Anti-interference transmission method for wireless low-power-consumption transmitter

Abstract

The invention relates to the technical field of wireless transmission, and discloses an anti-interference transmission method for a wireless low-power-consumption transmitter. The method comprises the steps of collecting a real-time electromagnetic interference signal frequency spectrum of an environment where a transmitter is located, and extracting multi-dimensional interference characteristics; constructing an interference feature library based on the features, and dividing interference feature clusters according to the feature distribution density and the intensity change trend; calculating a dynamic fluctuation index and a frequency domain concentration index of each cluster, and obtaining a peak frequency drift index and a spectral bandwidth variation index of each cluster; generating an environmental interference entropy value based on the indexes; constructing a transmission signal sequence covariance matrix and carrying out characteristic decomposition to obtain characteristic value distribution, a concentration ratio, a principal component contribution degree related to a discrete state and a discrete coefficient; and a signal transmission purity index is generated by combining the row vector correlation of the covariance matrix, the environment interference entropy value, the principal component contribution degree and the dispersion coefficient, anti-interference transmission control is executed according to the index, and the transmission stability of the transmitter in the complex electromagnetic environment is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless transmission technology, specifically to an anti-interference transmission method for a low-power wireless transmitter. Background Technology

[0002] In fields such as industrial automation, environmental monitoring, and intelligent sensing, wireless low-power transmitters are key devices for data acquisition and transmission, and their transmission stability directly affects the overall system performance. With the widespread application of various electronic devices, the electromagnetic environment in which transmitters operate is becoming increasingly complex. Electromagnetic interference signals from different devices superimpose each other, causing transmitters to frequently experience signal distortion, transmission delays, and even data loss during data transmission.

[0003] Most solutions for interference suppression in wireless low-power transmitters remain at the level of single interference suppression. For example, some technologies enhance their ability to shield against interference signals of specific frequencies by optimizing the transmitter's hardware circuit design. However, this approach can only cope with interference within a known frequency range. When new frequency interference signals appear in the environment, the anti-interference effect will drop significantly. Other technologies use fixed signal filtering algorithms to reduce noise in the transmitted signal. However, these algorithms cannot adjust the filtering parameters in real time according to the dynamic changes of the interference signal. In scenarios where the strength and frequency of the interference signal fluctuate, it is difficult to effectively filter out interference components. At the same time, it may also cause excessive attenuation of the useful signal, affecting the accuracy of data transmission.

[0004] Existing technologies generally lack systematic analysis and evaluation mechanisms for environmental interference signals, making it impossible to fully grasp the characteristic distribution patterns of interference signals. This results in a lack of scientific basis for formulating anti-interference strategies. For example, when multiple different types of interference signals exist simultaneously, existing technologies cannot accurately distinguish the degree of influence of each interference signal, nor can they judge the changing trend of the interference signals. Consequently, they cannot take targeted anti-interference measures, making it difficult to effectively guarantee the transmission stability of transmitters in complex electromagnetic environments. This severely restricts the application of wireless low-power transmitters in fields with high requirements for transmission reliability, such as industrial control and medical monitoring. Summary of the Invention

[0005] The purpose of this invention is to provide an anti-interference transmission method for a wireless low-power transmitter to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an anti-interference transmission method for a wireless low-power transmitter, the method comprising:

[0007] Collect the real-time electromagnetic interference signal spectrum of the environment where the transmitter is located, and extract multi-dimensional interference features;

[0008] An interference feature library is constructed based on multi-dimensional interference features, and interference feature clusters are divided according to the distribution density and intensity variation trend of features in the interference feature library.

[0009] Calculate the dynamic fluctuation index and frequency domain concentration index of each interference feature cluster;

[0010] Obtain the peak frequency drift index and spectral bandwidth variation index of each interference feature cluster;

[0011] An environmental interference entropy value is generated based on the peak frequency drift index, the spectral bandwidth variation index, the average dynamic fluctuation index of all interference feature clusters, and the average feature similarity between interference feature clusters.

[0012] Construct the covariance matrix of the current transmitted signal sequence, perform eigenvalue decomposition on the covariance matrix to obtain each eigenvalue, and obtain the principal component contribution and discrete coefficient of the eigenvalue sequence of the transmitted signal sequence based on the distribution, concentration and discreteness of the eigenvalues.

[0013] A signal transmission purity index is generated based on the correlation between row vectors in the covariance matrix, the entropy value of environmental interference, the contribution of principal components, and the discrete coefficients; anti-interference transmission control is then performed based on the signal transmission purity index.

[0014] Preferably, the method for dividing the interference feature clusters includes:

[0015] The spectrum peak detection algorithm is used to identify all the main peaks and secondary peaks in the spectrum of electromagnetic interference signals. The spectrum sequence is segmented from each secondary peak to form a feature subsequence, and the spectral energy range of each feature subsequence is calculated.

[0016] Input the spectral energy range of all feature subsequences into the clustering algorithm, and output the clusters of each feature;

[0017] Calculate the mean internal spectral energy of each feature cluster, and take the feature cluster with the largest mean internal spectral energy as the dominant interference cluster, and take the spectral subsequences corresponding to the features inside the dominant interference cluster as the interference feature cluster.

[0018] Preferably, the method for obtaining the dynamic volatility index includes:

[0019] For each interference feature cluster, locate the maximum spectral intensity point in the interference feature cluster, form the left spectral segment by combining the maximum spectral intensity point and the left spectral point, and form the right spectral segment by combining the maximum spectral intensity point and the right spectral point.

[0020] Obtain the first-order gradient sequence of the left-side spectral segment of each interference feature cluster, and use a nonlinear transformation function to process the first-order gradient sequence to generate a gradient transformation sequence;

[0021] The absolute value of the difference between the sum of the elements and absolute values ​​of the gradient transformation sequence and the length of the first-order gradient sequence of the left spectral segment is calculated and used as the fluctuation intensity index of the left spectral segment.

[0022] The same method was used to obtain the fluctuation intensity index of the right-side spectral band of each interference feature cluster;

[0023] Calculate the product of the absolute value and the mean of the difference in the fluctuation intensity index between the left and right spectral segments, and use it as the dynamic fluctuation index of the interference feature cluster.

[0024] Calculate the variance of the spectral intensity of the left-side spectrum segment and the variance of the spectral intensity of the right-side spectrum segment for each interference feature cluster, and use the mean of the variances on both sides as the frequency domain concentration index.

[0025] Preferably, the method for obtaining the spectral bandwidth variation index includes:

[0026] Extract the main peak intensity values ​​from all interference feature clusters and sort them according to their frequency points in the spectrum sequence to construct the main peak intensity sequence;

[0027] The frequency points of each main peak in the interference feature cluster are sorted by size to construct the main peak frequency point sequence;

[0028] Obtain the first-order difference sequences of the main peak intensity sequence and the main peak frequency sequence. Use the variance of the first-order difference sequence of the main peak intensity sequence as the peak frequency drift index, and use the variance of the first-order difference sequence of the main peak frequency sequence as the spectral bandwidth variation index.

[0029] Preferably, the method for generating the environmental disturbance entropy value includes:

[0030] The mean of the dynamic volatility indices of all disturbance feature clusters is calculated as the average volatility index.

[0031] The average spectral similarity coefficient of each interference feature cluster with all other interference feature clusters is obtained as the feature cluster similarity.

[0032] The environmental interference entropy value is generated by inputting the peak frequency drift index, the spectral bandwidth variation index, the average fluctuation index, and the mean similarity of feature clusters into the environmental interference entropy value calculation model.

[0033] Preferably, the method for obtaining the principal component contribution and the coefficient of variation includes:

[0034] Arrange all feature values ​​in descending order to construct a feature value sequence;

[0035] The feature value sequence is input into the adaptive threshold segmentation algorithm, which outputs the segmentation threshold. Feature values ​​greater than or equal to the segmentation threshold are listed as primary feature values, and feature values ​​less than the segmentation threshold are listed as secondary feature values.

[0036] The proportion of principal eigenvalues ​​to the total number of eigenvalues ​​is calculated as the principal component contribution.

[0037] The product of the mean and variance of the principal eigenvalues ​​is used as the principal eigenvalue volatility index. The secondary eigenvalue volatility index is calculated using the same method. The absolute value of the difference between the principal eigenvalue volatility index and the secondary eigenvalue volatility index is used as the coefficient of variation.

[0038] Preferably, the method for generating the signal transmission purity index includes:

[0039] The mean absolute value of the correlation coefficients between each row vector in the covariance matrix and other row vectors is used as the row correlation.

[0040] The mean of the correlations of all rows in the covariance matrix is ​​taken as the matrix autocorrelation.

[0041] The principal component contribution, dispersion coefficient, matrix autocorrelation, and environmental interference entropy are input into the purity calculation model to generate a signal transmission purity index.

[0042] Preferably, the anti-interference transmission control based on the signal transmission purity index includes:

[0043] Calculate the adaptive frequency hopping interval based on the signal transmission purity index;

[0044] The transmitter carrier frequency is dynamically adjusted based on adaptive frequency hopping interval;

[0045] On the adjusted carrier frequency, the data to be transmitted is divided into data block groups, and a unique cluster identifier and hierarchical check code are added to each data block to form a transmission cluster unit;

[0046] The data is transmitted sequentially according to the hierarchical order of the transmission cluster units, and multi-level verification is performed at the receiving end.

[0047] Preferably, the multi-level verification includes:

[0048] The receiving end reassembles the data block group based on the cluster identifier and extracts the hierarchical check code;

[0049] Perform the first level of parity check; if the check fails, activate the second level of cyclic redundancy check.

[0050] If the cyclic redundancy check fails, a check feature library is generated based on the feature distribution of the data block group.

[0051] Data recovery is performed by matching the optimal fault-tolerant decoding strategy based on the verification feature library.

[0052] Preferably, the matching of the fault-tolerant decoding strategy includes:

[0053] Monitor current channel quality indicators and transmitter remaining energy indicators;

[0054] The maximum number of allowed retransmissions is calculated based on the channel quality index, and the minimum power consumption transmission mode is calculated based on the remaining energy index.

[0055] If the number of data recovery failures does not reach the maximum allowed number of retransmissions, a partial retransmission request is initiated using the minimum power transmission mode.

[0056] Otherwise, based on the priority flag of the data block group, either discard or perform downgraded decoding.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] This anti-interference transmission method for wireless low-power transmitters acquires the real-time electromagnetic interference signal spectrum of the transmitter's environment and extracts multi-dimensional interference features. This allows for a comprehensive and detailed understanding of the specific attributes of interference signals in the environment, breaking the limitations of traditional technologies that can only handle single or fixed types of interference. Based on multi-dimensional interference features, an interference feature library is constructed, and interference feature clusters are divided according to the distribution density and intensity variation trends of the features. This allows for the classification and organization of complex and diverse interference signals, clearly presenting the distribution patterns and changing trends of different interference signals, and providing comprehensive information support for the subsequent development of precise anti-interference strategies.

[0059] Calculating the dynamic fluctuation index and frequency domain concentration index of each interference feature cluster, as well as obtaining the peak frequency drift index and spectral bandwidth variation index, allows for the quantification of the dynamic variation characteristics and frequency domain distribution features of interference signals from multiple dimensions. These quantitative indicators can accurately determine the stability, frequency variation range, and bandwidth fluctuation of interference signals. Compared to traditional techniques that cannot quantify interference signal characteristics, this method can more deeply analyze the essential attributes of interference signals, thus providing a precise basis for adjusting subsequent anti-interference measures.

[0060] The environmental interference entropy value generated based on the above multi-dimensional quantitative indicators can comprehensively reflect the complexity and uncertainty of interference signals in the environment. The introduction of environmental interference entropy value can transform the originally difficult-to-measure overall interference environment into a quantifiable indicator, making the assessment of the interference environment more objective and scientific. This avoids the problem that traditional technologies cannot accurately assess the degree of interference when facing complex interference environments, resulting in weak targeting of anti-interference measures.

[0061] By constructing the covariance matrix of the current transmitted signal sequence and performing eigenvalue decomposition, the distribution, concentration, and dispersion of eigenvalues ​​are obtained. This leads to the principal component contribution and dispersion coefficients, enabling a comprehensive analysis of the transmitted signal's characteristics. These signal characteristic indicators provide a clear understanding of the distribution of effective components and the degree of signal dispersion, offering a direct basis for judging the extent of interference affecting the signal and providing a reference direction for subsequent adjustments to signal transmission parameters.

[0062] Based on the correlation between row vectors in the covariance matrix, environmental interference entropy, principal component contribution, and discrete coefficients, a signal transmission purity index is generated. Anti-interference transmission control is then implemented based on this index, achieving dynamic and precise anti-interference measures. Compared to traditional techniques that employ fixed anti-interference strategies, this method can flexibly adjust anti-interference parameters and strategies according to the real-time signal transmission purity index. When interference signals change, it responds promptly and takes corresponding processing measures, effectively filtering interference components, reducing the impact on useful signals, and ensuring the stability and accuracy of data transmission.

[0063] The entire method does not rely on complex hardware upgrades. It achieves efficient anti-interference transmission simply by analyzing the interference signal and evaluating its characteristics, combined with a dynamically adjusted anti-interference control strategy. While ensuring the anti-interference effect, it effectively controls the power consumption of the transmitter, which meets the application requirements of wireless low-power transmitters and expands its applicability in various complex electromagnetic environments. It is especially suitable for scenarios with high requirements for transmission stability and device power consumption. Attached Figure Description

[0064] Figure 1 This is a schematic diagram illustrating the working principle of the anti-interference transmission method for wireless low-power transmitters described in this invention.

[0065] Figure 2 Flowchart of the method for dividing interference feature clusters;

[0066] Figure 3 Flowchart of the method for obtaining the spectrum bandwidth variation index and peak frequency drift index;

[0067] Figure 4 The flowchart shows the method for obtaining the principal component contribution and the coefficient of variation. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Please see Figure 1 This invention provides an anti-interference transmission method for a wireless low-power transmitter, the method comprising:

[0070] The system acquires the real-time electromagnetic interference signal spectrum of the transmitter's environment and extracts multi-dimensional interference features. Based on these features, it constructs an interference feature library and divides the interference feature clusters according to the distribution density and intensity trends of the features in the library. It calculates the dynamic fluctuation index and frequency domain concentration index of each interference feature cluster. It obtains the peak frequency drift index and spectral bandwidth variation index of each interference feature cluster. Based on the peak frequency drift index, spectral bandwidth variation index, the average dynamic fluctuation index of all interference feature clusters, and the average feature similarity between interference feature clusters, it generates an environmental interference entropy value. It constructs the covariance matrix of the current transmitted signal sequence, performs eigenvalue decomposition on the covariance matrix to obtain each eigenvalue, and obtains the principal component contribution and discrete coefficient of the eigenvalue sequence based on the distribution, concentration, and discrete states of the eigenvalues. Based on the correlation between the row vectors in the covariance matrix, the environmental interference entropy value, the principal component contribution, and the discrete coefficient, it generates a signal transmission purity index. Finally, it performs anti-interference transmission control based on the signal transmission purity index.

[0071] Example 1: See Figure 2 In the operating environment of a wireless low-power transmitter, the real-time acquisition of the electromagnetic interference signal spectrum in its vicinity is accomplished by a built-in spectrum analysis module. This module captures the radio frequency energy distribution within a specific frequency band at a fixed sampling period and outputs a digital sequence composed of frequency points and intensity values. This sequence is the original electromagnetic interference signal spectrum. Multi-dimensional interference features are extracted from this spectrum, including but not limited to peak intensity, average energy, number of zero-crossing points, spectral centroid, and spectral roll-off point. These features collectively constitute a multi-dimensional quantitative description of the current electromagnetic environment.

[0072] Based on these multi-dimensional interference characteristics, the system constructs a dynamically updated interference feature library. This library not only stores the feature vector at the current moment but also retains historical features within a certain time window to analyze the evolution trend of the features. The division of interference feature clusters depends on the analysis of the data distribution in the feature library. Specifically, a spectral peak detection algorithm is used to process the original electromagnetic interference signal spectrum. This algorithm scans the entire spectral sequence, identifies all frequency points that satisfy the local maximum condition and whose intensity exceeds the global average intensity by a certain proportion, and marks these points as main peaks or secondary peaks. The distinction between main peaks and secondary peaks is based on their relative intensity and their significance in the frequency domain.

[0073] The continuous spectral sequence is divided into multiple characteristic subsequences from the identified secondary peak positions. The boundary of each characteristic subsequence is determined by the adjacent secondary peak frequency points or the start and end points of the spectrum. The spectral energy range of each characteristic subsequence is calculated, which is the difference between the maximum and minimum spectral intensity values ​​within the subsequence. This energy range reflects the dynamic variation amplitude of the interference signal within this local frequency band. The calculated spectral energy range values ​​of all characteristic subsequences are input into a clustering algorithm for processing. This clustering algorithm analyzes the distribution of these energy range values, grouping energy ranges with similar values ​​into the same set, thus forming several characteristic clusters. Each cluster represents a local spectral region with similar energy fluctuation characteristics.

[0074] The internal spectral energy mean of each feature cluster is calculated; this mean is the arithmetic mean of the spectral energy ranges of all feature subsequences within the cluster. Comparing these internal means across all feature clusters, the cluster with the largest internal mean is identified as the dominant interference cluster. This means that the spectral region represented by this cluster exhibits the strongest energy variation characteristics, typically corresponding to the most significant interference source in the environment. Finally, all feature subsequences belonging to this dominant interference cluster are extracted from the original spectrum; these sequences are formally defined as the interference feature clusters required for subsequent analysis. For each identified interference feature cluster, its dynamic characteristics need further analysis. First, the frequency point with the highest spectral intensity within the interference feature cluster is located, i.e., the maximum spectral intensity point. Using this point as a boundary, the spectral sequence of the interference feature cluster is divided into two parts: the left spectral segment consists of the maximum intensity point and all frequencies to its left; the right spectral segment consists of all frequencies to the right of the maximum intensity point. This division helps to examine the different behaviors of the interference signal on both sides of the peak frequency.

[0075] Analyze the left-hand spectrum segment and calculate the rate of change of intensity values ​​at adjacent frequency points within this segment, i.e., the first-order gradient sequence. This sequence reflects the speed and direction of intensity change as it transitions from the left side of the spectrum towards the peak point. Process this first-order gradient sequence using a nonlinear transformation function, which amplifies significant gradient changes while suppressing minor fluctuations, resulting in a transformed gradient sequence. Calculate the sum of the absolute values ​​of all elements in this transformed sequence, and then calculate the absolute value of the difference between this value and the total length of the first-order gradient sequence in the left-hand spectrum segment. This result is defined as the fluctuation intensity index of the left-hand spectrum segment, quantifying the severity of signal changes in the left-hand frequency band. The exact same analysis procedure is applied to the right-hand spectrum segment. Calculate the first-order gradient sequence on its right side, apply the same nonlinear transformation, and calculate the absolute value of the difference between the sum of the absolute values ​​of the transformed sequence elements and the sequence length, obtaining the fluctuation intensity index of the right-hand spectrum segment. Calculate the absolute value of the difference between the left-hand and right-hand fluctuation intensity indices, and then calculate the arithmetic mean of these two indices. Multiply the absolute value of the difference by the mean; the result is the dynamic fluctuation index of this interference feature cluster. This index comprehensively reflects the asymmetry of the interference signal's variation on both sides of the peak and the overall level of fluctuation.

[0076] The variances of the intensity values ​​at all frequency points in the left and right frequency bands were calculated separately. Variance measures the dispersion of data around its mean. The arithmetic mean of these two variances was calculated and used as the frequency domain concentration index of the interference feature cluster, which characterizes the concentration of interference energy around its peak in the frequency domain. For each identified interference feature cluster, a set of quantitative indicators describing its dynamic and concentration characteristics were obtained: the dynamic fluctuation index and the frequency domain concentration index. These indicators provide a data foundation for subsequent comprehensive assessment of the complexity of the electromagnetic environment and the intensity of interference. The entire implementation process involves multi-stage processing of the spectral signals, gradually extracting key features from the raw spectral data, and ultimately transforming them into quantifiable indicator values ​​to guide anti-interference transmission decisions.

[0077] Example 2: See Figure 3After completing the segmentation and basic parameter extraction of each interference feature cluster, the implementation process enters the quantitative analysis stage of the macroscopic time-varying characteristics of the interference spectrum. The core of this stage lies in extracting key features from the identified interference feature clusters and calculating a set of comprehensive indices that characterize the complexity and instability of the entire electromagnetic environment. The object of processing is all the previously segmented interference feature clusters. Each interference feature cluster corresponds to a local energy concentration region in the spectrum and contains a significant main peak. The intensity value of the main peak is extracted from each interference feature cluster; this is a scalar value representing the peak energy level of the interference signal within the cluster. Subsequently, based on the frequency position order of these main peaks in the original global spectrum, all main peak intensity values ​​are sorted to construct an ordered main peak intensity sequence. The order of elements in this sequence reflects the distribution of different interference sources in the frequency domain, and the changes in their values ​​reflect the differences in the intensity of each interference source.

[0078] Similarly, for all these interference feature clusters, the specific frequency point positions corresponding to the main peak in each cluster are extracted. These frequency point position values ​​are then arranged in ascending or descending order according to their numerical values ​​to construct a main peak frequency point sequence. This sequence describes the specific distribution of the main interference sources in the environment along the frequency axis. To quantify the instability of these main peak characteristics over time or with environmental changes, the first-order difference characteristics of the two sequences mentioned above need to be analyzed. The first-order difference sequence of the main peak intensity sequence is calculated, that is, the difference between each element in the sequence and the next element. This difference sequence reflects the amplitude and direction of the intensity level changes between adjacent main peaks. The variance of this first-order difference sequence is calculated, and this variance value is defined as the peak frequency drift exponent. The magnitude of this exponent reflects the fluctuation of the intensity level of the main interference sources in the environment; the larger the value, the more drastic and unstable the change in interference intensity.

[0079] The first-order difference sequence of the main peak frequency points is calculated, which involves calculating the difference in frequency point positions between adjacent main peaks. This difference sequence reflects the changes in the distribution density of interference sources in the frequency domain and possible frequency shifts. The variance of this first-order difference sequence is calculated and defined as the spectral bandwidth variation index. This index quantifies the degree of change in the distribution of interference sources along the frequency axis; a larger value indicates that the locations of interference sources are more dispersed or that they move more frequently, implying a more complex spectrum occupancy. After obtaining the dynamic fluctuation index characterizing the dynamic properties of individual interference feature clusters, it is necessary to evaluate the overall interference fluctuation level from a global perspective. The arithmetic mean of the dynamic fluctuation indices of all interference feature clusters is calculated; this average is used as the average fluctuation index, which represents the average intensity of the dynamic changes of interference signals in the current electromagnetic environment.

[0080] Further analysis of the relationships between different interference sources is conducted. For each interference feature cluster, the similarity coefficient between its spectral waveform and the spectral waveforms of every other interference feature cluster is calculated. This coefficient is obtained by comparing the spectral shape, distribution, and other characteristics of the two clusters. For each interference feature cluster, the average of its similarity coefficients with all other clusters is calculated to obtain the feature cluster similarity. Subsequently, the arithmetic mean of this feature cluster similarity for all interference feature clusters is calculated to obtain the mean feature cluster similarity. This value reflects the degree of similarity between different interference sources in the environment; a high mean indicates that the interference source type may be relatively simple, while a low mean indicates that the interference sources are diverse and complex.

[0081] The four key indicators calculated above—peak frequency drift index, spectral bandwidth variation index, average fluctuation index, and mean similarity of feature clusters—are input into an environmental interference entropy calculation model. Based on the different weights and interrelationships of these indicators, the model generates a scalar output value, namely the environmental interference entropy, through a comprehensive mathematical operation. This entropy value is a highly generalized indicator that integrates multi-dimensional information such as interference intensity stability, frequency domain distribution characteristics, overall fluctuation level, and interference source diversity, thus comprehensively quantifying the complexity and unpredictability of interference faced by the current wireless environment. A higher environmental interference entropy value indicates a harsh transmission environment, requiring more proactive anti-interference strategies; while a lower entropy value indicates a relatively stable and simple environment. This entropy value provides crucial environmental state input for subsequent adaptive transmission decisions. The entire implementation process, through progressive analysis and calculation, transforms the raw spectrum data into comprehensive environmental assessment parameters with clear physical meaning and decision-making value.

[0082] Example 3: See Figure 4 In the signal processing flow of a wireless low-power transmitter, analyzing the intrinsic structure of the current signal sequence to be transmitted is a crucial step in generating a signal transmission purity index. This implementation begins with constructing a mathematical expression representing the intrinsic relationships within the signal sequence. Specifically, the system acquires discrete signal samples that the transmitter is about to send; these samples are arranged in chronological order to form a multidimensional signal vector. Based on this signal vector, its covariance matrix is ​​calculated. This matrix is ​​a symmetric square matrix, where the diagonal elements represent the variance of each dimension of the signal, and the off-diagonal elements represent the covariance relationship between different dimensions of the signal, reflecting the degree of linear correlation between the components within the signal sequence.

[0083] After constructing the covariance matrix, eigenvalue decomposition is performed. Eigenvalue decomposition is a matrix factorization technique that aims to decompose the original matrix into a set of eigenvectors and their corresponding eigenvalues. In this process, a numerically stable algorithm is used to solve for all the eigenvalues ​​of the matrix. These eigenvalues ​​are essentially scaling factors of the linear transformation represented by the covariance matrix, and their magnitudes are directly related to the energy or importance of each principal component in the signal sequence. All the solved eigenvalues ​​are then arranged in descending order of their numerical values, forming an ordered sequence of eigenvalues. The distribution of this sequence contains key information about the signal's intrinsic structure.

[0084] To quantify the distribution characteristics of the eigenvalue sequence, it is input into an adaptive threshold segmentation algorithm. This algorithm automatically analyzes the numerical distribution pattern of the sequence and finds an optimal segmentation point. The principle for determining this segmentation point is to maximize the inter-class difference between the two sub-sequences after segmentation. Elements in the eigenvalue sequence that are greater than or equal to the segmentation threshold are classified as principal eigenvalues, while elements that are less than the segmentation threshold are classified as secondary eigenvalues. Principal eigenvalues ​​represent the dominant, high-energy main components of the signal sequence, while secondary eigenvalues ​​correspond to lower-energy, minor components or noise.

[0085] The proportion of principal eigenvalues ​​to the total number of eigenvalues ​​is defined as the principal component contribution of the signal sequence. A higher proportion indicates that the signal energy is concentrated on a few principal components, resulting in a clearer signal structure. Conversely, a low proportion suggests dispersed signal energy, potentially indicating more interference or a complex structure. Further analysis of the statistical properties of the sets of principal and secondary eigenvalues ​​is performed, calculating the arithmetic mean of the principal eigenvalues ​​and the variance of the values ​​within that set. The product of the mean and variance of the principal eigenvalues ​​is defined as the principal eigenvalue fluctuation index. This index integrates the average energy level of the principal components with their internal fluctuations. The same calculation method is used to process the set of secondary eigenvalues, calculating the product of the mean and variance of each secondary eigenvalue to obtain the secondary eigenvalue fluctuation index. Finally, the absolute value of the difference between the principal and secondary eigenvalue fluctuation indices is calculated; this result is defined as the coefficient of variation. The magnitude of the coefficient of variation reflects the degree of difference in energy distribution and stability between the principal and secondary components of the signal sequence; the greater the difference, the higher the coefficient of variation.

[0086] In analyzing the correlation within a signal sequence, it's necessary to examine the strength of the correlation between the row vectors of the covariance matrix. The Pearson correlation coefficient is calculated between each row vector and all other row vectors in the matrix. The Pearson correlation coefficient measures the degree of linear correlation between two vectors. For each row, the absolute value of its correlation coefficients with all other rows is calculated, and the arithmetic mean of these absolute values ​​is taken to obtain the row correlation degree. A higher row correlation degree indicates a stronger linear dependence between the signal dimension represented by that row and other dimensions. Next, the arithmetic mean of the row correlation degrees of all rows in the covariance matrix is ​​calculated to obtain the matrix autocorrelation. Matrix autocorrelation is a global indicator that comprehensively characterizes the average linear correlation strength between the dimensions within the entire signal sequence. A high matrix autocorrelation usually indicates a tight internal structure of the signal sequence and significant mutual influence between dimensions.

[0087] Four key parameters have been obtained: environmental interference entropy (from previous analysis), principal component contribution of the signal sequence, coefficient of variation, and matrix autocorrelation. These parameters are then input into a pre-defined purity calculation model. This model is a trained multilayer perceptron neural network. Before input, all parameters are normalized to eliminate dimensional differences and ensure they fall within the model's manageable numerical range. The normalized parameters serve as the input layer node values ​​for the neural network. The neural network contains one or more hidden layers, each consisting of several neurons connected by weighted links. Each neuron performs a weighted summation of its inputs and generates an output using a non-linear activation function (such as ReLU or Sigmoid). Finally, the output layer nodes, after processing by the activation function, generate a scalar output value, which is the signal transmission purity index. This index is a comprehensive quantitative evaluation result, integrating the complexity of external electromagnetic interference (environmental interference entropy) and the structural characteristics of the internal signal sequence (principal component contribution, coefficient of variation, and matrix autocorrelation), ultimately providing a numerical basis for judging the purity of signal transmission under current channel conditions. The entire implementation process, through rigorous mathematical calculations and model interpretation, transforms the raw signal data and environmental interference information into a purity index with clear guiding significance, laying the foundation for subsequent adaptive anti-interference transmission control decisions. The formula is as follows:

[0088]

[0089] in: This represents the optimal segmentation threshold determined by the adaptive threshold segmentation algorithm; The variable representing all possible split points in the sequence of feature values; Indicated by When the point is a split point, the proportion of the number of principal eigenvalues ​​to the total number of eigenvalues; Indicated by When the point is a split point, the proportion of secondary eigenvalues ​​to the total number of eigenvalues; Indicates When the dividing point is the arithmetic mean of the principal eigenvalues; Indicates The arithmetic mean of the secondary eigenvalues ​​is given as a split point. The goal of this formula is to find a split point. This maximizes the inter-class variance between the two subsets (primary eigenvalue set and secondary eigenvalue set) generated by the split.

[0090] Example 4: In the anti-interference transmission control of a wireless low-power transmitter, after the signal transmission purity index is calculated, the system dynamically adjusts the transmission strategy based on the index value. The signal transmission purity index is a normalized value between 0 and 1. The lower the value, the more severe the current environmental interference or the less favorable the signal structure is for pure transmission, requiring more aggressive anti-interference measures; conversely, the higher the value, the better the transmission conditions.

[0091] The system internally uses a pre-defined frequency hopping interval mapping table, which defines adaptive frequency hopping interval values ​​corresponding to different ranges of signal transmission purity index. This mapping relationship is set based on the principle of balancing channel quality and frequency hopping overhead. A lower purity index means a poor channel environment or that the signal is susceptible to interference, requiring more frequent frequency switching to avoid interference; therefore, a smaller frequency hopping interval is allocated. A higher purity index indicates better channel conditions, allowing for a more appropriate increase in the frequency hopping interval to reduce unnecessary frequency switching overhead and potential channel discovery delay. The table below shows a typical mapping relationship:

[0092] Table 1: Mapping relationship between signal transmission purity index and adaptive frequency hopping interval.

[0093] Signal transmission purity index range Adaptive frequency hopping interval (ms) Applicable Scenarios Description [0.0,0.2) 5 The environmental interference is extremely strong, and the signal structure is unstable. [0.2,0.4) 10 Strong environmental interference, with significant sources of interference. [0.4,0.6) 25 Medium level of interference, with multiple sources of interference. [0.6,0.8) 50 Mild interference, relatively stable channel conditions [0.8,1.0] 100 Interference is weak and channel conditions are excellent.

[0094] The system uses a real-time calculated signal transmission purity index to look up the mapping table and determine the appropriate adaptive frequency hopping interval value. Upon receiving this frequency hopping interval command, the transmitter's frequency synthesizer module initiates a dynamic frequency adjustment mechanism. This mechanism includes a frequency hopping sequence generator and a precise timer. The frequency hopping sequence is typically generated based on a pre-shared key or a specific algorithm to ensure synchronization between the transmitter and receiver. The timer counts down according to the set adaptive frequency hopping interval. When the timer reaches zero, the frequency synthesizer immediately switches the transmitter's carrier frequency to the next predetermined frequency in the frequency hopping sequence. This dynamic adjustment allows the transmitted signal to actively avoid currently heavily interfered frequency bands and hop to potentially cleaner frequencies for communication.

[0095] After switching to the new carrier frequency, the transmitter begins processing the raw data to be transmitted. To enhance transmission robustness, the raw data stream is divided into a series of fixed-length data blocks. The length of the data block is typically determined based on factors such as the physical layer frame structure, modulation scheme, and expected bit error rate. The segmented data blocks are arranged sequentially to form data block groups. To facilitate identification, reassembly, and verification at the receiver, the system attaches two key pieces of information to each data block: a unique cluster identifier and a hierarchical checksum.

[0096] The unique cluster identifier is an incrementing sequence number used to clearly identify the position of the data block in the original data stream and the transmission batch to which it belongs. The hierarchical checksum contains two levels of verification information: the first level is a simple parity check code, used to quickly detect whether an odd number of bit errors have occurred during data block transmission; the second level is a more powerful cyclic redundancy check code, whose generator polynomial is selected based on the data block length and expected error detection capability, used to detect more complex error patterns. Data blocks with the cluster identifier and hierarchical checksum attached constitute a complete transmission cluster unit.

[0097] The transmitter strictly follows the hierarchical order of the transmission cluster units. The transmission order is based on the ascending order of the cluster identifier codes to ensure that the receiver can receive data in sequence. During transmission, each transmission cluster unit is modulated and transmitted as an independent transmission frame structure. At the receiver, the processing begins with the binary data stream obtained after demodulating the received wireless signal. The receiver first identifies and sorts the received data blocks according to the cluster identifier code information contained in the transmission cluster unit frame structure. By parsing the cluster identifier code, the receiver can determine the position of each data block in the original data block group and reassemble them in the correct order to recover the original data block group structure. After reassembly, the receiver extracts the hierarchical check code attached to each data block and initiates a multi-level check mechanism. The first level of check, parity check, is performed first. The receiver recalculates the parity bits based on the content of the received data block and compares them with the received parity check code. If they match, the data block is determined to have passed the parity check, and the data transmission is likely correct. If parity check fails, it indicates that at least one bit of the data block has been corrupted during transmission. At this point, the receiver automatically activates the second-level check mechanism, namely cyclic redundancy check.

[0098] The receiving end uses the same Cyclic Redundancy Check (CRC) generator polynomial as the sending end to recalculate the checksum for the received data block content and compares it with the received CRC checksum. If they match, the data block is considered to have passed CRC check, and although parity check fails, the data is still considered complete. If CRC check also fails, it is confirmed that the data block has encountered an error during transmission that cannot be corrected by the checksum. For data blocks that fail CRC check, the receiving end does not immediately discard or request a retransmission of the entire data, but instead attempts to recover the data. At this time, the receiving end analyzes the characteristic distribution of the entire group of received data blocks. These characteristics include: the cluster identifier position of the erroneous data block, the error mode (such as consecutive bit errors or random bit errors), the number of erroneous data blocks, and their distribution within the group. Based on this characteristic information, the receiving end constructs a checksum feature library.

[0099] Based on the contents of the verification feature library, the receiving end matches and selects the optimal fault-tolerant decoding strategy. For example, if the number of erroneous data blocks is small and their locations are scattered, a forward error correction algorithm may be used to attempt repair; if the errors are concentrated in a specific area, interpolation or context-based data reconstruction algorithms may be used. The selected fault-tolerant decoding strategy is applied to the erroneous data blocks to attempt to recover their original content. If recovery is successful, the data block group is reconstructed; if recovery fails, the verification failure of the data block group is recorded, and a decision on whether to request retransmission is made based on subsequent procedures. The entire receiving end processing flow aims to maximize the utilization of received information, reduce unnecessary retransmissions, and improve transmission efficiency.

[0100] Example 5: In the fault-tolerant processing flow of a wireless low-power transmitter system, when the receiver fails to recover data through a fault-tolerant decoding strategy, the system enters a decision-making phase that comprehensively considers channel conditions and device power consumption. This phase relies on continuous monitoring of the current communication link quality and the transmitter's own energy reserves. The system monitors two key operating parameters in real time. The first parameter is the current channel quality index. This index is derived by analyzing data from the receiver's physical layer and link layer, and its calculation integrates the stability of the received signal strength indicator and the success rate of frame reception at the link layer. The received signal strength indicator reflects the degree of signal attenuation during spatial propagation, while the frame reception success rate directly reflects the reliability of data transmission. These two sub-indicators are weighted and fused to form a value that comprehensively characterizes the current wireless channel transmission conditions. The lower the value, the worse the channel conditions, the higher the bit error rate, and the less reliable the transmission. The second monitored parameter is the transmitter's remaining energy index. This index is provided by the transmitter's built-in power management unit. The power management unit measures the battery voltage and discharge current through precision circuitry and estimates the remaining capacity, ultimately outputting a value reflecting the device's current available energy level. This value directly relates to how long the transmitter can continue to operate and how many additional communication operations it can support.

[0101] Based on real-time acquired channel quality metrics, the system calculates the maximum number of data retransmissions allowed under the current channel conditions. The calculation process references a preset mapping relationship between channel quality and the maximum number of retransmissions. This mapping relationship is typically set such that the worse the channel quality, the lower the allowed maximum number of retransmissions. This is because under poor channel conditions, the success rate of multiple retransmissions remains low, instead consuming valuable energy resources and increasing transmission latency. The mapping relationship may be implemented in the form of a lookup table or a simple function. For example, when the channel quality metric is below a certain threshold, the maximum allowed number of retransmissions is set to zero or a very small value; as the channel quality metric improves, the maximum allowed number of retransmissions increases accordingly.

[0102] Based on the real-time acquired remaining energy indicators, the system calculates and determines the minimum power transmission mode to be adopted. The core objective of the minimum power transmission mode is to minimize the energy consumption of a single communication operation. This is typically achieved by selecting the lowest transmit power level supported by the device and employing the simplest modulation and demodulation scheme with the lowest hardware processing requirements. While reducing transmit power may shorten communication distance or reduce signal quality, and using simplified modulation methods may reduce data transmission rates, prioritizing the device's endurance is crucial in energy-constrained scenarios.

[0103] The system maintains an internal counter that records the cumulative number of times the fault-tolerant decoding strategy has attempted to recover data from the same data block group being processed but ultimately failed. This failure count reflects the difficulty of recovering the data block group and the severity of the current channel conditions. The decision logic is as follows: if the recorded number of fault-tolerant decoding failures has not yet reached the maximum allowable retransmission count calculated based on the current channel quality, the system will initiate a partial data retransmission request. This retransmission request strictly follows the aforementioned minimum power transmission mode. Partial retransmission means that the receiver only requests the retransmission of those data blocks that failed verification and whose fault-tolerant decoding recovery was unsuccessful, rather than the entire original data block group. The request information is sent to the transmitter through the feedback channel. After receiving the retransmission request, the transmitter only remodulates and retransmits the specified failed data blocks. After receiving the retransmitted data blocks, the receiver re-attempts parity checking and cyclic redundancy check, and attempts fault-tolerant decoding if the verification fails. Simultaneously, the fault-tolerant decoding failure counter is updated based on the recovery result.

[0104] If the recorded number of fault-tolerant decoding failures has reached or exceeded the maximum allowed retransmission limit, the system will no longer initiate new retransmission requests and will instead proceed to the final processing decision. At this point, the system needs to determine subsequent operations based on the priority markers assigned to the data block group during segmentation and encapsulation. Priority markers are typically appended to each data block group when the sending end segments the original data to identify the importance level of that group of data. For example, high priority might correspond to critical control commands or sensor data with high real-time requirements; low priority might correspond to non-critical configuration information or historical data that can be delayed in transmission.

[0105] Based on priority markings, the system performs different operations: If a data block group is marked as high priority, the system will attempt to perform degraded decoding. Degraded decoding is a lossy data reconstruction method whose goal is to recover usable information fragments as much as possible when complete and accurate data is unavailable. Specific strategies may include: interpolating or extrapolating using information from other successfully received data blocks within the data block group; inferring based on correlations between data blocks; or extracting only the verified bits from the received data, ignoring the erroneous parts. The result of degraded decoding may be incomplete or reduced in precision, but it retains some informational value. If a data block group is marked as low priority, the system may choose to discard the data block group directly without any further recovery attempts to avoid further resource consumption. The discard operation will release the associated buffers and may report the data loss event to the upper-layer application.

[0106] The entire implementation process dynamically monitors channel status and device energy, and calculates key parameters (maximum allowed retransmission count, minimum power consumption mode) based on preset strategies. Ultimately, it selects between retransmission requests, downgraded decoding, or direct data discarding based on the number of failures in fault-tolerant recovery and the importance of the data. This mechanism aims to find a balance between data transmission reliability and device power consumption lifespan under the dual constraints of energy constraints and channel instability.

[0107] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0108] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for anti-interference transmission of a wireless low-power transmitter, characterized in that, Includes the following steps: Collect the real-time electromagnetic interference signal spectrum of the environment where the transmitter is located, and extract multi-dimensional interference features; An interference feature library is constructed based on multi-dimensional interference features, and interference feature clusters are divided according to the distribution density and intensity variation trend of features in the interference feature library. Calculate the dynamic fluctuation index and frequency domain concentration index of each interference feature cluster; Obtain the peak frequency drift index and spectral bandwidth variation index of each interference feature cluster; An environmental interference entropy value is generated based on the peak frequency drift index, the spectral bandwidth variation index, the average dynamic fluctuation index of all interference feature clusters, and the average feature similarity between interference feature clusters. Construct the covariance matrix of the current transmitted signal sequence, perform eigenvalue decomposition on the covariance matrix to obtain each eigenvalue, and obtain the principal component contribution and discrete coefficient of the eigenvalue sequence of the transmitted signal sequence based on the distribution, concentration and discreteness of the eigenvalues. A signal transmission purity index is generated based on the correlation between row vectors in the covariance matrix, the entropy value of environmental interference, the contribution of principal components, and the discrete coefficients; anti-interference transmission control is then performed based on the signal transmission purity index.

2. The anti-interference transmission method for a wireless low-power transmitter as described in claim 1, characterized in that, The method for dividing the interference feature clusters includes: The spectrum peak detection algorithm is used to identify all the main peaks and secondary peaks in the spectrum of electromagnetic interference signals. The spectrum sequence is segmented from each secondary peak to form a feature subsequence, and the spectral energy range of each feature subsequence is calculated. Input the spectral energy range of all feature subsequences into the clustering algorithm, and output the clusters of each feature; Calculate the mean internal spectral energy of each feature cluster, and take the feature cluster with the largest mean internal spectral energy as the dominant interference cluster, and take the spectral subsequences corresponding to the features inside the dominant interference cluster as the interference feature cluster.

3. The anti-interference transmission method for a wireless low-power transmitter as described in claim 1, characterized in that, The method for obtaining the dynamic volatility index includes: For each interference feature cluster, locate the maximum spectral intensity point in the interference feature cluster, form the left spectral segment by combining the maximum spectral intensity point and the left spectral point, and form the right spectral segment by combining the maximum spectral intensity point and the right spectral point. Obtain the first-order gradient sequence of the left-side spectral segment of each interference feature cluster, and use a nonlinear transformation function to process the first-order gradient sequence to generate a gradient transformation sequence; The absolute value of the difference between the sum of the elements and absolute values ​​of the gradient transformation sequence and the length of the first-order gradient sequence of the left spectral segment is calculated and used as the fluctuation intensity index of the left spectral segment. The same method was used to obtain the fluctuation intensity index of the right-side spectral band of each interference feature cluster; Calculate the product of the absolute value and the mean of the difference in the fluctuation intensity index between the left and right spectral segments, and use it as the dynamic fluctuation index of the interference feature cluster. Calculate the variance of the spectral intensity of the left-side spectrum segment and the variance of the spectral intensity of the right-side spectrum segment for each interference feature cluster, and use the mean of the variances on both sides as the frequency domain concentration index.

4. The anti-interference transmission method for a wireless low-power transmitter as described in claim 2, characterized in that, The method for obtaining the spectrum bandwidth variation index includes: Extract the main peak intensity values ​​from all interference feature clusters and sort them according to their frequency points in the spectrum sequence to construct the main peak intensity sequence; The frequency points of each main peak in the interference feature cluster are sorted by size to construct the main peak frequency point sequence; Obtain the first-order difference sequences of the main peak intensity sequence and the main peak frequency sequence. Use the variance of the first-order difference sequence of the main peak intensity sequence as the peak frequency drift index, and use the variance of the first-order difference sequence of the main peak frequency sequence as the spectral bandwidth variation index.

5. The anti-interference transmission method for a wireless low-power transmitter as described in claim 1, characterized in that, The method for generating the environmental disturbance entropy value includes: The mean of the dynamic volatility indices of all disturbance feature clusters is calculated as the average volatility index. The average spectral similarity coefficient of each interference feature cluster with all other interference feature clusters is obtained as the feature cluster similarity. The environmental interference entropy value is generated by inputting the peak frequency drift index, the spectral bandwidth variation index, the average fluctuation index, and the mean similarity of feature clusters into the environmental interference entropy value calculation model.

6. The anti-interference transmission method for a wireless low-power transmitter as described in claim 1, characterized in that, The methods for obtaining the principal component contribution and the coefficient of variation include: Arrange all feature values ​​in descending order to construct a feature value sequence; The feature value sequence is input into the adaptive threshold segmentation algorithm, which outputs the segmentation threshold. Feature values ​​greater than or equal to the segmentation threshold are listed as primary feature values, and feature values ​​less than the segmentation threshold are listed as secondary feature values. The proportion of principal eigenvalues ​​to the total number of eigenvalues ​​is calculated as the principal component contribution. The product of the mean and variance of the principal eigenvalues ​​is used as the principal eigenvalue volatility index. The secondary eigenvalue volatility index is calculated using the same method. The absolute value of the difference between the principal eigenvalue volatility index and the secondary eigenvalue volatility index is used as the coefficient of variation.

7. The anti-interference transmission method for a wireless low-power transmitter as described in claim 1, characterized in that, The method for generating the signal transmission purity index includes: The mean absolute value of the correlation coefficients between each row vector in the covariance matrix and other row vectors is used as the row correlation. The mean of the correlations of all rows in the covariance matrix is ​​taken as the matrix autocorrelation. The principal component contribution, dispersion coefficient, matrix autocorrelation, and environmental interference entropy are input into the purity calculation model to generate a signal transmission purity index.

8. The anti-interference transmission method for a wireless low-power transmitter as described in claim 1, characterized in that, The anti-interference transmission control based on the signal transmission purity index includes: Calculate the adaptive frequency hopping interval based on the signal transmission purity index; The transmitter carrier frequency is dynamically adjusted based on adaptive frequency hopping interval; On the adjusted carrier frequency, the data to be transmitted is divided into data block groups, and a unique cluster identifier and hierarchical check code are added to each data block to form a transmission cluster unit; The data is transmitted sequentially according to the hierarchical order of the transmission cluster units, and multi-level verification is performed at the receiving end.

9. The anti-interference transmission method for a wireless low-power transmitter as described in claim 8, characterized in that, The multi-level verification includes: The receiving end reassembles the data block group based on the cluster identifier and extracts the hierarchical check code; Perform the first level of parity check; if the check fails, activate the second level of cyclic redundancy check. If the cyclic redundancy check fails, a check feature library is generated based on the feature distribution of the data block group. Data recovery is performed by matching the optimal fault-tolerant decoding strategy based on the verification feature library.

10. The anti-interference transmission method for a wireless low-power transmitter as described in claim 9, characterized in that, The matching of the fault-tolerant decoding strategy includes: Monitor current channel quality indicators and transmitter remaining energy indicators; The maximum number of allowed retransmissions is calculated based on the channel quality index, and the minimum power consumption transmission mode is calculated based on the remaining energy index. If the number of data recovery failures does not reach the maximum allowed number of retransmissions, a partial retransmission request is initiated using the minimum power transmission mode. Otherwise, based on the priority flag of the data block group, either discard or perform downgraded decoding.

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