Wireless communication signal enhancement method based on frequency modulation

Abstract

The invention discloses a wireless communication signal enhancement method based on frequency modulation, and relates to the technical field of wireless communication, and the method comprises the steps: obtaining an initial detection waveform set of a carrier signal classification channel; the method comprises the following steps: receiving a to-be-modulated baseband signal set and a frequency modulation sensitivity constraint interval through a user side; constructing a frequency modulation solution space according to the frequency modulation sensitivity constraint interval set; and based on the frequency modulation solution space, in combination with the initial detection waveform set, performing frequency modulation optimization on the to-be-modulated baseband signal set to obtain a recommended frequency modulation scheme, and performing frequency modulation on the to-be-modulated baseband signal set. The technical problems that in the prior art, a traditional signal enhancement method depends on output power improvement, consequently, energy consumption and cost are high, and the signal recognition rate and the anti-interference capacity are reduced due to waveform similarity are solved, and the purposes that by avoiding the waveform similarity and optimizing frequency modulation, the signal recognition rate and the anti-interference capacity are improved while the power consumption and the cost are reduced are achieved. And the signal identification rate and the anti-interference capability are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a method for enhancing wireless communication signals based on frequency modulation. Background Art

[0002] With the rapid development of wireless communication technology, the quality and efficiency of signal transmission have become particularly important in modern communication systems. However, traditional signal enhancement methods usually rely on increasing the output power to improve signal strength. Although this method can improve the transmission effect of the signal to a certain extent, it also brings significant technical bottlenecks and practical problems, including high power consumption, high cost, and high requirements for hardware equipment performance. In addition, the method of increasing the output power may cause channel interference, especially in a multi-user communication environment, which will further reduce the signal recognition rate and transmission reliability.

[0003] In complex communication environments, the similarity of signal waveforms is another important factor that leads to a decrease in recognition rate. Waveform similarity not only reduces the decoding accuracy of the signal receiving end, but may also cause incorrect signal interference judgments, affecting the overall performance of the communication system. Therefore, how to effectively improve signal recognition rate and transmission quality without significantly increasing power consumption has become a technical problem that needs to be solved in the field of wireless communications. Summary of the invention

[0004] The present application provides a wireless communication signal enhancement method based on frequency modulation, which is used to solve the technical problems in the prior art that traditional signal enhancement methods rely on increasing output power, resulting in high energy consumption and high cost, and reducing signal recognition rate and anti-interference ability due to waveform similarity.

[0005] The present application provides a wireless communication signal enhancement method based on frequency modulation, the method comprising: obtaining an initial detection waveform set of a carrier signal classification channel; receiving a baseband signal set to be modulated and a frequency modulation sensitivity constraint interval through a user terminal; constructing a frequency modulation solution space according to the frequency modulation sensitivity constraint interval set; based on the frequency modulation solution space, in combination with the initial detection waveform set, performing frequency modulation optimization on the baseband signal set to be modulated to obtain a recommended frequency modulation scheme; and frequency modulating the baseband signal set to be modulated according to the recommended frequency modulation scheme.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The wireless communication signal enhancement method based on frequency modulation provided by the present application relates to the field of wireless communication technology. By obtaining the initial detection waveform set of the carrier signal classification channel, receiving the baseband signal to be modulated and the frequency modulation sensitivity constraint interval, constructing the frequency modulation solution space, and combining the initial waveform set to perform frequency modulation optimization to generate a recommended frequency modulation scheme, and finally optimizing the frequency modulation of the baseband signal to achieve signal enhancement. It solves the technical problem that the traditional signal enhancement method in the prior art relies on increasing the output power, resulting in high energy consumption and high cost, and reduces the signal recognition rate and anti-interference ability due to waveform similarity, and achieves the technical effect of significantly improving the signal recognition rate and anti-interference ability while reducing power consumption and cost by avoiding waveform similarity and optimizing frequency modulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a method for enhancing a wireless communication signal based on frequency modulation provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of a process for optimizing and obtaining a recommended frequency modulation scheme in a method for enhancing a wireless communication signal based on frequency modulation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The present application provides a wireless communication signal enhancement method based on frequency modulation, which is used to solve the technical problems in the prior art that traditional signal enhancement methods rely on increasing output power, resulting in high energy consumption and high cost, and reducing signal recognition rate and anti-interference ability due to waveform similarity.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a wireless communication signal enhancement method based on frequency modulation, the method comprising:

[0015] P10: Obtain an initial detection waveform set of the carrier signal classification channel.

[0016] Specifically, in wireless communication systems, the detection and classification of carrier signals is an important basis for achieving signal enhancement. All carrier signals are collected and identified from the channel, and their main waveform features are extracted to form a complete initial detection waveform set.

[0017] First, the signal samples in the channel are collected in real time through the receiving device (such as software defined radio, SDR). These samples include key parameters such as signal amplitude, frequency, phase and time distribution. High-precision analog-to-digital converters (ADCs) and low-noise amplifiers (LNAs) are required in the signal acquisition stage to ensure that weak signals are not masked by environmental noise. At the same time, the sampling time window is reasonably selected to ensure the complete capture of the signal periodic characteristics. After the signal is collected, it is subjected to spectrum analysis, such as converting the signal from the time domain to the frequency domain through fast Fourier transform (FFT), extracting the main frequency characteristics and signal spectrum distribution, so as to determine the presence of multi-carrier or interference signals in the channel.

[0018] Based on spectrum analysis, the signal is classified using pattern recognition algorithms or machine learning models (such as support vector machines (SVM) or convolutional neural networks (CNN). This process divides the signal into different categories based on the modulation mode (such as AM, FM, PSK, etc.) and waveform characteristics of the signal, so as to clarify the properties and uses of the signal in the channel. After the classification is completed, the signal waveform is denoised, and the environmental noise is effectively filtered out through wavelet transform or Kalman filtering technology, while retaining the main features of the signal, further optimizing the waveform quality and ensuring that it accurately reflects the actual situation of the channel. Finally, the processed waveform information is stored in a unified format to form an initial detection waveform set. This set is a complete description of the characteristics of the carrier signal in the channel, providing high-quality data input data for subsequent frequency modulation optimization and waveform similarity avoidance, and is also the key to avoiding waveform similarity and improving signal recognition rate.

[0019] P20: Receive the set of baseband signals to be modulated and the frequency modulation sensitivity constraint interval through the user end.

[0020] Optionally, during the frequency modulation optimization process, the baseband signal set to be modulated and the frequency modulation sensitivity constraint interval are received by the user end, which can provide accurate input data and design boundaries for subsequent frequency modulation optimization.

[0021] First, the baseband signal set to be modulated refers to the original signal that has not been frequency modulated. These signals usually contain actual data information that needs to be transmitted in the channel, such as voice, video or digital data. The user end uploads or receives these signal sets in real time through the input interface, and classifies and stores them according to time series, amplitude characteristics or spectrum distribution. This process requires the use of efficient signal sampling and caching technology to ensure signal integrity and low transmission latency.

[0022] At the same time, the user end also needs to receive the frequency modulation sensitivity constraint interval, which is a set of parameters that defines the frequency change range during the frequency modulation process. This interval can be set based on hardware equipment capabilities, communication standard restrictions, and channel environment characteristics, such as the maximum allowable frequency deviation, minimum frequency resolution, and power consumption limit. Frequency modulation sensitivity directly affects the frequency response and bandwidth requirements of the signal, where sensitivity indicates the degree of influence of the baseband signal amplitude change on the carrier frequency offset during frequency modulation. In order to ensure the stability and anti-interference of communication, the sensitivity constraint interval needs to comprehensively consider the system equipment characteristics and external interference conditions, and be optimized through technical means such as dynamic range analysis and noise tolerance calculation.

[0023] During the receiving process, the user side needs to ensure the stable transmission of signal data through a reliable communication protocol (such as TCP / IP or real-time transport protocol RTP), and perform integrity check on the baseband signal set (such as CRC check) and validity check on the sensitivity constraint interval (such as limiting it to the frequency range supported by the hardware). After this step, the baseband signal set and the frequency modulation sensitivity constraint interval are integrated as the basic data of the modulation input, and clear boundary conditions are provided for the subsequent frequency modulation solution space construction and optimization process. Through the flexible input interface and robust data processing capabilities of the user side, this step effectively guarantees the accuracy and adaptability of the frequency modulation scheme design.

[0024] P30: Construct the FM solution space based on the FM sensitivity constraint interval set.

[0025] Furthermore, step P30 of the embodiment of the present application further includes:

[0026] P31: Construct a first coordinate axis constraint interval based on the first frequency modulation sensitivity constraint interval of the frequency modulation sensitivity constraint interval set; P32: Construct an Nth coordinate axis constraint interval based on the Nth frequency modulation sensitivity constraint interval of the frequency modulation sensitivity constraint interval set; P33: Construct the frequency modulation solution space based on the first coordinate axis constraint interval up to the Nth coordinate axis constraint interval.

[0027] It should be understood that constructing the FM solution space is a key step in frequency modulation optimization. By defining the set of FM sensitivity constraint intervals, possible frequency modulation parameter combinations are limited to a multidimensional space, which is convenient for subsequent FM optimization operations. The core of the FM solution space is to use a mathematical model to describe the effective range of FM parameters, where each dimension represents a FM sensitivity constraint interval. By gradually parsing the set of constraint intervals, a multidimensional constraint space is constructed to form a complete FM solution space.

[0028] Specifically, first, the first coordinate axis constraint of the solution space is constructed according to the first frequency modulation sensitivity constraint interval of the frequency modulation sensitivity constraint interval set. The constraint interval here defines the minimum and maximum values ​​of the frequency modulation parameters in this dimension, such as the minimum frequency deviation and the maximum frequency deviation. This constraint can be determined by analyzing the characteristic parameters of the device (such as bandwidth capability, linear modulation coefficient, etc.). The first coordinate axis constraint interval actually corresponds to a basic degree of freedom of the frequency modulation parameter, and its construction method can adopt interval representation. At this time, the frequency modulation parameters of this dimension are fixed within the constraint range, providing an initial boundary for the solution space.

[0029] Based on the first dimension, the other dimensions in the FM sensitivity constraint interval set are analyzed in turn until the Nth FM sensitivity constraint interval. The FM sensitivity of each dimension usually represents different modulation parameters, such as frequency modulation depth, modulation speed or noise tolerance. When constructing these constraint intervals, it is necessary to comprehensively consider a variety of influencing factors, such as environmental interference, channel attenuation characteristics, and device power consumption limitations. The constraint intervals of each dimension are superimposed in the solution space in the form of intervals, forming a gradually expanding multi-dimensional matrix structure.

[0030] When the FM sensitivity constraint intervals of all dimensions are resolved, a complete FM solution space is constructed based on the constraint intervals of each coordinate axis. This solution space can be regarded as an N-dimensional hypercube, in which each point represents a specific FM parameter combination. During the construction process, it is necessary to ensure the solvability of the solution space, that is, all dimensional constraints together constitute a non-empty set. If some constraints conflict (such as the maximum value of a dimension is less than the minimum value of another dimension), the conflict needs to be resolved through a constraint correction algorithm (such as linear programming or heuristic adjustment) to ensure the completeness of the solution space.

[0031] The construction of the FM solution space is a multi-dimensional constrained geometric modeling process. By converting the constraints of FM sensitivity into the boundaries of the solution space, a clear mathematical description is provided for the subsequent FM optimization. This process can be completed by combining high-dimensional matrix operations and constrained optimization techniques, such as using Numpy or Matlab tools in Python to simulate and analyze the multi-dimensional space to ensure the efficient generation and availability of the solution space.

[0032] P40: Based on the frequency modulation solution space and in combination with the initial detection waveform set, the frequency modulation optimization is performed on the set of baseband signals to be modulated to obtain a recommended frequency modulation solution.

[0033] Further, such as Figure 2 As shown, step P40 of the embodiment of the present application also includes:

[0034] P41: Setting an initial population size through the user terminal; P42: Generate a number of initial frequency modulation solution particles in the frequency modulation solution space based on the uniform distribution function according to the initial population size, wherein any initial frequency modulation solution particle includes a frequency modulation sensitivity set corresponding one-to-one to the set of baseband signals to be modulated; P43: Based on the carrier frequency information, traverse the number of initial frequency modulation solution particles, perform modulation prediction on the set of baseband signals to be modulated, and obtain a number of groups of predicted waveforms, wherein the number of groups of predicted waveforms correspond one-to-one to the number of initial frequency modulation solution particles; P44: Based on the number of groups of predicted waveforms and the initial detection waveform set, perform frequency optimization on the set of baseband signals to be modulated to obtain the recommended frequency modulation scheme.

[0035] Optionally, by combining the FM solution space and the initial detection waveform set, the modulation of the baseband signal set to be modulated is optimized, and finally a recommended FM solution is output. The key to FM optimization is to explore the optimal combination of FM parameters through intelligent algorithms to maximize the signal recognition rate and transmission quality.

[0036] For example, the first step of frequency modulation optimization is to set the initial population size through the user end, that is, to define the number of initial solution particles involved in the calculation in the optimization algorithm. The setting of the population size directly affects the search breadth and efficiency of the algorithm. A smaller population size may lead to insufficient search of the solution space, while an excessively large population size may increase the computational complexity. The population size can be set using heuristic methods or empirical values ​​to ensure that the algorithm has sufficient coverage in the solution space.

[0037] Next, under the set population size, a number of initial FM solution particles are randomly generated in the FM solution space based on the uniform distribution function. The initial FM solution particle is a vector, each element of which represents the FM sensitivity of the corresponding baseband signal. The generation of initial solution particles needs to ensure uniform distribution to avoid excessive concentration in a certain part of the solution space, while providing diversity for subsequent optimization. Each particle corresponds to the set of baseband signals to be modulated one by one, ensuring that the setting of FM sensitivity covers all baseband signals.

[0038] Furthermore, based on the initial FM solution particles, the modulation prediction is performed on the set of modulated baseband signals. Through the carrier frequency information, each FM solution particle is traversed, its parameters are applied to the baseband signal, and the corresponding modulation waveform is generated. These waveforms are called predicted waveforms and are used to evaluate the effectiveness of the FM scheme. The generation of the predicted waveform needs to be combined with the FM formula, for example, the modulation result of each signal is calculated through the frequency offset function. The focus of this stage is to map the solution particles and the baseband signal into specific physical waveforms.

[0039] Finally, the predicted waveform is compared with the initial detection waveform set to evaluate the effectiveness of the modulation result. The matching degree between the predicted waveform and the initial detection waveform is evaluated through a similarity metric function (such as a correlation coefficient or the sum of squared errors), which is used as the fitness value of the FM solution particle. Based on these fitness values, an optimization algorithm (such as a particle swarm optimization PSO or a genetic algorithm GA) is applied to search for the best solution in the solution space, and the FM solution particle with the highest fitness value is selected as the recommended FM solution. The recommended FM solution outputs a set of FM sensitivity parameters corresponding to the baseband signal, which is used to guide the actual signal modulation.

[0040] Through the above steps, it is possible to efficiently search in the multi-dimensional FM solution space, combine the initial detection waveform and the prediction results of the FM solution particles, generate the optimal FM solution, and ensure that the recommended FM solution can achieve the best effect of signal enhancement and interference avoidance in practical applications.

[0041] Furthermore, step P43 of the embodiment of the present application also includes:

[0042] P43-1: Obtain the first baseband signal to be modulated from the set of baseband signals to be modulated, wherein the first baseband signal to be modulated has signal attributes; P43-2: Process the signal attributes through a signal modulation table to obtain a modulation unit step and an attribute benchmark characteristic value; P43-3: Extract the frequency modulation sensitivity of the first baseband signal to be modulated of the first initial frequency modulation solution particle of the several initial frequency modulation solution particles; P43-4: Based on the modulation unit step, the attribute benchmark characteristic value and the frequency modulation sensitivity of the first baseband signal to be modulated, modulate the first baseband signal to be modulated according to the carrier frequency information, obtain a frequency modulation waveform of the first baseband signal to be modulated, and add it to the first group of predicted waveforms; P43-5: Add the first group of predicted waveforms to the several groups of predicted waveforms.

[0043] In a possible embodiment of the present application, in the modulation prediction process, a predicted waveform corresponding to each initial frequency modulation solution particle is generated by gradually processing a set of baseband signals to be modulated.

[0044] First, the starting point of modulation prediction is to extract the first baseband signal to be modulated from the set of baseband signals to be modulated. The baseband signal to be modulated is the original signal that has not been modulated, and it has inherent signal properties, such as amplitude, voltage or power. These properties describe the basic characteristics of the signal and have a direct impact on the modulation sensitivity. When extracting the baseband signal to be modulated, it is necessary to ensure that its properties match the modulation requirements of the system, such as confirming the maximum and minimum values ​​of the signal amplitude through the data interface to avoid modulation beyond the linear range of the device.

[0045] Next, the signal properties of the first baseband signal to be modulated are processed through the signal modulation table. The modulation table defines the mapping relationship between signal properties and modulation parameters, including the modulation unit step and the attribute reference characteristic value. For example, when the attribute is voltage and the reference value is 0, the step size is 1V, and the frequency modulation sensitivity is 75Hz / V, the frequency increases or decreases linearly with the step size as the voltage changes. Specifically, when the voltage is 0, the frequency is equal to the carrier frequency; when the voltage is +2V, the frequency increases by 150Hz; when the voltage is -2V, the frequency decreases by 150Hz. The standardization of signal properties and step size processing ensure the flexibility and controllability of modulation.

[0046] After obtaining the signal attribute mapping relationship, the first initial FM solution particle is extracted from several initial FM solution particles, and the corresponding first baseband signal to be modulated FM sensitivity is obtained. FM sensitivity is a key parameter of a specific dimension of each solution particle, which describes the intensity of the influence of the change of baseband signal attributes on the frequency offset. When extracting this parameter, it is necessary to keep matching with the solution particle and signal attributes so that subsequent modulation can accurately reflect the particle characteristics.

[0047] Next, based on the modulation unit step size, attribute baseline eigenvalue, and FM sensitivity, the signal attribute of the first baseband signal to be modulated is mapped to a frequency offset. Combined with the carrier frequency information, the modulation process of the signal is completed to generate a FM waveform of the first baseband signal to be modulated. The core of this process is the application of the FM formula. By calculating the frequency offset (for example, frequency = carrier frequency + attribute offset × FM sensitivity), the corresponding waveform is generated in real time. The generated FM waveform represents the modulation effect of the baseband signal under specific particle parameters.

[0048] Finally, the frequency modulation waveform of the first baseband signal to be modulated is added to the first group of predicted waveforms, and the group of predicted waveforms is merged into several groups of predicted waveforms corresponding to all initial frequency modulation solution particles. Through this process, the generation of the predicted waveform of the first baseband signal to be modulated is completed, providing a specific waveform sample for subsequent frequency modulation optimization.

[0049] Through the above steps, accurate mapping from signal attributes to FM waveforms is achieved. Combined with the signal modulation table and FM sensitivity, predicted waveforms reflecting the characteristics of different solution particles are dynamically generated, providing a high-quality data foundation for FM optimization.

[0050] Furthermore, step P44 of the embodiment of the present application also includes:

[0051] P44-1: traverse the several groups of predicted waveforms to perform intra-group waveform similarity analysis to obtain several first fitness parameters; P44-2: based on the initial detection waveform set, traverse the several groups of predicted waveforms to perform extra-group waveform similarity analysis to obtain several second fitness parameters, wherein the several first fitness parameters correspond one-to-one to the several second fitness parameters; P44-3: add the one-to-one corresponding several first fitness parameters and the several second fitness parameters to obtain several initial frequency modulation solution particle fitnesses; P44-4: extract the initial frequency modulation solution particles whose fitnesses are equal to 0, and set them as the recommended frequency modulation scheme.

[0052] Specifically, the key to frequency modulation optimization is to select the best frequency modulation solution from the solution space by evaluating the fitness of the predicted waveform. Specifically, the embodiment of the present application further calculates the fitness of the frequency modulation solution particles through waveform similarity analysis within and outside the group, and selects the particle with the highest fitness as the recommended frequency modulation solution.

[0053] First, traverse several groups of predicted waveforms, perform waveform similarity analysis within each group, and calculate the first fitness parameter. The goal of intra-group waveform similarity analysis is to evaluate the modulation consistency of the same initial FM solution particle on different baseband signals to ensure that each group of predicted waveforms exhibits consistent FM sensitivity characteristics. Similarity analysis can calculate the similarity score between waveforms based on statistical methods (such as cosine similarity or dynamic time warping). The first fitness parameter is the quantitative result of intra-group waveform similarity, which reflects the FM consistency of particles within the group.

[0054] Subsequently, based on the initial detection waveform set, the predicted waveform is subjected to an out-of-group waveform similarity analysis to calculate the second fitness parameter. The purpose of the out-of-group similarity analysis is to evaluate the matching degree between the predicted waveform and the initial detection waveform to ensure that the modulation result has a high recognition rate and transmission stability in the target channel. Similarity analysis can use correlation measurement technology (such as Pearson correlation coefficient) or error evaluation (such as mean square error) to compare the predicted waveform with the detection waveform one by one. The second fitness parameter reflects the adaptability of the modulation waveform in the target channel.

[0055] For each initial FM solution particle, the first fitness parameter and the second fitness parameter are summed to calculate the initial FM solution particle fitness. The logic of fitness summation is to comprehensively evaluate the consistency within the group and the adaptability outside the group to ensure that the selected particles achieve a balance between the consistency of the predicted waveform and the matching of the target channel. The fitness summation formula can adjust the weight according to the task requirements to highlight the priority of specific performance.

[0056] Finally, the particles with fitness equal to 0 are extracted from all the initial FM solution particles and used as the recommended FM solution. A fitness equal to 0 indicates that the particle has reached the optimal state in terms of both waveform consistency within the group and channel matching outside the group. This step requires strict screening conditions to avoid suboptimal solutions being selected due to calculation errors. If no particle meets the condition of fitness equal to 0, the solution space can be further optimized by iterative optimization or adjusting algorithm parameters.

[0057] Through the above steps, the embodiment of the present application comprehensively evaluates the fitness parameters based on the multi-level similarity analysis of the predicted waveform and the initial detection waveform, and finally determines the recommended frequency modulation scheme. The output of the recommended frequency modulation scheme is a set of frequency modulation parameters, which can efficiently guide the frequency modulation process of the baseband signal to be modulated, and provide important support for signal enhancement and transmission optimization.

[0058] Furthermore, step P44-1 of the embodiment of the present application also includes:

[0059] P44-11: Extract the first group of predicted waveforms based on the several groups of predicted waveforms; P44-12: Perform pairwise waveform overlap comparison on the first group of predicted waveforms to obtain a waveform overlap set; P44-13: Extract the proportion of waveform overlaps in the waveform overlap set that are greater than or equal to the waveform overlap threshold, set it as the first fitness parameter, and add it to the several first fitness parameters.

[0060] It should be understood that in order to further refine the intra-group waveform similarity analysis, the embodiment of the present application quantifies the consistency of the predicted waveforms within the group by comparing the pairwise overlap of each group of predicted waveforms, and generates a first fitness parameter by proportion calculation.

[0061] First, the first group of predicted waveforms is extracted from several groups of predicted waveforms. This group of predicted waveforms is generated by the same initial FM solution particle and represents the modulation results of the particle on different baseband signals to be modulated. The first group of waveforms can be efficiently distinguished and extracted through clustering indexes or grouping labels to ensure that subsequent analysis is performed only within the group.

[0062] Next, the first set of predicted waveforms extracted are compared for waveform overlap. The overlap is an indicator to measure the similarity of two waveforms, usually defined as the degree of matching of the waveforms in amplitude and phase. During the comparison process, the overlap can be calculated using numerical integration or cross-correlation function. For example, for two waveforms W1(t) and W2(t), their overlap can be calculated using the formula:

[0063] After calculating the overlap of each pair of waveforms, the result is stored as a waveform overlap set, which reflects the consistency of the waveforms in the group on different baseband signals.

[0064] Next, the number of waveform pairs with a degree of overlap greater than or equal to the waveform overlap threshold is extracted from the waveform overlap set, and their proportion is calculated as the first fitness parameter. The overlap threshold is a preset similarity benchmark, such as 0.9, which means that the matching degree of the waveform is at least 90%. The numerical range of the first fitness parameter is 0 to 1, which reflects the consistency of the waveforms in the group: the closer the value is to 1, the more consistent the frequency modulation sensitivity of the waveforms in the group. The calculation results are added to several first fitness parameters to provide a basis for subsequent fitness addition and optimization screening.

[0065] This process ensures that the performance of each initial frequency modulation solution particle in the group can be objectively quantified, and at the same time provides reliable basic data for the optimization algorithm. The introduction of the first fitness parameter can effectively evaluate the stability of the frequency modulation solution in the group, providing key support for the subsequent frequency modulation optimization process.

[0066] Furthermore, step P44-2 of the embodiment of the present application also includes:

[0067] P44-21: Extract the first group of predicted waveforms based on the several groups of predicted waveforms; P44-22: Based on the first predicted waveform of the first group of predicted waveforms, traverse the initial detection waveform set to perform waveform overlap comparison to obtain the first waveform overlap set, when the first waveform overlap set has a waveform overlap greater than or equal to the waveform overlap threshold, mark the first predicted waveform as 1, otherwise, mark it as 0; P44-23: Until the Mth predicted waveform based on the first group of predicted waveforms, traverse the initial detection waveform set to perform waveform overlap comparison to obtain the Mth waveform overlap set, when the Mth waveform overlap set has a waveform overlap greater than or equal to the waveform overlap threshold, mark the Mth predicted waveform as 1, otherwise, mark it as 0; P44-24: Calculate the proportion of the number of 1 values ​​in the first group of predicted waveforms, set it as the second fitness parameter, and add it to the several second fitness parameters.

[0068] Optionally, in the out-of-group waveform similarity analysis, the predicted waveform is compared with the initial detection waveform set to evaluate the channel adaptability of the predicted waveform, thereby generating a second fitness parameter.

[0069] First, the first set of predicted waveforms is extracted from several sets of predicted waveforms. This set of waveforms is generated by the same initial FM solution particle and represents the modulation results of the particle on different baseband signals. After extraction, ensure that the sequence of this set of waveforms is associated with the initial FM solution particle to provide data input for subsequent one-to-one comparison.

[0070] Next, for the first predicted waveform in the first group of predicted waveforms, the initial detection waveform set is traversed, and the waveform overlap comparison is performed one by one to generate a first waveform overlap set. The waveform overlap is used to measure the degree of match between the predicted waveform and the initial detection waveform. The calculation method can use a cross-correlation function or a dynamic time warping algorithm. If there is an overlap greater than or equal to the waveform overlap threshold in the first waveform overlap set, the first predicted waveform is marked as 1; otherwise, it is marked as 0. This mark reflects whether the similarity between the first predicted waveform and the detection waveform meets the threshold standard. The waveform marked as 1 indicates that it has a high adaptability.

[0071] Repeat the above process, compare each waveform in the first set of predicted waveforms (from the 1st to the Mth predicted waveforms) one by one, and generate a corresponding waveform coincidence set. For each waveform coincidence set, if there is a waveform coincidence that meets the threshold, the corresponding predicted waveform is marked as 1; otherwise, it is marked as 0. Through this traversal operation, the adaptability identification of the first set of predicted waveforms is completed.

[0072] Finally, count the number of waveforms marked as 1 in the first group of predicted waveforms, and calculate their proportion in the number of waveforms in the entire group as the second fitness parameter. The value range of the second fitness parameter is 0 to 1, reflecting the overall performance of the predicted waveforms outside the group in terms of channel adaptability. Add the calculation results to several second fitness parameters to provide data support for subsequent fitness addition. Through this process, it can be ensured that the frequency modulation optimization results have high reliability and adaptability in the target channel.

[0073] Furthermore, step P44-4 of the embodiment of the present application also includes:

[0074] P44-41: When the number of initial FM solution particles whose fitness is equal to 0 is 0, the initial FM solution particles are sorted from large to small according to the fitness of the initial FM solution particles to obtain the sorting result of the initial FM solution particles; P44-42: A first number of head initial FM solution particles and a second number of tail initial FM solution particles are extracted from the sorting result of the initial FM solution particles, wherein the second number is greater than or equal to 2 times the first number; P44-43: In the FM solution space, with the first number of head initial FM solution particles as the target, the second number of tail initial FM solution particles are positionally disturbed to generate a number of expanded FM solution particles; P44-44: An optimization loop is executed according to the number of expanded FM solution particles.

[0075] In a possible embodiment of the present application, in the process of frequency modulation optimization, when the number of particles with initial frequency modulation solution particle fitness equal to 0 is insufficient, it is necessary to generate more potential solution particles in the solution space through further optimization and expansion operations to ensure that the recommended frequency modulation solution with the best adaptive performance can be finally obtained.

[0076] First, when the number of particles with fitness equal to 0 among all the initial FM solution particles is 0, that is, no particle fully meets the fitness requirements, all the initial FM solution particles are sorted from large to small according to fitness, and the initial FM solution particle sorting results are generated. The sorting results provide a priority reference for identifying which particles have a higher probability of approaching the optimal solution. The technical core of fitness sorting is to perform weighted synthesis of multi-dimensional fitness parameters (such as the first fitness and the second fitness) to ensure the accuracy and fairness of the sorting.

[0077] Next, a certain number of head and tail solution particles are extracted from the sorting results. The first number of head initial frequency modulation solution particles represent particles with higher fitness in the sorting, which usually have greater optimization potential; the second number of tail initial frequency modulation solution particles represent particles with lower fitness, which are usually suitable as perturbation targets. To enhance the optimization effect, the second number is set to twice or more of the first number to ensure that the perturbation range covers a sufficiently wide solution space.

[0078] Taking the first number of head particles as the target, the second number of tail particles are perturbed in position in the FM solution space to generate several expanded FM solution particles. Position perturbation refers to a small-range random adjustment of the FM parameters of the tail particles to make them approach the direction of the head particles, thereby generating new solution particles. The amplitude and direction of the perturbation are determined by the Gaussian distribution or uniform distribution function, and the perturbation range needs to be constrained to prevent particles from exceeding the boundary of the solution space. Through this operation, the expanded solution particles not only retain the diversity of the original solution particles, but also combine the optimization potential of the head particles, providing a richer solution set for subsequent optimization.

[0079] Finally, the expanded solution particles are added back to the FM solution space, and the optimization loop is re-executed based on the expanded solution particles. The expanded solution particles will participate in fitness evaluation and optimization sorting as new initial solution particles, and continuously approach the optimal solution through iteration. The core of the optimization loop is to continuously optimize the exploration depth and breadth of the solution space by dynamically expanding the solution particles, thereby increasing the probability of obtaining solution particles with a fitness equal to 0.

[0080] This process relies on sorting algorithms (such as quick sort) to accurately sort the fitness of solution particles, and generates new optimized solution particles with the help of position perturbation and solution space expansion technology. The combined use of head and tail particles not only utilizes the optimization potential of head particles, but also enhances the diversity of solution sets through the perturbation of tail particles, thereby improving the comprehensiveness and reliability of optimization. Through this optimization and expansion strategy, new candidate solutions can be dynamically generated when the initial solution particles do not meet the fitness requirements, providing a broader exploration space and a higher optimization success rate for the final recommended frequency modulation solution.

[0081] P50: Frequency modulate the set of baseband signals to be modulated according to the recommended frequency modulation scheme.

[0082] Specifically, in the final stage, frequency modulation is performed on the set of baseband signals to be modulated according to the optimized recommended frequency modulation scheme to generate a modulated waveform with enhanced signal characteristics. This step completes the conversion from theoretical optimization to actual signal output by applying the frequency modulation parameters in the recommended scheme to the set of baseband signals one by one.

[0083] Exemplarily, the recommended frequency modulation scheme provides a set of optimized frequency modulation sensitivity parameters, which correspond one-to-one to the set of baseband signals to be modulated. Each frequency modulation sensitivity parameter describes the linear or nonlinear relationship between the change of baseband signal attributes (such as amplitude or voltage) and the carrier frequency offset. These parameters are the core inputs of frequency modulation, ensuring that the modulation process can achieve optimal frequency adjustment according to channel characteristics and signal requirements.

[0084] During the modulation process, it is important to maintain the key characteristics of the signal, including signal bandwidth, spectrum distribution, and phase consistency. To this end, the carrier frequency offset range can be monitored in real time to ensure that the modulated frequency is always within the constraint range of the recommended frequency modulation scheme to avoid spectrum overflow. When the baseband signal is input, filtering technology (such as FIR filter) is used to eliminate high-frequency noise to ensure the stability of the modulated signal. At the same time, in the frequency modulation output stage, the output power is adjusted to meet the transmission requirements of the channel to avoid signal distortion caused by over-modulation.

[0085] After the modulation is completed, the generated modulated signal has enhanced frequency characteristics and excellent channel adaptability. These signals will be transmitted through the wireless transmission module, or saved as a waveform file through the storage module for subsequent analysis. The spectrum characteristics of the modulated signal are optimized by the recommended frequency modulation scheme, showing a higher signal recognition rate and lower interference risk, which can provide reliable protection for channel transmission.

[0086] In summary, the embodiments of the present application have at least the following technical effects:

[0087] This application obtains the initial detection waveform set of the carrier signal classification channel, receives the baseband signal to be modulated and the frequency modulation sensitivity constraint interval, constructs the frequency modulation solution space, and combines the initial waveform set to perform frequency modulation optimization to generate a recommended frequency modulation scheme, and finally optimizes the frequency modulation of the baseband signal to achieve signal enhancement. The technical effect of significantly improving the signal recognition rate and anti-interference ability while reducing power consumption and cost by avoiding waveform similarity and optimizing frequency modulation is achieved.

[0088] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0090] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A wireless communication signal enhancement method based on frequency modulation, characterized in that: include: Obtaining an initial detection waveform set of a carrier signal classification channel; Receiving, through a user terminal, a set of baseband signals to be modulated and a frequency modulation sensitivity constraint interval; According to the set of frequency modulation sensitivity constraint intervals, a frequency modulation solution space is constructed; Based on the frequency modulation solution space and in combination with the initial detection waveform set, the frequency modulation optimization is performed on the set of baseband signals to be modulated to obtain a recommended frequency modulation solution; The set of baseband signals to be modulated is frequency modulated according to the recommended frequency modulation scheme.

2. The method according to claim 1, characterized in that According to the set of frequency modulation sensitivity constraint intervals, the frequency modulation solution space is constructed, including: Constructing a first coordinate axis constraint interval according to a first frequency modulation sensitivity constraint interval of the frequency modulation sensitivity constraint interval set; Until the Nth coordinate axis constraint interval is constructed according to the Nth frequency modulation sensitivity constraint interval of the frequency modulation sensitivity constraint interval set; The frequency modulation solution space is constructed according to the first coordinate axis constraint interval to the Nth coordinate axis constraint interval.

3. The method according to claim 1, characterized in that Based on the frequency modulation solution space and in combination with the initial detection waveform set, the frequency modulation optimization is performed on the to-be-modulated baseband signal set to obtain a recommended frequency modulation solution, including: Set the initial population size through the user end; According to the initial population size, a plurality of initial FM solution particles are generated in the FM solution space based on a uniform distribution function, wherein any initial FM solution particle includes a FM sensitivity set corresponding one-to-one to the set of baseband signals to be modulated; Based on the carrier frequency information, traverse the plurality of initial frequency modulation solution particles, perform modulation prediction on the set of baseband signals to be modulated, and obtain a plurality of groups of predicted waveforms, wherein the plurality of groups of predicted waveforms correspond one-to-one to the plurality of initial frequency modulation solution particles; Based on the several groups of predicted waveforms and the initial detection waveform set, frequency modulation optimization is performed on the set of baseband signals to be modulated to obtain the recommended frequency modulation scheme.

4. The method according to claim 3, characterized in that Based on the plurality of groups of predicted waveforms and the initial detection waveform set, performing frequency modulation optimization on the to-be-modulated baseband signal set to obtain the recommended frequency modulation scheme includes: Traversing the plurality of groups of predicted waveforms to perform waveform similarity analysis within the group to obtain a plurality of first fitness parameters; Based on the initial detection waveform set, traverse the plurality of groups of predicted waveforms to perform waveform similarity analysis outside the group to obtain a plurality of second fitness parameters, wherein the plurality of first fitness parameters correspond one to one with the plurality of second fitness parameters; Adding the one-to-one corresponding first fitness parameters and the second fitness parameters to obtain a plurality of initial frequency modulation solution particle fitnesses; Extract the initial frequency modulation solution particles whose fitness is equal to 0 and set them as the recommended frequency modulation solution.

5. The method according to claim 4, characterized in that Also includes: When the number of initial frequency modulation solution particles whose fitness is equal to 0 is 0, the initial frequency modulation solution particles are sorted from large to small according to the fitness of the initial frequency modulation solution particles to obtain the sorting result of the initial frequency modulation solution particles; Extracting a first number of head initial frequency modulation solution particles and a second number of tail initial frequency modulation solution particles from the initial frequency modulation solution particle sorting result, wherein the second number is greater than or equal to 2 times the first number; In the FM solution space, taking the first number of head initial FM solution particles as the target, perturbing the positions of the second number of tail initial FM solution particles to generate a plurality of expanded FM solution particles; An optimization loop is executed according to the plurality of expanded frequency modulation solution particles.

6. The method according to claim 4, characterized in that Traversing the plurality of groups of predicted waveforms to perform waveform similarity analysis within the group, and obtaining a plurality of first fitness parameters, including: Extracting a first group of predicted waveforms according to the plurality of groups of predicted waveforms; Comparing the waveform coincidence between two of the first set of predicted waveforms to obtain a waveform coincidence set; The proportion of waveform coincidences in the waveform coincidence set that are greater than or equal to the waveform coincidence threshold is extracted and set as a first fitness parameter, which is added to the plurality of first fitness parameters.

7. The method according to claim 4, characterized in that Based on the initial detection waveform set, the plurality of groups of predicted waveforms are traversed to perform waveform similarity analysis outside the group to obtain a plurality of second fitness parameters, including: Extracting a first group of predicted waveforms according to the plurality of groups of predicted waveforms; Based on the first predicted waveform of the first group of predicted waveforms, traverse the initial detection waveform set to perform waveform coincidence comparison to obtain a first waveform coincidence set, and when the first waveform coincidence set has a waveform coincidence greater than or equal to a waveform coincidence threshold, mark the first predicted waveform as 1, otherwise, mark it as 0; Until the Mth predicted waveform based on the first group of predicted waveforms, the initial detection waveform set is traversed to perform waveform coincidence comparison to obtain the Mth waveform coincidence set, when the Mth waveform coincidence set has a waveform coincidence greater than or equal to a waveform coincidence threshold, the Mth predicted waveform is marked as 1, otherwise, it is marked as 0; The proportion of the number of 1 values ​​in the first group of predicted waveforms is calculated, set as a second fitness parameter, and added into the plurality of second fitness parameters.

8. The method according to claim 3, characterized in that Based on the carrier frequency information, the plurality of initial frequency modulation solution particles are traversed to perform modulation prediction on the set of baseband signals to be modulated, and a plurality of groups of predicted waveforms are obtained, including: Obtaining a first baseband signal to be modulated from the set of baseband signals to be modulated, wherein the first baseband signal to be modulated has a signal attribute; Processing the signal attributes through a signal modulation table to obtain a modulation unit step size and an attribute reference characteristic value; Extracting the frequency modulation sensitivity of the first baseband signal to be modulated of the first initial frequency modulation solution particle of the plurality of initial frequency modulation solution particles; Based on the modulation unit step size, the attribute reference characteristic value and the frequency modulation sensitivity of the first baseband signal to be modulated, modulate the first baseband signal to be modulated according to the carrier frequency information to obtain a frequency modulation waveform of the first baseband signal to be modulated, and add it to the first group of predicted waveforms; The first set of predicted waveforms is added to the plurality of sets of predicted waveforms.

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