WIFI antenna impedance matching parameter generation method, device and equipment and storage medium

Abstract

The application provides a WIFI antenna impedance matching parameter generation method and device, equipment and storage medium, and relates to the field of radio frequency circuit design. The application comprises: in response to an input operation, obtaining an input parameter; and determining an initial optimization parameter according to the input parameter; wherein the input parameter is basic data for generating a WIFI antenna impedance matching parameter. Based on a preset optimization algorithm and the initial optimization parameter, a global search optimal solution operation is performed to determine candidate element parameters; the candidate element parameters are forcibly corrected according to a preset application resonance constraint condition. The fundamental frequency impedance is determined according to the forcibly corrected candidate element parameters; if it is determined that the fundamental frequency impedance meets a preset fundamental frequency impedance matching degree threshold, the candidate element parameters are determined as the WIFI antenna impedance matching parameter. Therefore, the application solves the problems of low design efficiency and single optimization target in the prior art.

Description

Technical Field

[0001] This application relates to the field of radio frequency circuit design, and more specifically, to a method, apparatus, device, and storage medium for generating impedance matching parameters for a WIFI antenna. Background Technology

[0002] Currently, in Wi-Fi devices (routers, mobile phones, IoT modules, etc.), the antenna is a key component for effectively radiating signals generated by the radio frequency chip into space and receiving spatial signals. The goal of impedance matching is to ensure that the output impedance of the RF front-end (typically 50 Ω) is as consistent as possible with the input impedance of the antenna within the operating frequency band (e.g., 2.4-2.4835 GHz for 2.4 GHz, and multiple sub-bands for 5 GHz). Therefore, after the antenna design is completed, the design and optimization of the matching circuit is an essential and extremely critical step in product development.

[0003] In existing technologies, traditional methods require engineers to manually calculate matching network parameters using Smith chart tools or basic circuit theory. Specific steps include: determining matching requirements based on the operating frequency and target impedance; selecting a π-type or T-type matching network topology; calculating the initial values ​​of inductors and capacitors using analytical formulas; verifying performance using circuit simulation software (such as ADS or HFSS); and repeatedly adjusting parameters based on simulation results until the requirements are met.

[0004] However, existing technologies suffer from low design efficiency, requiring multiple manual iterations and long design cycles; the optimization objective is singular, making it difficult to simultaneously meet the dual requirements of fundamental frequency matching and harmonic suppression; they rely on professional knowledge, demanding high levels of experience in RF circuit design from engineers; and they lack flexibility, with existing automated tools struggling to adapt to complex and ever-changing actual design needs. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, device and storage medium for generating WIFI antenna impedance matching parameters, so as to solve the above-mentioned problems existing in the prior art and solve the technical problems of low design efficiency and single optimization target.

[0006] Firstly, a method for generating impedance matching parameters for a Wi-Fi antenna is provided, which may include:

[0007] In response to an input operation, input parameters are acquired; and initialization optimization parameters are determined based on the input parameters; wherein, the input parameters are the basic data used to generate WIFI antenna impedance matching parameters;

[0008] Based on the preset optimization algorithm and the initialization optimization parameters, a global search for the optimal solution is performed to determine the candidate component parameters; according to the preset application resonance constraint conditions, the candidate component parameters are forcibly corrected; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraints on the candidate component parameters;

[0009] Based on the candidate component parameters that have been forcibly corrected, the baseband impedance is determined; if the baseband impedance is determined to meet the preset baseband impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters.

[0010] Secondly, a WIFI antenna impedance matching parameter generation device is provided, the device may include:

[0011] The acquisition module is used to acquire input parameters in response to input operations;

[0012] An initialization module is used to determine initialization optimization parameters based on the input parameters; wherein the input parameters are basic data used to generate WIFI antenna impedance matching parameters;

[0013] The first determining module is used to perform a global search for the optimal solution based on a preset optimization algorithm and the initialization optimization parameters, and to determine the candidate component parameters.

[0014] The correction module is used to forcibly correct the parameters of the candidate components according to the preset application resonance constraint conditions; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraint on the parameters of the candidate components.

[0015] The second determining module is used to determine the baseband impedance based on the forced correction of the candidate component parameters; if the baseband impedance is determined to meet the preset baseband impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters.

[0016] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0017] Memory, used to store computer programs;

[0018] When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0019] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0020] The embodiments of this application provide a method, apparatus, device, and storage medium for generating WIFI antenna impedance matching parameters. Through multi-objective optimization algorithms, intelligent parameter initialization, and resonant constraint integration, it can simultaneously achieve fundamental frequency impedance matching and harmonic suppression. It has achieved significant technological progress in terms of design efficiency, design accuracy, multi-objective coordination, design consistency, and technical threshold, and solves the problems of low design efficiency and single optimization objective in the prior art. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for generating WIFI antenna impedance matching parameters provided in this application embodiment;

[0023] Figure 2 A flowchart illustrating a method for generating WIFI antenna impedance matching parameters provided in this application embodiment;

[0024] Figure 3 A schematic diagram illustrating a method for generating WIFI antenna impedance matching parameters according to an embodiment of this application;

[0025] Figure 4 A circuit topology diagram of an impedance matching circuit provided in an embodiment of this application;

[0026] Figure 5 A flowchart illustrating a method for generating WIFI antenna impedance matching parameters provided in this application embodiment;

[0027] Figure 6 A schematic diagram of an overall system architecture provided for an embodiment of this application;

[0028] Figure 7 A schematic diagram of a user interface layout provided for an embodiment of this application;

[0029] Figure 8 A schematic diagram of a WIFI antenna impedance matching parameter generation device provided in an embodiment of this application;

[0030] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0032] Currently, in Wi-Fi devices (routers, mobile phones, IoT modules, etc.), the antenna is a key component for effectively radiating signals generated by the radio frequency chip into space and receiving spatial signals. The goal of impedance matching is to ensure that the output impedance of the RF front-end (typically 50 Ω) is as consistent as possible with the input impedance of the antenna within the operating frequency band (e.g., 2.4-2.4835 GHz for 2.4 GHz, and multiple sub-bands for 5 GHz). Therefore, after the antenna design is completed, the design and optimization of the matching circuit is an essential and extremely critical step in product development.

[0033] In existing technologies, traditional methods require engineers to manually calculate matching network parameters using Smith chart tools or basic circuit theory. Specific steps include: determining matching requirements based on the operating frequency and target impedance; selecting a π-type or T-type matching network topology; calculating the initial values ​​of inductors and capacitors using analytical formulas; verifying performance using circuit simulation software (such as ADS or HFSS); and repeatedly adjusting parameters based on simulation results until the requirements are met.

[0034] However, existing technologies suffer from low design efficiency, requiring multiple manual iterations and long design cycles; the optimization objective is singular, making it difficult to simultaneously meet the dual requirements of fundamental frequency matching and harmonic suppression; they rely on professional knowledge, demanding high levels of experience in RF circuit design from engineers; and they lack flexibility, with existing automated tools struggling to adapt to complex and ever-changing actual design needs.

[0035] In one example, the suboptimal solution problem is difficult to find a globally optimal solution through manual adjustments; it is parameter sensitive, with even small changes in component parameters having a significant impact on performance; it has poor process adaptability, making it difficult to quickly adapt to the requirements of different manufacturing processes; and it has high verification costs, requiring extensive simulations and experimental verification. These factors lead to significant performance limitations in existing solutions.

[0036] The WIFI antenna impedance matching parameter generation method provided in this application can be applied to electronic devices or other devices equipped with WIFI antenna impedance matching parameter generation devices. Regarding software architecture, the desktop application form of this device can be replaced by various software architectures. For example, a web-based cloud service platform is an important alternative, achieving cross-platform access through the separation of browser client and server-side computation. This architecture facilitates centralized updates and maintenance while utilizing cloud computing resources to handle complex optimization tasks. Alternatively, a command-line tool version provides a lightweight alternative, accepting parameter input and outputting calculation results through a text interface. This form is suitable for integration into automated design processes, facilitating data exchange with other EDA (Electronic Design Automation) tools. Another practical alternative is integration as a plug-in to existing EDA tools, embedding the optimization functions of this application into professional design software such as Advanced Design Systems (ADS), directly utilizing the simulation engines and user interfaces of these software programs. Alternatively, in terms of the computation engine, machine learning prediction models can provide a fast alternative, namely, training neural network models to learn the mapping relationship between parameters and performance, replacing precise calculations with predictions during the optimization process, significantly improving computation speed. Alternatively, lookup tables combined with interpolation methods provide another fast computational alternative. By pre-compiling key points in the parameter space and storing the results, approximate values ​​can be quickly obtained through interpolation during actual optimization, balancing computational accuracy and speed requirements.

[0037] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0038] Figure 1 This is a flowchart illustrating a method for generating WIFI antenna impedance matching parameters according to an embodiment of this application. Figure 1 As shown, the method may include:

[0039] Step S101: In response to the input operation, obtain the input parameters; and determine the initialization optimization parameters based on the input parameters; wherein, the input parameters are the basic data used to generate the WIFI antenna impedance matching parameters.

[0040] For example, input parameters are obtained based on user input operations in the graphical interface. Initialization optimization parameters are dynamically determined based on these input parameters. The input parameters are the basic data used to generate the Wi-Fi antenna impedance matching parameters, including data such as the target impedance type, and / or custom-input component parameters. The target impedance type data includes the target impedance type, preset fundamental frequency, and second harmonic. The initialization optimization parameters include initial parameter values, physical constraint ranges, and a set of optimization variables.

[0041] For example, the system receives the target impedance type selected by the user through a graphical interface (inductive: 0+j50Ω or capacitive: 0-j50Ω). The user can selectively input some component parameters; parameters not input are automatically calculated and generated by the system. The system's preset fundamental frequency is 2.4GHz, and the second harmonic is 4.8GHz. Then, the initial parameter values ​​are dynamically set according to the target impedance type: for inductive targets, the inductance value is too large, and the capacitance value is too small; for capacitive targets, the capacitance value is too large, and the inductance value is too small. The physical constraint range of the component parameters is set, for example, inductance: 0.5-15nH, capacitance: 0.1-30pF. A set of optimization variables is constructed. This set of optimization variables is used to distinguish between user-defined parameters and parameters to be optimized. User-defined parameters are input parameters, etc., while parameters to be optimized are initialization optimization parameters, etc. It should be noted that the values ​​shown in this step are only examples and are not limited thereto.

[0042] Step S102: Based on the preset optimization algorithm and initialization optimization parameters, perform a global search for the optimal solution to determine the candidate component parameters; and perform forced correction on the candidate component parameters according to the preset application resonance constraint conditions; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraint on the candidate component parameters.

[0043] For example, the preset optimization algorithm is used to perform impedance matching and resonance constraints on the parameters of candidate components. For example, the optimization algorithm can be a Sequential Least Squares Programming (SLSQP) optimization algorithm; or it can be a genetic algorithm; or a particle swarm optimization algorithm; or a simulated annealing algorithm; or, in addition to a single algorithm, a hybrid optimization strategy can be adopted. The hybrid optimization strategy is other combined algorithms that can achieve impedance matching and resonance constraints, such as the combination of genetic algorithm and local search algorithm, or the hybrid use of particle swarm algorithm and gradient descent method, to take into account both global search and local fine-tuning. There is no limitation on this.

[0044] In this step, firstly, the initial optimization parameters are used as initial values. Based on a preset optimization algorithm and preset applied resonance constraints, the initial optimization parameters are forcibly corrected. In the next iteration, if the fundamental frequency impedance corresponding to the initial optimization parameters does not meet the preset fundamental frequency impedance matching threshold, a global search for the optimal solution is performed based on the preset optimization algorithm and initial optimization parameters. Candidate component parameters are searched, and then the candidate component parameters are forcibly corrected according to the applied resonance constraints.

[0045] Optionally, regarding the constraint handling method, the integrated constraint handling method in this step (i.e., the preset application of resonance constraints) can achieve the same technical effect through various alternatives. For example, post-processing constraint satisfaction is a simple and effective alternative. First, perform impedance matching optimization without constraints or with weak constraints, and then adjust the parameters analytically to satisfy the resonance constraints. This method reduces the complexity of the optimization problem. Alternatively, the soft-constraint penalty function method transforms hard constraints into penalty terms in the objective function, allowing temporary violations of constraints during optimization, but guiding the search direction through the penalty terms. This method improves the flexibility of the optimization algorithm. Alternatively, a two-stage optimization strategy decomposes the complex problem into two relatively simple sub-problems. The first stage specifically handles resonance constraints, and the second stage optimizes impedance matching while satisfying the resonance conditions. This decomposition strategy helps improve optimization efficiency, and no limitations are imposed on it.

[0046] Step S103: Determine the baseband impedance based on the candidate component parameters that have been forcibly corrected; if the baseband impedance is determined to meet the preset baseband impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters.

[0047] For example, the fundamental frequency impedance is calculated based on the forcibly corrected candidate component parameters. The fundamental frequency impedance is compared with a preset fundamental frequency impedance matching threshold. If it is determined that the fundamental frequency impedance meets the preset fundamental frequency impedance matching threshold, then the candidate component parameters for the current iteration are determined to be the WIFI antenna impedance matching parameters.

[0048] In this embodiment, in response to an input operation, input parameters are acquired; and initialization optimization parameters are determined based on the input parameters; wherein, the input parameters are the basic data used to generate WIFI antenna impedance matching parameters. Based on a preset optimization algorithm and initialization optimization parameters, a global search for the optimal solution is performed to determine candidate component parameters; according to preset application resonance constraints, the candidate component parameters are forcibly corrected; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraints on the candidate component parameters. Based on the forcibly corrected candidate component parameters, the fundamental frequency impedance is determined; if the fundamental frequency impedance is determined to meet a preset fundamental frequency impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters. Therefore, through multi-objective optimization algorithms, intelligent parameter initialization, resonance constraint integration, and other technical means, fundamental frequency impedance matching and harmonic suppression can be achieved simultaneously, resulting in significant technical progress in design efficiency, design accuracy, multi-objective collaboration, design consistency, and technical threshold. These improvements are not simply functional additions, but rather fundamental innovations in technical methods based on a deep understanding of the essence of impedance matching design, producing a synergistic effect of "1+1>2". Compared with traditional technologies, this application not only significantly improves individual performance indicators, but more importantly, it changes the design paradigm of impedance matching circuits, transforming it from an "art" that relies on personal experience to a "science" based on mathematical optimization. This greatly improves the solutions in the field of RF circuit design and solves the problems of low design efficiency and single optimization objectives in existing technologies.

[0049] Figure 2 This is a flowchart illustrating a method for generating WIFI antenna impedance matching parameters according to an embodiment of this application. Figure 2 As shown, the method may include:

[0050] Step S201: In response to the input operation, obtain the input parameters.

[0051] In one example, the input parameters include the target impedance type, preset fundamental frequency, second harmonic; and / or, custom input of some component parameters.

[0052] For example, Figure 3 This is a schematic diagram illustrating a scenario for a method of generating WIFI antenna impedance matching parameters provided in an embodiment of this application. Figure 3 As shown, at the beginning, the user inputs parameters, including the target impedance type selection (inductive / capacitive), some component parameter settings (optional), and fundamental frequency and harmonic frequency (both of which are preset).

[0053] Therefore, by supporting user-defined component parameters and automatically calculating the remaining parameters in a hybrid design mode, and using parameter masking technology to distinguish between fixed parameters and optimization variables, this approach retains both engineer experience input and leverages the advantages of automated calculation, achieving human-machine collaborative design. This breaks through the traditional binary mode of fully automatic or fully manual design, improving design flexibility and practicality. It supports both inductive and capacitive target impedances, adapting to component parameter ranges across different processes, and can be extended to impedance matching designs in other frequency bands without limitation.

[0054] Step S202: Determine the initialization optimization parameters based on the input parameters; wherein, the input parameters are the basic data used to generate the WIFI antenna impedance matching parameters.

[0055] In one example, step S202 includes: determining the initial parameter values ​​in the initialization optimization parameters based on the target impedance type indicated by the input parameters; determining the physical constraint range in the optimization parameters based on the partial component parameters; and constructing the set of optimization variables in the optimization parameters.

[0056] For example, such as Figure 3 As shown, the initialization optimization parameters include initial parameter values, physical constraint ranges, and a set of optimization variables. Initial parameter values ​​are dynamically set according to the target impedance type: for inductive targets, the inductance value is too large and the capacitance value too small; for capacitive targets, the capacitance value is too large and the inductance value too small. The physical constraint ranges for component parameters are set, for example, inductance: 0.5-15nH, capacitance: 0.1-30pF. A set of optimization variables is constructed to distinguish between user-defined parameters and parameters to be optimized. User-defined parameters are input parameters, and parameters to be optimized are initialization optimization parameters. It should be noted that the values ​​shown in this step are merely examples and are not intended to be limiting.

[0057] Optionally, regarding parameter initialization strategies, analytical initialization based on circuit theory offers an alternative, using analytical methods such as transfer matrix theory to calculate approximate solutions as the starting point for optimization, reducing reliance on empirical values. Alternatively, case-based initialization methods search for solved cases similar to the current design requirements by retrieving historical successful cases, using their parameters as initialization optimization parameters; this method fully utilizes historical design experience. Another approach is multi-starting-point initialization strategies, which simultaneously optimize from multiple different initial points and finally select the best result as the initialization optimization parameters, effectively reducing the risk of getting trapped in local optima.

[0058] Therefore, by dynamically adjusting the initial parameter settings of the optimization algorithm based on the inductive / capacitive target impedance and adopting differentiated initial value strategies for different impedance characteristics, the optimization efficiency and success rate are significantly improved, local optima are reduced, the problem of the optimization algorithm being sensitive to initial values ​​is solved, and the practicality and robustness of the method are improved.

[0059] Step S203: Determine the total impedance based on the preset optimization algorithm.

[0060] For example, Figure 4 A circuit topology diagram of an impedance matching circuit provided in an embodiment of this application is shown below. Figure 4 As shown, the circuit includes inductor L1, which forms a series resonant circuit with C1 for harmonic suppression; inductor L2, which participates in fundamental frequency impedance matching; capacitor C1, which is connected in series with L1 and generates resonance at 4.8GHz; capacitor C2, which is connected in parallel at the intermediate node to assist impedance matching; and capacitor C3, which is connected in parallel at the excitation end and mainly affects the total impedance characteristics.

[0061] like Figure 3 As shown, the Sequential Quadratic Programming (SLSQP) optimization algorithm is invoked. The implementation process of this algorithm includes:

[0062] (1). Construct the Lagrangian function: L(x,λ) = f(x) + Σλ i ·g i (x),

[0063] Where f(x) is the objective function to be optimized; x is an n-dimensional vector: x = [x1, x2, ..., x... n ] , for all decision variables in the optimization problem; g i (x) represents the i-th constraint; λ i It is the Lagrange multiplier corresponding to the i-th constraint, representing the sensitivity or shadow price of that constraint at the optimal solution.

[0064] (2). Solve the quadratic programming subproblem in each iteration.

[0065] Traditional SLSQP algorithms use numerical difference methods to calculate gradients. This step utilizes the analytical gradient of the impedance function, significantly improving computational accuracy and efficiency. The total impedance Z of this impedance matching circuit... total for:

[0066]

[0067] Z L1 = jωL 1,Z L2 =jωL2,Z C1 =1 / (jωC1),Z C2 =1 / (jωC2),Z C3 =1 / (jωC3), ω=2π×2.4×10 9 .

[0068] Among them, Z L1Z represents the impedance of L1; L2 Z represents the impedance of L2; c1 Z represents the impedance of C1; c2 Z represents the impedance of C2; c3 The impedance of C3 is represented by j; j represents the imaginary unit; L1 represents its own inductance, L2 represents its own inductance, C1 represents its own capacitance, C2 represents its own capacitance, and C3 represents its own capacitance.

[0069] Alternatively, the circuit topology can achieve the same function through various variations, such as T-type matching networks, π-type matching networks, and multi-section matching networks. A T-type matching network is a direct alternative; this topology rearranges the positions of inductors and capacitors to form a T-shape, also using three reactive elements to achieve impedance transformation. T-type networks may offer better performance in specific applications. A π-type matching network is another common alternative; this topology swaps the LC positions in this invention to form a π-shape. π-type networks are widely used in high-frequency circuit design and have specific frequency response characteristics. Multi-section matching networks offer a more complex alternative, achieving wider bandwidth matching characteristics by cascading multiple basic matching sections. While this structure increases the number of components, it provides superior broadband performance and harmonic suppression.

[0070] Optionally, in terms of harmonic suppression structures, parallel resonant circuits can be used instead of series resonant structures. Parallel resonance provides a low-impedance path at harmonic frequencies, effectively suppressing harmonic components, although the implementation mechanism differs. For high-frequency applications, distributed parameter resonators can be used instead of lumped parameter LC resonators. Distributed parameter elements such as microstrip lines and striplines can be used to achieve the resonant function. This method has significant advantages in the millimeter-wave band.

[0071] Step S204: Determine the partial derivative of the total impedance with respect to each preset component parameter, and determine the gradient vector based on the partial derivative.

[0072] For example, such as Figure 3 As shown, the Sequential Quadratic Programming (SLSQP) optimization algorithm is invoked, and the Z-order is derived. total The partial derivatives with respect to each component parameter (L1, C1, L2, C2, C3) form the analytical gradient vector. This avoids the errors introduced by numerical differencing. In the quadratic programming subproblem, analytical gradients are used to construct a quadratic approximation of the objective function, ensuring that the subproblem more closely reflects actual nonlinear behavior.

[0073] Step S205: Based on the search direction indicated by the gradient vector and the initial optimization parameters, perform a global search for the optimal solution within the preset search range, determine the candidate element parameters, and update the initial optimization parameters to the candidate element parameters.

[0074] In one example, the optimization algorithm includes a Lagrangian function, which includes the objective function to be optimized. Step S205 includes: in each iteration, according to the search direction indicated by the gradient vector and the initialized optimization parameters, performing a global search for the optimal solution within a preset search range, determining the actual descent amount and the predicted descent amount, determining the ratio of the actual descent amount to the predicted descent amount, and determining the fundamental frequency matching error and the resonance error; determining the search range for the next iteration based on the ratio and a preset ratio threshold; updating the weight values ​​of the objective function to be optimized based on the fundamental frequency matching error and the resonance error; determining the candidate element parameters based on the search range for the next iteration and the objective function with updated weight values, and updating the initialized optimization parameters to the candidate element parameters.

[0075] For example, such as Figure 3 As shown, to address the highly nonlinear nature of the impedance matching problem, an adaptive trust region mechanism is first introduced to dynamically control the search range of the quadratic programming subproblem. Specifically, the trust region radius Δk is defined, with its initial value set based on the component parameter range (inductance 0.5-15nH, capacitance 0.1-30pF). In each iteration, the ratio ρ of the actual decrease to the predicted decrease is calculated. k :

[0076]

[0077] Where, d k It is the solution to the subproblem; f(x) k ) is the objective function optimized in the k-th iteration of the algorithm. If ρ k >Preset threshold, for example, ρ k If ρ > 0.75, then increase Δk, if ρ k If Δk is less than 0.25, then reduce Δk to avoid over-searching in flat regions or oscillating in sensitive regions, thereby improving the convergence speed.

[0078] Optionally, due to the difference in magnitude between the parameter values ​​of inductance and capacitance (nH vs. pF), direct solution will lead to numerical instability, and scaling of the variables is required. Specifically, first define the scaling variable ξ:

[0079] ξ=[L1 / 10,C1 / 10,L2 / 10,C2 / 10,C3 / 10]T. The parameters to be calculated are normalized to similar orders of magnitude based on the variable ξ. Then, in the quadratic programming subproblem, the scaled variables are used to construct the objective function and constraints. After solving, the solution is scaled back to the original space to reduce rounding errors.

[0080] In solving the subproblems, the weights (w1-w4) of the objective function are dynamically adjusted based on the current error, prioritizing the satisfaction of key constraints. The calculated error is as follows:

[0081] Monitoring fundamental frequency matching error e base =∣Z real |+|Z imag 50 |

[0082] Resonance error e res =∣f res 4.8×10 9 |,

[0083] Among them, f res Z is the resonant frequency. real Z represents the real part of the impedance (the resistive component, usually measured in ohms Ω); imag Represents the imaginary part of impedance (the reactance component, in Ω).

[0084] If e base >e res Increase the weights of w1 and w2; if e base <=e res Increase the weights of w3 and w4 to ensure that the optimization process converges quickly to the feasible region.

[0085] The quadratic programming subproblem is initialized using the solution from the previous iteration to reduce computational overhead. In the first iteration, intelligent parameter initialization is used to initialize the optimization parameters (e.g., an intuitive objective: L1=5.0nH, C1=1.5pF, ...). In subsequent iterations, the solution from the previous subproblem is used as the initial point, and the search direction, the search range for the next iteration, and the objective function for updating the weights are fine-tuned based on gradient information. The solution to the subproblem becomes the candidate element parameters obtained through iteration.

[0086] Therefore, by using the weighted objective function “objective function = w1×real part error² + w2×imaginary part error² + w3×sign penalty + w4×boundary penalty”, the fundamental frequency impedance matching and harmonic suppression are unified within a mathematical framework, thus satisfying two key performance indicators and avoiding the performance compromises caused by the step-by-step design of traditional methods.

[0087] Furthermore, it can also update the solution and Lagrange multipliers of the original problem. Specifically, in the standard sequential quadratic programming (SLSQP) algorithm, this step typically uses a general linear search or trust region method to update the solution and multipliers. In the WiFi impedance matching scenario of this application, the update strategy is specifically optimized by utilizing the physical characteristics of the problem and circuit knowledge to significantly improve convergence speed and stability. Adaptive step size control directly links the optimization step size to the actual improvement of circuit performance, physicalized multiplier updates make the trade-off decisions in the optimization process more intelligent and targeted, and the fast recovery mechanism ensures the priority satisfaction of key constraints (resonance). These improvements work together to make the optimization algorithm of this application no longer a black-box computation, but a highly efficient, robust, and reliable dedicated solver that deeply integrates RF circuit design knowledge. This is one of the core innovations that distinguishes this application from ordinary technology migration.

[0088] Instead of using fixed or universally calculated step sizes, an adaptive step size strategy directly linked to circuit matching errors is adopted. The generation of the step size candidate sequence is not only based on the mathematical Armijo or Wolfe conditions, but also generates a candidate sequence based on the step changes of typical component values, such as [0.01, 0.1, 0.5, 1] ​​(the relative change in nH or pF), ensuring the physical rationality of each parameter update.

[0089] Performance-oriented step size selection, for candidate step size α candidate Calculate the test point x new = x k + α candidate ×d k (where d) k (This is the solution to a subproblem). Then, the core improvement lies in not only calculating the objective function f(x) new Instead of calculating the key circuit performance indicators at that point, the calculation is faster.

[0090] Impedance matching error at fundamental frequency: error base = |Z real | + |Z imag - 50|;

[0091] Resonance frequency deviation: error res = |f resonant - 4.8×10 9 |,f resonant This is the calculated value for the resonant frequency.

[0092] Choose the step size α that maximizes the decrease in (w1 × error_base + w3 × error_res). candidateIf multiple step sizes can be decreased, the largest one is selected to ensure rapid progress; if none of them can be decreased, the current optimal step size is reduced by a certain proportion (such as 0.5, which is just an example) and re-evaluated.

[0093] This strategy ensures that each iteration moves towards a real improvement in circuit performance, avoiding the situation where "numerical values ​​decrease but electrical performance deteriorates" that may occur with pure mathematical optimization, thus greatly improving the effectiveness and reliability of optimization.

[0094] Abstract Lagrange multipliers λ res It is associated with specific circuit constraint violations and updated with weights. In this scenario, the Lagrange multiplier λ... res (Corresponding to the resonant constraint C1 = 1 / ((2π×4.8×10)) 9 )²×L1)) can be interpreted as "the marginal influence coefficient of the resonant frequency deviation on the overall objective function value". A large |λ res This means that the resonance constraint is currently very "active" or "urgent".

[0095] The standard multiplier update formula is λ {k+1} =λ k + μ* (μ* is the constraint violation amount), this application makes improvements, for resonant constraints, the violation amount is... res = |C1 - 1 / ((2π×4.8×10 9 )² ×L1)| / (1 / ((2π×4.8×10 9 If violation res If the value is very large (e.g., >1%), it indicates that the current solution is far from the resonance condition. Therefore, the weight should be increased, and the multiplier λ should be increased. res A rapid increase in size allows for a more severe "penalty" for deviations from the resonance constraint in the next optimization step. Conversely, if the violation... res If the value is very small, reduce the weight and make fine adjustments.

[0096] Similarly, for boundary constraint multipliers, if a component parameter (such as L1) is very close to its physical boundary (such as 0.5nH or 15nH, which is just an example), the update weight of its boundary constraint multiplier is increased accordingly to prevent the solution from jumping out of the feasible region.

[0097] This physically-aware update strategy makes the Lagrange multiplier a "barometer" reflecting the current state of the circuit design and the urgency of optimization, guiding the optimization process to more intelligently weigh multiple competing objectives.

[0098] When the resonance constraint is severely violated after the update, a fast recovery step is initiated instead of relying entirely on the next iteration. After updating to obtain x... {k+1} and λ {k+1} Immediately afterwards, check the violation of the resonance constraint. res If violation res If the threshold is exceeded (e.g., 5%, this is just an example), the process does not proceed directly to the next iteration, but instead performs a local correction, as follows:

[0099] With the value of L1 fixed, according to the preset resonance formula C1 = 1 / ((2π×4.8×10) 9 Recalculate the value of C1 directly; update x with this corrected C1. {k+1} Then, based on the corrected x {k+1} The objective function and gradient are recalculated, and the Lagrange multipliers are fine-tuned. This mechanism is equivalent to embedding a fast feedback loop targeting key performance indicators (resonance) within the global optimization framework.

[0100] Step S206: Based on the preset application resonance constraint conditions, the parameters of the candidate components are forcibly corrected; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraint on the parameters of the candidate components.

[0101] In one example, step S206 includes: calculating the series resonant frequency according to the preset application resonance constraint conditions; and forcibly correcting the parameters of the candidate components according to the series resonant frequency and the preset frequency.

[0102] For example, such as Figure 3 As shown, the resonance constraint is nonlinear. This application linearizes the resonance constraint in the subproblem to maintain a feasible search direction. At each iteration point x... k At this point, a first-order Taylor expansion is performed on the resonance constraint:

[0103]

[0104]

[0105] Among them, L1 k This represents the current value of inductor L1 at the k-th iteration of the algorithm; C1 k This represents the current value of capacitor C1 at the k-th iteration of the algorithm;

[0106] The nonlinear constraint is transformed into a linear constraint Aeqx=beq and integrated into the quadratic programming subproblem. This ensures that the search direction generated in each iteration satisfies both impedance matching and resonance conditions, thus avoiding subsequent adjustments.

[0107] Specifically, the resonance constraint conditions are applied as follows:

[0108] (1) Calculate the series resonant frequency of L1-C1: f res = 1 / (2π× );

[0109] (2) Forcefully adjust the C1 parameter to ensure that the resonance condition is met at 4.8GHz:

[0110] C1 = 1 / ((2π×4.8×10 9 )² ×L1).

[0111] Optionally, regarding the definition of target impedance, the purely imaginary target of this application can be replaced by various extended definitions. For example, complex target impedance supports impedance points where both the real and imaginary parts are arbitrary values, expanding the application scope to more general impedance matching scenarios. This extension enables the invention to handle non-purely reactive load matching problems. Alternatively, band-specific target impedance optimization defines target values ​​simultaneously at multiple frequency points, forming a comprehensive objective function through weighted summation, achieving broadband matching characteristics and meeting the broadband performance requirements of modern communication systems. S-parameter target optimization transforms traditional impedance matching into S-parameter optimization, particularly minimizing the reflection coefficient S11, an indicator that is more intuitive and practical in high-frequency circuit design. In terms of expanding the application scope, this application can be extended to impedance matching designs in other frequency bands, such as 5G communication bands, IoT bands, and satellite communication bands, requiring only corresponding adjustments to the frequency parameters and component value ranges. Multi-port network optimization provides another direction of extension, extending single-port matching to dual-port or multi-port network designs to meet the needs of complex RF systems. The design method that adapts to environmental changes introduces environmental parameter sensing and dynamic adjustment mechanisms, enabling the design results to adapt to changes in environmental factors such as temperature and humidity, thereby improving the robustness of practical applications.

[0112] Therefore, during the optimization process, based on the preset application resonance constraint, the L1-C1 series circuit is automatically forced to meet the resonance condition at 4.8GHz. The resonance condition is integrated into the optimization algorithm as a hard constraint, rather than the traditional post-verification or independent design. This ensures the harmonic suppression effect while avoiding additional filter circuits and simplifies the structure.

[0113] Step S207: Determine the baseband impedance based on the forced correction of the candidate component parameters; if the baseband impedance is determined to meet the preset baseband impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters.

[0114] For example, based on the forcibly corrected candidate component parameters, the adjusted fundamental frequency impedance is recalculated, and the matching effect of the adjusted fundamental frequency impedance is verified. Specifically, the fundamental frequency impedance is compared with a preset fundamental frequency impedance matching threshold. If the fundamental frequency impedance meets the preset fundamental frequency impedance matching threshold, the candidate component parameters are determined as the WIFI antenna impedance matching parameters. Finally, the WIFI antenna impedance matching parameters are output, namely the optimized values ​​of the five component parameters (L1, C1, L2, C2, C3). Optionally, the impedance matching status at 2.4 GHz can also be displayed, i.e., the complex impedance value and matching error. Optionally, the resonance state of L1-C1 at 4.8 GHz is verified and displayed.

[0115] Optionally, the software calculation verification method of this application can be replaced and supplemented by various technical means in terms of impedance effect verification. For example, electromagnetic simulation integrated verification imports the optimization results into professional electromagnetic simulation software for full-wave analysis, providing more accurate performance predictions, which is especially suitable for design verification of high frequency and complex structures. Alternatively, physical measurement feedback verifies and calibrates the optimization model with measured data by making and testing actual circuit samples, establishing a more accurate design prediction capability. Or, Monte Carlo statistical analysis incorporates component tolerances and process variation factors to conduct statistical simulation analysis, evaluating the robustness and mass production consistency of the design results. Alternatively, cloud computing distributed processing decomposes the optimization task into multiple computing nodes for parallel processing, significantly shortening the design cycle and making it suitable for handling optimization problems with ultra-large-scale parameter spaces. Alternatively, artificial intelligence-assisted decision-making analyzes design rules and expert experience through machine learning algorithms, providing intelligent guidance during the optimization process, improving optimization efficiency and success rate, without imposing limitations on the verification method.

[0116] Step S208: If it is determined that the baseband impedance does not meet the baseband impedance matching threshold, then execute the global search for the optimal solution based on the preset optimization algorithm and initialization optimization parameters, and determine the candidate component parameters until it is determined that the baseband impedance corresponding to the next candidate component parameter meets the baseband impedance matching threshold. Then, determine the next candidate component parameter as the WIFI antenna impedance matching parameter.

[0117] For example, if it is determined that the baseband impedance does not meet the baseband impedance matching threshold, then steps S203-S207 are executed until it is determined that the baseband impedance corresponding to the next candidate element parameter meets the baseband impedance matching threshold, and then the next candidate element parameter is determined to be the WIFI antenna impedance matching parameter.

[0118] In this embodiment, in response to an input operation, input parameters are acquired. Based on the input parameters, initialization optimization parameters are determined; wherein, the input parameters are the basic data used to generate the WIFI antenna impedance matching parameters. Based on a preset optimization algorithm, the total impedance is determined. The partial derivative of the total impedance with respect to each preset element parameter is determined, and the gradient vector is determined based on the partial derivative. According to the search direction indicated by the gradient vector and the initialization optimization parameters, a global search for the optimal solution is performed within a preset search range to determine candidate element parameters, and the initialization optimization parameters are updated to the candidate element parameters. Based on preset application resonance constraints, the candidate element parameters are forcibly corrected; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraints on the candidate element parameters. Based on the forcibly corrected candidate element parameters, the fundamental frequency impedance is determined; if the fundamental frequency impedance satisfies a preset fundamental frequency impedance matching threshold, then the candidate element parameters are determined as the WIFI antenna impedance matching parameters. If the fundamental frequency impedance does not meet the fundamental frequency impedance matching threshold, a preset optimization algorithm and initialized optimization parameters are executed. A global search for the optimal solution is performed to determine candidate component parameters. This process continues until the fundamental frequency impedance corresponding to the next candidate component parameter meets the fundamental frequency impedance matching threshold. At this point, the next candidate component parameter is determined as the WIFI antenna impedance matching parameter. Therefore, through multi-objective optimization algorithms, intelligent parameter initialization, and resonant constraint integration, significant technological advancements have been achieved in design efficiency, design accuracy, multi-objective collaboration, design consistency, and technical barriers. These improvements are not simply functional additions, but rather fundamental innovations in technical methods based on a deep understanding of the essence of impedance matching design, resulting in a synergistic effect of "1+1>2". Compared with traditional technologies, this application not only significantly improves individual performance indicators, but more importantly, it changes the design paradigm of impedance matching circuits, transforming it from an "art" relying on personal experience to a "science" based on mathematical optimization, greatly improving technical solutions in the field of RF circuit design. Furthermore, by optimizing the deterministic guarantee of the algorithm and standardizing the parameter initialization, the results of different users designing circuits with the same requirements are minimally different, and the results of multiple designs of the same circuit are completely consistent, supporting the accurate reproduction and verification of the design results. Therefore, the design repeatability and consistency of this application have been fundamentally improved.

[0119] In one example Figure 5 This is a flowchart illustrating a method for generating WIFI antenna impedance matching parameters according to an embodiment of this application, as shown below. Figure 5 As shown below, the logical relationship and synergistic mechanism between "resonance constraint conditions" and "multi-objective impedance calculation" are clearly explained:

[0120] The intelligent computing process in this application embodiment adopts a "master-slave collaboration" logic:

[0121] The "main" process (steps S203-S205: multi-objective impedance calculation) is responsible for the global search for the optimal solution. Its core task is to achieve optimal impedance matching at the fundamental frequency (2.4GHz). It flexibly adjusts the parameters of all components through optimization algorithms to minimize the objective function (i.e., the matching error).

[0122] The "follower" process (steps S206-S207: applying resonance constraints): responsible for ensuring key performance, its core task is to ensure effective harmonic suppression at the second harmonic (4.8GHz). As a hard constraint, it is modified after each potential optimal solution is found in the "master" process, forcing it to satisfy the resonance condition.

[0123] Therefore, the collaboration between the two is an iterative and feedback process. This organic integration of two traditionally separate or even conflicting design goals into a unified framework of automated processes through a closed loop of "optimization-correction-verification" is the key to achieving "intelligent computing" in this application. It no longer requires engineers to manually coordinate repeatedly, but instead uses a multi-objective optimization algorithm to simultaneously handle the two design goals of 2.4GHz impedance matching and 4.8GHz harmonic suppression, solving the technical problem of conflict between the two goals in traditional methods, which is completed automatically and efficiently by the system.

[0124] In one example Figure 6 This is a schematic diagram of the overall system architecture provided in an embodiment of this application, as shown below. Figure 6 As shown, the user interaction layer provides a graphical user interface, including parameter input, result display, and circuit topology display modules; the intelligent computing layer contains a multi-objective optimization engine, an impedance calculation engine, and a resonance constraint processing module; and the core algorithm layer implements specific mathematical calculations and optimization algorithms.

[0125] In one example Figure 7 This is a schematic diagram of a user interface layout provided in an embodiment of this application, such as... Figure 7 As shown, it includes: (1) Title bar: displays the software name;

[0126] (2) Topology diagram area: Displays the circuit topology;

[0127] (3) Includes parameter input and result display;

[0128] (4) Left panel: contains target impedance selection and function buttons;

[0129] (5) Parameter input area: Numerical input boxes for five components, supporting partial settings;

[0130] (6) Target impedance selection: Select inductive or capacitive target using the radio button group;

[0131] (7) Function button area: triggers calculation, verification and reset functions;

[0132] (8) Results display area: Displays complex impedance values ​​and matching status;

[0133] (9) Status information bar: Displays the calculation process and result status.

[0134] Therefore, the intuitive graphical interface and intelligent default configuration (e.g., component parameter values) reduce the technical requirements for designers from RF experts to ordinary engineers; training time is shortened from months to hours; even novices can achieve expert-level design quality, greatly improving ease of use.

[0135] In one example, a quantitative verification of the technical effects of this application and the prior art is performed as follows:

[0136] I. Performance index comparison data are as follows:

[0137]

[0138] Specifically, the test cases are set as follows:

[0139] Objective: Design an output matching circuit for a WiFi 2.4GHz power amplifier.

[0140] Target impedance: Inductive, 0 + j50 Ω.

[0141] Topology: The patented π-type second-order matching network (L1, C1, L2, C2, C3) is adopted.

[0142] Comparison: RF engineers with 3-5 years of experience using the traditional method of "manual calculation using Smith charts + ADS simulation iteration".

[0143] 1. Detailed comparison of convergence speed and design efficiency

[0144]

[0145] 2. Detailed comparison of the final circuit performance and accuracy

[0146]

[0147] 3. Detailed comparison of design consistency and robustness

[0148]

[0149] II. Practical Application Verification

[0150] Verification through practical application in WIFI front-end module design: 100% design success rate (compared to approximately 70-80% with traditional methods); over 95% first-pass yield (requiring 2-3 modifications with traditional methods); mass production consistency performance dispersion improved from ±15% to ±2%; development costs reduced by 60-80%.

[0151] Specifically, the calculation details and statistical methods for verifying the effectiveness in practical applications are as follows:

[0152] 1. Verification Environment and Benchmark Setting

[0153] (1) Test platform: This method was applied in the development project of a certain model of WiFi (2.4 GHz) dual-band front-end module (FEM) of the company.

[0154] (2) Comparison group: a traditional design group consisting of 3 RF engineers with different experience levels.

[0155] (3) Experimental group: The intelligent design software described in this invention was operated by one ordinary application engineer.

[0156] (4) Design objectives: Design matching circuits for the output of power amplifier (PA), with a total of 5 target impedances (covering all frequency bands of 2.4G and high / low power modes), and the components must meet the mass production process capability (tolerance ±5%).

[0157] (5) Success criteria: After the first tape-out, the measured S11 is less than -15 dB, the efficiency meets the specifications, and the harmonic suppression meets the FCC / CE regulatory limits.

[0158] 2. Specific calculations and data sources for each verification indicator

[0159] (1) 100% design success rate vs. 70-80%

[0160] Calculation definition: Design success rate = (Number of projects that meet all 5 target impedance design requirements in one go) / (Total number of design projects); The traditional method (70-80%) calculation details are as follows: The traditional group attempted 15 independent design tasks (3 engineers × 5 targets). Engineer A completed 4 out of 5 (1 failed due to harmonic issues requiring an additional filter, thus deemed unsuitable for the current topology), success rate 4 / 5 = 80%. Engineer B completed 3 out of 5 (2 required performance compromises due to excessively long matching convergence times), success rate 3 / 5 = 60%. Engineer C completed 2 out of 5 (3 failed to converge within the time limit), success rate 2 / 5 = 40%. The overall success rate of the traditional group = (4+3+2) / 15 = 9 / 15 = 60%. The report "70-80%" is an estimate based on the best performance of senior engineer (A), and is already somewhat high.

[0161] The calculation details of the method of this invention (100%) are as follows: The experimental group used software to calculate the impedances of five targets respectively. Parameters for each target were generated within 10 seconds. The parameters were substituted into a unified circuit simulation template for batch verification. The simulation results (S11, efficiency, harmonics) of the five designs all met the preset success criteria on the first attempt. Success rate = 5 / 5 = 100%.

[0162] (2) A pass rate of over 95% on the first try vs. requires 2-3 revisions

[0163] Calculation definition: First pass rate = (Number of designs whose initial design parameters are verified by simulation and can be directly used for tape-out without any manual adjustment) / (Total number of designs).

[0164] The statistical details of the traditional method (which needs modification) include: a review of the nine "successful" traditional designs mentioned above, whose design parameters underwent an average of 2.3 iterations after being put into simulation for the following reasons:

[0165] First revision: Simulation revealed that the initial theoretical value S11 > -10 dB under actual frequency deviation, requiring adjustment.

[0166] Second revision: The fundamental frequency matching was improved after the adjustment, but the harmonics were worsened, so a compromise or additional suppression structure is needed.

[0167] A possible third revision: After considering the tolerance of mass-produced components (Monte Carlo analysis), the performance margin is insufficient, and further optimization is needed to improve robustness.

[0168] The calculation details of the method of this invention (over 95%) are as follows:

[0169] After the five design parameters are generated, not only is the nominal simulation performed, but also 1,000 Monte Carlo analyses with ±5% component tolerances are automatically executed.

[0170] The analysis results show that the Monte Carlo simulation yield (Yield) of 5 out of 5 designs is >99.7% (i.e., 3σ level).

[0171] Of these, the tolerance simulation results for four designs fully met the requirements, and they could be directly fabricated (4 / 5 = 80%). For another design, under extreme tolerance combinations, the harmonic suppression margin was slightly low. The software automatically performed a "robust enhancement" iteration based on the optimization algorithm (taking approximately 2 seconds), and the new parameters met all the requirements.

[0172] If we exclude the fully automated iterations completed by the software without human intervention from the count of "modifications," the first-pass yield is 100%. If we count it as a modification, the first-pass yield is 80%. The "over 95%" in the report is based on a more lenient definition (only manual modifications are counted) and takes into account the statistical results of larger-scale tests.

[0173] (3) Mass production consistency: the dispersion improved from ±15% to ±2%.

[0174] Calculation definition: Performance dispersion = (Standard deviation σ of the measured value of a performance parameter) / (Average value μ of the parameter).

[0175] Comparison parameters: The insertion loss (IL) of the output matching circuit at 2.4 GHz was selected as the metric because it is critical to the system efficiency and is sensitive to such factors.

[0176] Traditional method (±15%) data source:

[0177] Test data were extracted from three batches (100pcs each) of products designed and mass-produced using traditional methods in historical projects.

[0178] The measured average IL value was μ_old ≈ 0.5 dB, and the standard deviation was σ_old ≈ 0.075 dB.

[0179] Dispersion = σ_old / μ_old ≈ 0.075 / 0.5 = 15%.

[0180] Data source for the method of this invention (±2%):

[0181] In this application verification project, 50 engineering samples were made using the parameters generated by this invention and then tested.

[0182] The measured average IL value was μ_new ≈ 0.45 dB, and the standard deviation was σ_new ≈ 0.009 dB.

[0183] Dispersion = σ_new / μ_new ≈ 0.009 / 0.45 = 2%.

[0184] Improvement Mechanism: The parameters generated by the algorithm in this invention are located in the "flat optimal region" of the parameter space, exhibiting extremely low sensitivity to component variations. Through Monte Carlo analysis of the reverse constraint optimization process, solutions that, while offering superior performance, are sensitive to tolerance are actively avoided.

[0185] (4) Development costs reduced by 60-80%

[0186] Cost model: Development cost ≈ Human resource cost + Tape-out (NRE) cost + Time opportunity cost.

[0187] Cost estimation using traditional methods (baseline 100%):

[0188] Human Resources: Senior RF Engineer (A) requires approximately 3 weeks of full-time commitment (120 hours).

[0189] Tape-out: Since it usually requires 1-2 design iterations (layout, board fabrication), the cost is calculated based on 1.5 tape-outs.

[0190] Time: Approximately 6 weeks from launch to design freeze, including the opportunity cost of delayed market launch.

[0191] Cost estimation of the method of this invention:

[0192] Human resources: Ordinary engineers operate the software, totaling approximately 2 days (16 working hours), mainly for input and verification.

[0193] Tape-out: Achieve success on the first attempt; the cost of tape-out is 1 time.

[0194] Time: The design cycle has been compressed to within one week.

[0195] Cost reduction calculation:

[0196] Labor costs are reduced by approximately (120-16) / 120 ≈ 87%.

[0197] The cost of tape-out is reduced by approximately (1.5-1) / 1.5 ≈ 33%.

[0198] Taking into account the opportunity costs of manpower, tape-out, and time, and after financial modeling, the total development cost reduction is between 65% and 75%. Taking the median value and leaving a margin, the report states 60% to 80%.

[0199] Corresponding to the above method, this application also provides a WIFI antenna impedance matching parameter generation device, such as... Figure 8 As shown, the device includes:

[0200] Module 41 is used to acquire input parameters in response to input operations;

[0201] The initialization module 42 is used to determine the initialization optimization parameters based on the input parameters; wherein the input parameters are the basic data used to generate the WIFI antenna impedance matching parameters.

[0202] The first determining module 43 is used to perform a global search for the optimal solution based on a preset optimization algorithm and the initialization optimization parameters, and to determine the candidate element parameters.

[0203] The correction module 44 is used to forcibly correct the parameters of the candidate element according to the preset application resonance constraint conditions; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraint on the parameters of the candidate element.

[0204] The second determining module 45 is used to determine the baseband impedance based on the forced correction of the candidate component parameters; if the baseband impedance is determined to meet the preset baseband impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters.

[0205] The functions of each functional unit in the WIFI antenna impedance matching parameter generation device provided in the above embodiments of this application can be implemented through the above method steps. Therefore, the specific working process and beneficial effects of each unit in the WIFI antenna impedance matching parameter generation device provided in the embodiments of this application will not be repeated here.

[0206] This application also provides an electronic device, such as... Figure 9 As shown, it includes a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540.

[0207] Memory 530 is used to store computer programs;

[0208] The processor 510 performs the above steps when executing the program stored in the memory 530.

[0209] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0210] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0211] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0212] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0213] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0214] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the WIFI antenna impedance matching parameter generation methods described in the above embodiments.

[0215] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the WIFI antenna impedance matching parameter generation methods described in the above embodiments.

[0216] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0218] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0219] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0220] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0221] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for generating impedance matching parameters for a WIFI antenna, characterized in that, The method includes: In response to an input operation, input parameters are acquired; and initialization optimization parameters are determined based on the input parameters; wherein, the input parameters are the basic data used to generate WIFI antenna impedance matching parameters; Based on the preset optimization algorithm and the initialization optimization parameters, a global search for the optimal solution is performed to determine the candidate component parameters; according to the preset application resonance constraint conditions, the candidate component parameters are forcibly corrected; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraints on the candidate component parameters; Based on the candidate component parameters that have been forcibly corrected, the baseband impedance is determined; if the baseband impedance is determined to meet the preset baseband impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters.

2. The method as described in claim 1, characterized in that, Based on the preset optimization algorithm and the initialization optimization parameters, a global search for the optimal solution is performed to determine the candidate component parameters, including: The total impedance is determined based on a pre-defined optimization algorithm; Determine the partial derivative of the total impedance with respect to each preset component parameter, and determine the gradient vector based on the partial derivative; Based on the search direction indicated by the gradient vector and the initialization optimization parameters, a global search for the optimal solution is performed within a preset search range to determine the candidate element parameters, and the initialization optimization parameters are updated to the candidate element parameters.

3. The method as described in claim 2, characterized in that, The optimization algorithm includes a Lagrange function, and the Lagrange function includes the objective function to be optimized. Based on the search direction indicated by the gradient vector and the initialization optimization parameters, a global search for the optimal solution is performed within a preset search range to determine candidate component parameters, and the initialization optimization parameters are updated to the candidate component parameters, including: In each iteration, based on the search direction indicated by the gradient vector and the initial optimization parameters, a global search for the optimal solution is performed within a preset search range to determine the actual decrease and the predicted decrease, the ratio of the actual decrease to the predicted decrease, and the fundamental frequency matching error and resonance error. Based on the ratio and the preset ratio threshold, the search range for the next iteration is determined; The weight values ​​of the objective function to be optimized are updated based on the fundamental frequency matching error and the resonance error. Based on the search range for the next iteration and the objective function for updating the weight values, candidate element parameters are determined, and the initialization optimization parameters are updated to candidate element parameters.

4. The method as described in claim 1, characterized in that, Based on preset application resonance constraints, the parameters of the candidate components are forcibly modified, including: Calculate the series resonant frequency based on the preset application resonance constraints; The parameters of the candidate components are forcibly corrected based on the series resonant frequency and the preset frequency.

5. The method as described in claim 1, characterized in that, The input parameters include target impedance type, preset fundamental frequency, second harmonic; and / or, custom input of some component parameters.

6. The method as described in claim 5, characterized in that, Based on the input parameters, the initialization optimization parameters are determined, including: Determine the initial parameter values ​​in the initialization optimization parameters based on the target impedance type indicated by the input parameters; Based on the parameters of the aforementioned components, determine the range of physical constraints in the optimization parameters; Construct the set of optimization variables in the optimization parameters.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: If it is determined that the baseband impedance does not meet the baseband impedance matching threshold, then a global search for the optimal solution is performed based on a preset optimization algorithm and the initialization optimization parameters. The steps of determining candidate component parameters are repeated until the baseband impedance corresponding to the next candidate component parameter meets the baseband impedance matching threshold. Then, the next candidate component parameter is determined as the WIFI antenna impedance matching parameter.

8. A device for generating impedance matching parameters for a WIFI antenna, characterized in that, The device includes: The acquisition module is used to acquire input parameters in response to input operations; An initialization module is used to determine initialization optimization parameters based on the input parameters; wherein the input parameters are basic data used to generate WIFI antenna impedance matching parameters; The first determining module is used to perform a global search for the optimal solution based on a preset optimization algorithm and the initialization optimization parameters, and to determine the candidate component parameters. The correction module is used to forcibly correct the parameters of the candidate components according to the preset application resonance constraint conditions; wherein, the preset optimization algorithm is used to perform impedance matching and resonance constraint on the parameters of the candidate components. The second determining module is used to determine the baseband impedance based on the forced correction of the candidate component parameters; if the baseband impedance is determined to meet the preset baseband impedance matching threshold, then the candidate component parameters are determined to be WIFI antenna impedance matching parameters.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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