Digital signal dynamic adaptation method and system under adaptive modulation and coding

Abstract

The application provides a digital signal dynamic adaptation method and system under adaptive modulation and coding, and relates to the technical field of transmission. The method comprises: performing frame synchronization, symbol timing synchronization and carrier frequency offset compensation on a digital baseband signal; performing time domain anomaly detection on the synchronized received signal, and performing signal separation according to the time domain anomaly detection result; performing digital predistortion processing on the useful signal component based on the nonlinear model of a power amplifier; performing fractional Fourier transform on the nonlinear correction signal; performing signal observation embedding on the multipath suppression signal, and performing double uncertainty analysis based on the signal observation embedding vector; when there is an anomaly, triggering the predictive adjustment of adaptive modulation and coding, and realizing digital signal dynamic adaptation. Through the application, the problem that the signal anomaly is not identified and responded in time in the prior art can be solved, real-time anomaly prediction and modulation and coding optimization are realized, and the technical effect of improving the anti-interference ability of a communication system is achieved.

Description

Adaptive Modulation and Coding Method and System for Dynamic Adaptation of Digital Signals Technical Field

[0001] This application relates to the field of transmission technology, and in particular to a method and system for dynamic adaptation of digital signals under adaptive modulation and coding. Background Technology

[0002] With the rapid development of wireless communication technology, especially in 5G and future 6G networks, the demand for high speed, low latency, and massive connectivity is increasing. Communication systems for next-generation information networks are facing increasingly stringent requirements for signal transmission quality and stability in business scenarios such as internet access and related services, next-generation internet operation services, and IPv6-based network platform services. In the field of high-end equipment manufacturing, such as intelligent manufacturing equipment, industrial robots, unmanned operation platforms, aerospace equipment, and energy equipment, communication systems, as core supporting technologies for communication technologies and next-generation information network infrastructure, undertake important functions such as equipment status perception, control command issuance, and multi-device collaboration. Related communication links typically rely on next-generation information networks to achieve cross-device and cross-system data interconnection and resource collaboration; that is, their communication reliability and real-time performance directly affect equipment operation safety and overall system performance. To meet these demands, the research and application of modulation and coding technologies have become one of the key technologies for improving the performance of wireless communication systems. Adaptive modulation and coding technology, which flexibly adjusts under different channel conditions, is widely used and is gradually becoming an important physical layer technology supporting next-generation information network service transmission, adapting to changing channel environments and improving data transmission efficiency.

[0003] Currently, existing adaptive modulation and coding techniques mainly rely on the accurate acquisition of Channel State Information (CSI) and optimize modulation schemes, coding schemes, and other transmission parameters based on CSI. These technologies are typically designed for traditional wireless access or single-service transmission scenarios. They usually select the most suitable modulation scheme and coding redundancy through real-time signal measurement, thereby improving transmission rate and anti-interference capabilities. However, in high-end equipment manufacturing applications and in scenarios involving collaborative internet resource services and multi-service concurrent transmission based on next-generation information networks, the communication environment often exhibits characteristics such as strong electromagnetic interference, significant multipath effects, high-speed equipment movement, or rapid switching of operating conditions. Channel states exhibit obvious non-stationarity and abrupt changes, placing higher demands on adaptive modulation and coding techniques. However, existing technologies often overlook abnormal situations during signal transmission, such as signal fading caused by multipath propagation, phase distortion caused by frequency offset, and sudden signal anomalies due to environmental changes, network load fluctuations, dynamic service scheduling, or changes in the IPv6 network environment. Especially in application scenarios where high-end equipment manufacturing systems are deeply integrated with next-generation information networks, if communication anomalies are not identified and handled in a timely manner, it may lead to control failure, coordination disorder, or even equipment security risks. In abnormal situations, traditional adaptive modulation and coding techniques may fail to adjust their parameters in a timely manner, leading to a decline in signal transmission quality, or even packet loss or demodulation errors, affecting the overall performance of the system and further impacting the overall performance and service reliability of communication systems supported by next-generation information networks.

[0004] In summary, existing technologies suffer from several problems. During the transmission of services for next-generation information networks, such as Internet access and related services, network platform services based on IPv6 technology, and Internet resource collaboration services, various uncertainties and sudden anomalies in signal transmission cannot be identified and effectively addressed in a timely manner. This results in poor adaptability of existing adaptive modulation and coding technologies under extreme or complex channel conditions that support next-generation Internet operation services. Consequently, these technologies further affect the reliability and performance of communication systems in the high-efficiency and high-stability environment of next-generation information network services. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for dynamic adaptation of digital signals under adaptive modulation and coding, in order to solve the technical problem that in the existing technology, due to the failure to timely identify and effectively deal with various uncertainties and sudden anomalies in signal transmission during the service transmission process of Internet access and related services for next-generation information networks, network platform services based on IPv6 technology, and Internet resource collaboration services, the existing adaptive modulation and coding technology has poor adaptability under extreme or complex channel conditions supporting next-generation Internet operation services, which further affects the reliability and performance of communication systems in the high-efficiency and high-stability environment of next-generation information network services.

[0006] In view of the above problems, this application provides a method and system for dynamic adaptation of digital signals under adaptive modulation and coding.

[0007] In a first aspect, this application provides a method for dynamic adaptation of digital signals under adaptive modulation and coding, implemented through a dynamic adaptation system for digital signals under adaptive modulation and coding. The method includes: a receiving end acquiring a digital baseband signal carrying service data in a next-generation information network, and performing frame synchronization, symbol timing synchronization, and carrier frequency offset compensation on the digital baseband signal to obtain a synchronized received signal; performing time-domain anomaly detection on the synchronized received signal, and performing signal separation based on the time-domain anomaly detection result to obtain a useful signal component and a pulse interference component; constructing a nonlinear model of a power amplifier, and performing digital predistortion processing on the useful signal component based on the nonlinear model to obtain a nonlinear correction signal; performing a fractional Fourier transform on the nonlinear correction signal to obtain a multipath suppression signal; performing signal observation embedding on the multipath suppression signal to obtain a signal observation embedding vector, and performing dual uncertainty analysis based on the signal observation embedding vector. When the dual uncertainty analysis result indicates the existence of anomalies, predictive adjustment of adaptive modulation and coding is triggered to achieve dynamic adaptation of digital signals for next-generation information network service transmission.

[0008] Preferably, the adaptive modulation and coding method for dynamic digital signal adaptation further includes: analyzing the time-domain characteristics of the synchronously received signal to identify anomalous signal components with bursty and non-stationary characteristics; performing signal separation on the synchronously received signal based on the anomalous signal components to generate anomalous marking information; extracting pulse interference components from the synchronously received signal based on the anomalous marking information, wherein the pulse interference components include signal components with abrupt amplitude changes and a duration of less than 1 microsecond; and suppressing, replacing, or reconstructing the signal components in the synchronously received signal that are not marked as anomalous to obtain useful signal components.

[0009] Preferably, the method for dynamic adaptation of digital signals under adaptive modulation and coding further includes: constructing a nonlinear model of the power amplifier based on the historical calibration data of the power amplifier at the transmitting end; constructing an inverse nonlinear model of the power amplifier based on the nonlinear model; inputting the useful signal component into the inverse nonlinear model, performing digital predistortion processing on the useful signal component, and using the signal after digital predistortion processing as a nonlinear correction signal.

[0010] Preferably, the dynamic adaptation method for digital signals under adaptive modulation coding further includes: the nonlinear model of the power amplifier is used to characterize at least one of amplitude-to-amplitude conversion distortion, amplitude-to-phase conversion distortion, and dynamic nonlinear distortion caused by the power amplifier memory effect, and the nonlinear model adopts at least one of a polynomial model, a memory polynomial model, a piecewise linearized model, or a parameterized empirical model.

[0011] Preferably, the dynamic adaptation method for digital signals under adaptive modulation and coding further includes: determining the order parameters of the fractional Fourier transform, and performing a fractional Fourier transform on the nonlinear correction signal based on the order parameters to obtain a fractional domain signal; analyzing the signal energy distribution within the fractional domain signal, and suppressing the signal components corresponding to multipath propagation; and performing an inverse fractional Fourier transform on the suppressed fractional domain signal to obtain a multipath suppressed signal.

[0012] Preferably, the dynamic adaptation method for digital signals under adaptive modulation and coding further includes: pre-constructing a signal observation reference embedding vector; performing observation-side uncertainty analysis on the signal observation embedding vector using the signal observation reference embedding vector to determine a first uncertainty coefficient, wherein the first uncertainty coefficient is used to characterize the degree of deviation between the signal observation and the reference situation; under the current modulation and coding configuration, performing decision-side uncertainty analysis based on the multipath suppression signal to obtain a second uncertainty coefficient, wherein the second uncertainty coefficient is used to characterize the degree of uncertainty in the demodulation decision; when the first uncertainty coefficient is greater than or equal to a first coefficient threshold and the second uncertainty coefficient is greater than or equal to a second coefficient threshold, an anomaly is considered as the result of the double uncertainty analysis.

[0013] Preferably, the method for dynamic adaptation of digital signals under adaptive modulation and coding further includes: obtaining a set of historical signal observation embedding vectors under a reference condition; performing mean drift filtering on the set of historical signal observation embedding vectors, and using the mean drift center obtained by the filtering as the signal observation reference embedding vector.

[0014] Preferably, the dynamic adaptation method for digital signals under adaptive modulation and coding further includes: performing multi-hypothesis demodulation based on the multipath suppression signal under the current modulation and coding configuration to obtain multiple sets of candidate demodulation results; constructing a demodulation output distribution based on the multiple sets of candidate demodulation results; and calculating a decision-side uncertainty metric based on the demodulation output distribution to obtain a second uncertainty coefficient.

[0015] Preferably, the adaptive modulation and coding method for dynamic digital signal adaptation further includes: the signal observation embedding vector includes at least three of the following: subcarrier, frequency band energy distribution characteristics, time domain statistical characteristics, spectral morphology characteristics, multipath residual energy, delay spread correlation characteristics, and nonlinear residual or signal distortion statistical characteristics.

[0016] Secondly, this application also provides a dynamic digital signal adaptation system under adaptive modulation and coding, used to execute the dynamic digital signal adaptation method under adaptive modulation and coding as described in the first aspect, including: a synchronous received signal acquisition module, used by the receiving end to acquire the digital baseband signal carrying service data in the next-generation information network, and to perform frame synchronization, symbol timing synchronization, and carrier frequency offset compensation on the digital baseband signal to obtain a synchronous received signal; a useful signal component acquisition module, used to perform time-domain anomaly detection on the synchronous received signal, and to perform signal separation based on the time-domain anomaly detection result to obtain a useful signal component and a pulse interference component; and a nonlinear correction signal acquisition module. The system includes a nonlinear model for constructing a power amplifier and a digital predistortion processing module for the useful signal components based on the nonlinear model to obtain a nonlinear correction signal; a multipath suppression signal acquisition module for performing a fractional Fourier transform on the nonlinear correction signal to obtain a multipath suppression signal; and a digital signal dynamic adaptation module for performing signal observation embedding on the multipath suppression signal to obtain a signal observation embedding vector. Based on the signal observation embedding vector, a dual uncertainty analysis is performed. When the result of the dual uncertainty analysis indicates the presence of an anomaly, a predictive adjustment of adaptive modulation and coding is triggered to achieve dynamic adaptation of digital signals for next-generation information network service transmission.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by implementing a signal anomaly prediction and dynamic adaptation method based on dual uncertainty analysis, it achieves the technical effect of real-time identification of signal anomalies and predictive adjustment and optimization of modulation and coding parameters in business scenarios of Internet access and related services for next-generation information networks, network platform services based on IPv6 technology, and Internet resource collaboration services in complex and dynamic channel environments. This improves the anti-interference capability and signal transmission quality of wireless communication systems supporting next-generation Internet operation services under variable channel conditions, and ensures efficient and stable operation in the next-generation information network service transmission environment and extreme environments.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 is a flowchart illustrating the dynamic adaptation method for digital signals under adaptive modulation and coding of this application.

[0021] Figure 2 is a schematic diagram of the structure of the digital signal dynamic adaptation system under adaptive modulation and coding of this application.

[0022] Explanation of reference numerals in the attached diagram: Synchronous received signal acquisition module 1, Useful signal component acquisition module 2, Nonlinear correction signal acquisition module 3, Multipath suppression signal acquisition module 4, Digital signal dynamic adaptation module 5. Detailed Implementation

[0023] This application provides a method and system for dynamic digital signal adaptation under adaptive modulation and coding. It addresses the technical problem in existing technologies where various uncertainties and sudden anomalies in signal transmission during service transmission for next-generation internet access and related services, IPv6-based network platform services, and internet resource collaboration services fail to be identified and effectively addressed in a timely manner. This results in poor adaptability of existing adaptive modulation and coding technologies under extreme or complex channel conditions supporting next-generation internet operations, further impacting the reliability and performance of communication systems in the high-efficiency and high-stability environments of next-generation information network services. The application implements a signal anomaly prediction and dynamic adaptation method based on dual uncertainty analysis. This method achieves real-time identification of signal anomalies in service scenarios for next-generation internet access and related services, IPv6-based network platform services, and internet resource collaboration services in complex and dynamic channel environments. It then predictively adjusts and optimizes modulation and coding parameters, thereby improving the anti-interference capability and signal transmission quality of wireless communication systems supporting next-generation internet operations under varying channel conditions, ensuring efficient and stable operation in next-generation information network service transmission environments and extreme environments.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to Figure 1. This application provides a method for dynamic adaptation of digital signals under adaptive modulation and coding, applied to a dynamic adaptation system for digital signals under adaptive modulation and coding, specifically including the following steps:

[0026] The receiving end acquires the digital baseband signal carrying service data in the next-generation information network, and performs frame synchronization, symbol timing synchronization and carrier frequency offset compensation on the digital baseband signal to obtain a synchronized reception signal.

[0027] Specifically, acquiring the digital baseband signal carrying service data in the next-generation information network through the receiving end refers to the process in a communication system that transmits data based on the next-generation information network architecture. After completing radio frequency down-conversion and analog-to-digital conversion, the receiving side of the communication system obtains a digital baseband signal represented in discrete sampling form. This digital baseband signal contains the valid data information to be recovered, as well as distortion components introduced by channel fading, frequency offset, and noise. This provides the basic input for subsequent signal processing, thereby supporting the stable and efficient transmission of service data in the next-generation information network. Specifically, the sampling rate of the digital baseband signal is set to be no less than twice the signal bandwidth, preferably 5MHz to 200MHz, with a sampling accuracy of 10bit to 16bit. The signal-to-noise ratio of the digital baseband signal ranges from -5dB to 30dB to cover weak signal and strong interference scenarios.

[0028] Frame synchronization of digital baseband signals refers to determining the start position of data frames in the digital baseband signal through methods such as sequence matching or energy decision, thereby achieving time alignment between the data frame boundaries at the receiving end and the data frame structure at the transmitting end, and avoiding the impact of frame boundary misalignment on the demodulation and decoding processes. Frame synchronization uses known synchronization sequences of 64 to 512 symbols in length for correlation matching, with the correlation peak threshold set to 3 to 6 times the noise mean. The timing error of symbol timing synchronization is controlled within ±0.05 symbol periods to suppress inter-symbol interference to below -20dB.

[0029] Performing symbol timing synchronization on digital baseband signals refers to adjusting the sampling time based on the symbol timing error signal to keep the sampling time at the receiving end consistent with the symbol interval at the transmitting end, thereby reducing inter-symbol interference caused by sampling offset and improving the accuracy and stability of symbol decision.

[0030] Furthermore, carrier frequency offset compensation for digital baseband signals involves estimating the frequency deviation between the receiver's local oscillator and the transmitter's carrier, and then performing phase rotation or frequency correction on the digital baseband signal based on this deviation. This reduces the phase accumulation error and constellation rotation caused by the frequency offset, thereby obtaining a synchronized received signal and ensuring the reliability of the subsequent demodulation process. The carrier frequency offset estimation range is ±10ppm to ±50ppm, corresponding to a frequency deviation of tens of hertz to thousands of hertz. After compensation, the residual frequency offset is controlled within 0.1ppm of the carrier frequency to ensure that the constellation rotation angle is less than 5°.

[0031] The synchronous received signal is subjected to time-domain anomaly detection, and signal separation is performed based on the time-domain anomaly detection result to obtain the useful signal component and the impulse interference component.

[0032] Furthermore, this application also includes: analyzing the time-domain characteristics of the synchronously received signal to identify anomalous signal components with bursty and non-stationary characteristics; performing signal separation on the synchronously received signal based on the anomalous signal components to generate anomalous marking information; extracting pulse interference components from the synchronously received signal based on the anomalous marking information, wherein the pulse interference components include signal components with abrupt amplitude changes and a duration of less than 1 microsecond; and suppressing, replacing, or reconstructing the signal components in the synchronously received signal that are not marked as anomalous to obtain useful signal components.

[0033] Specifically, analyzing the time-domain characteristics of synchronously received signals involves using time-series analysis methods, such as statistical feature extraction and time-domain correlation analysis, within the time domain of the received signal to identify components exhibiting sudden and non-stationary characteristics. Abnormal signal components manifest as frequent amplitude fluctuations, sudden changes, or periodic instability, significantly differing from normal signal components. These components require separate identification and processing to avoid interfering with subsequent demodulation and information extraction. The amplitude abrupt changes of abnormal signal components are 2 to 10 times the average of normal signals, lasting from 10 ns to 1 μs, with an occurrence probability of 1 to 100 times per millisecond, meeting the typical characteristics of industrial electromagnetic interference and high-power transient interference.

[0034] Based on anomalous signal components, signal separation is performed on the synchronously received signal. This involves identifying anomalous signal components and then using techniques such as filtering, blind source separation, and signal demixing to separate them from the synchronously received signal, providing markers for subsequent processing. The signal separation process effectively identifies and extracts anomalous components, generating anomalous marker information to facilitate targeted processing of different types of signal components.

[0035] Extracting pulse interference components from synchronously received signals based on anomaly marker information refers to further extracting pulse interference components from the synchronously received signals after signal separation, based on the anomaly marker information. Pulse interference components are signal components with abrupt amplitude changes and a duration of less than 1 microsecond, such as pulse noise and sudden interference. Pulse interference components have significantly different time and spectral characteristics from normal signals, causing considerable interference to subsequent signal processing.

[0036] The process involves suppressing, replacing, or reconstructing unmarked signal components in the synchronously received signal. This means that after separating the impulse interference component from the synchronously received signal, the remaining unmarked components are further processed to eliminate or reduce the impact of noise. The processing includes using filtering, interpolation, or reconstruction algorithms to repair or optimize the remaining signal, making the remaining signal components closer to their ideal state, thereby obtaining effective and useful signal components.

[0037] A nonlinear model of the power amplifier is constructed, and the useful signal component is digitally predistorted based on the nonlinear model to obtain a nonlinear correction signal.

[0038] Furthermore, this application also includes: constructing a nonlinear model of the power amplifier based on historical calibration data of the power amplifier at the transmitting end; constructing an inverse nonlinear model of the power amplifier based on the nonlinear model; inputting the useful signal component into the inverse nonlinear model, performing digital predistortion processing on the useful signal component, and using the signal after digital predistortion processing as a nonlinear correction signal.

[0039] Furthermore, this application also includes: the nonlinear model of the power amplifier is used to characterize at least one of amplitude-to-amplitude conversion distortion, amplitude-to-phase conversion distortion, and dynamic nonlinear distortion caused by the power amplifier memory effect, and the nonlinear model adopts at least one of a polynomial model, a memory polynomial model, a piecewise linearized model, or a parameterized empirical model.

[0040] Specifically, the historical calibration data includes output amplitude and phase response data of the power amplifier under input power ranges of -30dBm to +10dBm and frequencies of 700MHz to 6GHz, with no fewer than 1000 calibration points. Amplitude-to-amplitude (AM-AM) distortion is characterized by the output power compression point being near the 1dB compression point, with an amplitude error not exceeding ±2dB. Amplitude-to-phase (AM-PM) distortion is characterized by a phase shift range of 0° to 30°. The memory effect has a time span of 1 to 10 symbol periods. Based on the historical calibration data of the power amplifier at the transmitting end, a nonlinear model of the power amplifier is constructed. This involves acquiring historical calibration data of the power amplifier under different input powers and frequencies, including the amplitude and frequency of the input signal and the corresponding amplitude and phase information of the power amplifier output. A nonlinear model of the power amplifier is constructed using data fitting methods such as regression analysis and least squares. The nonlinear model of the power amplifier is used to characterize at least one of amplitude-to-amplitude conversion distortion, amplitude-to-phase conversion distortion, and dynamic nonlinear distortion caused by the power amplifier memory effect. Amplitude-to-amplitude conversion distortion refers to the nonlinear change in signal amplitude as the input signal amplitude changes. Amplitude-to-phase conversion distortion refers to the phase error caused by changes in the input signal amplitude. Dynamic nonlinear distortion caused by the memory effect of the power amplifier refers to the dynamic nonlinear distortion caused by forward or backward propagation effects. By characterizing the distortion types, the non-ideal behavior of the power amplifier under high power input can be accurately described.

[0041] The use of at least one of the following nonlinear models—polynomial model, memory polynomial model, piecewise linearized model, or parametric empirical model—means that various mathematical models can be employed to accurately describe the nonlinear characteristics of a power amplifier. For example, a polynomial model uses a polynomial function to fit nonlinear distortion and is suitable for simple nonlinear cases; a memory polynomial model considers the historical input-output relationship of the power amplifier to characterize nonlinear distortion with memory effects; a piecewise linearized model simplifies the handling of complex nonlinear problems by dividing the nonlinear function into pieces, making it approximately linear within each piece; and a parametric empirical model fits parameters using empirical data to adapt to the nonlinear behavior of the actual system.

[0042] Constructing an inverse nonlinear model of a power amplifier, based on the nonlinear model, involves deriving its dual inverse nonlinear model according to the structure and characteristics of the power amplifier's nonlinear model. The inverse nonlinear model can compensate for the power amplifier's output signal through inverse operations, eliminating the nonlinear distortion caused by the power amplifier and restoring the signal to its ideal state. The construction of the inverse nonlinear model is based on the distortion characteristics of the power amplifier, compensating for the distortion effects caused by nonlinearity through corresponding inverse adjustments.

[0043] Inputting useful signal components into the inverse nonlinear model refers to passing the pre-processed effective signal components extracted from the synchronously received signal as input to the constructed inverse nonlinear model. The input signal undergoes processing by the inverse nonlinear model, eliminating amplitude and phase distortion caused by the power amplifier's nonlinearity, thereby improving signal quality. Specifically, after digital predistortion processing, the error vector amplitude of the power amplifier's output signal is reduced from the original 8% to 15% to 2% to 5%, and the adjacent channel leakage ratio is improved by at least 10 dB.

[0044] Performing digital predistortion processing on the useful signal components and using the predistorted signal as the nonlinear correction signal refers to further digitally predistorting the signal after inverse nonlinear model processing. The purpose of predistortion processing is to appropriately transform the signal using digital algorithms so that when the signal passes through a power amplifier, its output can better compensate for the nonlinear distortion introduced by the power amplifier, ultimately obtaining a nonlinearly corrected signal. The predistorted signal is the nonlinear correction signal, which can be used in subsequent processing and demodulation stages to reduce the impact of nonlinear distortion on the signal.

[0045] A fractional Fourier transform is performed on the nonlinear correction signal to obtain a multipath suppression signal.

[0046] Furthermore, this application also includes: determining the order parameters of the fractional Fourier transform, and performing a fractional Fourier transform on the nonlinear correction signal based on the order parameters to obtain a fractional domain signal; analyzing the signal energy distribution within the fractional domain signal, and suppressing the signal components corresponding to multipath propagation; and performing an inverse fractional Fourier transform on the suppressed fractional domain signal to obtain a multipath suppressed signal.

[0047] Specifically, determining the order parameters of the fractional Fourier transform refers to selecting appropriate fractional Fourier transform order parameters based on the characteristics of the input signal. The order parameters determine the degree of signal transformation and affect the signal's performance in different transform domains. By selecting appropriate order parameters, the signal can achieve its optimal representation in the fractional domain, thereby improving multipath suppression. The selection of order parameters depends on the signal's spectral characteristics and the influence of multipath propagation, aiming to better separate the various frequency components of the signal in the fractional domain. Specifically, the order parameter α of the fractional Fourier transform ranges from 0.3 to 0.9, and the maximum time delay spread of the multipath signal is 50 ns to 5 μs; after multipath suppression, the signal's time-domain broadening is reduced by more than 30%.

[0048] Performing a fractional Fourier transform on a nonlinear correction signal based on order parameters to obtain a fractional-order domain signal refers to performing a fractional Fourier transform on the nonlinear correction signal using the determined order parameters. The fractional Fourier transform is a generalized Fourier transform method that effectively transforms a signal from the time domain to a transform domain more suitable for processing signal characteristics. By adjusting the order parameters, a signal representation with better adaptability to multipath effects and other signal distortions can be obtained in the fractional-order domain.

[0049] Analyzing signal energy distribution in the fractional-order domain involves further processing the signal obtained from the fractional-order Fourier transform to analyze its energy distribution within that domain. Signal energy distribution analysis helps identify multipath propagation components in a signal because multipath propagation typically causes changes in the signal's energy distribution across different time and frequency ranges. By analyzing the signal's energy distribution, signal components related to multipath propagation can be distinguished.

[0050] Suppressing signal components caused by multipath propagation refers to taking appropriate processing methods, such as filtering or weighted suppression, after identifying the signal components caused by multipath propagation. This helps to reduce the impact of multipath effects on the signal, reduce distortion caused by multipath effects, and thus improve signal quality.

[0051] Performing an inverse fractional Fourier transform on the suppressed fractional-order domain signal to obtain a multipath-suppressed signal means that after suppressing multipath signal components, the inverse fractional Fourier transform is applied to restore the fractional-order domain signal from the fractional-order domain to the time or frequency domain. Through the inverse fractional Fourier transform, the signal after suppressing multipath effects can be converted back into a form usable for subsequent processing, thus obtaining the multipath-suppressed signal.

[0052] The multipath suppression signal is subjected to signal observation embedding to obtain a signal observation embedding vector. Based on the signal observation embedding vector, a dual uncertainty analysis is performed. When the result of the dual uncertainty analysis is that there is an anomaly, a predictive adjustment of adaptive modulation and coding is triggered to realize dynamic adaptation of digital signals for next-generation information network service transmission.

[0053] Furthermore, this application also includes: pre-constructing a signal observation reference embedding vector; performing observation-side uncertainty analysis on the signal observation embedding vector using the signal observation reference embedding vector to determine a first uncertainty coefficient, wherein the first uncertainty coefficient is used to characterize the degree of deviation between the signal observation and the reference situation; under the current modulation and coding configuration, performing decision-side uncertainty analysis based on the multipath suppression signal to obtain a second uncertainty coefficient, wherein the second uncertainty coefficient is used to characterize the degree of uncertainty in the demodulation decision; when the first uncertainty coefficient is greater than or equal to a first coefficient threshold and the second uncertainty coefficient is greater than or equal to a second coefficient threshold, an anomaly is considered as the result of the dual uncertainty analysis.

[0054] Furthermore, this application also includes: obtaining a set of historical signal observation embedding vectors under the reference conditions; performing mean drift filtering on the set of historical signal observation embedding vectors, and using the mean drift center obtained by the filtering as the signal observation reference embedding vector.

[0055] Furthermore, this application also includes: under the current modulation and coding configuration, performing multi-hypothesis demodulation based on the multipath suppression signal to obtain multiple sets of candidate demodulation results; constructing a demodulation output distribution based on the multiple sets of candidate demodulation results; and calculating a decision-side uncertainty metric based on the demodulation output distribution to obtain a second uncertainty coefficient.

[0056] Furthermore, this application also includes: the signal observation embedding vector includes at least three of the following: subcarrier or frequency band energy distribution characteristics, time domain statistical characteristics, spectral morphology characteristics, multipath residual energy, delay spread correlation characteristics, and nonlinear residual or signal distortion statistical characteristics.

[0057] Specifically, obtaining the historical signal observation embedding vector set under baseline conditions refers to acquiring the signal observation data set by collecting and processing historical signals under normal conditions without any interference or abnormal influences. Each signal observation vector in the historical signal observation embedding vector set represents the signal characteristics received at a specific time point, including the signal's time domain, frequency domain, or other relevant characteristics. The historical signal observation embedding vector set under baseline conditions provides a reference for the normal signal state for subsequent signal analysis and processing.

[0058] Mean shift filtering of historical signal observation embedding vector sets refers to data processing of the historical signal observation vector set, using a mean shift algorithm to filter the signal vectors and remove abnormal or deviating data points. The mean shift algorithm is used for pattern recognition and data clustering. By finding the central trend of the data distribution, it filters out outliers that deviate from the center, ensuring that the final retained signal vectors represent the signal characteristics under normal conditions.

[0059] Using the mean drift center obtained through screening as the signal observation benchmark embedding vector means that the signal data center obtained through the mean drift algorithm, i.e., the average position of the signal observation vector, serves as the standard signal observation vector under the benchmark condition. It represents the typical characteristics of the signal received under normal and interference-free conditions, providing a reference for subsequent signal comparison and analysis, and ensuring that the judgment of signal anomalies is based on the correct standard.

[0060] Uncertainty analysis of signal observation embedding vectors using a reference embedding vector refers to comparative analysis based on a pre-constructed reference signal observation vector to assess the degree of deviation between the currently received signal and the reference signal. Comparative analysis quantifies the difference between the signal and the expected normal state, determines its uncertainty level, and further assesses the signal's reliability. The signal observation embedding vector includes at least three of the following: subcarrier or frequency band energy distribution characteristics, time-domain statistical characteristics, spectral morphology characteristics, multipath residual energy, delay spread correlation characteristics, and nonlinear residual or signal distortion statistical characteristics. The signal observation embedding vector has a dimension of 16 to 128, where each feature is normalized. The first uncertainty coefficient threshold is set to 0.6 to 0.8, and the second uncertainty coefficient threshold is set to 0.5 to 0.7. When both thresholds are exceeded, the anomaly detection accuracy is no less than 95%. Frequency band energy distribution characteristics refer to the energy distribution characteristics of each subcarrier or frequency band extracted through frequency band segmentation in signal spectrum analysis. This describes the energy distribution of the signal across different frequency bands, reflecting the distribution characteristics of the signal spectrum and helping to identify the spectral structure and frequency variations caused by multipath effects. Time-domain statistical characteristics refer to the analysis of the statistical properties of a signal in the time domain. These characteristics include the signal's mean, variance, skewness, and kurtosis, reflecting the waveform shape and its variation patterns in the time domain, and helping to identify periodic and sudden changes in the signal, as well as noise interference. Spectral morphology characteristics refer to the extraction of the signal's morphological features in the frequency domain through spectral analysis methods such as Fourier transform. This describes the signal's distribution, peaks, and troughs in the spectrum, reflecting the frequency components of the signal and their distribution in the spectrum, and helping to distinguish different types of signals and interference. Multipath residual energy refers to the multipath effect that occurs when signals travel through different paths, undergoing reflection and refraction before reaching the receiver in a multipath propagation environment. Multipath residual energy describes the residual signal energy caused by multipath effects, and can identify signal attenuation or distortion caused by multipath effects. Delay spread correlation characteristics refer to the signal time delay and time spread effects caused by multipath propagation during signal propagation, describing the time difference of different paths reaching the receiver and the impact of delay on signal shape. Nonlinear residual or signal distortion statistical characteristics refer to the signal residuals generated due to nonlinear distortion during signal transmission. Nonlinear distortion may be caused by power amplifiers, transmission media, etc., and signal distortion statistical characteristics can characterize the impact of this distortion on the signal, thus providing a basis for signal recovery.

[0061] A first-level uncertainty coefficient is determined, which characterizes the degree of deviation between the observed signal and the baseline situation. Specifically, through observation-side uncertainty analysis, an uncertainty coefficient is calculated that reflects the degree of difference between the received signal and the signal observation reference embedding vector. The magnitude of the first-level uncertainty coefficient effectively measures the deviation of the observed signal; a larger coefficient indicates a more significant deviation between the signal and the normal baseline situation.

[0062] Under the current modulation and coding configuration, multi-hypothesis demodulation is performed based on the multipath suppression signal to obtain multiple sets of candidate demodulation results. That is, given the current signal modulation and coding configuration, the multi-hypothesis demodulation method is used to demodulate the signal after multipath suppression. Multi-hypothesis demodulation is a demodulation strategy that considers multiple different demodulation hypotheses and calculates the demodulation result under each hypothesis, thereby obtaining multiple candidate demodulation results. This helps to address the multipath propagation effects that may exist in the signal, improving the accuracy and robustness of demodulation through multiple hypothetical results.

[0063] Constructing a demodulation output distribution based on multiple candidate demodulation results refers to building a demodulation output distribution after acquiring multiple candidate demodulation results. This distribution reflects the probability distribution of the demodulation output under different assumptions and is used to describe the overall demodulation results. The demodulation output distribution not only provides confidence information for each candidate demodulation result but also reveals the uncertainties in the signal demodulation process.

[0064] Calculating the decision-side uncertainty measure based on the demodulated output distribution to obtain the second-level uncertainty coefficient refers to quantifying the reliability of the demodulation decision by calculating the decision-side uncertainty measure based on the demodulated output distribution. The decision-side uncertainty measure reflects the degree of uncertainty of the demodulation result, that is, whether there is significant uncertainty or deviation in the demodulation decision under the current demodulated output distribution. By calculating the uncertainty measure, the second-level uncertainty coefficient can be obtained. The second-level uncertainty coefficient is used to characterize the degree of uncertainty in the demodulation decision, reflecting the reliability or uncertainty of the decision during the demodulation process, thus affecting the final decision of the signal.

[0065] When both the first and second uncertainty coefficients are greater than or equal to a first threshold, an anomaly is identified as a result of dual uncertainty analysis. That is, when judging the signal state, if both the first and second uncertainty coefficients simultaneously exceed predetermined thresholds, indicating that the signal's reliability, accuracy, or stability is below a preset standard, the signal is judged as an abnormal signal. This triggers predictive adjustment of adaptive modulation and coding, automatically adjusting the modulation and coding schemes based on the nature of the anomaly to adapt to changes in the current channel. Predictive adjustment predicts the signal's needs in the next transmission cycle based on historical signal performance and the current signal state. Modulation, coding, or other parameters are adjusted based on the prediction results to ensure the reliability and efficiency of signal transmission. Dynamic adaptation of digital signals for next-generation information network service transmission refers to the process of adaptive modulation and coding predictively adjusting parameters such as modulation, coding, and transmit power of signals during transmission based on anomalies in the current signal conditions. This is done within the next-generation information network architecture to optimize signal quality, improve anti-interference capabilities, and enhance transmission stability, thereby providing stable and efficient communication support for various types of services in the next-generation information network. The predictive adjustment reconfigures at least one of the following: modulation scheme, coding scheme, coding redundancy, transmit power, or subcarrier mapping scheme, before proceeding to the next transmission cycle. After predictive modulation and coding adjustment, system throughput is increased by 10% to 25% under complex channel conditions, the bit error rate is reduced by more than one order of magnitude, and the link maintenance rate is improved by no less than 20% in high-speed mobile or strong interference scenarios.

[0066] In summary, the adaptive modulation and coding-based digital signal dynamic adaptation method provided in this application has the following technical effects: by implementing a signal anomaly prediction and dynamic adaptation method based on dual uncertainty analysis, it achieves the technical effect of real-time identification of signal anomalies and predictive adjustment and optimization of modulation and coding parameters in business scenarios such as Internet access and related services for next-generation information networks, network platform services based on IPv6 technology, and Internet resource collaboration services in complex and dynamic channel environments. This improves the anti-interference capability and signal transmission quality of wireless communication systems supporting next-generation Internet operation services under variable channel conditions, and ensures efficient and stable operation in the next-generation information network service transmission environment and extreme environments.

[0067] Example 2: Based on the same inventive concept as the adaptive modulation and coding-based digital signal dynamic adaptation method in the aforementioned examples, this application also provides an adaptive modulation and coding-based digital signal dynamic adaptation system, as shown in Figure 2. The system includes: a synchronous received signal acquisition module 1, used to acquire a digital baseband signal at the receiving end and perform frame synchronization, symbol timing synchronization, and carrier frequency offset compensation on the digital baseband signal to obtain a synchronous received signal; a useful signal component acquisition module 2, used to perform time-domain anomaly detection on the synchronous received signal, and perform signal separation based on the time-domain anomaly detection result to obtain a useful signal component and a pulse interference component; and a nonlinear correction signal. The acquisition module 3 is used to construct a nonlinear model of the power amplifier and perform digital predistortion processing on the useful signal components based on the nonlinear model to obtain a nonlinear correction signal; the multipath suppression signal acquisition module 4 is used to perform a fractional Fourier transform on the nonlinear correction signal to obtain a multipath suppression signal; the digital signal dynamic adaptation module 5 is used to perform signal observation embedding on the multipath suppression signal to obtain a signal observation embedding vector, and perform double uncertainty analysis based on the signal observation embedding vector. When the result of the double uncertainty analysis is that there is an anomaly, the predictive adjustment of the adaptive modulation and coding is triggered to realize the dynamic adaptation of the digital signal.

[0068] Furthermore, the adaptive modulation and coding-based digital signal dynamic adaptation system is also used for: analyzing the time-domain characteristics of the synchronously received signal to identify anomalous signal components with bursty and non-stationary characteristics; performing signal separation on the synchronously received signal based on the anomalous signal components to generate anomalous marking information; extracting pulse interference components from the synchronously received signal based on the anomalous marking information, wherein the pulse interference components include signal components with abrupt amplitude changes and a duration of less than 1 microsecond; and suppressing, replacing, or reconstructing signal components in the synchronously received signal that are not marked as anomalous to obtain useful signal components.

[0069] Furthermore, the adaptive modulation and coding-based digital signal dynamic adaptation system is also used to: construct a nonlinear model of the power amplifier based on the historical calibration data of the power amplifier at the transmitting end; construct an inverse nonlinear model of the power amplifier based on the nonlinear model; input the useful signal component into the inverse nonlinear model, perform digital predistortion processing on the useful signal component, and use the signal after digital predistortion processing as a nonlinear correction signal.

[0070] Furthermore, the adaptive modulation and coding-based digital signal dynamic adaptation system is also used in the following ways: the nonlinear model of the power amplifier is used to characterize at least one of amplitude-to-amplitude conversion distortion, amplitude-to-phase conversion distortion, and dynamic nonlinear distortion caused by the power amplifier memory effect, and the nonlinear model adopts at least one of a polynomial model, a memory polynomial model, a piecewise linearized model, or a parameterized empirical model.

[0071] Furthermore, the adaptive modulation and coding-based digital signal dynamic adaptation system is also used to: determine the order parameters of the fractional Fourier transform, and perform a fractional Fourier transform on the nonlinear correction signal based on the order parameters to obtain a fractional domain signal; analyze the signal energy distribution within the fractional domain signal, and suppress the signal components corresponding to multipath propagation; and perform an inverse fractional Fourier transform on the suppressed fractional domain signal to obtain a multipath suppressed signal.

[0072] Furthermore, the adaptive modulation and coding-based digital signal dynamic adaptation system is also used for: pre-constructing a signal observation reference embedding vector; performing observation-side uncertainty analysis on the signal observation embedding vector using the signal observation reference embedding vector to determine a first uncertainty coefficient, wherein the first uncertainty coefficient is used to characterize the degree of deviation between the signal observation and the reference situation; under the current modulation and coding configuration, performing decision-side uncertainty analysis based on the multipath suppression signal to obtain a second uncertainty coefficient, wherein the second uncertainty coefficient is used to characterize the degree of uncertainty in the demodulation decision; when the first uncertainty coefficient is greater than or equal to a first coefficient threshold and the second uncertainty coefficient is greater than or equal to a second coefficient threshold, an anomaly is considered as the result of the dual uncertainty analysis.

[0073] Furthermore, the adaptive modulation and coding-based digital signal dynamic adaptation system is also used to: obtain a set of historical signal observation embedding vectors under reference conditions; perform mean drift filtering on the set of historical signal observation embedding vectors, and use the mean drift center obtained by the filtering as the signal observation reference embedding vector.

[0074] Furthermore, the adaptive modulation and coding digital signal dynamic adaptation system is also used to: perform multi-hypothesis demodulation based on the multipath suppression signal under the current modulation and coding configuration to obtain multiple sets of candidate demodulation results; construct a demodulation output distribution based on the multiple sets of candidate demodulation results; and calculate the decision-side uncertainty metric based on the demodulation output distribution to obtain a second uncertainty coefficient.

[0075] Furthermore, the adaptive modulation and coding-based digital signal dynamic adaptation system is also used to include at least three of the following in the signal observation embedding vector: subcarrier, frequency band energy distribution characteristics, time domain statistical characteristics, spectral morphology characteristics, multipath residual energy, delay spread correlation characteristics, and nonlinear residual or signal distortion statistical characteristics.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The adaptive modulation coding-based digital signal dynamic adaptation method and specific examples in the aforementioned embodiment one are also applicable to the adaptive modulation coding-based digital signal dynamic adaptation system of this embodiment. Through the foregoing detailed description of the adaptive modulation coding-based digital signal dynamic adaptation method, those skilled in the art can clearly understand the adaptive modulation coding-based digital signal dynamic adaptation system of this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for dynamic adaptation of digital signals under adaptive modulation and coding, characterized in that, The method includes: a receiving end acquiring a digital baseband signal carrying service data in a next-generation information network, and performing frame synchronization, symbol timing synchronization, and carrier frequency offset compensation on the digital baseband signal to obtain a synchronized received signal; performing time-domain anomaly detection on the synchronized received signal, and performing signal separation based on the time-domain anomaly detection result to obtain a useful signal component and a pulse interference component; constructing a nonlinear model of a power amplifier, and performing digital predistortion processing on the useful signal component based on the nonlinear model to obtain a nonlinear correction signal; performing a fractional Fourier transform on the nonlinear correction signal to obtain a multipath suppression signal; performing signal observation embedding on the multipath suppression signal to obtain a signal observation embedding vector, and performing dual uncertainty analysis based on the signal observation embedding vector; when the result of the dual uncertainty analysis is... In case of anomalies, predictive adjustments to adaptive modulation and coding are triggered to achieve dynamic adaptation of digital signals for next-generation information network service transmission. A pre-constructed signal observation reference embedding vector is used to perform observation-side uncertainty analysis on the signal observation embedding vector to determine a first-level uncertainty coefficient, which characterizes the degree of deviation between the signal observation and the reference situation. Under the current modulation and coding configuration, decision-side uncertainty analysis is performed based on the multipath suppression signal to obtain a second-level uncertainty coefficient, which characterizes the degree of uncertainty in the demodulation decision. When the first-level uncertainty coefficient is greater than or equal to a first coefficient threshold and the second-level uncertainty coefficient is greater than or equal to a second coefficient threshold, an anomaly is considered as a result of the dual uncertainty analysis.

2. The method for dynamic adaptation of digital signals under adaptive modulation and coding as described in claim 1, characterized in that, The synchronously received signal is subjected to time-domain anomaly detection, and signal separation is performed based on the time-domain anomaly detection results to obtain useful signal components and impulse interference components. This includes: analyzing the time-domain characteristics of the synchronously received signal to identify anomalous signal components with sudden and non-stationary characteristics; performing signal separation on the synchronously received signal based on the anomalous signal components to generate anomaly marking information; extracting impulse interference components from the synchronously received signal based on the anomaly marking information, wherein the impulse interference components include signal components with abrupt amplitude changes and a duration of less than 1 microsecond; and suppressing, replacing, or reconstructing the signal components in the synchronously received signal that are not marked as anomalous to obtain useful signal components.

3. The method for dynamic adaptation of digital signals under adaptive modulation and coding as described in claim 1, characterized in that, Constructing a nonlinear model of a power amplifier and performing digital predistortion processing on the useful signal component based on the nonlinear model to obtain a nonlinear correction signal includes: constructing a nonlinear model of the power amplifier based on historical calibration data of the power amplifier at the transmitting end; constructing an inverse nonlinear model of the power amplifier based on the nonlinear model; inputting the useful signal component into the inverse nonlinear model, performing digital predistortion processing on the useful signal component, and using the signal after digital predistortion processing as the nonlinear correction signal.

4. The method for dynamic adaptation of digital signals under adaptive modulation and coding as described in claim 3, characterized in that, The nonlinear model of the power amplifier is used to characterize at least one of amplitude-to-amplitude conversion distortion, amplitude-to-phase conversion distortion, and dynamic nonlinear distortion caused by the power amplifier memory effect, and the nonlinear model adopts at least one of a polynomial model, a memory polynomial model, a piecewise linearized model, or a parameterized empirical model.

5. The method for dynamic adaptation of digital signals under adaptive modulation and coding as described in claim 1, characterized in that, Performing a fractional Fourier transform on the nonlinear correction signal to obtain a multipath suppression signal includes: determining the order parameters of the fractional Fourier transform, and performing a fractional Fourier transform on the nonlinear correction signal based on the order parameters to obtain a fractional domain signal; analyzing the signal energy distribution within the fractional domain signal, and suppressing the signal components corresponding to multipath propagation; and performing an inverse fractional Fourier transform on the suppressed fractional domain signal to obtain the multipath suppression signal.

6. The method for dynamic adaptation of digital signals under adaptive modulation and coding as described in claim 1, characterized in that, The pre-construction of a signal observation benchmark embedding vector includes: obtaining a set of historical signal observation embedding vectors under benchmark conditions; performing mean drift filtering on the set of historical signal observation embedding vectors, and using the mean drift center obtained from the filtering as the signal observation benchmark embedding vector.

7. The method for dynamic adaptation of digital signals under adaptive modulation and coding as described in claim 1, characterized in that, Under the current modulation and coding configuration, decision-side uncertainty analysis is performed based on the multipath suppression signal to obtain the second uncertainty coefficient, including: under the current modulation and coding configuration, multi-hypothesis demodulation is performed based on the multipath suppression signal to obtain multiple sets of candidate demodulation results; based on the multiple sets of candidate demodulation results, a demodulation output distribution is constructed; and based on the demodulation output distribution, a decision-side uncertainty metric is calculated to obtain the second uncertainty coefficient.

8. The method for dynamic adaptation of digital signals under adaptive modulation and coding as described in claim 1, characterized in that, The signal observation embedding vector includes at least three of the following: subcarrier, frequency band energy distribution characteristics, time domain statistical characteristics, spectral morphology characteristics, multipath residual energy, delay spread correlation characteristics, and nonlinear residual or signal distortion statistical characteristics.

9. A dynamic adaptation system for digital signals under adaptive modulation and coding, characterized in that, The steps for implementing the adaptive modulation and coding-based digital signal dynamic adaptation method according to any one of claims 1 to 8 include: a synchronous received signal acquisition module, used by a receiving end to acquire a digital baseband signal carrying service data in a next-generation information network, and to perform frame synchronization, symbol timing synchronization, and carrier frequency offset compensation on the digital baseband signal to obtain a synchronous received signal; a useful signal component acquisition module, used to perform time-domain anomaly detection on the synchronous received signal, and to perform signal separation based on the time-domain anomaly detection result to obtain a useful signal component and a pulse interference component; a nonlinear correction signal acquisition module, used to construct a nonlinear model of a power amplifier, and to perform digital predistortion processing on the useful signal component based on the nonlinear model to obtain a nonlinear correction signal; a multipath suppression signal acquisition module, used to perform fractional Fourier transform on the nonlinear correction signal to obtain a multipath suppression signal; and a digital signal dynamic adaptation module, used to perform signal observation embedding on the multipath suppression signal to obtain a signal observation embedding vector, and to perform dual uncertainty analysis based on the signal observation embedding vector, and when the dual uncertainty analysis result indicates the existence of anomalies, to trigger predictive adjustment of adaptive modulation and coding to achieve dynamic adaptation of digital signals for next-generation information network service transmission.

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