A method for enhancing digital communication signals

Abstract

The present invention proposes a method for enhancing digital communication signals, which relates to the technical field of signal processing. The specific steps are as follows: constructing a digital communication signal data set; constructing an adaptive basis function by combining local instantaneous phase and energy centroid offset; encoding the digital communication signal into the adaptive basis function by Gaussian weighted dynamic focusing on key sampling points to construct an encoding matrix; performing noise suppression on the encoding matrix by using dynamic threshold and nonlinear compression; fusing multi-scale back projection and global energy balance to perform final enhancement processing on the digital communication signal; and at the same time, proposing a digital communication signal enhancement processing model to efficiently complete the enhancement processing of digital communication signals through the model.

Description

Technical Field

[0001] The present invention belongs to the technical field of signal processing, and particularly relates to a method for enhancing digital communication signals. Background Art

[0002] With the continuous development of information technology, digital communication systems have been widely used in various industries; however, with the complexity of the communication environment and the increase in signal noise, existing digital communication signal processing methods face many challenges; traditional digital communication signal enhancement technologies often rely on global processing methods, ignoring the local characteristics of signals at different frequency bands and instantaneous changes, resulting in poor restoration effects of digital communication signals, and it is difficult to effectively ensure the clarity and reliability of digital communication signals, affecting the overall performance of the communication system.

[0003] In addition, existing noise suppression technologies usually adopt static threshold settings and cannot be dynamically adjusted according to the characteristics and real-time changes of digital communication signals, thus reducing the accuracy and adaptability of noise suppression; traditional digital communication signal enhancement methods are also difficult to balance high-precision restoration and anti-interference ability of signals, often resulting in distortion and information loss during the reconstruction process of digital communication signals; therefore, how to effectively improve the quality of digital communication signals in a complex communication environment, reduce noise interference and ensure the stable transmission of digital communication signals has become a major problem in current digital communication technologies. Summary of the Invention

[0004] The present invention provides a method for enhancing digital communication signals, aiming to propose an enhancement processing model for digital communication signals. By performing windowing processing on digital communication signals and constructing an adaptive basis function based on the instantaneous phase and energy centroid offset, the local characteristics of digital communication signals are accurately captured; the Gaussian weighting technique is used to focus on key sampling points within each window to enhance the detail performance of digital communication signals and construct an encoding matrix; based on the signal-to-noise ratio-driven dynamic threshold and nonlinear compression technology, noise is further suppressed, the core information of digital communication signals is retained, and the clarity of digital communication signals is ensured; through multi-scale backprojection and global energy balancing technology, digital communication signals are reconstructed and enhanced to improve the robustness and stability of digital communication signals; the enhancement processing of digital communication signals is completed using the digital communication signal enhancement processing model.

[0005] To achieve the above object, the present invention provides the following technical solution: A method for enhancing digital communication signals, the specific steps are as follows:

[0006] S1. Collect and construct a digital communication signal dataset;

[0007] S2. Perform windowing processing on the digital communication signal, and construct an adaptive basis function based on the local instantaneous phase and the energy centroid offset;

[0008] S3. Dynamically focus on key sampling points through Gaussian weighting, encode the digital communication signal into the adaptive basis function, and construct an encoding matrix;

[0009] S4. Perform noise suppression on the encoding matrix using a signal-to-noise ratio-driven dynamic threshold and nonlinear compression;

[0010] S5. Fuse multi-scale backprojection and global energy equalization to reconstruct the signal, and perform final enhancement processing on the digital communication signal;

[0011] S6. Construct a digital communication signal enhancement processing model, input the digital communication signal dataset into the model, sequentially perform S2 to S5 and conduct model training to complete the digital communication signal enhancement processing.

[0012] Preferably, in step S1, for the digital communication signal dataset, use a high-precision digital communication receiving device to collect digital communication signals in real time, including cellular network, satellite communication, and shortwave communication signals, covering typical noise scenarios such as Gaussian white noise, multipath fading, and impulse interference, store them in binary files in complex form; set the sampling rate and the total number of sampling points, divide the training set and the test set proportionally, and label each sample with the true signal-to-noise ratio, modulation method, and noise type to construct the digital communication signal dataset.

[0013] Preferably, in step S2, the specific steps for constructing the adaptive basis function are as follows: The input is the original digital communication signal , the sampling point index is , the total number of signal sampling points is P, divide the input into windows, the window length is W, , the windows slide point by point with a step size of 1, the total number of windows is , generate the adaptive basis function by calculating the instantaneous phase and the energy centroid offset of each window; the mathematical model of the adaptive basis function is:

[0014] ;

[0015] In the formula, ω is the window index, , is the total number of windows, b is the adaptive basis function index, , is the number of adaptive basis functions, is the instantaneous phase, is the energy centroid offset, is a zero-prevention constant, and m is the index;

[0016] Instantaneous phase The mathematical model of

[0017] ;

[0018] In the formula, is the average frequency of the signal within the window;

[0019] Energy center of gravity offset The mathematical model of

[0020] .

[0021] Preferably, in step S2, the design of the adaptive basis function aims to effectively cope with the variability of instantaneous characteristics and noise interference in digital communication signals, and improve the recovery effect of digital communication signals in complex environments; First, by performing windowing processing on digital communication signals and calculating the instantaneous phase and energy center of gravity offset of each window, the fluctuation characteristics of digital communication signals within a local range can be captured, providing accurate local signal information for constructing the adaptive basis function; Subsequently, based on local characteristics, the generated adaptive basis function can dynamically adjust the signal processing strategy, avoid distortion caused by global processing methods, and ensure enhanced accuracy; The overall design can flexibly adjust the enhancement strategy according to the characteristics of digital communication signals, improve the recovery effect in high-noise environments, reduce noise interference, and thus ensure the stability and reliability of communication quality.

[0022] Preferably, in step S3, the specific steps for constructing the coding matrix are as follows: project the input original digital communication signal onto the adaptive basis function, calculate the projection coefficients for each window and the adaptive basis function, weight the signal points within the window through the Gaussian kernel function, focus on the key sampling points of the energy center of gravity, and construct the coding matrix using the calculated projection coefficients as matrix elements The projection coefficient of the w-th window on the b-th adaptive basis function

[0023] ;

[0024] In the formula, is the Gaussian kernel function, is the signal fluctuation intensity of the w-th window;

[0025] The signal fluctuation intensity of the w-th window

[0026] ;

[0027] In the formula, is the mean value of the w-th window.

[0028] Preferably, in step S3, the construction of the encoding matrix aims to further improve the quality and enhancement effect of digital communication signals through refined signal processing. First, by projecting the original digital communication signal onto the adaptive basis function space and calculating the projection coefficients of each window, the local characteristics of the digital communication signal can be accurately captured. Subsequently, the signal points within each window are weighted in combination with the Gaussian kernel function, focusing on the key sampling points of the energy center of gravity, effectively enhancing the core features of the digital communication signal and avoiding information loss caused by global processing. The overall design, by constructing the encoding matrix, while ensuring the integrity of the digital communication signal, effectively improves the clarity and accuracy of the signal, reduces noise interference, enhances the transmission effect of the digital communication signal, and provides a solid foundation for subsequent noise suppression and signal reconstruction.

[0029] Preferably, in step S4, the specific steps for performing noise suppression on the encoding matrix are as follows: The input is the encoding matrix. Combining the projection coefficients, a dynamic threshold is calculated for each adaptive basis function. Nonlinear compression is performed on each projection coefficient, and all the projection coefficients after the nonlinear compression process constitute the enhanced encoding matrix. The dynamic threshold of the b-th adaptive basis function has the mathematical model of:

[0030] ;

[0031] In the formula, is the signal-to-noise ratio of the original digital communication signal, defined as the ratio of the power of the original digital communication signal to the noise power, is the power of the original digital communication signal, is the noise power, is the noise-free reference signal;

[0032] The enhanced projection coefficient has the mathematical model of:

[0033] ;

[0034] In the formula, is the steepness coefficient of the b-th adaptive basis function, .

[0035] Preferably, in step S4, the specific steps of noise suppression are aimed at effectively reducing the noise interference in the encoding matrix and further improving the quality of digital communication signals; first, by combining the projection coefficients, the dynamic threshold of each adaptive basis function is calculated, and the noise suppression intensity is dynamically adjusted according to the signal-to-noise ratio of the digital communication signal, adaptively adjusting the noise suppression effect for different signal characteristics to ensure that the important information in the digital communication signal is retained; subsequently, nonlinear compression is used to process the projection coefficients, which can effectively reduce the impact of noise on the signal while retaining the key features of the digital communication signal and improving the clarity of the digital communication signal; the overall design dynamically adjusts the parameters of noise suppression to achieve adaptive noise suppression under different signal-to-noise ratio conditions, optimizes the recovery effect of digital communication signals, enhances the anti-interference ability of the system in the communication environment, and provides a reliable basis for subsequent signal enhancement and reconstruction.

[0036] Preferably, in step S5, the specific steps for the final enhancement processing of the digital communication signal are as follows: the input is the enhanced encoding matrix , for each window, the time-domain digital communication signal is generated through multi-scale backprojection; the window reconstruction results are spliced through the impulse function to obtain the reconstructed digital communication signal; energy equalization is performed on the global signal and combined with the original digital communication signal to obtain the finally enhanced digital communication signal; the time-domain digital communication signal has the following mathematical model:

[0037] ;

[0038] In the formula, is the anti-zero constant;

[0039] The reconstructed digital communication signal has the following mathematical model:

[0040] ;

[0041] In the formula, is the impulse function, which is 1 when p = w and 0 otherwise;

[0042] The finally enhanced digital communication signal has the following mathematical model:

[0043] ;

[0044] In the formula, is the energy equalization smoothing coefficient, is the original digital communication signal, is the anti-zero constant.

[0045] Preferably, in step S5, the final enhancement processing of the digital communication signal aims to effectively improve the quality and stability of the digital communication signal through multi-scale backprojection and global energy equalization. First, a time-domain digital communication signal is generated through multi-scale backprojection, and the signal information at different frequencies and scales is fused to restore the detailed information in the digital communication signal, enhance the detailed performance of the signal, and at the same time reduce the distortion that occurs during signal reconstruction. Subsequently, the reconstructed digital communication signal is spliced through impulse functions to ensure the continuity and integrity of the time-domain signal. Finally, the overall strength of the digital communication signal is optimized by combining global energy equalization, the energy distribution of the signal is adjusted, and the phenomenon of local overstrength or overweakness is avoided to ensure the smoothness of the digital communication signal. Through the combination of multi-scale processing and global energy equalization in the overall design, the robustness and stability of the digital communication signal are effectively improved, the fidelity of the digital communication signal is enhanced, and the efficient transmission and accurate recovery of the digital communication signal are ensured.

[0046] Preferably, in step S6, for the digital communication signal enhancement processing model, first, a digital communication signal data set is input, and each processing step is executed in sequence. The specific steps are as follows: In the digital communication signal windowing stage, the input digital communication signal is windowed according to the characteristics of the signal, and the instantaneous phase and energy centroid offset of each window are calculated to construct an adaptive basis function. Then, the key sampling points are dynamically focused using Gaussian weighting, the signal of each window is projected into the adaptive basis function space, and the projection coefficients are calculated to generate a coding matrix. In the noise suppression stage, the coding matrix is processed based on the signal-to-noise ratio-driven dynamic threshold and non-linear compression technology to effectively reduce noise interference and retain the key information in the digital communication signal. Finally, multi-scale backprojection and global energy equalization technologies are used to reconstruct and enhance the digital communication signal to ensure the clarity and stability of the digital communication signal. Through the training and parameter optimization of the model, the model can automatically adapt to the enhancement requirements under different signal conditions, effectively improve the quality of the digital communication signal and ensure stable transmission, and finally output the processing result of the enhanced digital communication signal.

[0047] Compared with the prior art, the present invention has the following technical effects: The technical solution provided by the present invention proposes a digital communication signal enhancement processing model, in which the digital communication signal is windowed, and an adaptive basis function is constructed based on the instantaneous phase and the energy centroid offset to accurately capture the local features of the digital communication signal; the Gaussian weighting technique is used to focus on the key sampling points in each window to enhance the detail performance of the digital communication signal and construct an encoding matrix; based on the signal-to-noise ratio-driven dynamic threshold and non-linear compression technique, the noise is further suppressed, and the core information of the digital communication signal is retained to ensure the clarity of the digital communication signal; through the multi-scale backprojection and global energy equalization technique, the digital communication signal is reconstructed and enhanced to improve the robustness and stability of the digital communication signal; the digital communication signal enhancement processing model is used to complete the enhancement processing of the digital communication signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flowchart of the digital communication signal enhancement processing method provided by the present invention.

[0049] Figure 2 is a flowchart of constructing an adaptive basis function provided by the present invention.

[0050] Figure 3 is a flowchart of constructing an encoding matrix provided by the present invention.

[0051] Figure 4 is a flowchart of performing noise suppression on the encoding matrix provided by the present invention.

[0052] Figure 5 is a flowchart of performing final enhancement processing on the digital communication signal provided by the present invention.

[0053] Figure 6 is the digital communication signal not processed by the digital communication signal enhancement processing model provided by the present invention.

[0054] Figure 7 is the digital communication signal processed by the digital communication signal enhancement processing model provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The present invention aims to propose a method for enhancing digital communication signals, and proposes an enhanced processing model for digital communication signals. By performing windowing on digital communication signals and constructing an adaptive basis function based on the instantaneous phase and energy centroid offset, the local characteristics of digital communication signals can be accurately captured; the Gaussian weighting technique is used to focus on key sampling points within each window, enhancing the detail performance of digital communication signals and constructing an encoding matrix; based on the dynamic threshold driven by the signal-to-noise ratio and the non-linear compression technique, noise is further suppressed, the core information of digital communication signals is retained, and the clarity of digital communication signals is ensured; through multi-scale backprojection and global energy equalization techniques, digital communication signals are reconstructed and enhanced, improving the robustness and stability of digital communication signals; using the enhanced processing model for digital communication signals, the enhancement processing of digital communication signals is completed.

[0056] Please refer to Figure 1 As shown, a method for enhancing digital communication signals in an embodiment of the present application is as follows.

[0057] S1. Collect and construct a digital communication signal dataset.

[0058] Furthermore, in step S1, for the digital communication signal dataset, a software-defined radio module is used to collect digital communication signals in the actual communication environment, including cellular network, satellite communication, and shortwave communication signals, covering typical noise scenarios such as Gaussian white noise, multipath fading, and impulse interference, with a signal-to-noise ratio range of -10 dB to 20 dB. The digital communication signals are stored as binary files in complex form; the sampling rate is set to 10 MHz, the quantization bit number is 16 bits, the duration of each signal segment is 1 millisecond, corresponding to the total number of sampling points P = 10 4 , the total number of samples in the digital communication signal dataset is 10 5 , and the training set and test set are divided in a ratio of 8:2. Each sample is labeled with the true signal-to-noise ratio, modulation method, and noise type to construct a digital communication signal dataset.

[0059] S2. Perform windowing on the digital communication signals and construct an adaptive basis function based on the local instantaneous phase and energy centroid offset.

[0060] Furthermore, in step S2, to construct an adaptive basis function, the process is as Figure 2 shown, and the specific steps for constructing the adaptive basis function are as follows.

[0061] The input is the original digital communication signal , the sampling point index is , the total number of sampling points of the signal is P, P = 10 4 , the input is divided into windows, and the window length is W, = 1000, the window slides point by point with a step size of 1, and the total number of windows is , = P - W + 1 = 9001. An adaptive basis function is generated by calculating the offset between the instantaneous phase and the energy center of gravity for each window; the adaptive basis function has the following mathematical model:

[0062] ;

[0063] In the formula, w is the window index, , is the total number of windows, b is the adaptive basis function index, , is the number of adaptive basis functions, is the instantaneous phase, is the offset of the energy center of gravity, m is the index, is a constant to prevent zero, = 9, ;

[0064] The instantaneous phase has the following mathematical model:

[0065] ;

[0066] In the formula, is the average frequency of the signal within the window;

[0067] The offset of the energy center of gravity has the following mathematical model:

[0068] .

[0069] S3. Dynamically focus on the key sampling points through Gaussian weighting, encode the digital communication signal into the adaptive basis function, and construct an encoding matrix.

[0070] Furthermore, in step S3, for constructing the encoding matrix, the process is as Figure 3 shown, and the specific steps for constructing the encoding matrix are as follows.

[0071] Project the input original digital communication signal onto the adaptive basis function, calculate the projection coefficients for each window and adaptive basis function, weight the signal points within the window through the Gaussian kernel function, focus on the key sampling points of the energy center of gravity, and use the calculated projection coefficients as matrix elements to construct the encoding matrix ; The projection coefficient of the w-th window on the b-th adaptive basis function

[0072] ;

[0073] In the formula, is the Gaussian kernel function, is the signal fluctuation intensity of the w-th window;

[0074] The signal fluctuation intensity of the w-th window has the following mathematical model:

[0075] ;

[0076] In the formula, is the mean value of the w-th window.

[0077] S4. Use the dynamic threshold and non-linear compression driven by the signal-to-noise ratio to perform noise suppression on the coding matrix.

[0078] Furthermore, in step S4, the process of performing noise suppression on the coding matrix is as Figure 4 shown, and the specific steps of performing noise suppression on the coding matrix are as follows.

[0079] The input is the coding matrix. Combining the projection coefficients, calculate the dynamic threshold for each adaptive basis function; perform non-linear compression on each projection coefficient. All the projection coefficients after non-linear compression processing form the enhanced coding matrix; the dynamic threshold of the b-th adaptive basis function has the following mathematical model:

[0080] ;

[0081] In the formula, is the signal-to-noise ratio of the original digital communication signal, defined as the ratio of the power of the original digital communication signal to the noise power, is the power of the original digital communication signal, is the noise power, is the noise-free reference signal, and the noise-free reference signal is generated through simulation;

[0082] The noise-free reference signal has the following mathematical model:

[0083] ;

[0084] In the formula, is the symbol amplitude of the quadrature phase shift keying QPSK modulation, taking values of normalized levels, , is the carrier frequency, = 1 MHz, is the phase state of the QPSK modulation, , is the sampling interval, and the sampling rate = 10 MHz, , K is the total number of modulation symbols, K = 1000;

[0085] Enhanced back-projection coefficient The mathematical model is as follows:

[0086] ;

[0087] In the formula, is the steepness coefficient of the b-th adaptive basis function, .

[0088] S5. Fuse multi-scale back-projection and global energy equalization to reconstruct the signal, and perform final enhancement processing on the digital communication signal.

[0089] Furthermore, in step S5, the final enhancement processing of the digital communication signal is as follows Figure 5 shown, and the specific steps for the final enhancement processing of the digital communication signal are as follows.

[0090] The input is the enhanced coding matrix . For each window, generate a time-domain digital communication signal through multi-scale back-projection; splice the window reconstruction results through an impulse function to obtain a reconstructed digital communication signal; perform energy equalization on the global signal and combine it with the original digital communication signal to obtain the finally enhanced digital communication signal; the time-domain digital communication signal The mathematical model is as follows:

[0091] ;

[0092] In the formula, is the anti-zero constant, ;

[0093] The mathematical model of the reconstructed digital communication signal is as follows:

[0094] ;

[0095] In the formula, is the impulse function, which is 1 when p = w and 0 otherwise;

[0096] The mathematical model of the finally enhanced digital communication signal is as follows:

[0097] ;

[0098] In the formula, is the energy equalization smoothing coefficient, is the original digital communication signal, is the anti-zero constant, , .

[0099] S6. Construct a digital communication signal enhancement processing model, input the digital communication signal dataset into the model, sequentially execute S2 to S5 and perform model training to complete the digital communication signal enhancement processing.

[0100] Furthermore, in step S6, for the digital communication signal enhancement processing model, first input the digital communication signal dataset and sequentially execute each processing step. The specific steps are as follows: In the digital communication signal windowing stage, perform windowing processing on the input digital communication signal according to the signal characteristics, and calculate the instantaneous phase and energy centroid offset of each window, thereby constructing an adaptive basis function; then, use Gaussian weighted dynamic focusing on key sampling points, project the signal of each window into the adaptive basis function space, and calculate the projection coefficients to generate an encoding matrix; in the noise suppression stage, process the encoding matrix based on the signal-to-noise ratio-driven dynamic threshold and nonlinear compression technology to effectively reduce noise interference and retain the key information in the digital communication signal; finally, use multi-scale backprojection and global energy equalization technology to reconstruct and enhance the digital communication signal to ensure the clarity and stability of the digital communication signal; through the training and parameter optimization of the model, the model can automatically adapt to the enhancement requirements under different signal conditions, effectively improve the quality of the digital communication signal and ensure stable transmission, and finally output the processed result of the enhanced digital communication signal.

[0101] Even further, in step S6, for the digital communication signal enhancement processing model, it is developed based on the Python programming language, uses the PyTorch framework, the input is the digital communication signal dataset, the batch size is set to 64, uses the Adam optimizer, the learning rate is set to 0.001, and the number of training times is 100 times. After the iterative training is completed, the digital communication signal enhancement processing model outputs the processed result of the digital communication signal enhancement.

[0102] Even further, in step S6, input the digital communication signal into the digital communication signal enhancement processing model for processing. The effects before and after processing are as Figure 6 and Figure 7 shown. The abscissa represents time in milliseconds, and the ordinate represents the amplitude of the digital communication signal in volts; before being processed by the model, the digital communication signal fluctuates greatly in the time domain, and the noise interference is obvious, resulting in unstable signal amplitude and affecting the clarity and usability of the digital communication signal; after being enhanced by the model, the noise is effectively suppressed, the amplitude fluctuation of the digital communication signal is significantly reduced, and the overall digital communication signal becomes more stable and clear; the processing results shown in the figure indicate that the digital communication signal enhancement processing model can significantly improve the quality and stability of the digital communication signal, thereby enabling more reliable digital communication signal transmission and verifying the effectiveness of the model proposed in this paper.

[0103] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the inventive concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention.

Claims

1. A digital communication signal enhancement processing method, characterized in that: The specific steps include: S1, collect and construct digital communication signal data set; S2, perform window processing on the digital communication signal, and construct an adaptive basis function based on the local instantaneous phase and energy center of gravity offset, the adaptive basis function is: the input is the original digital communication signal , the sampling point index is , the total number of signal sampling points is P, and the input is divided into windows, the window length is , the window slides point by point, and the adaptive basis function is generated by calculating the instantaneous phase and energy center of gravity offset of each window; Adaptive Basis Function The mathematical model is: ； In the formula, is the window index, , is the total number of windows, b is the adaptive basis function index, , is the number of adaptive basis functions, is the instantaneous phase, is the energy center of gravity offset, is the zero-proof constant, m is the index; Instantaneous phase The mathematical model is: ； In the formula, is the average frequency of the signal in the window; Energy center of gravity offset The mathematical model is: ； S3, dynamically focusing on key sampling points by Gaussian weighting, encoding the digital communication signal into an adaptive basis function, and constructing a coding matrix; S4, performing noise suppression on the coding matrix using a dynamic threshold and nonlinear compression driven by a signal-to-noise ratio; S5, fusing multi-scale back-projection and global energy balance to reconstruct the signal and perform final enhancement processing on the digital communication signal; S6. Build a digital communication signal enhancement processing model, input the digital communication signal data set into the model, execute S2 to S5 in sequence and perform model training to complete the digital communication signal enhancement processing.

2. A digital communication signal enhancement processing method according to claim 1, characterized in that: In the step S3, the specific steps of constructing the coding matrix are: projecting the input original digital communication signal to the adaptive basis function, calculating the projection coefficient for each window and the adaptive basis function, weighting the signal points in the window by the Gaussian kernel function, focusing on the key sampling points of the energy center of gravity, and using the calculated projection coefficients as matrix elements to construct the coding matrix ; The projection coefficient of the wth window on the bth adaptive basis function The mathematical model is: ； In the formula, For the The signal fluctuation strength of each window.

3. A digital communication signal enhancement processing method according to claim 2, characterized in that: In the step S4, the specific steps of performing noise suppression on the coding matrix are: the input is the coding matrix, combined with the projection coefficient, and the dynamic threshold is calculated for each adaptive basis function; nonlinear compression is performed on each projection coefficient, and all projection coefficients after nonlinear compression constitute the enhanced coding matrix; the dynamic threshold of the bth adaptive basis function The mathematical model is: ； Where SNR is the signal-to-noise ratio of the original digital communication signal; Enhanced projection coefficient The mathematical model is: ； In the formula, is the steepness coefficient of the bth adaptive basis function.

4. A digital communication signal enhancement processing method according to claim 3, characterized in that: In the step S5, the specific steps of performing the final enhancement processing on the digital communication signal are as follows: the input is the enhanced coding matrix For each window, a time domain digital communication signal is generated through multi-scale back projection; the window reconstruction results are spliced ​​through impulse functions to obtain a reconstructed digital communication signal; energy balancing is performed on the global signal, and the final enhanced digital communication signal is obtained by combining the original digital communication signal; the time domain digital communication signal The mathematical model is: ； In the formula, To prevent zero constant; Reconstructing digital communication signals The mathematical model is: ； In the formula, is the impulse function; Final enhanced digital communication signal The mathematical model is: ； In the formula, is the energy balance smoothing coefficient, is the original digital communication signal, To prevent zero constant.

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