method applicable to this invention is as follows: This invention proposes a decision theory identification scheme based on multiple feature parameters, including research on parameters such as higher-order cumulants, instantaneous parameters, and cyclic spectrum parameters.

[0057] Table 1 illustrates higher-order cumulants. The higher-order cumulants of signals vary significantly under different modulation schemes. Signals can be classified by extracting higher-order cumulant features. Gaussian noise has zero cumulants of orders two and above, so identification methods using higher-order cumulants as features have good noise suppression effects. The higher-order cumulants of Gaussian signals with zero mean are also zero, so Gaussian noise interference can be ignored when calculating the higher-order cumulants. For mixed signals of non-Gaussian signals and white noise, higher-order cumulants have excellent denoising effects. By deriving the higher-order cumulants of various signals through statistical theory, it can be concluded that the higher-order cumulant of Gaussian random signals is zero. These cumulants contain the characteristics of the signal, which can be used to identify and classify the signal type. The theoretical values ​​of the cumulants for each type of digital modulation signal are shown in the table, assuming the energy of the input signal is E.

[0058] Table 1 Higher-order accumulation

[0059]

[0060] Instantaneous feature parameters are as follows: For signals with different modulation schemes, their instantaneous time-domain feature parameters are mostly different. By extracting the instantaneous time-domain parameters of the modulated signal, signals with different modulation schemes can be distinguished from each other. Recognition algorithms based on instantaneous feature parameters have low computational cost but weak noise resistance; in low signal-to-weight ratio environments, they cannot guarantee the accuracy of signal recognition. Let the input signal... The result after Hilbert transformation is The instantaneous characteristic parameters used in this invention include: the maximum value of the zero-center normalized instantaneous amplitude spectral density. Absolute amplitude standard deviation Frequency mean Variance of the normalized central instantaneous frequency .

[0061] Maximum instantaneous amplitude spectral density with zero center normalization for:

[0062]

[0063] In the formula, Indicates the number of samples of the input signal; It represents the instantaneous amplitude value of the signal; express The normalized value; yes The zero-centered amplitude; It means The mean, The calculation method is as follows:

[0064]

[0065] To minimize the impact of channel gain on characteristic parameters The impact requires consideration of instantaneous amplitude. Standardization is performed. Frequency-domain modulated signals (such as 2FSK and MFSK) are not modulated in amplitude; their envelope curve is constant, therefore its... It can be known that For both MPSK and MASK signals, their amplitude envelopes will change. It can be deduced that Therefore, through The parameters can be used to infer whether the envelope of a signal is constant, thus distinguishing between the two major types of signals.

[0066] Absolute amplitude standard deviation for:

[0067]

[0068] In the formula To determine the strength of the signal. It is usually set to 1. c represents the number of strong signals in the input signal. Definition. It reflects the absolute amplitude information of the signal; the 2ASK signal does not contain normalized amplitude information. The value is low; the 4ASK signal contains normalized amplitude information. The value is relatively high.

[0069] Frequency mean for:

[0070]

[0071] In the formula, ,in This represents the instantaneous frequency of the signal. This indicates the number of input signal points. The time-domain frequency characteristics of the signal can be used to distinguish between MFSK and MPSK signals.

[0072] Variance of normalized central instantaneous frequency for:

[0073]

[0074] In the formula, And the definition of c, The definition is the same as the formula above. 2FSK It will be less than 4FSK Values ​​can be obtained through instantaneous parameters. Complete the differentiation between 2FSK and 4FSK signals.

[0075] This invention calculates the cyclic spectrum of a signal using a time-domain smoothing periodicity method. The specific implementation steps are as follows:

[0076] Step 1: Calculation The spectrum of N points over a time period of T.

[0077] Using Wiener-Khinchin's theorem, the power spectrum function of a signal is derived from its autocorrelation function as follows:

[0078]

[0079] In the formula, It is the cyclic autocorrelation function of the signal. It is the cycle frequency. It is the cyclic spectral density function of the signal.

[0080] A rectangular window (center time t, width T) is used for finite signals. To extract, The spectrum of the signal is ,definition The spectrum of the signal after truncation is .

[0081]

[0082] In the formula, for The signal undergoes a short Fourier transform, which is equivalent to the signal It passes through a frequency filter, the center frequency of which is bandwidth is .

[0083] Step 2: Calculate spectral correlation.

[0084] Will Spectrum shifted to both sides and The time-varying spectrum of the truncated signal was obtained. and , The cycle frequency.

[0085]

[0086] right and After performing the multiplication operation, the window width is adjusted without mean smoothing the result, and finally the cyclic spectral density function is obtained. :

[0087]

[0088] In the formula, T is the same as T in step one. It is a time-varying spectrum . conjugate.

[0089] Step 3: Spectral smoothing.

[0090] Will and Substitute the value The expression yields:

[0091]

[0092] Iterate through each specific loop frequency Calculate its cyclic spectral density function, and after traversing the entire spectrum, obtain the complete cyclic spectral parameter matrix of the finite signal. .

[0093] The specific implementation process of the debugging method identification based on higher-order accumulative quantity and cyclic spectrum parameters proposed in this invention is as follows:

[0094] MASK, MQAM, MPSK, MFSK, and DSSS modulation schemes can be distinguished using second- and fourth-order statistics and autocorrelation functions. Due to the sub-Gaussian distribution characteristics of OFDM modulation, conventional high-order feature extraction methods are ineffective for identifying mixed multi-carrier and single-carrier signals. To address this issue, this invention combines fourth-order cumulants and cyclic spectrum features for OFDM identification. First, the eigenvalues ​​are defined. The generalized fourth-order cumulant is defined as:

[0095]

[0096] in It is a baseband signal. It is a custom nonlinear transformation. It defines the maximum instantaneous amplitude power spectral density normalized to zero center. for:

[0097]

[0098] In the formula, N is the sampling length. This represents the instantaneous envelope normalized to zero, and is calculated using the following formula: , Represents the instantaneous envelope expectation. Defines the standard deviation of the instantaneous absolute value of the phase of a zero-centered non-weak signal segment. for:

[0099]

[0100] Where C is the instantaneous phase of the non-weak signal. Indicates instantaneous amplitude. The threshold for determining a non-weak signal is typically the expected value of the instantaneous envelope. The phase is nonlinear and instantaneous. The generalized cyclic spectrum is defined as follows:

[0101]

[0102]

[0103] Feature parameters Indicates the position of the spectral peak in the generalized cyclic spectrum. This indicates the number of spectral peaks.

[0104] Combination Figure 1This section describes the modulation scheme identification process. It considers a scenario where OFDM, MASK (M=2, 4), BPSK, 4FSK, and 16QAM coexist. The normalized generalized fourth-order cumulant estimate is calculated to separate multi-carrier OFDM from single-carrier signals. The normalized fourth-order cumulant estimate of the OFDM signal is much larger than that of other signals, and the difference gradually increases with the increase of the mixed signal-to-noise ratio. Therefore, by appropriately setting the decision domain, OFDM identification and extraction can be achieved. Parameters based on instantaneous features are calculated to classify single-carrier signals into three categories: 2ASK, 4ASK and 16QAM, and BPSK and 4FSK. These parameters are used to distinguish between 4FSK and BPSK. Characteristic parameters based on the cyclic spectrum are calculated. These parameters are used to distinguish between 4ASK and 16QAM.

[0105] The air-sea cross-domain communication gateway is equipped with the modulation mode identification method proposed in this invention. It identifies the modulation mode of the received communication signal and changes its own modulation mode to adapt to the current underwater environment, thereby improving the success rate and reliability of underwater information transmission.

[0106] The above description is merely of preferred embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention, and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. An underwater information transmission method adapted to an air-sea cross-domain communication gateway, characterized in that, Includes the following steps: S1. Receive underwater acoustic communication signals sent by the underwater node; S2. Identify the modulation method of the underwater acoustic communication signal; S3. Adjust the modulation scheme of the air-sea cross-domain communication gateway according to the identified modulation scheme; S4. Use the adjusted modulation method to communicate with the corresponding underwater node.

2. The underwater information transmission method adapted to an air-sea cross-domain communication gateway as described in claim 1, characterized in that, The modulation scheme identification algorithm includes: a. Signal preprocessing, including separating the modulated signal from the mixed signal and filtering and noise reduction of the modulated signal; b. Feature extraction: By analyzing the time-domain features and spectral characteristics of the signal, suitable feature parameters are constructed and thresholds are determined for use in the classifier. c. Classifier selection: The type of signal is determined by comparing the feature parameters of the input signal with a threshold.

3. The underwater information transmission method adapted to an air-sea cross-domain communication gateway as described in claim 2, characterized in that, The feature extraction includes: a. Extract higher-order cumulants to suppress Gaussian white noise; b. Extract instantaneous parameters for signal identification in low signal-to-noise ratio environments; c. Extract cyclic spectrum parameters to improve noise immunity.

4. The underwater information transmission method adapted to an air-sea cross-domain communication gateway as described in claim 2, characterized in that, The classifier chosen is the decision tree classifier because it is easy to understand and implement, and has good real-time performance.

5. The underwater information transmission method adapted to an air-sea cross-domain communication gateway as described in claim 3, characterized in that, The modulation method based on higher-order cumulants and cyclic spectrum parameters is identified as follows: a. For MASK, MQAM, MPSK, MFSK and DSSS modulation schemes, they are distinguished by second- and fourth-order statistics and autocorrelation functions; b. For OFDM modulation, identification and extraction are performed by combining fourth-order cumulants and cyclic spectrum features.

6. The underwater information transmission method adapted to an air-sea cross-domain communication gateway as described in claim 5, characterized in that, The method for identifying the OFDM modulation scheme includes: a. Calculate the normalized generalized fourth-order cumulant estimate to separate multicarrier OFDM from single-carrier signals; b. Calculate parameters based on instantaneous characteristics, and classify single-carrier signals into three categories based on these parameters: 2ASK, 4ASK and 16QAM, and BPSK and 4FSK; c. Calculate characteristic parameters based on the cyclic spectrum, and use these parameters to distinguish between 4ASK and 16QAM.

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