Signal quality enhancement processing method and system based on adaptive modulation identification

Abstract

The application provides a signal quality enhancement processing method and system based on adaptive modulation identification, receives a wireless communication signal, collects multi-dimensional signal characteristics of the wireless communication signal, calls an adaptive modulation identification model to analyze and generate a modulation mode set. A signal quality enhancement strategy library is constructed based on the modulation mode set, and each strategy corresponds to a specific modulation mode. A strategy matching the current modulation mode is selected from the strategy library to enhance the signal, and an enhanced signal is obtained. The enhanced signal is subjected to modulation mode identification again, the identification result is compared with the initial set, and the strategy parameters in the strategy library are adjusted according to the comparison result, so that the dynamic optimization of the enhancement strategy is realized. The application can adaptively enhance the signal quality according to the signal modulation mode, effectively reduce the communication error rate, and improve the performance of the wireless communication system.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and more specifically, to a signal quality enhancement processing method and system based on adaptive modulation recognition. Background Technology

[0002] In the field of wireless communication, signal quality is a key factor affecting communication performance. With the continuous development of communication technology, wireless communication signals face complex transmission environments, such as multipath effects, noise interference, and signal fading. These factors can severely degrade signal quality, leading to problems such as increased bit error rate and decreased transmission rate. Traditional signal quality enhancement methods typically employ fixed processing strategies, such as simple filtering and amplification. However, these methods lack targeted processing for signal modulation modes. Different modulation modes have different signal characteristics, and fixed processing strategies cannot adaptively adjust according to the actual modulation mode of the signal, making it difficult to achieve optimal signal quality enhancement in various complex communication scenarios. Therefore, how to adaptively enhance signal quality based on the signal's modulation mode has become a pressing technical problem in the field of wireless communication. Summary of the Invention

[0003] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a signal quality enhancement processing method based on adaptive modulation identification, the method comprising:

[0004] Receive the wireless communication signal to be processed, perform signal feature acquisition on the wireless communication signal, and obtain the multi-dimensional signal features of the wireless communication signal;

[0005] An adaptive modulation recognition model is invoked to perform modulation mode analysis on the multi-dimensional signal features, generating a set of modulation modes corresponding to the wireless communication signal.

[0006] A signal quality enhancement strategy library is constructed based on the modulation mode set, and each strategy in the signal quality enhancement strategy library corresponds to a specific modulation mode in the modulation mode set.

[0007] An enhancement strategy matching the current modulation mode is selected from the signal quality enhancement strategy library, and enhancement processing is performed on the wireless communication signal to obtain the enhanced signal.

[0008] The enhanced signal is subjected to modulation mode identification again, and the identification result is compared with the initial modulation mode set. Based on the comparison result, the strategy parameters in the signal quality enhancement strategy library are adjusted to achieve dynamic optimization of the enhancement strategy.

[0009] In another aspect, embodiments of the present invention also provide a signal quality enhancement processing system based on adaptive modulation identification, including a processor and a machine-readable storage medium connected to the processor. The machine-readable storage medium is used to store programs, instructions, or code, and the processor is used to execute the programs, instructions, or code in the machine-readable storage medium to implement the above-described method.

[0010] Based on the above, this invention collects multi-dimensional signal features of wireless communication signals and accurately analyzes the set of modulation modes using an adaptive modulation recognition model. A signal quality enhancement strategy library constructed based on this modulation mode set enables the design of specialized enhancement strategies for different modulation modes, achieving personalized customization of signal quality enhancement strategies. During the enhancement process, an enhancement strategy matching the current modulation mode is selected, and the strategy parameters are fine-tuned in real time based on intermediate processing results, ensuring the accuracy and effectiveness of the enhancement process. By re-identifying the modulation mode of the enhanced signal and dynamically optimizing the strategy parameters in the signal quality enhancement strategy library based on comparison results, the entire signal quality enhancement system can continuously adapt to different communication environments and signal characteristics, significantly improving the effect and stability of signal quality enhancement, effectively reducing the communication bit error rate, and enhancing the overall performance of the wireless communication system. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the execution flow of the signal quality enhancement processing method based on adaptive modulation recognition provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of exemplary hardware and software components of the signal quality enhancement processing system based on adaptive modulation recognition provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a signal quality enhancement processing method based on adaptive modulation recognition provided in one embodiment of the present invention. The following is a detailed description of this signal quality enhancement processing method based on adaptive modulation recognition.

[0014] Step S110: Receive the wireless communication signal to be processed, collect signal features of the wireless communication signal, and obtain the multi-dimensional signal features of the wireless communication signal.

[0015] In this embodiment, the wireless communication signal to be processed is a downlink communication signal sent by the user equipment to the base station, which contains communication data between the user equipment and the base station. After receiving the wireless communication signal, signal features are acquired, including amplitude features, phase features, frequency features, signal-to-noise ratio (SNR) features, and bit error rate (BER) features. Specifically, amplitude features are a sequence of amplitude values ​​at different time points; phase features are a sequence of phase values ​​at different time points; frequency features are the carrier frequency; SNR features are the ratio of signal power to noise power; and BER features are the ratio of the number of erroneous symbols to the total number of symbols during signal transmission. By acquiring these features, a multi-dimensional signal feature is obtained, consisting of amplitude feature sequences, phase feature sequences, frequency feature values, SNR feature values, and BER feature values.

[0016] Step S120: Call the adaptive modulation recognition model to perform modulation mode analysis on the multi-dimensional signal features and generate a set of modulation modes corresponding to the wireless communication signal.

[0017] In this embodiment, the adaptive modulation recognition model is a pre-trained deep learning model for recognizing the modulation patterns of wireless communication signals. Its input is multi-dimensional signal features, and its output is a set of modulation patterns.

[0018] Step S121: Input the multi-dimensional signal features into the feature processing layer of the adaptive modulation recognition model, perform feature correlation modeling on the multi-dimensional signal features, and obtain the feature correlation matrix.

[0019] In this embodiment, the feature processing layer is a neural network layer in the adaptive modulation recognition model, and its function is to analyze the correlation between features in the multi-dimensional signal features. After the multi-dimensional signal features are input into the feature processing layer, the feature processing layer first preprocesses each feature, converting sequence features such as amplitude feature sequences and phase feature sequences into fixed-length vectors, and normalizing numerical features such as frequency feature values ​​and signal-to-noise ratio feature values ​​to ensure that all features are within the same numerical range. Then, the feature processing layer analyzes the correlation between features by calculating the correlation coefficients. The correlation coefficients are calculated based on the value distribution of each feature, obtained by analyzing the linear or non-linear relationships between features. Based on the calculated correlation coefficients, a feature correlation matrix is ​​constructed. The rows and columns of the feature correlation matrix correspond to each feature in the multi-dimensional signal features, and the element values ​​in the matrix are the correlation coefficients between corresponding two features. For example, if the multi-dimensional signal features include amplitude features, phase features, frequency features, signal-to-noise ratio features, and bit error rate features, then the feature correlation matrix is ​​a 5-row, 5-column matrix, where the element in the first row and second column is the correlation coefficient between amplitude features and phase features, the element in the second row and third column is the correlation coefficient between phase features and frequency features, and so on.

[0020] Step S1211: Extract each feature item from the multi-dimensional signal features and determine the attribute type and value range of each feature item.

[0021] In this embodiment, the feature items in the multi-dimensional signal features include amplitude features, phase features, frequency features, signal-to-noise ratio (SNR) features, and bit error rate (BER) features. Specifically, the amplitude feature is a continuous numerical feature with a value range from 0 to the maximum amplitude of the signal; the phase feature is a continuous numerical feature with a value range from 0 to 2π; the frequency feature is a continuous numerical feature with a value range within the carrier frequency range allowed by the wireless communication system; the SNR feature is a continuous numerical feature with a value range greater than 0; and the BER feature is a continuous numerical feature with a value range from 0 to 1.

[0022] Step S1212: Analyze the mutual influence relationship between different feature items, and determine the correlation strength between feature items through feature interaction analysis.

[0023] In this embodiment, feature interaction analysis is achieved through statistical analysis of historical data on multi-dimensional signal features. Historical data includes a large number of multi-dimensional signal features of wireless communication signals under different modulation modes. During the analysis, two sets of feature items are selected from the historical data, such as amplitude and phase features. Their value combinations under different modulation modes are statistically analyzed, and their covariance and correlation coefficient are calculated. The covariance reflects the common trend of the changes in the values ​​of the two feature items, while the correlation coefficient reflects the degree of linear correlation between the two feature items. Based on the calculation results of the covariance and correlation coefficient, the correlation strength between the two feature items is determined. The magnitude of the correlation strength is proportional to the absolute value of the correlation coefficient; the larger the absolute value of the correlation coefficient, the stronger the correlation. This analysis is performed on all combinations of feature items in the multi-dimensional signal features to obtain the correlation strength between each pair of feature items.

[0024] Step S1213: Construct the initial structure of the feature correlation matrix based on the correlation strength, where the rows and columns of the feature correlation matrix correspond to different feature terms.

[0025] In this embodiment, the dimension of the feature correlation matrix is ​​determined based on the number of feature terms in the multi-dimensional signal features. For example, if there are 5 feature terms, the initial structure of the feature correlation matrix is ​​a 5x5 matrix. The rows and columns of the matrix correspond to the 5 feature terms, and the order of the rows and columns is consistent with the selection order of the feature terms. In the initial structure, the diagonal elements of the matrix represent the correlation strength of the feature term itself, and the off-diagonal elements represent the correlation strength between two different feature terms.

[0026] Step S1214: Convert the association strength into the values ​​of the elements of the feature association matrix, and fill the initial structure to form a preliminary feature association matrix.

[0027] In this embodiment, the correlation strength between every two feature items obtained in step S1212 is directly used as the value of the corresponding element in the feature correlation matrix, filling it into the initial structure of the feature correlation matrix to form a preliminary feature correlation matrix. For example, if the correlation strength between the amplitude feature and the phase feature is 0.8, then in the preliminary feature correlation matrix, the element value at the corresponding row of the amplitude feature and column of the phase feature is 0.8; if the correlation strength between the phase feature and the frequency feature is 0.5, then the element value at the corresponding row of the phase feature and column of the frequency feature is 0.5, and so on.

[0028] Step S1215: Normalize the preliminary feature correlation matrix to ensure that the values ​​of the feature correlation matrix elements are within a uniform range, thus obtaining the final feature correlation matrix.

[0029] In this embodiment, the normalization process is achieved by dividing each element value in the initial feature association matrix by the maximum value among all element values. After this process, the element values ​​in the feature association matrix are in the range of 0 to 1, which is within a uniform range.

[0030] Step S122: Perform preliminary modulation mode classification on the feature correlation matrix through the mode classification layer of the adaptive modulation recognition model to obtain a preliminary modulation mode candidate set.

[0031] In this embodiment, the pattern classification layer is a fully connected neural network layer in the adaptive modulation recognition model. Its input is the feature correlation matrix, and its output is a preliminary modulation mode candidate set. During processing, the feature correlation matrix is ​​first converted into a one-dimensional vector, and then input into the pattern classification layer. The pattern classification layer performs linear transformation and nonlinear activation processing on the one-dimensional vector to output the probability value of each possible modulation mode. Modulation modes with probability values ​​greater than a preset probability threshold are selected into the preliminary modulation mode candidate set.

[0032] Step S123: Perform mode discrimination enhancement processing on the preliminary modulation mode candidate set to extract feature difference information between different candidate modes.

[0033] In this embodiment, the mode discrimination enhancement processing is achieved by analyzing the features corresponding to each candidate mode in the preliminary modulation mode candidate set.

[0034] Step S1231: Obtain the feature set corresponding to each candidate mode in the preliminary modulation mode candidate set. The feature set is derived from the classification results of multi-dimensional signal features.

[0035] In this embodiment, the feature set corresponding to each candidate mode is the typical feature value range and feature combination of that candidate mode in multi-dimensional signal features. These feature sets are obtained by statistical analysis of the multi-dimensional signal features of the wireless communication signals corresponding to the candidate mode in historical data. For example, for the quadrature amplitude modulation mode, its corresponding feature set includes a specific amplitude value range, a phase value range, and the combination relationship between amplitude and phase, etc.

[0036] Step S1232: Calculate the overlap between the feature sets of any two candidate patterns, and measure the overlap by the ratio of the number of feature intersections to the number of feature unions.

[0037] In this embodiment, for any two candidate pattern feature sets, their feature intersection is first determined, which consists of feature items and feature value ranges belonging to both feature sets. Then, their feature union is determined, which consists of feature items and feature value ranges belonging to either of the two feature sets. The overlap is calculated as the ratio of the number of elements in the feature intersection to the number of elements in the feature union. A larger ratio indicates a higher degree of overlap between the feature sets of the two candidate patterns and a lower pattern distinguishability; a smaller ratio indicates a lower degree of overlap between the feature sets of the two candidate patterns and a higher pattern distinguishability.

[0038] Step S1233: Construct a pattern discrimination evaluation function based on overlap. The output value of the pattern discrimination evaluation function increases as the overlap decreases.

[0039] In this embodiment, the pattern discrimination evaluation function is constructed based on the reciprocal of the overlap, that is, the output value of the pattern discrimination evaluation function is equal to 1 divided by the overlap. In this way, when the overlap decreases, the output value of the pattern discrimination evaluation function increases, which can intuitively reflect the change in pattern discrimination.

[0040] Step S1234: Based on the output value of the pattern discrimination evaluation function, perform differential enhancement on the feature set of candidate patterns, sort them by overlap value from small to large, and increase the representation weight of the corresponding features. The smaller the overlap value, the greater the weight increase.

[0041] In this embodiment, the candidate modes in the preliminary modulation mode candidate set are first sorted in ascending order of overlap value. Then, based on the sorting result, different representation weights are assigned to the feature items in the feature set of each candidate mode. The smaller the overlap value of the candidate mode, the greater the increase in the representation weight of the feature items in its feature set. The increase in representation weight is achieved by increasing the occurrence frequency of the feature item in the feature set or expanding the value range of the feature item, so as to enhance the distinguishability of the candidate mode from other candidate modes.

[0042] Step S1235: Extract the significantly different feature items from the enhanced feature set to form a feature difference list.

[0043] In this embodiment, the significantly different feature items are those that, compared with the feature sets of other candidate modes, have significantly different value ranges or feature combinations in the enhanced feature set. By analyzing the enhanced feature set one by one, these significantly different feature items are extracted and sorted according to the type and degree of difference of the feature items to form a feature difference list.

[0044] Step S1236: Organize the correspondence between the feature difference list and the candidate patterns to generate feature difference information containing pattern identifiers and difference features.

[0045] In this embodiment, the mode identifier is a unique identifier for the candidate mode. For example, the identifier for the quadrature amplitude modulation mode is QAM, and the identifier for the frequency keying modulation mode is FSK. Each difference feature item in the feature difference list is associated with the mode identifier of the corresponding candidate mode to form feature difference information. The feature difference information includes the mode identifier of the candidate mode and the difference feature item corresponding to that mode.

[0046] Step S1237: Supplement the missing difference feature items in the feature difference information to cover all differences between different candidate patterns.

[0047] In this embodiment, by comprehensively comparing the feature sets of all candidate modes in the preliminary modulation mode candidate set, the missing difference features in the feature difference information are identified, and these missing difference features are added to the feature difference information to ensure that the feature difference information covers all differences between different candidate modes.

[0048] Step S1238: Construct a pattern discrimination feedback mechanism based on feature difference information, adjust the overlap calculation weight according to the enhanced discrimination effect, and optimize the accuracy of pattern discrimination enhancement processing.

[0049] In this embodiment, the mode discrimination feedback loop is implemented by evaluating the preliminary modulation mode candidate set after mode discrimination enhancement processing. During the evaluation, the mode discrimination of each candidate mode in the enhanced preliminary modulation mode candidate set is calculated. Mode discrimination is measured by the overlap between the feature sets of the candidate modes and those of other candidate modes. Based on the evaluation results, the weights of the feature terms in the overlap calculation are adjusted. For candidate mode pairs with low mode discrimination, the weights of the significantly different feature terms in their feature sets are increased.

[0050] Step S124: Based on the feature difference information, the preliminary modulation mode candidate set is screened, and modulation modes whose feature differences meet the set conditions are retained.

[0051] In this embodiment, the set condition is that the number of difference feature items in the feature difference information is greater than a preset number threshold, or the degree of difference of the difference feature items is greater than a preset degree threshold. By checking the feature difference information corresponding to each candidate mode in the preliminary modulation mode candidate set, modulation modes that meet the set conditions are retained, and modulation modes that do not meet the set conditions are eliminated.

[0052] Step S125: Sort the filtered modulation modes according to their frequency of occurrence to generate a set of modulation modes containing the sorting results.

[0053] In this embodiment, the frequency of occurrence refers to the number of times the selected modulation mode appears in historical data. The frequency of occurrence of each selected modulation mode is obtained by statistical analysis of the historical data. The selected modulation modes are sorted in descending order of frequency of occurrence, generating a set of modulation modes containing the sorting results. Each modulation mode in the set corresponds to a frequency of occurrence value.

[0054] Step S130: Construct a signal quality enhancement strategy library based on the modulation mode set, wherein each strategy in the signal quality enhancement strategy library corresponds to a specific modulation mode in the modulation mode set.

[0055] In this embodiment, the signal quality enhancement strategy library is used to store signal quality enhancement strategies for different modulation modes.

[0056] Step S131: Extract the signal characteristic information of each modulation mode in the modulation mode set.

[0057] In this embodiment, the signal characteristic information includes the modulation method, modulation order, signal bandwidth, symbol rate, constellation structure, etc. This signal characteristic information is obtained by analyzing the technical standards and historical data of the modulation mode. For example, the modulation method of the quadrature amplitude modulation mode is quadrature amplitude modulation, the modulation order is a specific value, the signal bandwidth is a specific range, the symbol rate is a specific value, and the constellation structure is a specific point distribution, etc.

[0058] Step S132: Design basic enhancement strategy units based on the signal characteristic information, with each basic enhancement strategy unit corresponding to a type of signal characteristic information.

[0059] In this embodiment, the basic enhancement strategy unit is a signal quality enhancement processing step for specific signal characteristic information, and different basic enhancement strategy units correspond to different signal characteristic information.

[0060] Step S1321: Classify the signal characteristic information and determine the signal quality influencing factors corresponding to each type of signal characteristic information.

[0061] In this embodiment, the classification of signal characteristic information is based on its impact on signal quality. For example, the signal quality influencing factor corresponding to the modulation order is the signal's noise immunity; the higher the modulation order, the weaker the signal's noise immunity. The signal quality influencing factor corresponding to the constellation diagram structure is the bit error rate; the denser the distribution of points in the constellation diagram, the higher the bit error rate. By classifying the signal characteristic information, the signal quality influencing factor corresponding to each type of signal characteristic information is determined.

[0062] Step S1322: Design corresponding processing logic for different influencing factors.

[0063] In this embodiment, the processing logic designed for the signal quality influencing factors corresponding to the modulation order is to reduce the modulation order in order to improve the signal's noise immunity; the processing logic designed for the signal quality influencing factors corresponding to the constellation diagram structure is to adjust the distribution of points in the constellation diagram and increase the distance between points in order to reduce the signal's bit error rate.

[0064] Step S1323: Transform the processing logic into an executable sequence of steps, with each sequence of steps corresponding to a basic enhancement strategy unit.

[0065] In this embodiment, the processing logic for reducing the modulation order is transformed into an executable sequence of steps, including detecting the current modulation order, determining the target modulation order, and adjusting the modulator parameters to achieve the target modulation order. Similarly, the processing logic for adjusting the distribution of points in the constellation diagram is transformed into an executable sequence of steps, including obtaining the current constellation diagram structure, determining the target constellation diagram structure, and adjusting the constellation diagram generation parameters to achieve the target constellation diagram structure. Each sequence of steps corresponds to a basic enhancement strategy unit.

[0066] Step S1324: Verify the effectiveness of the step sequence by testing the improvement effect of the strategy unit through simulated signal processing.

[0067] In this embodiment, effectiveness verification is achieved by testing the step sequence in a simulated environment. The simulated environment includes devices such as a signal generator, a channel simulator, and a signal receiver. The signal generator generates a wireless communication signal with specific signal characteristics, the channel simulator simulates the transmission characteristics of a wireless communication channel, and the signal receiver receives the signal after transmission through the channel. During testing, the step sequence is first applied to the signal processing in the simulated environment. Then, the signal quality indicators, such as signal-to-noise ratio and bit error rate, are compared before and after applying the step sequence. The improvement effect of the step sequence is evaluated based on the changes in these quality indicators.

[0068] Step S1325: Adjust the parameters in the step sequence according to the verification results so that the basic enhancement strategy unit can effectively respond to the corresponding signal characteristic information.

[0069] In this embodiment, if the verification results show that the improvement effect of the step sequence does not reach the preset standard, the parameters affecting the improvement effect are analyzed, such as the adjustment range of the modulation order and the degree of adjustment of the distribution of points in the constellation diagram. These parameters are adjusted, and then the effectiveness verification is performed again until the improvement effect of the step sequence reaches the preset standard. At this time, the step sequence is an effective basic enhancement strategy unit.

[0070] Step S133: Perform combination optimization on the basic enhancement strategy unit to generate combined enhancement strategies for different modulation modes.

[0071] In this embodiment, combinatorial optimization combines multiple basic enhancement strategy units in a certain order and manner to form a combinatorial enhancement strategy for a specific modulation mode.

[0072] For example, in step S1331: obtain the signal characteristic information of each modulation mode and the corresponding basic enhancement strategy unit, and establish a mapping table between modulation modes and basic enhancement strategy units.

[0073] In this embodiment, the mapping table is used to record the signal characteristic information and basic enhancement strategy unit corresponding to each modulation mode. By analyzing each modulation mode in the modulation mode set, the corresponding signal characteristic information and basic enhancement strategy unit are determined, and then this information is recorded in the mapping table.

[0074] Step S1332: Based on the mapping relationship table, select multiple basic enhancement strategy units for the same modulation mode, and determine the functional priority of each basic enhancement strategy unit. The functional priority is set based on the improvement weight of the basic enhancement strategy unit on the signal characteristics.

[0075] In this embodiment, functional priority is determined by evaluating the improvement effect of the basic enhancement strategy unit on signal characteristics. The better the improvement effect, the higher the functional priority of the basic enhancement strategy unit. The evaluation of the improvement effect is achieved by testing the basic enhancement strategy unit in a simulated environment and comparing the changes in signal characteristics before and after applying the basic enhancement strategy unit.

[0076] Step S1333: Analyze the functional compatibility between basic enhancement strategy units with different functional priorities, and eliminate combinations of basic enhancement strategy units with functional conflicts.

[0077] In this embodiment, functional compatibility analysis is achieved by analyzing the processing logic of the basic enhancement strategy units. If the processing logic of two basic enhancement strategy units contradicts each other, for example, one basic enhancement strategy unit requires a reduction in modulation order and the other basic enhancement strategy unit requires an increase in modulation order, then the two basic enhancement strategy units have a functional conflict and combinations containing these two basic enhancement strategy units need to be excluded.

[0078] Step S1334: Introduce timing adaptability analysis of strategy combination, and adjust the execution interval of the basic enhancement strategy unit based on the signal transmission period of the modulation mode.

[0079] In this embodiment, timing adaptability analysis is achieved by analyzing the signal transmission period of the modulation mode, where the signal transmission period is the time interval of signal transmission under the modulation mode. Based on the signal transmission period, the execution interval of the basic enhancement strategy unit is adjusted to match the execution time of the basic enhancement strategy unit with the signal transmission period, ensuring that the basic enhancement strategy unit can process the signal at the appropriate time.

[0080] Step S1335: Predict the effect of combining compatible basic enhancement strategy units and simulate the degree of improvement in signal quality by the combined strategy.

[0081] In this embodiment, the effect prediction is achieved by testing the combination of basic enhancement strategy units in a simulated environment. The testing process is similar to the effectiveness verification process in step S1324. By comparing the quality indicators of the signal before and after applying the combination of basic enhancement strategy units, the degree of improvement of the combined strategy on the signal quality is predicted.

[0082] Step S1336: Adjust the execution order of the basic enhancement strategy unit combination according to the prediction results, and optimize the timing of the action of different basic enhancement strategy units.

[0083] In this embodiment, if the prediction result shows that the improvement effect of the combination of basic enhancement strategy units does not reach the preset standard, the impact of the execution order on the improvement effect is analyzed, the execution order of the basic enhancement strategy units is adjusted, and then the effect prediction is performed again until the improvement effect reaches the preset standard.

[0084] Step S1337: Calculate the overall fit of the adjusted combination strategy and compare it with the fit threshold of the modulation mode.

[0085] In this embodiment, the overall fit is calculated by comprehensively evaluating the improvement effect of the adjusted combination strategy. The evaluation indicators include the improvement in signal-to-noise ratio and the reduction in bit error rate. The fit threshold is a pre-set minimum standard for the fit of the combination strategy for this modulation mode.

[0086] Step S1338: If the overall fit does not reach the fit threshold, replace some basic enhancement strategy units and re-predict; if the overall fit reaches the fit threshold, determine the combination and execution order of the basic enhancement strategy units as a combined enhancement strategy for the modulation mode.

[0087] In this embodiment, if the overall fit does not reach the fit threshold, the reasons for the failure are analyzed, some basic enhancement strategy units are replaced, for example, basic enhancement strategy units with poor improvement effects are replaced, and then the effect prediction and overall fit calculation are re-performed; if the overall fit reaches the fit threshold, the combination and execution order of the basic enhancement strategy units are determined as the combined enhancement strategy for the modulation mode.

[0088] Step S1339: Associate the generated combined enhancement strategy with the corresponding modulation mode and store it to form a combined strategy index table for calling and querying the signal quality enhancement strategy library.

[0089] In this embodiment, the combination strategy index table records the combination enhancement strategy corresponding to each modulation mode. Through the combination strategy index table, the combination enhancement strategy for a specific modulation mode can be quickly queried.

[0090] Step S13310: Based on the actual application effect of the combination strategy, dynamically update the functional priority of the basic enhancement strategy unit in the mapping relationship table, and optimize the accuracy of the pattern discrimination enhancement processing.

[0091] In this embodiment, the actual application effect is obtained by statistical analysis of the application of the combined enhancement strategy in the actual wireless communication system. Based on the actual application effect, the functional priority of the basic enhancement strategy unit in the mapping table is adjusted. For example, if a basic enhancement strategy unit has a good improvement effect in actual application, its functional priority is increased; if a basic enhancement strategy unit has a poor improvement effect in actual application, its functional priority is decreased.

[0092] Step S134: Calculate the fit between each combination enhancement strategy and the corresponding modulation mode, and quantify the fit by measuring the characteristic changes after the strategy is applied to the signal characteristics.

[0093] In this embodiment, the fit is calculated by analyzing the changes in characteristics of the signal after the combined enhancement strategy is applied to the corresponding modulation mode. These changes include variations in signal-to-noise ratio, bit error rate, and signal bandwidth. The fit is quantified as a weighted sum of these changes, with the weights set according to the importance of each feature to signal quality; features with higher importance receive greater weights.

[0094] Step S135: Store the combined enhancement strategy together with the corresponding modulation mode and adaptation degree to build a signal quality enhancement strategy library, and set the dynamic update rules for the signal quality enhancement strategy library.

[0095] In this embodiment, the signal quality enhancement strategy library is stored in the form of a database. Each record in the database includes a combined enhancement strategy, the corresponding modulation mode, and the suitability. The dynamic update rules include periodic updates and real-time updates. Periodic updates involve re-evaluating and adjusting the combined enhancement strategies in the signal quality enhancement strategy library at preset time intervals. Real-time updates involve updating the combined enhancement strategies in the signal quality enhancement strategy library promptly when the actual application effect of the combined enhancement strategies changes significantly.

[0096] Step S140: Select an enhancement strategy that matches the current modulation mode from the signal quality enhancement strategy library, perform enhancement processing on the wireless communication signal, and obtain the enhanced signal.

[0097] In this embodiment, the current modulation mode is the modulation mode in the modulation mode set generated in step S120. Based on the current modulation mode, the corresponding enhancement strategy is selected from the signal quality enhancement strategy library to enhance the wireless communication signal.

[0098] Step S141: Determine the modulation mode corresponding to the current wireless communication signal, and retrieve all combined enhancement strategies corresponding to the currently determined modulation mode from the signal quality enhancement strategy library.

[0099] In this embodiment, the modulation mode corresponding to the current wireless communication signal is the modulation mode that appears most frequently in the modulation mode set. By querying the combination strategy index table of the signal quality enhancement strategy library, all combination enhancement strategies corresponding to the modulation mode are retrieved.

[0100] Step S142: Calculate the current fitness of each combined enhancement strategy, and select the combined enhancement strategy with the largest current fitness value as the target enhancement strategy.

[0101] In this embodiment, the current fit is calculated by evaluating the suitability of the combined enhancement strategy in the current wireless communication signal environment. The evaluation process considers factors such as the signal-to-noise ratio, bit error rate, and signal bandwidth of the current wireless communication signal. The fit of the combined enhancement strategy is adjusted based on these factors to obtain the current fit. The combined enhancement strategy with the largest current fit value is selected as the target enhancement strategy.

[0102] Step S143: Analyze the execution steps of the target enhancement strategy and adjust the multi-dimensional signal characteristics of the wireless communication signal step by step.

[0103] In this embodiment, the execution steps of the target enhancement strategy are the execution order of the basic enhancement strategy units in the combined enhancement strategy. By parsing these execution steps, the multi-dimensional signal characteristics of the wireless communication signal are adjusted in sequence. For example, if the execution step includes reducing the modulation order, the modulation order of the wireless communication signal is adjusted; if the execution step includes adjusting the constellation diagram structure, the constellation diagram structure of the wireless communication signal is adjusted.

[0104] Step S144: During the adjustment process, the intermediate processing results of the signal are collected in real time, and the strategy parameters are fine-tuned based on the intermediate processing results.

[0105] In this embodiment, the intermediate processing result is the signal output by each execution step of the target enhancement strategy. By analyzing the intermediate processing result, the strategy parameters are fine-tuned to ensure the effectiveness of the enhancement processing.

[0106] Step S1441: After each execution step of the target enhancement strategy is completed, collect the intermediate signal output by that execution step.

[0107] In this embodiment, the intermediate signal is the wireless communication signal processed by the execution step, which is acquired by a signal acquisition device.

[0108] Step S1442: Extract the key features of the intermediate signal and compare them with the ideal features preset in this execution step.

[0109] In this embodiment, the key feature is the signal characteristic information corresponding to the execution step. For example, if the execution step is to reduce the modulation order, the key feature is the modulation order; if the execution step is to adjust the constellation diagram structure, the key feature is the distribution of points in the constellation diagram. The preset ideal feature is the signal characteristic information that the execution step is expected to achieve.

[0110] Step S1443: Calculate the degree of deviation between the key features and the ideal features, and quantify the degree of deviation by using the difference between the feature values ​​of the two.

[0111] In this embodiment, the degree of deviation is calculated as the absolute value of the difference between the value of the key feature and the value of the ideal feature. The larger the absolute value of the difference, the greater the degree of deviation.

[0112] Step S1444: If the deviation exceeds the allowable range, analyze the cause of the deviation and determine the strategy parameters that need to be adjusted.

[0113] In this embodiment, the allowable range is the maximum permissible deviation between the pre-defined key features and the ideal features. If the deviation exceeds the allowable range, the cause of the deviation is analyzed, such as insufficient modulation order adjustment or inaccurate constellation structure adjustment. Based on the cause, the strategy parameters that need to be adjusted are determined, such as the adjustment range of the modulation order and the adjustment parameters of the constellation structure.

[0114] Step S1445: Adjust the corresponding parameters according to the cause of the deviation, and re-execute the execution step until the key features of the intermediate signal meet the ideal feature requirements.

[0115] In this embodiment, the strategy parameters are adjusted according to the cause of the deviation. For example, if the modulation order adjustment range is insufficient, the modulation order adjustment range is increased; if the constellation diagram structure is not accurately adjusted, the constellation diagram structure adjustment parameters are adjusted. After adjustment, the execution step is re-executed, intermediate signals are acquired, key features are extracted, and compared with ideal features. The above process is repeated until the key features of the intermediate signal meet the ideal feature requirements.

[0116] Step S145: After completing all adjustment steps, output the enhanced signal and record the strategy parameters and processing results during this enhancement process.

[0117] In this embodiment, the enhanced signal is the wireless communication signal processed through all execution steps, and is output through a signal output device. The strategy parameters in this enhancement process include the execution steps of the target enhancement strategy, the adjusted strategy parameters, etc., and the processing results include the multi-dimensional signal characteristics, signal-to-noise ratio, and bit error rate of the enhanced signal.

[0118] Step S150: Perform modulation mode identification on the enhanced signal again, compare the identification result with the initial modulation mode set, and adjust the strategy parameters in the signal quality enhancement strategy library according to the comparison result to achieve dynamic optimization of the enhancement strategy.

[0119] In this embodiment, the effect of the enhancement strategy is evaluated by performing modulation pattern recognition on the enhanced signal, and the strategy parameters are adjusted according to the evaluation results to achieve dynamic optimization of the enhancement strategy.

[0120] Step S151: Call the adaptive modulation recognition model to collect multi-dimensional signal features of the enhanced signal to obtain the enhanced multi-dimensional signal features.

[0121] In this embodiment, the acquisition process of multi-dimensional signal features is the same as the acquisition process in step S110. Signal features are acquired from the enhanced signal to obtain the enhanced multi-dimensional signal features.

[0122] Step S152: Based on the enhanced multi-dimensional signal features, perform modulation mode identification to generate an enhanced modulation mode set.

[0123] In this embodiment, the modulation pattern recognition process is the same as the modulation pattern analysis process in step S120. The adaptive modulation recognition model is called to process the enhanced multi-dimensional signal features and generate an enhanced modulation pattern set.

[0124] Step S153: Compare the enhanced modulation mode set with the initial modulation mode set and analyze the consistency of the modulation modes in the two sets.

[0125] In this embodiment, consistency analysis is achieved by comparing the types and frequencies of modulation modes in the enhanced modulation mode set with those in the initial modulation mode set. The ratio of the number of identical modulation modes in the two sets to the total number of modulation modes in the initial modulation mode set is calculated. The larger the ratio, the higher the consistency.

[0126] Step S154: Calculate the degree of influence of the enhancement processing on the stability of the modulation mode based on the consistency results, and quantify the degree of influence by the ratio of the number of matching modulation modes in the two sets to the total number.

[0127] In this embodiment, the degree of influence is quantified as the ratio of the number of identical modulation modes in the enhanced modulation mode set to the total number of modulation modes in the initial modulation mode set. The larger the ratio, the smaller the influence of the enhancement process on the stability of the modulation mode, and the more stable the modulation mode; the smaller the ratio, the greater the influence of the enhancement process on the stability of the modulation mode, and the less stable the modulation mode.

[0128] Step S155: If the impact level does not reach the set standard, adjust the parameters of the corresponding enhancement strategy based on the difference point and update the stored information in the signal quality enhancement strategy library; if the impact level reaches the set standard, retain the current enhancement strategy parameters.

[0129] In this embodiment, the set standard is a pre-defined minimum standard for the impact of enhancement processing on modulation mode stability. If the impact does not reach the set standard, the differences between the enhanced modulation mode set and the initial modulation mode set are analyzed, such as changes in modulation mode types or frequency of occurrence. Based on these differences, the parameters of the corresponding enhancement strategy are adjusted, such as the execution order of the basic enhancement strategy units and the values ​​of the strategy parameters. Then, the stored information in the signal quality enhancement strategy library is updated. If the impact reaches the set standard, the current enhancement strategy parameters are retained.

[0130] Figure 2 The illustration shows exemplary hardware and software components of an adaptive modulation identification-based signal quality enhancement processing system 100, which can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the adaptive modulation identification-based signal quality enhancement processing system 100 and to perform the functions in this application.

[0131] The signal quality enhancement processing system 100 based on adaptive modulation identification can be a general-purpose server or a special-purpose server; both can be used to implement the signal quality enhancement processing method based on adaptive modulation identification of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0132] For example, the signal quality enhancement processing system 100 based on adaptive modulation identification may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the signal quality enhancement processing system 100 based on adaptive modulation identification may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The signal quality enhancement processing system 100 based on adaptive modulation identification also includes an I / O interface 150 between the computer and other input / output devices.

[0133] For ease of explanation, only one processor is described in the signal quality enhancement processing system 100 based on adaptive modulation recognition. However, it should be noted that the signal quality enhancement processing system 100 based on adaptive modulation recognition in this application may also include multiple processors. Therefore, the steps performed by one processor as described in this application may also be performed jointly or individually by multiple processors. For example, if the processor of the signal quality enhancement processing system 100 based on adaptive modulation recognition performs steps A and B, it should be understood that steps A and B may also be performed jointly by two different processors or individually by one processor. For example, the first processor performs step A, the second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0134] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-described signal quality enhancement processing method based on adaptive modulation recognition is implemented.

[0135] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A signal quality enhancement processing method based on adaptive modulation recognition, characterized in that, The method includes: Receive the wireless communication signal to be processed, perform signal feature acquisition on the wireless communication signal, and obtain the multi-dimensional signal features of the wireless communication signal; An adaptive modulation recognition model is invoked to perform modulation mode analysis on the multi-dimensional signal features, generating a set of modulation modes corresponding to the wireless communication signal, specifically including: The multi-dimensional signal features are input into the feature processing layer of the adaptive modulation recognition model to perform feature correlation modeling on the multi-dimensional signal features, thereby obtaining the feature correlation matrix; The feature correlation matrix is ​​initially classified into modulation modes by the mode classification layer of the adaptive modulation recognition model to obtain a preliminary modulation mode candidate set. The preliminary modulation mode candidate set is subjected to mode discrimination enhancement processing to extract feature difference information between different candidate modes; Based on the aforementioned feature difference information, the preliminary modulation mode candidate set is filtered, and modulation modes whose feature differences meet the set conditions are retained. The filtered modulation modes are sorted by frequency of occurrence to generate a set of modulation modes containing the sorted results; A signal quality enhancement strategy library is constructed based on the modulation mode set, and each strategy in the signal quality enhancement strategy library corresponds to a specific modulation mode in the modulation mode set. An enhancement strategy matching the current modulation mode is selected from the signal quality enhancement strategy library, and enhancement processing is performed on the wireless communication signal to obtain an enhanced signal, specifically including: Determine the modulation mode corresponding to the current wireless communication signal, and retrieve all combined enhancement strategies corresponding to the currently determined modulation mode from the signal quality enhancement strategy library; Calculate the current fitness of each combined enhancement strategy, and select the combined enhancement strategy with the largest current fitness value as the target enhancement strategy; The execution steps of the target enhancement strategy are analyzed, and the multi-dimensional signal characteristics of the wireless communication signal are adjusted step by step. During the adjustment process, the intermediate processing results of the signal are collected in real time, and the strategy parameters are fine-tuned based on the intermediate processing results. After completing all adjustment steps, output the enhanced signal and record the strategy parameters and processing results during this enhancement process; The enhanced signal is subjected to modulation mode identification again, and the identification result is compared with the initial modulation mode set. Based on the comparison result, the strategy parameters in the signal quality enhancement strategy library are adjusted to achieve dynamic optimization of the enhancement strategy.

2. The signal quality enhancement processing method based on adaptive modulation recognition according to claim 1, characterized in that, The method of constructing a signal quality enhancement strategy library based on the modulation mode set includes: Extract the signal characteristic information of each modulation mode in the modulation mode set; Based on the signal characteristic information, basic enhancement strategy units are designed, and each basic enhancement strategy unit corresponds to a type of signal characteristic information. The basic enhancement strategy units are combined and optimized to generate combined enhancement strategies for different modulation modes; Calculate the fit between each combination enhancement strategy and the corresponding modulation mode, and quantify the fit by measuring the change in characteristics after the strategy is applied to the signal characteristics. The combined enhancement strategies, along with their corresponding modulation modes and adaptability, are stored together to form a signal quality enhancement strategy library, and dynamic update rules for the signal quality enhancement strategy library are set.

3. The signal quality enhancement processing method based on adaptive modulation recognition according to claim 1, characterized in that, The process of re-identifying the modulation mode of the enhanced signal, comparing the identification result with the initial modulation mode set, and adjusting the strategy parameters in the signal quality enhancement strategy library based on the comparison result to achieve dynamic optimization of the enhancement strategy includes: The adaptive modulation recognition model is invoked to collect multi-dimensional signal features of the enhanced signal, thereby obtaining the enhanced multi-dimensional signal features. Modulation mode identification is performed based on the enhanced multi-dimensional signal features to generate an enhanced set of modulation modes. The enhanced modulation mode set is compared with the initial modulation mode set to analyze the consistency of the modulation modes in the two sets; The impact of enhancement processing on modulation mode stability is calculated based on the consistency results, and the impact is quantified by the ratio of the number of matching modulation modes in the two sets to the total number. If the impact does not reach the set standard, adjust the parameters of the corresponding enhancement strategy based on the difference point and update the stored information in the signal quality enhancement strategy library; if the impact reaches the set standard, retain the current enhancement strategy parameters.

4. The signal quality enhancement processing method based on adaptive modulation recognition according to claim 1, characterized in that, The step of inputting the multi-dimensional signal features into the feature processing layer of the adaptive modulation recognition model, performing feature correlation modeling on the multi-dimensional signal features to obtain a feature correlation matrix includes: Extract each feature item from the multi-dimensional signal features and determine the attribute type and value range of each feature item; Analyze the mutual influence relationships between different feature items, and determine the correlation strength between feature items through feature interaction analysis; The initial structure of the feature correlation matrix is ​​constructed based on the correlation strength, and the rows and columns of the feature correlation matrix correspond to different feature terms. The correlation strength is converted into the values ​​of the elements of the feature correlation matrix, and the initial structure is filled to form a preliminary feature correlation matrix; The preliminary feature correlation matrix is ​​normalized to ensure that the values ​​of the feature correlation matrix elements are within a uniform range, thus obtaining the final feature correlation matrix.

5. The signal quality enhancement processing method based on adaptive modulation recognition according to claim 2, characterized in that, The design of basic enhancement strategy units based on the signal characteristic information, each basic enhancement strategy unit corresponding to one type of signal characteristic information, includes: Classify signal characteristic information and determine the signal quality influencing factors corresponding to each type of signal characteristic information; Design corresponding processing logic for different influencing factors; The processing logic is transformed into an executable sequence of steps, with each sequence of steps corresponding to a basic enhancement strategy unit. The effectiveness of the step sequence is verified by testing the improvement effect of the strategy unit through simulated signal processing. Adjust the parameters in the step sequence based on the verification results so that the basic enhancement strategy unit can effectively respond to the corresponding signal characteristic information.

6. The signal quality enhancement processing method based on adaptive modulation recognition according to claim 1, characterized in that, The process of real-time acquisition of intermediate signal processing results during adjustment, and fine-tuning of strategy parameters based on these intermediate processing results, includes: After each execution step of the target enhancement strategy is completed, the intermediate signal output by that execution step is collected; Extract the key features of the intermediate signal and compare them with the ideal features preset for this execution step; Calculate the degree of deviation between key features and ideal features, and quantify the degree of deviation by the difference between the feature values ​​of the two. If the deviation exceeds the allowable range, analyze the cause of the deviation and determine the strategy parameters that need to be adjusted. Adjust the corresponding parameters according to the cause of the deviation, and re-execute the execution step until the key characteristics of the intermediate signal meet the ideal characteristic requirements.

7. The signal quality enhancement processing method based on adaptive modulation recognition according to claim 1, characterized in that, The step of performing mode discrimination enhancement processing on the preliminary modulation mode candidate set and extracting feature difference information between different candidate modes includes: Obtain the feature set corresponding to each candidate mode in the preliminary modulation mode candidate set. The feature set is derived from the classification results of multi-dimensional signal features. Calculate the overlap between the feature sets of any two candidate patterns, and measure the overlap by the ratio of the number of feature intersections to the number of feature unions. A pattern discrimination evaluation function is constructed based on overlap. The output value of the pattern discrimination evaluation function increases as the overlap decreases. Based on the output value of the pattern discrimination evaluation function, the feature set of candidate patterns is enhanced by differentiation. The features are sorted by overlap value from small to large, and the representation weight of the features is increased accordingly. The smaller the overlap value, the greater the increase in weight. Extract the significantly different features from the enhanced feature set to form a feature difference list; Organize the correspondence between the feature difference list and the candidate patterns to generate feature difference information containing pattern identifiers and difference features; Supplement the missing difference feature items in the feature difference information to cover all differences between different candidate patterns; A pattern discrimination feedback mechanism is constructed based on feature difference information. The overlap calculation weight is adjusted according to the enhanced discrimination effect to optimize the accuracy of pattern discrimination enhancement processing.

8. A signal quality enhancement processing system based on adaptive modulation recognition, characterized in that, The signal quality enhancement processing system based on adaptive modulation identification includes a processor and a memory, the memory and the processor being connected. The memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the signal quality enhancement processing method based on adaptive modulation identification as described in any one of claims 1-7.

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