Wireless communication signal enhancement method based on frequency modulation

Abstract

The present invention discloses a wireless communication signal enhancement method based on frequency modulation, which relates to the field of wireless communication technology. The method comprises: 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, obtaining a recommended frequency modulation scheme, and frequency modulating the baseband signal set to be modulated. The present invention solves the technical problem that traditional signal enhancement methods in the prior art 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. The present invention achieves the technical effect of significantly improving signal recognition rate and anti-interference ability while reducing power consumption and cost by avoiding waveform similarity and optimizing frequency modulation.

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, signal transmission quality and efficiency have become increasingly important in modern communication systems. However, traditional signal enhancement methods typically rely on increasing output power to boost signal strength. While this approach can improve signal transmission to a certain extent, it also presents significant technical bottlenecks and practical challenges, including high power consumption, high costs, and demanding hardware performance. Furthermore, increasing output power can cause channel interference, particularly in multi-user communication environments, further reducing signal recognition and transmission reliability.

[0003] In complex communication environments, signal waveform similarity is another significant factor contributing to reduced recognition rates. This waveform similarity not only reduces decoding accuracy at the receiving end but can also lead to erroneous signal interference determinations, impacting the overall performance of the communication system. Therefore, effectively improving signal recognition and transmission quality without significantly increasing power consumption has become a pressing technical challenge in the wireless communications field. 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 set of baseband signals to be modulated and a frequency modulation sensitivity constraint interval through a user terminal; constructing a frequency modulation solution space according to the set of frequency modulation sensitivity constraint intervals; based on the frequency modulation solution space and in combination with the initial detection waveform set, performing frequency modulation optimization on the set of baseband signals to be modulated to obtain a recommended frequency modulation scheme; and frequency modulating the set of baseband signals 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 an initial detection waveform set of a carrier signal classification channel, receiving a baseband signal to be modulated and a frequency modulation sensitivity constraint interval, constructing a frequency modulation solution space, and combining the initial waveform set to perform frequency modulation optimization to generate a recommended frequency modulation scheme, the baseband signal is finally optimized for frequency modulation to achieve signal enhancement. This method solves the technical problem 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. This method achieves the technical effect of significantly improving 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 paying any creative work.

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

[0010] Figure 2 This is a flow chart of optimizing and obtaining a recommended frequency modulation scheme in a frequency modulation-based wireless communication signal enhancement method 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 accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts 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 clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0014] Example 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 the initial detection waveform set of the carrier signal classification channel.

[0016] Specifically, in wireless communication systems, carrier signal detection and classification are important foundations 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, a receiving device (such as a software-defined radio (SDR)) collects real-time signal samples within the channel. These samples include key parameters such as the signal's amplitude, frequency, phase, and time distribution. During the signal acquisition phase, a high-precision analog-to-digital converter (ADC) and low-noise amplifier (LNA) are required to ensure that weak signals are not obscured by ambient noise. Furthermore, a well-selected sampling time window ensures that the signal's periodic characteristics are fully captured. After the signal is acquired, spectral analysis is performed. For example, a fast Fourier transform (FFT) is used to convert the signal from the time domain to the frequency domain, extracting the dominant frequency characteristics and signal spectral distribution to determine the presence of multi-carrier or interfering signals in the channel.

[0018] Based on spectrum analysis, signals are classified using pattern recognition algorithms or machine learning models (such as support vector machines (SVMs) or convolutional neural networks (CNNs). This process categorizes signals into different categories based on their modulation scheme (such as AM, FM, PSK) and waveform characteristics, clarifying the properties and purpose of the signals in the channel. After classification, the signal waveform is denoised using wavelet transforms or Kalman filtering to effectively filter out ambient noise while preserving the signal's key features. This further optimizes the waveform quality and ensures it accurately reflects the actual channel conditions. Finally, the processed waveform information is stored in a unified format to form an initial detection waveform set. This set provides a complete description of the carrier signal characteristics within the channel, providing high-quality input data for subsequent frequency modulation optimization and waveform similarity avoidance. It is also key to avoiding waveform similarity and improving signal recognition.

[0019] P20: Receives the baseband signal set 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 before frequency modulation. These signals typically contain the actual data information to be transmitted over the channel, such as voice, video, or digital data. The user end uploads these signal sets through an input interface or receives them in real time. They are then classified and stored according to time series, amplitude characteristics, or spectral distribution. This process requires efficient signal sampling and buffering 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 range, which is a set of parameters that defines the frequency variation range during the frequency modulation process. This range can be set based on hardware device 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. Among them, sensitivity indicates the degree of influence of the baseband signal amplitude change on the carrier frequency offset during frequency modulation. To ensure communication stability and anti-interference, the sensitivity constraint range needs to comprehensively consider the characteristics of the system equipment and external interference conditions, and be optimized through technical means such as dynamic range analysis and noise tolerance calculation.

[0023] During the reception process, the user end needs to ensure the stable transmission of signal data through a reliable communication protocol (such as TCP / IP or Real-time Transport Protocol RTP), while performing an integrity check (such as CRC check) on the baseband signal set and a 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 FM sensitivity constraint interval are integrated as the basic data for the modulation input, and clear boundary conditions are provided for the subsequent FM solution space construction and optimization process. Through the user end's flexible input interface and robust data processing capabilities, this step effectively guarantees the accuracy and adaptability of the FM solution design.

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

[0025] Furthermore, step P30 in 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 a set of FM sensitivity constraint intervals, possible frequency modulation parameter combinations are confined to a multidimensional space, facilitating subsequent FM optimization. The core of the FM solution space lies in using a mathematical model to describe the valid range of FM parameters, where each dimension represents a FM sensitivity constraint interval. By gradually resolving the set of constraint intervals, a multidimensional constraint space is constructed, thus forming a complete FM solution space.

[0028] Specifically, first, based on the first frequency modulation sensitivity constraint interval of the frequency modulation sensitivity constraint interval set, the first coordinate axis constraint of the solution space is constructed. The constraint interval here defines the minimum and maximum values ​​of the frequency modulation parameter 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 the interval representation method. At this time, the frequency modulation parameter of this dimension is fixed within the constraint range, providing an initial boundary for the solution space.

[0029] Building on the first dimension, the remaining dimensions in the set of FM sensitivity constraint intervals are analyzed sequentially, until the Nth FM sensitivity constraint interval is reached. Each dimension of FM sensitivity typically represents a different modulation parameter, such as frequency modulation depth, modulation speed, or noise margin. When constructing these constraint intervals, a variety of factors must be considered, such as environmental interference, channel attenuation characteristics, and device power consumption limits. The constraint intervals for each dimension are superimposed in the solution space as intervals, forming a progressively expanding multidimensional matrix structure.

[0030] Once the FM sensitivity constraint intervals for all dimensions are resolved, a complete FM solution space is constructed based on the constraint intervals for each coordinate axis. This solution space can be viewed as an N-dimensional hypercube, where 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 form a non-empty set. If some constraints conflict (for example, the maximum value of one dimension is less than the minimum value of another dimension), a constraint correction algorithm (such as linear programming or heuristic adjustment) is used to resolve the conflict and ensure the completeness of the solution space.

[0031] Constructing the FM solution space involves a multi-dimensional, constrained geometric modeling process. By transforming the constraints on FM sensitivity into the boundaries of the solution space, this provides a clear mathematical description for subsequent FM optimization. This process can be accomplished by combining high-dimensional matrix operations with constrained optimization techniques, such as using Python's Numpy or Matlab tools to simulate and analyze the multi-dimensional space, ensuring efficient generation and usability 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 in this embodiment of the application also includes:

[0034] P41: Setting an initial population size through the user terminal; P42: Generate a number of initial FM solution particles in the FM solution space based on the uniform distribution function according to the initial population size, wherein any initial FM solution particle includes a FM 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 FM 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 FM solution particles; P44: Based on the number of groups of predicted waveforms and the initial detection waveform set, perform FM optimization on the set of baseband signals to be modulated to obtain the recommended FM 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 a recommended FM solution is ultimately output. The key to FM optimization lies in exploring the optimal combination of FM parameters through intelligent algorithms to maximize signal recognition rate and transmission quality.

[0036] For example, the first step in frequency modulation optimization is to set the initial population size through the user. This defines the number of initial solution particles involved in the optimization algorithm's calculations. The population size directly impacts the algorithm's search breadth and efficiency. A smaller population size may result in an insufficient search of the solution space, while an excessively large population size may increase computational complexity. Heuristics or empirical values ​​can be used to set the population size to ensure sufficient coverage of the solution space.

[0037] Next, within the set population size, a number of initial FM solution particles are randomly generated in the FM solution space based on a uniform distribution function. These initial FM solution particles are vectors, each element of which represents the FM sensitivity of the corresponding baseband signal. The generation of these initial solution particles ensures a uniform distribution to avoid excessive concentration in a single part of the solution space while providing diversity for subsequent optimization. Each particle corresponds one-to-one to the set of baseband signals to be modulated, ensuring that the FM sensitivity settings cover all baseband signals.

[0038] Furthermore, based on the initial FM solution particles, modulation prediction is performed on the set of baseband signals to be modulated. Using the carrier frequency information, each FM solution particle is traversed, and its parameters are applied to the baseband signal to generate the corresponding modulation waveform. These waveforms, called predicted waveforms, are used to evaluate the effectiveness of the FM scheme. The generation of predicted waveforms requires integrating the FM formula, for example, by calculating the modulation result of each signal using a frequency offset function. The focus of this stage is on mapping the solution particles and baseband signals into specific physical waveforms.

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

[0040] Through the above steps, we can efficiently search in the multi-dimensional FM solution space, combine the initial detection waveform and the prediction results of the FM solution particles, and generate the optimal FM solution, ensuring that the recommended FM solution can achieve the best signal enhancement and interference avoidance effects in actual applications.

[0041] Furthermore, step P43 of the embodiment of the present application further 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 the signal modulation table to obtain the modulation unit step and the attribute benchmark characteristic value; P43-3: Extract the first baseband signal to be modulated frequency modulation sensitivity 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 to obtain the 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, during the modulation prediction process, a predicted waveform corresponding to each initial FM solution particle is generated by gradually processing a set of baseband signals to be modulated.

[0044] Modulation prediction begins by extracting the first baseband signal to be modulated from the set of baseband signals to be modulated. This baseband signal is the original, unmodulated signal and possesses inherent signal properties, such as amplitude, voltage, or power. These properties describe the fundamental characteristics of the signal and directly impact modulation sensitivity. When extracting the baseband signal to be modulated, it is important to ensure that its properties match the system's modulation requirements. For example, the maximum and minimum signal amplitudes must be confirmed through the data interface to prevent modulation from exceeding 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 size 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 change of voltage in steps. 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, the first initial FM solution particle is extracted from the initial FM solution particles, and its corresponding FM sensitivity for the first baseband signal to be modulated is obtained. FM sensitivity is a key parameter for each solution particle's specific dimension, describing the impact of changes in baseband signal attributes on frequency offset. This parameter must be extracted to ensure that the properties of the solution particle and the signal are aligned, ensuring that subsequent modulation accurately reflects the particle's characteristics.

[0047] Next, based on the modulation unit step size, attribute baseline eigenvalue, and FM sensitivity, the signal attributes of the first baseband signal to be modulated are mapped to a frequency offset. Combined with the carrier frequency information, the modulation process of the signal is completed, and the FM waveform of the first baseband signal to be modulated is generated. 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 FM waveform of the first baseband signal to be modulated is added to the first set of predicted waveforms, and this set of predicted waveforms is then merged into the sets of predicted waveforms corresponding to all initial FM solution particles. This process completes the generation of the predicted waveform of the first baseband signal to be modulated, providing a specific waveform sample for subsequent FM optimization.

[0049] Through the above steps, accurate mapping from signal properties 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 further 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 from the several initial frequency modulation solution particles and set them as the recommended frequency modulation scheme.

[0052] Specifically, the key to FM optimization lies in selecting the optimal FM solution from the solution space by evaluating the fitness of the predicted waveform. Specifically, this embodiment further calculates the fitness of the FM solution particles by analyzing the similarity of waveforms within and outside the group, and selects the particle with the highest fitness as the recommended FM solution.

[0053] First, we 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, ensuring 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, reflecting the FM consistency of particles within the group.

[0054] Subsequently, based on the initial set of detected waveforms, the predicted waveforms are subjected to out-of-group waveform similarity analysis to calculate a second fitness parameter. The purpose of out-of-group similarity analysis is to assess the degree of match between the predicted waveform and the initial detected waveform, ensuring that the modulation result has a high recognition rate and transmission stability in the target channel. Similarity analysis can use correlation metrics (such as the Pearson correlation coefficient) or error assessment (such as mean square error) to perform a one-to-one comparison between the predicted and detected waveforms. The second fitness parameter reflects the adaptability of the modulation waveform in the target channel.

[0055] For each initial FM solution particle, the corresponding first and second fitness parameters are summed to calculate the initial FM solution particle fitness. The fitness summation logic comprehensively evaluates both intra-group consistency and extra-group adaptability, ensuring that the selected particles strike a balance between predicted waveform consistency and target channel compatibility. The fitness summation formula can adjust weights based on task requirements to prioritize specific performance characteristics.

[0056] Finally, particles with a fitness of 0 are selected from all initial FM solution particles and used as the recommended FM solution. A fitness of 0 indicates that the particle has achieved optimal waveform consistency within the group and channel matching outside the group. This step requires strict screening criteria to avoid selecting suboptimal solutions due to computational errors. If no particle meets the fitness of 0, the solution space can be further optimized through iterative optimization or adjustment of algorithm parameters.

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

[0058] Furthermore, step P44-1 of the embodiment of the present application further 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 set of predicted waveforms is extracted from several sets of predicted waveforms. This set of predicted waveforms is generated by the same initial FM solution particle and represents the modulation results of that particle on different baseband signals to be modulated. Using cluster indexes or group labels, the first set of waveforms can be efficiently distinguished and extracted, ensuring that subsequent analysis is performed only within that group.

[0062] Next, the first set of predicted waveforms extracted are compared for pairwise overlap. Overlap is a measure of the similarity between two waveforms, typically defined as the degree of matching in amplitude and phase. During the comparison process, numerical integration or cross-correlation functions can be used to calculate overlap. 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 results are 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, for example, 0.9, which means that the waveform matching degree is at least 90%. The numerical range of the first fitness parameter is 0 to 1, which reflects the degree of 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 FM solution particle within the group can be objectively quantified, while also providing reliable basic data for the optimization algorithm. The introduction of the first fitness parameter effectively evaluates the stability of the FM solution within the group, providing key support for the subsequent FM 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, the first predicted waveform is marked as 1, otherwise, it is marked 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, the Mth predicted waveform is marked as 1, otherwise, it is marked 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. This set of waveforms, generated by the same initial FM solution particle, represents the modulation results of the particle on different baseband signals. After extraction, the sequence of this set of waveforms is ensured to be associated with the initial FM solution particle, providing 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. Waveform overlap is used to measure the degree of matching 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 identified as 1; otherwise, it is identified as 0. This identification reflects whether the similarity between the first predicted waveform and the detection waveform meets the threshold standard. The waveform identified as 1 indicates that it has a high adaptability.

[0071] Repeat the above process, comparing each waveform in the first set of predicted waveforms (from the 1st to the Mth predicted waveform) one by one, generating 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. This traversal operation completes the compatibility identification of the first set of predicted waveforms.

[0072] Finally, the number of waveforms marked as 1 in the first group of predicted waveforms is counted, and their proportion to the total number of waveforms in the group is calculated as the second fitness parameter. The value of the second fitness parameter ranges from 0 to 1 and reflects the overall performance of the predicted waveforms outside the group in terms of channel adaptability. The calculated result is added to several second fitness parameters to provide data support for subsequent fitness summation. This process ensures that the frequency modulation optimization results are highly reliable and adaptable 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 perturbed to generate a number of expanded FM solution particles; P44-44: An optimization loop is executed based on the number of expanded FM solution particles.

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

[0076] First, when the number of particles with a fitness equal to 0 among all initial FM solution particles is zero, meaning no particle fully meets the fitness requirements, all initial FM solution particles are sorted from highest to lowest according to fitness, generating a ranking result for the initial FM solution particles. This ranking result provides a priority reference, identifying which particles have a higher probability of approaching the optimal solution. The core technology of fitness ranking is the weighted combination of multidimensional fitness parameters (such as primary fitness and secondary fitness) to ensure the accuracy and fairness of the ranking.

[0077] Next, a certain number of leading and trailing solution particles are extracted from the sorted results. The first number of leading initial frequency modulation solution particles represents particles with higher fitness in the sort and generally have greater optimization potential; the second number of trailing initial frequency modulation solution particles represents particles with lower fitness and are generally suitable as perturbation targets. To enhance optimization effectiveness, the second number is set to twice or more the first number to ensure that the perturbation range covers a sufficiently wide range of the solution space.

[0078] Using the first number of leading particles as the target, the second number of tail particles are positionally perturbed in the FM solution space to generate several expanded FM solution particles. Position perturbation involves making small, random adjustments to the FM parameters of the tail particles, aligning them toward the leading particles, thereby generating new solution particles. The amplitude and direction of the perturbation are determined by a Gaussian or uniform distribution function, while the perturbation range is constrained to prevent particles from exceeding the boundaries of the solution space. This operation preserves the diversity of the original solution particles while incorporating the optimization potential of the leading 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. These expanded solution particles serve as the new initial solution particles for fitness evaluation and optimization sorting, iterating to continuously approach the optimal solution. The core of the optimization loop lies in continuously optimizing the depth and breadth of the solution space through dynamic expansion of solution particles, thereby increasing the probability of obtaining a solution particle with a fitness of 0.

[0080] This process relies on sorting algorithms (such as quick sort) to accurately rank the fitness of solution particles, and then generates new optimized solution particles using position perturbation and solution space expansion techniques. The combined use of head and tail particles leverages the optimization potential of the head particles while enhancing the diversity of the solution set through the perturbation of the tail particles, thereby improving the comprehensiveness and reliability of the optimization process. This optimization and expansion strategy allows for the dynamic generation of new candidate solutions even when the initial solution particles do not meet 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 FM scheme to generate a modulated waveform with enhanced signal characteristics. This step completes the transition from theoretical optimization to actual signal output by applying the FM parameters in the recommended scheme one by one to the set of baseband signals.

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

[0084] During the modulation process, it is crucial to maintain key signal characteristics, including signal bandwidth, spectral distribution, and phase consistency. To this end, real-time monitoring of the carrier frequency offset ensures that the modulated frequency remains within the constraints of the recommended frequency modulation scheme, preventing spectrum overflow. Furthermore, filtering techniques (such as FIR filters) are used during baseband signal input to eliminate high-frequency noise and ensure the stability of the modulated signal. Furthermore, during the frequency modulation output stage, the output power is adjusted to meet the channel transmission requirements to avoid signal distortion caused by overmodulation.

[0085] After modulation is complete, the resulting modulated signal has enhanced frequency characteristics and excellent channel adaptability. These signals are transmitted via the wireless transmitter module or stored as waveform files in the storage module for subsequent analysis. The spectral characteristics of the modulated signal, optimized using the recommended frequency modulation scheme, demonstrate higher signal recognition rates and lower interference risks, providing reliable support for channel transmission.

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

[0087] This application obtains an initial detection waveform set for 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 solution. Ultimately, it optimizes the frequency modulation of the baseband signal to achieve signal enhancement. By avoiding waveform similarity and optimizing frequency modulation, the application achieves the technical effect of significantly improving signal recognition rate and anti-interference capability while reducing power consumption and cost.

[0088] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[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 replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0090] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

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; Constructing a frequency modulation solution space according to the frequency modulation sensitivity constraint interval; Based on the frequency modulation solution space and in combination with the initial detection waveform set, the frequency modulation optimization of the baseband signal set to be modulated is performed to obtain a recommended frequency modulation solution; Frequency modulating the set of baseband signals to be modulated according to the recommended frequency modulation scheme; Based on the frequency modulation solution space and in combination with the initial detection waveform set, frequency modulation optimization is performed on the set of baseband signals to be modulated to obtain a recommended frequency modulation solution, including: Set the initial population size through the user end; generating a plurality of initial FM solution particles in the FM solution space based on the initial population size and a uniform distribution function, wherein any one of the initial FM solution particles includes a set of FM sensitivities corresponding one-to-one to the set of baseband signals to be modulated; Based on the carrier frequency information, traverse the plurality of initial FM 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 FM 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.

2. The method according to claim 1, wherein According to the frequency modulation sensitivity constraint interval, a 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; Until an Nth coordinate axis constraint interval is constructed according to the Nth frequency modulation sensitivity constraint interval of the frequency modulation sensitivity constraint interval; 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, wherein Based on the plurality of groups of predicted waveforms and the initial detection waveform set, performing frequency modulation optimization on the set of baseband signals to be modulated to obtain the recommended frequency modulation scheme includes: Traversing the plurality of groups of predicted waveforms to perform intra-group waveform similarity analysis to obtain a plurality of first fitness parameters; Based on the initial detection waveform set, traversing the plurality of groups of predicted waveforms to perform out-of-group waveform similarity analysis to obtain a plurality of second fitness parameters, wherein the plurality of first fitness parameters correspond one-to-one to the plurality of second fitness parameters; Adding the one-to-one corresponding first fitness parameters and the one-to-one corresponding second fitness parameters to obtain a plurality of initial FM 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.

4. The method according to claim 3, wherein Also includes: 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 a sorting result of the initial FM 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 sorting result of the initial frequency modulation solution particles, 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 initial FM solution particles at the head as the target, positionally perturbing the second number of initial FM solution particles at the tail to generate a plurality of expanded FM solution particles; An optimization loop is executed according to the plurality of expanded frequency modulation solution particles.

5. The method according to claim 3, wherein Traversing the plurality of groups of predicted waveforms to perform intra-group waveform similarity analysis, 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; performing pairwise waveform coincidence comparison on 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 a waveform coincidence threshold is extracted and set as a first fitness parameter, which is added to the plurality of first fitness parameters.

6. The method according to claim 3, wherein Based on the initial detection waveform set, the plurality of groups of predicted waveforms are traversed to perform out-of-group waveform similarity analysis 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, traversing 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, marking the first predicted waveform as 1, otherwise marking 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 an Mth waveform coincidence set, and 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 and set as a second fitness parameter, which is then added to the plurality of second fitness parameters.

7. The method according to claim 1, wherein 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 to obtain a plurality of groups of predicted waveforms, 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 benchmark characteristic value; Extracting the first baseband signal FM sensitivity to be modulated of the first initial FM solution particle of the plurality of initial FM 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, modulating 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 adding the waveform to the first set of predicted waveforms; The first set of predicted waveforms is added to the plurality of sets of predicted waveforms.

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