UHF signal dynamic pre-correction method and system for terrestrial digital television

Abstract

The invention discloses a UHF signal dynamic pre-correction method and system for a terrestrial digital television, and relates to the field of signal pre-correction, and the method comprises the steps: collecting a detection signal sequence of a UHF frequency band in real time, and obtaining an actual output signal processed by a power amplifier; separating the actual output signal into a detection signal component and a television data signal component; distortion feature extraction is carried out, and a nonlinear distortion parameter matrix is obtained through calculation; inputting the nonlinear distortion parameter matrix into the model as an input feature for learning training based on time domain-frequency domain dual paths of the double-circulation convolutional neural network, and obtaining a pre-distortion function; a television data signal is input into a pre-distortion function, a pre-correction signal is output, and the pre-correction intensity is dynamically adjusted through a closed-loop control mechanism. The method has the advantages that through the deep learning technology, signal components are accurately separated, distortion features are extracted, the pre-correction intensity is dynamically adjusted, and the transmission quality and stability of terrestrial digital television signals are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of signal pre-correction, and in particular to a UHF signal dynamic pre-correction method and system for terrestrial digital television. Background Art

[0002] The UHF signal dynamic pre-correction method and system for terrestrial digital television is a technical solution designed to address the multipath interference and fading issues that can arise in modern digital television signal transmission. This system monitors and analyzes received UHF signal characteristics in real time, such as signal strength, phase offset, and delay. It then uses pre-set algorithms and models to dynamically correct these signals, improving signal quality and stability at the receiving end.

[0003] Current UHF signal dynamic pre-correction methods for terrestrial digital television are limited in their detection signal separation and distortion feature extraction. They typically employ basic signal processing techniques, making it difficult to accurately capture the nonlinear distortion characteristics of signals in complex environments. This results in an inability to accurately predict and adjust signal distortion during the pre-correction process, impacting the ultimate transmission quality and stability. Furthermore, common pre-correction methods in existing systems often lack effective closed-loop control mechanisms, preventing real-time monitoring and response to actual performance changes during signal transmission. This makes it difficult for the system to instantly adjust its correction strategy in the face of dynamic channel conditions or external interference, impacting its ability to adapt to complex environments and its continued stability. Furthermore, some traditional systems lack sufficient frequency domain analysis and time domain modeling capabilities for signal pre-processing, limiting their precision and accuracy in capturing dynamic signal changes and nonlinear distortion characteristics. Summary of the Invention

[0004] In order to improve the existing methods and systems, a UHF signal dynamic pre-correction method and system for terrestrial digital television is provided. This method collects UHF signals in real time and uses deep learning technology to accurately separate signal components, extract distortion features, and dynamically adjust the pre-correction strength, thereby significantly improving the transmission quality and stability of terrestrial digital television signals.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A dynamic pre-correction method for UHF signals for terrestrial digital television includes:

[0007] Collect the UHF band detection signal sequence in real time, extract the pilot sequence in the signal as a reference benchmark, and obtain the actual output signal after being processed by the power amplifier;

[0008] Based on the sparse Fourier transform algorithm, the actual output signal is separated into the detection signal component and the TV data signal component;

[0009] Based on the separated detection signal components, distortion features are extracted and the nonlinear distortion parameter matrix is ​​calculated, including the amplitude-phase conversion coefficient, amplitude-amplitude conversion gradient, memory effect depth factor and intermodulation product distribution weight;

[0010] Based on the dual-path of time domain and frequency domain of double-cycle convolutional neural network, the nonlinear distortion parameter matrix is ​​input into the model as input feature for learning and training to obtain the pre-distortion function;

[0011] Based on obtaining a predistortion function, the television data signal acquired in real time is input into the predistortion function, a precorrection signal is output, and the precorrection intensity is dynamically adjusted through a closed-loop control mechanism.

[0012] Preferably, the real-time acquisition of the UHF band detection signal sequence, extraction of the pilot sequence in the signal as a reference, and acquisition of the actual output signal after being processed by the power amplifier specifically include:

[0013] A directional coupler is configured at the output end of the power amplifier to extract a mixed feedback signal containing the detection signal and the TV signal;

[0014] Convert the collected feedback signal into a digital signal stream;

[0015] According to the predefined frame synchronization header, the time slot starting position of the detection signal is determined, the pure pilot component is obtained through orthogonal demodulation separation, and the reference signal is output through phase calibration and amplitude normalization;

[0016] The actual output signal is obtained through signal alignment processing using the complex analytical form of the power amplifier output signal.

[0017] Preferably, the separation of the actual output signal into the detection signal component and the television data signal component based on the sparse Fourier transform algorithm specifically includes:

[0018] Based on the actual output signal obtained, data preprocessing is performed and the signal spectrum is divided into multiple "buckets", each bucket representing a set of frequency ranges;

[0019] Perform discrete Fourier transform on each bucket to convert the signal from time domain to frequency domain;

[0020] Obtain the main frequency components of the signal in the frequency domain representation based on the matching pursuit algorithm and separate the spectral components in the signal;

[0021] Extracting the detection signal frequency band components that conform to the detection signal spectrum distribution in the frequency domain, and extracting the frequency components related to the television data signal from the frequency domain;

[0022] Based on the extracted frequency band components, inverse sparse Fourier transform is performed respectively to restore the frequency domain signal to the time domain to obtain the detection signal component and the television data signal component.

[0023] Preferably, the distortion feature extraction is performed based on the separated detection signal components, and the nonlinear distortion parameter matrix is ​​calculated and obtained, including the amplitude-phase conversion coefficient, the amplitude-amplitude conversion gradient, the memory effect depth factor and the intermodulation product distribution weight, which specifically includes:

[0024] Based on the phase offset of the output signal of the detection signal component relative to the input signal, the amplitude-to-phase conversion coefficient is obtained by fitting a cubic polynomial to the data points;

[0025] Calculating and obtaining an amplitude-amplitude conversion gradient based on an amplitude change rate of a signal of the detection signal component;

[0026] Calculate the memory effect depth factor based on the third-order cross-correlation algorithm;

[0027] Extract the intermodulation components from the detection signal components, and calculate the power ratio of the intermodulation products by combining the subcarriers of the detection signal in pairs, and obtain the intermodulation product distribution weight matrix;

[0028] Based on the parameter data obtained above, a nonlinear distortion parameter matrix is ​​constructed.

[0029] Preferably, the time-domain-frequency-domain dual-path based on the double-cycle convolutional neural network inputs the nonlinear distortion parameter matrix into the model as an input feature for learning and training, and obtaining the predistortion function specifically includes:

[0030] Construct a time-domain-frequency-domain dual pathway based on a double-cycle convolutional neural network;

[0031] The time domain pathway extracts the memory effect delay characteristics through a causal convolution layer, and the convolution kernel length is related to the thermal time constant of the power amplifier;

[0032] The frequency domain pathway learns the frequency domain intermodulation response through the complex convolutional layer and fuses the time-frequency features at the output layer;

[0033] Based on the obtained nonlinear distortion parameter matrix, it is input into the time domain-frequency domain dual channel for learning and training;

[0034] The predistortion function is generated through dual-path learning in the time domain and frequency domain.

[0035] Preferably, the step of obtaining a predistortion function, inputting the television data signal acquired in real time into the predistortion function, outputting a precorrection signal, and dynamically adjusting the precorrection strength through a closed-loop control mechanism specifically includes:

[0036] Based on the obtained predistortion function, the television data signal acquired in real time is inputted into the predistortion function;

[0037] The predistortion function generates a corresponding precorrection signal according to the nonlinear distortion characteristics of the input signal, and preliminarily verifies the characteristics of the output precorrection signal;

[0038] Build a closed-loop control mechanism to monitor the performance of the output signal in real time and collect the error between the actual output signal and the target signal through the feedback loop;

[0039] Based on error analysis, the parameters of the predistortion function or the precorrection strength are dynamically adjusted through PID control.

[0040] Furthermore, a UHF signal dynamic pre-correction system for terrestrial digital television is proposed, including:

[0041] Signal acquisition module: The signal acquisition module collects the detection signal sequence of the UHF frequency band in real time, extracts the pilot sequence as a reference benchmark, and obtains the actual output signal after the power amplifier is processed;

[0042] Signal separation module: The signal separation module separates the actual output signal into a detection signal component and a TV data signal component based on a sparse Fourier transform algorithm;

[0043] Distortion feature extraction module: The distortion feature extraction module extracts distortion features and calculates a nonlinear distortion parameter matrix based on the separated detection signal components;

[0044] Predistortion function module: The predistortion function module learns the nonlinear distortion parameter matrix through the time domain and frequency domain dual channels of the double-loop convolutional neural network to generate a predistortion function;

[0045] Pre-correction signal module: The pre-correction signal module inputs the real-time collected TV data signal into the pre-distortion function to generate a pre-correction signal;

[0046] Closed-loop control module: The closed-loop control module is used to monitor the output signal performance in real time and dynamically adjust the pre-distortion function parameters or pre-correction strength through feedback loop and PID control;

[0047] Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0048] Compared with the prior art, the advantages of the present invention are:

[0049] By acquiring UHF-band detection signals in real time and extracting pilot sequences, the actual signal output status can be accurately reflected. The sparse Fourier transform algorithm effectively separates the detection signal and TV data signal components, extracts nonlinear distortion characteristics, and thus obtains a precise distortion parameter matrix. A dual-loop convolutional neural network, combining time-domain and frequency-domain channels, enables the pre-distortion function to more accurately adapt to various signal distortion patterns and dynamically adjust the correction strength. A closed-loop control mechanism further enhances the system's real-time and adaptability, enabling the pre-correction signal to be optimized based on error feedback during the output process. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the method proposed in the present invention;

[0051] Figure 2 This is a schematic diagram of obtaining the actual output signal proposed by the present invention;

[0052] Figure 3 This is a schematic diagram of separating actual output signals proposed by the present invention;

[0053] Figure 4 This is a schematic diagram of the distortion feature extraction proposed by the present invention;

[0054] Figure 5 This is a schematic diagram of obtaining a pre-distortion function proposed by the present invention;

[0055] Figure 6 This is a schematic diagram of the output pre-correction signal proposed by the present invention. DETAILED DESCRIPTION

[0056] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0057] The UHF signal dynamic pre-correction system for terrestrial digital television includes:

[0058] Signal acquisition module: The signal acquisition module collects the detection signal sequence of the UHF frequency band in real time, extracts the pilot sequence as a reference benchmark, and obtains the actual output signal after the power amplifier is processed;

[0059] Signal separation module: The signal separation module separates the actual output signal into a detection signal component and a TV data signal component based on a sparse Fourier transform algorithm;

[0060] Distortion feature extraction module: The distortion feature extraction module extracts distortion features and calculates a nonlinear distortion parameter matrix based on the separated detection signal components;

[0061] Predistortion function module: The predistortion function module learns the nonlinear distortion parameter matrix through the time domain and frequency domain dual channels of the double-loop convolutional neural network to generate a predistortion function;

[0062] Pre-correction signal module: The pre-correction signal module inputs the real-time collected TV data signal into the pre-distortion function to generate a pre-correction signal;

[0063] Closed-loop control module: The closed-loop control module is used to monitor the output signal performance in real time and dynamically adjust the pre-distortion function parameters or pre-correction strength through feedback loop and PID control;

[0064] Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0065] See Figure 1 As shown, the UHF signal dynamic pre-correction method for terrestrial digital television includes:

[0066] Step 1: Real-time acquisition of the UHF band detection signal sequence, extraction of the pilot sequence in the signal as a reference benchmark, and acquisition of the actual output signal after power amplifier processing;

[0067] Step 2: Based on the sparse Fourier transform algorithm, the actual output signal is separated into a detection signal component and a TV data signal component;

[0068] Step 3: Based on the separated detection signal components, perform distortion feature extraction and calculate the nonlinear distortion parameter matrix, including the amplitude-phase conversion coefficient, amplitude-amplitude conversion gradient, memory effect depth factor, and intermodulation product distribution weight;

[0069] Step 4: Based on the time-domain and frequency-domain dual-path of the double-cycle convolutional neural network, the nonlinear distortion parameter matrix is ​​input into the model as input features for learning and training to obtain the pre-distortion function;

[0070] Step 5: Based on the obtained pre-distortion function, the television data signal acquired in real time is input into the pre-distortion function, a pre-correction signal is output, and the pre-correction intensity is dynamically adjusted through a closed-loop control mechanism.

[0071] See Figure 2 As shown, real-time acquisition of the UHF band detection signal sequence, extraction of the pilot sequence in the signal as a reference benchmark, and acquisition of the actual output signal after processing by the power amplifier specifically include:

[0072] A directional coupler is configured at the output end of the power amplifier to extract a mixed feedback signal containing the detection signal and the TV signal;

[0073] Convert the collected feedback signal into a digital signal stream;

[0074] According to the predefined frame synchronization header, the time slot starting position of the detection signal is determined, the pure pilot component is obtained through orthogonal demodulation separation, and the reference signal is output through phase calibration and amplitude normalization;

[0075] The actual output signal is obtained through signal alignment processing using the complex analytical form of the power amplifier output signal.

[0076] Specifically, a directional coupler is configured at the output end of the power amplifier to extract a mixed feedback signal containing the detection signal and the television signal from the output signal. The directional coupler extracts part of the output signal through the coupling port while maintaining the main signal transmission, and converts the analog feedback signal extracted by the directional coupler into a digital signal stream for subsequent digital signal processing;

[0077] Search for a predefined frame synchronization header sequence in the digital signal stream, calculate the cross-correlation between the signal and the frame synchronization header, find the peak position, and determine the time slot start point;

[0078] Through orthogonal demodulation, the pilot component of the detection signal is separated from the mixed signal. Based on the starting point of the time slot, the signal segment of the time slot where the detection signal is located is extracted. Using orthogonal demodulation, the signal is multiplied by the local carrier to separate the in-phase and quadrature components. The two components are low-pass filtered to remove the high-frequency components to obtain the baseband pilot signal. The formulas for the in-phase component and the quadrature component are:

[0079] I[n]=s pilot [n]·cos(2πf c nT s )

[0080] Q[n]=s pilot [n]·sin(2πf c nT s )

[0081] Among them, I[n] is the in-phase component, Q[n] is the quadrature component, s pilot [n] is the signal segment of the time slot where the detection signal is located, f c is the local carrier, i.e. frequency, T s is the sampling period, n is the sampling point index;

[0082] The separated pilot components are phase-calibrated and amplitude-normalized to generate a reference signal, which is represented by a complex analytical signal and used to process the power amplifier output signal to generate an actual output signal in a complex analytical form.

[0083] See Figure 3 As shown, based on the sparse Fourier transform algorithm, the actual output signal is separated into the detection signal component and the TV data signal component, specifically including:

[0084] Based on the actual output signal obtained, data preprocessing is performed and the signal spectrum is divided into multiple "buckets", each bucket representing a set of frequency ranges;

[0085] Perform discrete Fourier transform on each bucket to convert the signal from time domain to frequency domain;

[0086] Obtain the main frequency components of the signal in the frequency domain representation based on the matching pursuit algorithm and separate the spectral components in the signal;

[0087] Extracting the detection signal frequency band components that conform to the detection signal spectrum distribution in the frequency domain, and extracting the frequency components related to the television data signal from the frequency domain;

[0088] Based on the extracted frequency band components, inverse sparse Fourier transform is performed respectively to restore the frequency domain signal to the time domain to obtain the detection signal component and the television data signal component.

[0089] Specifically, the spectrum of the actual output signal is divided into multiple "buckets", each bucket corresponds to a frequency range. According to the frequency range of each bucket, the signal component of the corresponding frequency is extracted through a bandpass filter, and the discrete Fourier transform is applied to the signal sub-segment of each bucket to obtain the frequency domain representation;

[0090] In the frequency domain representation of each bucket, a dictionary is constructed containing possible frequency basis functions. The frequency basis function that best matches the signal is iteratively selected to extract the main frequency components. Based on the known spectral distribution characteristics of the detection signal and the TV signal, the respective frequency components are separated. The matching pursuit iteration formula is:

[0091]

[0092] Among them, S k [m] is the frequency domain representation of each bucket, m is the frequency index, d i [m] is the i-th frequency basis function in the dictionary, d i the complex conjugate of [m];

[0093] Based on the known spectral characteristics of the detection signal (such as bandwidth and center frequency), the frequency band components of the detection signal are extracted from the frequency domain representation of each bucket. Based on the spectral characteristics of the TV signal (such as modulation mode and bandwidth), the frequency band components of the TV signal are extracted.

[0094] Apply inverse sparse Fourier transform to the frequency domain components of the detection signal and the TV signal to restore the corresponding time domain signals, merge the time domain signals of all buckets, and obtain the complete detection signal component and TV signal component.

[0095] See Figure 4As shown, based on the separated detection signal components, distortion features are extracted and the nonlinear distortion parameter matrix is ​​calculated, including the amplitude-phase conversion coefficient, amplitude-amplitude conversion gradient, memory effect depth factor and intermodulation product distribution weight. Specifically, it includes:

[0096] Based on the phase offset of the output signal of the detection signal component relative to the input signal, the amplitude-to-phase conversion coefficient is obtained by fitting a cubic polynomial to the data points;

[0097] Calculating and obtaining an amplitude-amplitude conversion gradient based on an amplitude change rate of a signal of the detection signal component;

[0098] Calculate the memory effect depth factor based on the third-order cross-correlation algorithm;

[0099] Extract the intermodulation components from the detection signal components, and calculate the power ratio of the intermodulation products by combining the subcarriers of the detection signal in pairs, and obtain the intermodulation product distribution weight matrix;

[0100] Based on the parameter data obtained above, a nonlinear distortion parameter matrix is ​​constructed.

[0101] Specifically, the phase shift of the detection signal output relative to the input signal reflects the phase distortion characteristics of the nonlinear system. The amplitude-to-phase conversion coefficient is obtained by calculating the phase difference between the input detection signal and the output signal and fitting a cubic polynomial.

[0102] The first-order derivative calculation based on the relationship between input and output amplitudes can be approximated by the fitting method, and the calculation result is the amplitude-amplitude conversion gradient;

[0103] By constructing a third-order cross-correlation function and scanning, the delay order corresponding to the maximum correlation value is obtained, which is the memory effect depth factor;

[0104] The detection signal contains multiple subcarriers. The intermodulation frequencies are calculated for all combinations, the power at the corresponding frequencies of the intermodulation components is extracted, the total intermodulation power is normalized, and an intermodulation product power ratio matrix is ​​formed to obtain the intermodulation product distribution weight matrix.

[0105] The above nonlinear parameters are unified and integrated to form a comprehensive modeling matrix that describes the behavior of the power amplifier.

[0106] See Figure 5 As shown in the figure, based on the time-domain and frequency-domain dual-path of the double-cycle convolutional neural network, the nonlinear distortion parameter matrix is ​​input into the model as the input feature for learning and training. The pre-distortion function is obtained specifically including:

[0107] Construct a time-domain-frequency-domain dual pathway based on a double-cycle convolutional neural network;

[0108] The time domain pathway extracts the memory effect delay characteristics through a causal convolution layer, and the convolution kernel length is related to the thermal time constant of the power amplifier;

[0109] The frequency domain pathway learns the frequency domain intermodulation response through the complex convolutional layer and fuses the time-frequency features at the output layer;

[0110] Based on the obtained nonlinear distortion parameter matrix, it is input into the time domain-frequency domain dual channel for learning and training;

[0111] The predistortion function is generated through dual-path learning in the time domain and frequency domain.

[0112] Specifically, the purpose of the time domain path is to capture the nonlinear distortion characteristics of the signal in the time domain and extract the memory effect delay characteristics of the power amplifier. The input signal is processed through a causal convolution operation. The length of the convolution kernel is related to the thermal time constant of the power amplifier. Causal convolution means that the output signal depends on the current and previous inputs. The formula is:

[0113]

[0114] Among them, x(t) is the input signal, y(t) is the output signal, and w k is the convolution kernel, L is the length of the convolution kernel;

[0115] The frequency domain path is used to capture the frequency domain characteristics of the signal, especially the frequency domain intermodulation response of the power amplifier. The frequency domain path uses complex convolution operations to process the frequency domain characteristics of the input signal. Complex convolution is more suitable for processing complex signals, especially for intermodulation distortion problems. The formula is:

[0116]

[0117] Where f(f) is the frequency domain representation of the input signal, y f (f) is the frequency domain output signal, W k is the complex convolution kernel, L f is the length of the frequency domain convolution kernel;

[0118] After the time domain path and frequency domain path extract the time domain and frequency domain features respectively, they are fused in the output layer by weighted summation or splicing.

[0119] Based on the learned nonlinear distortion parameter matrix, a predistortion function is generated, which is designed to compensate for the nonlinear distortion of the power amplifier.

[0120] See Figure 6 As shown, based on obtaining a pre-distortion function, the television data signal acquired in real time is input into the pre-distortion function, a pre-correction signal is output, and the pre-correction strength is dynamically adjusted through a closed-loop control mechanism. Specifically, the following steps are included:

[0121] Based on the obtained predistortion function, the television data signal acquired in real time is inputted into the predistortion function;

[0122] The predistortion function generates a corresponding precorrection signal according to the nonlinear distortion characteristics of the input signal, and preliminarily verifies the characteristics of the output precorrection signal;

[0123] Build a closed-loop control mechanism to monitor the performance of the output signal in real time and collect the error between the actual output signal and the target signal through the feedback loop;

[0124] Based on error analysis, the parameters of the predistortion function or the precorrection strength are dynamically adjusted through PID control.

[0125] Specifically, at the system output end, the error between the output signal and the target signal is monitored, and the error information is fed back to the control system in real time through a feedback loop. The error is input into the PID controller, which generates a regulating signal based on the error and its derivative and integral parts.

[0126] During each cycle, the system continues to monitor the error between the output signal and the target signal and continuously adjusts the parameters of the predistortion function according to the PID control algorithm.

[0127] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A dynamic pre-correction method for UHF signals for terrestrial digital television, characterized in that: include: Collect the UHF band detection signal sequence in real time, extract the pilot sequence in the signal as a reference benchmark, and obtain the actual output signal after being processed by the power amplifier; Based on the sparse Fourier transform algorithm, the actual output signal is separated into the detection signal component and the TV data signal component; Based on the separated detection signal components, distortion features are extracted and the nonlinear distortion parameter matrix is ​​calculated, including the amplitude-phase conversion coefficient, amplitude-amplitude conversion gradient, memory effect depth factor and intermodulation product distribution weight; Based on the dual-path of time domain and frequency domain of double-cycle convolutional neural network, the nonlinear distortion parameter matrix is ​​input into the model as input feature for learning and training to obtain the pre-distortion function; Based on obtaining a predistortion function, the television data signal acquired in real time is input into the predistortion function, a precorrection signal is output, and the precorrection intensity is dynamically adjusted through a closed-loop control mechanism.

2. The method for dynamic pre-correction of UHF signals for terrestrial digital television according to claim 1, characterized in that: The real-time acquisition of the UHF band detection signal sequence, extraction of the pilot sequence in the signal as a reference, and acquisition of the actual output signal after being processed by the power amplifier specifically include: A directional coupler is configured at the output end of the power amplifier to extract a mixed feedback signal containing the detection signal and the TV signal; Convert the collected feedback signal into a digital signal stream; According to the predefined frame synchronization header, the time slot starting position of the detection signal is determined, the pure pilot component is obtained through orthogonal demodulation separation, and the reference signal is output through phase calibration and amplitude normalization; The actual output signal is obtained through signal alignment processing using the complex analytical form of the power amplifier output signal.

3. The method for dynamic pre-correction of UHF signals for terrestrial digital television according to claim 1, characterized in that: The separation of the actual output signal into the detection signal component and the television data signal component based on the sparse Fourier transform algorithm specifically includes: Based on the actual output signal obtained, data preprocessing is performed and the signal spectrum is divided into multiple "buckets", each bucket representing a set of frequency ranges; Perform discrete Fourier transform on each bucket to convert the signal from time domain to frequency domain; Obtain the main frequency components of the signal in the frequency domain representation based on the matching pursuit algorithm and separate the spectral components in the signal; Extracting the detection signal frequency band components that conform to the detection signal spectrum distribution in the frequency domain, and extracting the frequency components related to the television data signal from the frequency domain; Based on the extracted frequency band components, inverse sparse Fourier transform is performed respectively to restore the frequency domain signal to the time domain to obtain the detection signal component and the television data signal component.

4. The method for dynamic pre-correction of UHF signals for terrestrial digital television according to claim 1, characterized in that: The method of extracting distortion features based on the separated detection signal components and calculating and obtaining a nonlinear distortion parameter matrix, including an amplitude-phase conversion coefficient, an amplitude-amplitude conversion gradient, a memory effect depth factor, and an intermodulation product distribution weight, specifically includes: Based on the phase offset of the output signal of the detection signal component relative to the input signal, the amplitude-to-phase conversion coefficient is obtained by fitting a cubic polynomial to the data points; Calculating and obtaining an amplitude-amplitude conversion gradient based on an amplitude change rate of a signal of the detection signal component; Calculate the memory effect depth factor based on the third-order cross-correlation algorithm; Extract the intermodulation components from the detection signal components, and calculate the power ratio of the intermodulation products by combining the subcarriers of the detection signal in pairs, and obtain the intermodulation product distribution weight matrix; Based on the parameter data obtained above, a nonlinear distortion parameter matrix is ​​constructed.

5. The method for dynamic pre-correction of UHF signals for terrestrial digital television according to claim 1, characterized in that: The time-domain-frequency-domain dual-path based on the double-cycle convolutional neural network inputs the nonlinear distortion parameter matrix into the model as an input feature for learning and training, and obtains the predistortion function specifically including: Construct a time-domain-frequency-domain dual pathway based on a double-cycle convolutional neural network; The time domain pathway extracts the memory effect delay characteristics through a causal convolution layer, and the convolution kernel length is related to the thermal time constant of the power amplifier; The frequency domain pathway learns the frequency domain intermodulation response through the complex convolutional layer and fuses the time-frequency features at the output layer; Based on the obtained nonlinear distortion parameter matrix, it is input into the time domain-frequency domain dual channel for learning and training; The predistortion function is generated through dual-path learning in the time domain and frequency domain.

6. The method for dynamic pre-correction of UHF signals for terrestrial digital television according to claim 1, characterized in that: The method of obtaining a predistortion function, inputting the television data signal acquired in real time into the predistortion function, outputting a precorrection signal, and dynamically adjusting the precorrection strength through a closed-loop control mechanism specifically includes: Based on the obtained predistortion function, the television data signal acquired in real time is inputted into the predistortion function; The predistortion function generates a corresponding precorrection signal according to the nonlinear distortion characteristics of the input signal, and preliminarily verifies the characteristics of the output precorrection signal; Build a closed-loop control mechanism to monitor the performance of the output signal in real time and collect the error between the actual output signal and the target signal through the feedback loop; Based on error analysis, the parameters of the predistortion function or the precorrection strength are dynamically adjusted through PID control.

7. A UHF signal dynamic pre-correction system for terrestrial digital television, configured to implement the UHF signal dynamic pre-correction method for terrestrial digital television according to any one of claims 1 to 6, characterized in that: include: Signal acquisition module: The signal acquisition module collects the detection signal sequence of the UHF frequency band in real time, extracts the pilot sequence as a reference benchmark, and obtains the actual output signal after the power amplifier is processed; Signal separation module: The signal separation module separates the actual output signal into a detection signal component and a TV data signal component based on a sparse Fourier transform algorithm; Distortion feature extraction module: The distortion feature extraction module extracts distortion features and calculates a nonlinear distortion parameter matrix based on the separated detection signal components; Predistortion function module: The predistortion function module learns the nonlinear distortion parameter matrix through the time domain and frequency domain dual channels of the double-loop convolutional neural network to generate a predistortion function; Pre-correction signal module: The pre-correction signal module inputs the real-time collected TV data signal into the pre-distortion function to generate a pre-correction signal; Closed-loop control module: The closed-loop control module is used to monitor the output signal performance in real time and dynamically adjust the pre-distortion function parameters or pre-correction strength through feedback loop and PID control; Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

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