A data link communication method and system based on adaptive phase modulation

Abstract

This invention discloses a data link communication method and system based on adaptive phase modulation, comprising: S1, a local receiver acquiring current channel state information, including signal-to-noise ratio, channel fading coefficient, and interference intensity; S2, a local transmitter selecting the optimal phase modulation scheme from a preset set of phase modulation schemes based on the channel state information obtained in S1; S3, the local transmitter modulating the channel-coded data using the optimal phase modulation scheme selected in S2 to generate a modulated signal and transmitting it; S4, a remote receiver receiving the modulated signal generated in S3 and demodulating and decoding the channel according to the optimal phase modulation scheme selected in S2 to obtain the recovered original data. This invention, by adaptively selecting the optimal phase modulation scheme based on channel state information, can improve data transmission rate while ensuring communication quality, thereby meeting the high requirements of modern warfare for data link systems.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology. More specifically, this invention relates to a data link communication method and system based on adaptive phase modulation. Background Technology

[0002] With the increasing demands for real-time and accurate information in modern warfare, data link systems have become a crucial component of military communications. Traditional data link systems often employ fixed-phase modulation methods, such as BPSK and QPSK. While simple to implement, these methods struggle to achieve an optimal balance between data transmission rate and bit error rate in complex electromagnetic environments and when channel conditions dynamically change. When channel quality is poor, higher-order modulation results in a higher bit error rate; conversely, when channel quality is good, lower-order modulation wastes channel capacity and reduces transmission efficiency. Summary of the Invention

[0003] One object of the present invention is to solve at least the above-mentioned problems and / or defects, and to provide at least the advantages described below.

[0004] To achieve these objectives and other advantages of the present invention, a data link communication method based on adaptive phase modulation is provided, comprising: S1. The local receiver obtains the current channel state information, which includes: signal-to-noise ratio, channel fading coefficient, and interference intensity. S2. Based on the channel state information obtained in S1, the local transmitting end selects the optimal phase modulation scheme from the preset set of phase modulation schemes. S3. The local transmitting end uses the optimal phase modulation method selected in S2 to modulate the channel-coded data, generate a modulated signal, and transmit it. S4. The remote receiver receives the modulated signal generated by S3, and performs demodulation and channel decoding according to the optimal phase modulation method selected by S2 to obtain the recovered original data. The selection rule for the optimal phase modulation method is as follows: When the signal-to-noise ratio of the channel state information is less than the first threshold, the first phase modulation method BPSK is selected as the optimal phase modulation method. When the signal-to-noise ratio of the channel state information is greater than or equal to the first threshold and less than the second threshold, the second phase modulation method QPSK is selected as the optimal phase modulation method. When the signal-to-noise ratio of the channel state information is greater than or equal to the second threshold and less than the third threshold, the third phase modulation method 8PSK is selected as the optimal phase modulation method. When the signal-to-noise ratio of the channel state information is greater than or equal to the third threshold, the fourth phase modulation method, 16PSK, is selected as the optimal phase modulation method.

[0005] Preferably, in S1, the current channel state information is obtained in the following way: S10. Based on time-frequency transformation, the energy of the signal is mapped from the time domain to a two-dimensional time-frequency plane to obtain the corresponding time-domain feature vector and frequency-domain feature vector. S11. Concatenate the time-domain feature vector and frequency-domain feature vector obtained in S10 to obtain the corresponding fused feature vector; S12. Use known modulated signal samples with different signal-to-noise ratios to train the fused feature vector in order to build a mapping model from the fused feature vector to the signal-to-noise ratio value. S13. For an input signal with an unknown signal-to-noise ratio, a mapping model is used to give an estimate of the signal-to-noise ratio.

[0006] Preferably, the time-domain feature vector is a second-order and fourth-order cumulant based on the real and imaginary parts of the signal; The correlation function of the second-order cumulant includes: the real part autocorrelation function. ,and Imaginary autocorrelation function ,and Cross-correlation function of real and imaginary parts ,and Total power function of signal P ,and ,in, and These are the real and imaginary parts, respectively, and E[﹒ ] represents the mathematical expectation, which is the statistical average of the random signal; The second-order cumulants include: in, This indicates cumulative calculation. Indicates a complex baseband signal. yes . conjugate.

[0007] Preferably, the frequency domain feature vector is extracted from key features of the signal's power spectral density, and the key features include: center frequency. f c Bandwidth B, spectral flatness, peak frequency; Wherein, the center frequency f c It is characterized by the following formula: In the above formula, fk It is a frequency point. That is the corresponding PSD value. k This represents each specific frequency sequence number; Bandwidth B is characterized by the following formula: .

[0008] Preferably, in S12, the mapping model is constructed using Support Vector Regression (SVR), and the final regression function... f ( x It is characterized by the following formula: In the above formula, This indicates that it corresponds to the location located at The Lagrange multipliers introduced by the sample points at the upper boundary of the insensitive interval This indicates that it corresponds to the location located at The Lagrange multipliers introduced by the sample points at the lower boundary of the insensitive interval Represents the kernel function; b Represents a constant offset, and only if it satisfies... Only sample points that meet certain criteria can be used as support vectors.

[0009] Preferably, in S13, the signal-to-noise ratio estimate is obtained by the following formula: In the above formula, h The channel fading coefficient, P t For transmission power, P interference For interference intensity, P noiseo This represents noise power.

[0010] A data link communication system, comprising: A channel state information acquisition module is set up at the local receiving end; A phase modulation mode selection module is set at the local transmitting end; A modulation module set at the local transmitting end; A demodulation module and a channel decoding module are installed at the remote receiving end. This invention offers at least the following advantages: This invention adaptively selects the optimal phase modulation scheme based on channel state information, thereby increasing data transmission rate while ensuring communication quality, thus meeting the high demands of modern warfare for data link systems. Furthermore, this invention is simple to implement and easy to apply in practical systems.

[0011] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0012] Figure 1 This is a block diagram showing the composition of the local or remote end of the present invention; Figure 2 This is a flowchart of the data link communication method based on adaptive phase modulation according to the present invention. Detailed Implementation

[0013] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0014] like Figure 1 As shown, a data link communication system based on adaptive phase modulation includes: a local end and a remote end, both of which include a transmitter and a receiver. The transmitting end is used to perform channel coding and modulation on the data to be transmitted, generate a modulated signal, and transmit it. Specifically, the transmitting end includes: The phase modulation scheme selection module is used to select the optimal phase modulation scheme from a preset set of phase modulation schemes based on the channel state information. The set of phase modulation schemes includes BPSK, QPSK, 8PSK, and 16PSK. The modulation module is used to modulate the channel-coded data using the optimal phase modulation method.

[0015] The receiving end is used to receive the modulated signal, demodulate and decode the channel to recover the original data. Specifically, the receiving end includes: The channel state information acquisition module is used to acquire the current channel state information. Channel state information includes signal-to-noise ratio, channel fading coefficient, and interference intensity. The demodulation module is used to demodulate the received signal according to the optimal phase modulation method. The channel decoding module is used to perform channel decoding on the demodulated data.

[0016] A data link communication system method based on adaptive phase modulation is proposed, which can adaptively select the optimal phase modulation scheme for data communication according to channel conditions, such as... Figure 2 As shown, its communication process includes: Step 1: The local receiver obtains the current channel state information; In this step, the core idea of ​​the channel state information acquisition method is to combine time-domain (second-order / fourth-order cumulants) and frequency-domain (PSD features) analysis techniques to obtain more robust features to noise. A machine learning model is then used to learn the complex mapping between these features and the signal-to-noise ratio (SNR), achieving stable estimation of the SNR in noisy environments. Feature fusion employs simple vector concatenation, while model training follows a standard supervised regression process. The implementation steps are as follows: 1) Signal and Processing: The receiving end acquires the modulated signal contaminated by additive white Gaussian noise; 2) Time-Frequency Transform: Perform a Short-Time Fourier Transform (STFT) on the signal to obtain its joint time-frequency representation. This step maps the signal's energy from the time domain to a two-dimensional time-frequency plane, which helps suppress noise and reveal the signal's instantaneous frequency characteristics; 3) Feature extraction: a) Time-domain characteristics f time vector This feature is primarily based on the second and fourth order cumulants (cumulants) of the real and imaginary parts of the signal. For the received complex baseband signal... ,in and Real and imaginary parts, time-domain features f time A vector can contain one or more of the following specific feature vectors. Commonly used cumulant feature vectors include: The correlation function of the second-order cumulant includes: the real part autocorrelation function. ,and Imaginary autocorrelation function ,and Cross-correlation function of real and imaginary parts ,and Total power function of signal P ,and ,in, and These are the real and imaginary parts, respectively, and E[﹒ ] represents the mathematical expectation, which is the statistical average of the random signal; Fourth-order cumulants are insensitive to Gaussian noise, and therefore robust to additive white Gaussian noise (AWGN). The characteristics of fourth-order cumulants include: in, This indicates cumulative calculation. yes The conjugate of the accumulators. These accumulators can be calculated separately for the real and imaginary parts of the signal, or directly for complex signals. In practical applications, to simplify calculations and ensure the invariance of features to phase shifts, normalized fourth-order accumulators are used: and

[0017] b) Frequency domain characteristics vector: Extracting key features from the power spectral density (PSD) of a signal, such as its center frequency and bandwidth, requires first estimating the signal's PSD. Common methods include the periodogram method or averaging the squared amplitudes of the STFT results. Frequency domain characteristics. f freq A vector may contain one or more of the following specific feature vectors. Key feature vectors extracted from a PSD include: Center frequency f c : Reflects the centroid of information energy distribution in the frequency domain, and ,in f k It is a frequency point. That is the corresponding PSD value.

[0018] Bandwidth B: Describes the extent to which signal energy spreads in the frequency domain; it is typically expressed as the root mean square bandwidth. .

[0019] Spectral flatness (SF): Measures the flatness of the spectrum; it is the ratio of the geometric mean to the arithmetic mean. It helps distinguish between noise and modulated signals. .

[0020] Spectral entropy H: reflects the randomness of the energy distribution of the spectrum, and ,in .

[0021] Peak frequency f peak The frequency corresponding to the largest amplitude value in the PSD, and .

[0022] c) Fusion features: The time-domain and frequency-domain feature vectors are concatenated to form a more comprehensive fused feature vector. The specific steps are as follows: Feature standardization: Before concatenation, the time-domain feature vectors need to be standardized. f time and frequency domain eigenvectors f frek Standardize them separately (e.g., Z-score standardization) to eliminate the effects of differences in the dimensions and numerical ranges of different features.

[0023] Vector concatenation: Directly concatenate the standardized time-domain feature vector and the frequency-domain feature vector to form a higher-dimensional fused feature vector. f fusion ,and For example, if four time-domain cumulative features and three frequency-domain features (such as center frequency, bandwidth, and spectral entropy) are extracted, the fused feature vector is a 7-dimensional vector.

[0024] Dimensionality reduction (optional): If the feature dimension is too high after fusion, consider using temporal principal component analysis (PCA) or linear discriminant analysis (LDA) to reduce the computational cost and improve model performance.

[0025] 4) Machine Learning Training: The fused feature vector is trained using a large number of known modulated signal samples at different signal-to-noise ratios (SNR). The goal of the training is to establish a mapping model from the fused feature vector to the SNR value. The specific process is as follows: a) Dataset construction: Use a large number of signal samples with known modulation types and known signal-to-noise ratios (SNR). The SNR range should cover the intended application scenario (e.g., -10dB to 20dB).

[0026] For each sample signal, calculate its corresponding fused feature vector according to the above steps (time domain transformation, feature extraction, fusion). .

[0027] b) Mapping model: Support Vector Regression (SVR): SVR is commonly used in datasets due to its good generalization ability. The final regression function (predictive model) can be expressed as: in, This indicates that it corresponds to the location located at The Lagrange multipliers introduced by the sample points at the upper boundary of the insensitive interval This indicates that it corresponds to the location located at The Lagrange multiplier introduced by the sample points at the lower boundary of the insensitive interval; This refers to the kernel function, which is used to process input samples. x i and new input x Map to a high-dimensional feature space and compute the inner product of the two; b Represents a constant offset, and only if it satisfies... Only a few sample points contribute to the final regression function; these sample points are called support vectors. Therefore, the solution of SVR is sparse, and the complexity of the model depends only on the number of support vectors, and is independent of the dimensionality of the original data.

[0028] c) Training process: Divide the dataset into training, validation, and test sets (e.g., 70%-15%-15%).

[0029] Train the SVR model on the training set.

[0030] Use a validation set for hyperparameter tuning to prevent overfitting.

[0031] Finally, the model's performance was evaluated using a test set, denoted by mean squared error (MSE). , where n is the number of samples in the test set; y i This represents the true value of the i-th sample. Let be the predicted value for the i-th sample; This involves summing the squares of the differences between the predicted and actual values ​​for all samples.

[0032] 5) Online estimation: For an input signal with an unknown signal-to-noise ratio (SNR), repeat steps 2) to 4) to extract its fusion features and input them into the trained model to output the estimated value of the SNR.

[0033]

[0034] Where: channel fading coefficient h and transmit power P t Located in the molecule, it is positively correlated with the signal-to-noise ratio (SNR). The better the channel conditions, the higher the SNR. Interference intensity P interference and noise power P noiseo Located in the denominator, it is negatively correlated with the signal-to-noise ratio (SNR). The stronger the interference, the lower the SNR.

[0035] Step 2: The local transmitting end selects the optimal phase modulation scheme from a preset set of phase modulation schemes based on the channel state information; In this step, the phase modulation mode selection module selects the optimal phase modulation mode according to a preset phase modulation mode selection strategy, which includes: 1) When the channel state information satisfies the condition that the signal-to-noise ratio is less than the first threshold, the first phase modulation method BPSK is selected; 2) When the channel state information satisfies the condition that the signal-to-noise ratio is greater than or equal to the first threshold and less than the second threshold, the second phase modulation method, QPSK, is selected; 3) When the channel state information satisfies the condition that the signal-to-noise ratio is greater than or equal to the second threshold and less than the third threshold, the third phase modulation method, 8PSK, is selected; 4) When the channel state information satisfies the signal-to-noise ratio greater than or equal to the third threshold, the fourth phase modulation method 16PSK is selected.

[0036] Threshold selection: The first threshold can be set to 10dB, the second threshold to 15dB, and the third threshold to 20dB. When the signal-to-noise ratio (SNR) is less than 10dB, BPSK phase modulation is selected; when the SNR is greater than or equal to 10dB and less than 15dB, QPSK phase modulation is selected; when the SNR is greater than or equal to 15dB and less than 20dB, 8PSK phase modulation is selected; and when the SNR is greater than or equal to 20dB, 16PSK phase modulation is selected.

[0037] Step 3: The local transmitting end uses the optimal phase modulation method to modulate the channel-coded data, generates a modulated signal, and transmits it. Step four: The remote receiver receives the modulated signal and performs demodulation and channel decoding according to the optimal phase modulation method to recover the original data.

[0038] The above solution is merely an illustration of a preferred example and is not limited thereto. When implementing this invention, appropriate substitutions and / or modifications can be made according to the user's needs.

[0039] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Other modifications can be readily made by those skilled in the art. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. A data link communication method based on adaptive phase modulation, characterized in that, include: S1. The local receiver obtains the current channel state information, which includes: signal-to-noise ratio, channel fading coefficient, and interference intensity. S2. Based on the channel state information obtained in S1, the local transmitting end selects the optimal phase modulation scheme from the preset set of phase modulation schemes. S3. The local transmitting end uses the optimal phase modulation method selected in S2 to modulate the channel-coded data, generate a modulated signal, and transmit it. S4. The remote receiver receives the modulated signal generated by S3, and performs demodulation and channel decoding according to the optimal phase modulation method selected by S2 to obtain the recovered original data. The selection rule for the optimal phase modulation method is as follows: When the signal-to-noise ratio of the channel state information is less than the first threshold, the first phase modulation method BPSK is selected as the optimal phase modulation method. When the signal-to-noise ratio of the channel state information is greater than or equal to the first threshold and less than the second threshold, the second phase modulation method QPSK is selected as the optimal phase modulation method. When the signal-to-noise ratio of the channel state information is greater than or equal to the second threshold and less than the third threshold, the third phase modulation method 8PSK is selected as the optimal phase modulation method. When the signal-to-noise ratio of the channel state information is greater than or equal to the third threshold, the fourth phase modulation method, 16PSK, is selected as the optimal phase modulation method.

2. The data link communication method based on adaptive phase modulation as described in claim 1, characterized in that, In S1, the current channel state information is obtained as follows: S10. Based on time-frequency transformation, the energy of the signal is mapped from the time domain to a two-dimensional time-frequency plane to obtain the corresponding time-domain feature vector and frequency-domain feature vector. S11. Concatenate the time-domain feature vector and frequency-domain feature vector obtained in S10 to obtain the corresponding fused feature vector; S12. Use known modulated signal samples with different signal-to-noise ratios to train the fused feature vector in order to build a mapping model from the fused feature vector to the signal-to-noise ratio value. S13. For an input signal with an unknown signal-to-noise ratio, a mapping model is used to give an estimate of the signal-to-noise ratio.

3. The data link communication method based on adaptive phase modulation as described in claim 2, characterized in that, The time-domain feature vector is based on the second- and fourth-order cumulants of the real and imaginary parts of the signal; The correlation function of the second-order cumulant includes: the real part autocorrelation function. ,and Imaginary autocorrelation function ,and Cross-correlation function of real and imaginary parts ,and Total signal power function P ,and ,in, and These are the real and imaginary parts, respectively, and E[﹒ ] represents the mathematical expectation, which is the statistical average of the random signal; The second-order cumulants include: in, This indicates cumulative calculation. Indicates a complex baseband signal. yes . conjugate.

4. The data link communication method based on adaptive phase modulation as described in claim 2, characterized in that, The frequency domain feature vector is extracted from key features of the signal's power spectral density, and these key features include: center frequency. f c Bandwidth B, spectral flatness, peak frequency; Wherein, the center frequency f c It is characterized by the following formula: In the above formula, f k It is a frequency point. That is the corresponding PSD value. k This represents each specific frequency sequence number; Bandwidth B is characterized by the following formula: 。 5. The data link communication method based on adaptive phase modulation as described in claim 2, characterized in that, In S12, the mapping model is constructed using Support Vector Regression (SVR), and the final regression function... f ( x It is characterized by the following formula: In the above formula, This indicates that it corresponds to the location located at The Lagrange multipliers introduced by the sample points at the upper boundary of the insensitive interval This indicates that it corresponds to the location located at The Lagrange multipliers introduced by the sample points at the lower boundary of the insensitive interval Represents the kernel function; b Represents a constant offset, and only if it satisfies... Only sample points that meet certain criteria can be used as support vectors.

6. The data link communication method based on adaptive phase modulation as described in claim 2, characterized in that, In S13, the signal-to-noise ratio estimate is obtained by the following formula: In the above formula, h The channel fading coefficient, P t For transmission power, P interference For interference intensity, P noiseo This represents noise power.

7. A data link communication system, applied in the data link communication method based on adaptive phase modulation as described in any one of claims 1-6, characterized in that, include: A channel state information acquisition module is set up at the local receiving end; A phase modulation mode selection module is set at the local transmitting end; A modulation module set at the local transmitting end; The demodulation module and channel decoding module are set at the remote receiving end.

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