Self-adaptive frequency point sampling frequency sweep method, system and related equipment

Abstract

The present invention belongs to the field of wireless communication technology, and particularly relates to a self-adaptive frequency point sampling frequency sweep method, system and related equipment, and the frequency sweep method is applied to the simulation of radio frequency devices. The present invention adopts a self-adaptive frequency point sampling method that does not rely on a specific numerical method, and can perform accurate simulation for the part where the frequency response changes rapidly and perform rough simulation for other uninteresting areas, thereby improving the accuracy and efficiency of the design process of radio frequency devices, and at the same time, uses different evaluation indexes for the characteristics of Y parameters and S parameters in the frequency response to reduce the sampling points required for frequency sweeping and improve the frequency sweeping speed.

Description

[Technical Field]

[0001] The present invention relates to the field of wireless communication technology, and more particularly to a self-adaptive frequency point sampling frequency sweep method, system and related equipment. [Background technology]

[0002] With the development of wireless communication technology, the demand for radio frequency devices in various consumer electronics products is increasing. Accordingly, designers are placing increasing demands on the efficiency and accuracy of frequency sweeps in simulation software during the design process of radio frequency devices. While increasing the frequency sweep points typically improves the accuracy of the frequency sweep curve, it also significantly increases the time required for calculations. Therefore, a faster algorithm is needed to minimize calculation time while maintaining accuracy.

[0003] The basic principle of the finite element method is to calculate the frequency response at a few typical frequency points, and then use mathematical techniques such as Taylor series or Pade rational approximation to approximate and expand the entire matrix to estimate the frequency response at neighboring frequency points with minimal computational overhead. The problem is that Taylor series expansions based on methods such as AWE often only achieve high accuracy near the sampling frequency points. However, the frequency response estimation accuracy is poor far from the sampling frequency points or in areas where the frequency response changes rapidly. Areas where the frequency response changes rapidly are often areas of concern during the sampling process.

[0004] In practical simulations, high accuracy of frequency response is often required only for a certain region of the bandwidth, such as within the passband, while other regions require less accuracy. Conventional estimation methods indiscriminately divide the entire bandwidth into several narrow frequency bands and then perform frequency sweep analysis for each band individually, resulting in low efficiency. Therefore, conventional estimation methods may lack accuracy where the frequency response changes rapidly, while consuming large amounts of computational resources for sampling regions of no interest. Furthermore, conventional estimation methods often rely on specific numerical simulation algorithms. For example, the asymptotic waveform evaluation method requires decomposing the global matrix of the finite element method to obtain an analytical expression for the electric field value that changes with frequency. Such methods are not suitable for environments such as experimental measurements and machine learning, where explicit control equations do not exist. Summary of the Invention [Problem to be solved by the invention]

[0005] Embodiments of the present invention provide a self-adaptive frequency point sampling frequency sweep method, system, and related equipment that solves the problem that conventional frequency sweep methods rely on specific numerical values ​​and are not suitable for scenarios such as experimental measurements and machine learning. [Means for solving the problem]

[0006] In a first aspect, an embodiment of the present invention provides a self-adaptive frequency point sampling frequency sweep method applied to a simulation of a radio frequency device, the frequency sweep method comprising: The number of frequency points sampled n, the final number of frequency points sampled n', and the sampling range [f min ,f max ], sampling within the sampling range using a preset simulation method, and calculating the frequency response of the n-th sampling point, nStep S1, where the frequency response at is f(x); a step S2 of calculating the first and second derivatives of said frequency response f(x); performing frequency response curve estimation for the n-th sampling point based on the first derivative and the second derivative to obtain n-1 estimated curves, n The estimated curve corresponding to g xn (x) and the scope is [x n-1 ,x n+1 Step S3: The adjacent estimated curve g xn (x) and g xn+1 (x) Maximum error frequency point x p Sequentially obtaining n-1 maximum error frequency points, each having an error value; Step S5: calculating the error value of each of the maximum error frequency points according to a preset evaluation method to obtain an evaluation index for each of the maximum error frequency points; performing a ranking based on the evaluation index and selecting the first m maximum error frequency points as sampled points, wherein m is less than n, and both m and n are integers greater than 0; If the cumulative number of sampled points has not reached the final sampling number n' of frequency points, return to step S2 and repeat the process; Step S6: stopping the repetition when the cumulative number of sampled points reaches the final sampling number n' of frequency points; and step S7 of outputting all the sampled points as final sampling points.

[0007] Furthermore, in step S1, the sampling patterns within the sampling range according to the preset simulation method are all uniform sampling.

[0008] Furthermore, in step S2, the method used to calculate the first and second derivatives of the frequency response f(x) is the central difference method.

[0009] Furthermore, in step S3, the estimated curve g xn (x) is g xn (x)=f(x n )+f'(x n )*(xx n )+1 / 2*f”(x n )*(xx n ) 2 (1) The relation (1) is satisfied.

[0010] Furthermore, in step S4, the maximum error frequency point x p satisfies the following relation (2), x p =(x n +x n+1 ) / twenty two) The error value is Abs[g xn (x p )-g xn+1 (x p )] (3) The relation (3) is satisfied.

[0011] Furthermore, in step S5, if the type of the frequency response is a Y parameter, the preset evaluation method is Among the sampling points, the frequency point where the Y parameter value is maximum is the maximum value x max and frequency point minimum x min and the corresponding maximum estimation curve g xmax (x) and minimum value estimated curve g xmin Step S51 of calculating (x); The maximum value estimation curve g xmax (x) and the minimum value estimation curve g xmin (x) based on the resonance point x r and anti-resonance point x a and obtain the resonance point x r and the anti-resonance point xa a step S52 of adding the value of the sample points to be repeatedly used; The evaluation index of the maximum error frequency point is expressed as F(x p ) and the evaluation index F(x p )but, F(x p )=Abs[g xn (x p )-g xn+1 (x p )] (4) and step S53, which satisfies the relational expression (4).

[0012] Furthermore, in step S5, if the type of the frequency response is an S parameter, the preset evaluation method is Step S51 of defining the S-parameters of the frequency response to include a first S-parameter and a second S-parameter; Evaluation index F of the estimated curves of the first S parameter and the second S parameter 11 (x p ) and F 21 (x p ) and the evaluation index F of the first S parameter is calculated. 11 (x p ) satisfies the following relation (5), F 11 (x p )=Abs[g xn (x p )-g xn+1 (x p )] / Abs[(g xn (x p )+g xn+1 (x p ))] 2 (5) The evaluation index F of the second S parameter 21 (x p )but, F 21 (x p )=Abs[g xn (x p )-g xn+1 (x p )]*Abs[(g xn (xp )+g xn+1 (x p ))] 2 (6) Step S52, which satisfies the relational expression (6), The evaluation index of the maximum error frequency point is expressed as F(x p ) and the evaluation index F(x p )but, F(x p )=F 11 (x p )+F 21 (x p ) (7) and step S53, which satisfies the relational expression (7).

[0013] In a second aspect, embodiments of the present invention also provide a self-adaptive frequency point sampling frequency sweep system applied to a simulation of a radio frequency device, comprising: The number of frequency points sampled n, the final number of frequency points sampled n', and the sampling range [f min ,f max ] is determined, and a predetermined simulation method is used to sample within the sampling range, and a frequency response of the n-th sampling point is calculated, and the sampling point x is used to calculate the frequency response of the n-th sampling point. n an initialization module where the frequency response at is f(x); a derivative determination module for calculating first and second derivatives of the frequency response f(x); Based on the first derivative and the second derivative, a frequency response curve estimation is performed for the nth sampling point, which is used to obtain n-1 estimated curves, where n The estimated curve corresponding to g xn (x) and the scope is [x n-1 ,x n+1 ], and a frequency response estimation module, The adjacent estimated curve g xn (x) and g xn+1 (x) Maximum error frequency point x pa maximum error calculation module used to sequentially obtain n-1 maximum error frequency points, each of which has an error value; an evaluation module for calculating the error value of each of the maximum error frequency points according to a preset evaluation method to obtain an evaluation index of each of the maximum error frequency points; ranking the first m maximum error frequency points based on the evaluation index and selecting them as sampled points, where m is less than n, and both m and n are integers greater than 0; If the cumulative number of sampled points has not reached the final sampling number n' of frequency points, return to the frequency response estimation module and execute iteratively; a repeating module for stopping the repeat when the cumulative number of sampled points reaches the final sampling number n' of frequency points; and an output module for outputting all the sampled points as final sampled points.

[0014] In a third aspect, an embodiment of the present invention also provides a computer device, comprising: a memory; a processor; and a computer program stored in the memory and executable by the processor, the computer device implementing the steps of the method for self-adaptive frequency point sampling according to any one of the preceding embodiments when the processor executes the computer program.

[0015] In a fourth aspect, an embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being configured to implement steps in the method for self-adaptive frequency point sampling according to any one of the preceding embodiments when executed by a processor. [Effects of the Invention]

[0016] The beneficial effect achieved by this invention is that it employs a self-adaptive frequency point sampling method that does not rely on a specific numerical method, allowing it to perform accurate simulations of areas where the frequency response changes rapidly and rough simulations of other areas of no interest, thereby improving the accuracy and efficiency of the radio frequency device design process. At the same time, it uses different evaluation indicators for the Y-parameter and S-parameter characteristics of the frequency response, reducing the sampling points required for frequency sweeping and improving the frequency sweep speed. [Brief explanation of the drawings]

[0017] [Figure 1] 3 is a step flowchart of a frequency sweep method for self-adaptive frequency point sampling provided by an embodiment of the present invention; [Figure 2] FIG. 2 is a schematic diagram of S-parameter sampling according to a frequency sweep method of self-adaptive frequency point sampling provided by an embodiment of the present invention; [Figure 3] 1 is a schematic diagram of sampling a Y parameter according to a frequency sweep method of self-adaptive frequency point sampling provided by an embodiment of the present invention; [Figure 4] 1 is a structural schematic diagram of a self-adaptive frequency point sampling frequency sweep system provided by an embodiment of the present invention; [Figure 5] 1 is a structural schematic diagram of a computer device provided by an embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0018] In order to further clarify the objectives, technical means and advantages of the present invention, the present invention will be described in more detail below with reference to the drawings and examples. It should be understood that the specific examples described herein are only used to interpret the present invention and are not intended to limit the present invention.

[0019] Referring to FIG. 1, FIG. 1 is a step flowchart of a self-adaptive frequency point sampling frequency sweep method provided by an embodiment of the present invention, which is applied to the simulation of a radio frequency device, and the frequency sweep method includes the following steps:

[0020] S1: The number of frequency points sampled n, the final number of frequency points sampled n', and the sampling range [f min ,f max ] is determined, and a preset simulation method is used to sample within the sampling range, and a frequency response of the n-th sampling point is calculated, where the sampling point x n The frequency response at is f(x).

[0021] Furthermore, in step S1, the sampling patterns within the sampling range according to the preset simulation method are all uniform sampling.

[0022] Specifically, the preset simulation method may be any numerical simulation calculation method such as Matlab.

[0023] S2: Calculate the first and second derivatives of the frequency response f(x).

[0024] Furthermore, in step S2, the method used to calculate the first and second derivatives of the frequency response f(x) is the central difference method.

[0025] S3: Based on the first derivative and the second derivative, perform frequency response curve estimation for the n-th sampling point to obtain n-1 estimated curves, where n The estimated curve corresponding to g xn (x) and the scope is [x n-1 ,x n+1 ].

[0026] Specifically, when frequency response curve estimation is performed for the nth sampling point based on the first derivative and the second derivative, it is performed for every two adjacent sampling points, and can be further estimated using the Taylor formula to obtain the estimated curve.

[0027] Furthermore, in step S3, the estimated curve g xn (x) is g xn (x)=f(x n )+f'(x n )*(xx n )+1 / 2*f”(x n )*(xx n ) 2 (1) The relation (1) is satisfied.

[0028] S4: The adjacent estimated curve g xn (x) and g xn+1 (x) Maximum error frequency point x p until n-1 of the maximum error frequency points are obtained, each of the maximum error frequency points having an error value.

[0029] Specifically, in any range [x n ,x n+1 ] point x in m For each of the two sampling points, the estimated curve g xn (x) and g xn+1 (x), that is, g xn (x m ) and g xn+1 (x m ) can be predicted by

[0030] At the same time, in order to facilitate the calculation of subsequent evaluation indices and iterations, the embodiment of the present invention determines the maximum error frequency point x through data verification. p The value of is set to the midpoint of the sampling points corresponding to the estimated curve used in the calculation.

[0031] Furthermore, in step S4, the maximum error frequency point xp satisfies the following relation (2), x p =(x n +x n+1 ) / twenty two) The error value is Abs[g xn (x p )-g xn+1 (x p )] (3) The relation (3) is satisfied.

[0032] S5: Calculate the error value of each of the maximum error frequency points according to a preset evaluation method to obtain an evaluation index for each of the maximum error frequency points.

[0033] Specifically, the parameter type of the frequency response can be various types, such as S parameter and Y parameter, and although different parameters are essentially equivalent to the two-port network parameters of the two-port network and later, the process of obtaining the numerical values ​​is different, and in the embodiment of the present invention, the calculation method of the evaluation index is also different according to the type of numerical value.

[0034] Furthermore, in step S5, if the type of the frequency response is a Y parameter, the preset evaluation method is S51: Among the sampling points, the frequency point maximum value x at which the Y parameter value is maximum max and frequency point minimum x min and the corresponding maximum estimation curve g xmax (x) and minimum value estimated curve g xmin Calculate (x), S52: The maximum value estimation curve g xmax (x) and the minimum value estimation curve g xmin (x) based on the resonance point x r and anti-resonance point x a and obtain the resonance point x r and the anti-resonance point x a to the iteratively used sampling points, S53: The evaluation index of the maximum error frequency point is expressed as F(xp ) and the evaluation index F(x p )but, F(x p )=Abs[g xn (x p )-g xn+1 (x p )] (4) The relation (4) is satisfied.

[0035] The resonance point x r and the anti-resonance point x a is added to the sampling point used repeatedly, the collection pattern presented by the sampling point gradually approaches the resonance point x r and the anti-resonance point x a By approaching the position, frequency sweep of the target's bandwidth of interest is achieved.

[0036] Correspondingly, the present invention provides yet another embodiment, in which in step S5, if the type of the frequency response is an S parameter, the preset evaluation method is: S51: Define the S-parameters of the frequency response to include a first S-parameter and a second S-parameter; S52: Evaluation index F of the estimated curves of the first S parameter and the second S parameter 11 (x p ) and F 21 (x p ) and calculate the evaluation index F of the first S parameter. 11 (x p ) satisfies the following relation (5), F 11 (x p )=Abs[g xn (x p )-g xn+1 (x p )] / Abs[(g xn (x p )+g xn+1 (x p ))] 2 (5) The evaluation index F of the second S parameter 21 (x p )but, F21 (x p )=Abs[g xn (x p )-g xn+1 (x p )]*Abs[(g xn (x p )+g xn+1 (x p ))] 2 (6) The relation (6) is satisfied, S53: The evaluation index of the maximum error frequency point is expressed as F(x p ) and the evaluation index F(x p )but, F(x p )=F 11 (x p )+F 21 (x p ) (7) The relation (7) is satisfied.

[0037] S6: Perform ranking based on the evaluation index, and select the first m maximum error frequency points as sampled points, where m is less than n, and both m and n are integers greater than 0; If the cumulative number of sampled points has not reached the final sampling number n' of frequency points, return to step S2 and repeat the process; When the cumulative number of sampled points reaches the final sampling number of frequency points n', the iteration stops.

[0038] Specifically, in an embodiment of the present invention, the final sampling number n' of frequency points is set, and steps S2 to S6 are one complete iteration process, where after each iteration, frequency estimation is performed again based on the initially divided sampling points, resulting in more sampling points for calculation than the number of sampling points used in the previous iteration.Through multiple iterations, the maximum error frequency point that satisfies the evaluation index in each iteration is selected as the sampling point, thereby efficiently sampling the target bandwidth with fewer frequency points.

[0039] S7: All the sampled points are output as final sampling points.

[0040] 2 and 3 are schematic diagrams of S-parameter and Y-parameter sampling, respectively, according to a self-adaptive frequency point sampling frequency sweep method provided by an embodiment of the present invention. For the S-parameter, the connecting lines of the sampling points within the passband closely match the real curve, while for the Y-parameter, the connecting lines of the sampling points closely match the entire real curve. The scattered points at the bottom of FIGS. 2 and 3 are the points selected for each sampling, and the sampling patches change hierarchically. From these figures, it can be seen that the sampling points of the first and second patches both exhibit uniform sampling, while the sampling points of the third and subsequent patches are concentrated in a relatively concentrated area. In FIGS. 2 and 3, the sampling points of the third and subsequent patches are all close to the in-band region of the S-parameter and the resonance and anti-resonance points of the Y-parameter.

[0041] The beneficial effect achieved by this invention is that it employs a self-adaptive frequency point sampling method that does not rely on a specific numerical method, allowing it to perform accurate simulations of areas where the frequency response changes rapidly and rough simulations of other areas of no interest, thereby improving the accuracy and efficiency of the radio frequency device design process. At the same time, it uses different evaluation indicators for the Y-parameter and S-parameter characteristics of the frequency response, reducing the sampling points required for frequency sweeping and improving the frequency sweep speed.

[0042] The embodiment of the present invention also provides a self-adaptive frequency point sampling frequency sweep system. Referring to FIG. 4, FIG. 4 is a structural schematic diagram of the self-adaptive frequency point sampling frequency sweep system provided by the embodiment of the present invention. The self-adaptive frequency point sampling frequency sweep system 200 includes: The number of frequency points sampled n, the final number of frequency points sampled n', and the sampling range [f min ,f max ] is determined, and a predetermined simulation method is used to sample within the sampling range, and a frequency response of the n-th sampling point is calculated, and the sampling point x is used to calculate the frequency response of the n-th sampling point. n an initialization module 201, where the frequency response at is f(x); a derivative determination module 202 for calculating first and second derivatives of the frequency response f(x); Based on the first derivative and the second derivative, a frequency response curve estimation is performed for the nth sampling point, which is used to obtain n-1 estimated curves, where n The estimated curve corresponding to g xn (x) and the scope is [x n-1 ,x n+1 ], and a frequency response estimation module 203, The adjacent estimated curve g xn (x) and g xn+1 (x) Maximum error frequency point x p a maximum error calculation module 204 which is used to sequentially obtain n-1 maximum error frequency points, each of which has an error value; an evaluation module 205 for calculating the error value of each of the maximum error frequency points according to a preset evaluation method to obtain an evaluation index for each of the maximum error frequency points; ranking the first m maximum error frequency points based on the evaluation index and selecting them as sampled points, where m is less than n, and both m and n are integers greater than 0; If the cumulative number of sampled points has not reached the final sampling number n' of frequency points, return to the frequency response estimation module and execute iteratively; a repeating module 206 for stopping the repeat when the cumulative number of sampled points reaches the final sampling number n' of frequency points; and an output module 207 for outputting all the sampled points as final sampling points.

[0043] The self-adaptive frequency point sampling frequency sweeping system 200 can realize the steps in the self-adaptive frequency point sampling frequency sweeping method in the above embodiment and can achieve the same technical effects, so please refer to the description in the above embodiment and the details will not be repeated here.

[0044] An embodiment of the present invention also provides a computer device. Referring to FIG. 5, FIG. 5 is a structural schematic diagram of a computer device provided by an embodiment of the present invention, where the computer device 300 includes a memory 302, a processor 301, and a computer program stored in the memory 302 and executable by the processor 301.

[0045] The processor 301 calls the computer program stored in the memory 302 to execute the steps of the self-adaptive frequency point sampling frequency sweep method provided by the embodiment of the present invention. As shown in Figure 1, specifically, The number of frequency points sampled n, the final number of frequency points sampled n', and the sampling range [f min ,f max ], sampling within the sampling range using a preset simulation method, and calculating the frequency response of the n-th sampling point, n Step S1, where the frequency response at is f(x); a step S2 of calculating the first and second derivatives of said frequency response f(x); performing frequency response curve estimation for the n-th sampling point based on the first derivative and the second derivative to obtain n-1 estimated curves, n The estimated curve corresponding to g xn (x) and the scope is [xn-1 ,x n+1 Step S3: The adjacent estimated curve g xn (x) and g xn+1 (x) Maximum error frequency point x p Sequentially obtaining n-1 maximum error frequency points, each having an error value; Step S5: calculating the error value of each of the maximum error frequency points according to a preset evaluation method, and setting the error value as an evaluation index for each of the maximum error frequency points; performing a ranking based on the evaluation index and selecting the first m maximum error frequency points as sampled points, wherein m is less than n, and both m and n are integers greater than 0; If the cumulative number of sampled points has not reached the final sampling number n' of frequency points, return to step S2 and repeat the process; Step S6: stopping the repetition when the cumulative number of sampled points reaches the final sampling number n' of frequency points; and step S7 of outputting all the sampled points as final sampling points.

[0046] Furthermore, in step S1, the sampling patterns within the sampling range according to the preset simulation method are all uniform sampling.

[0047] Furthermore, in step S2, the method used to calculate the first and second derivatives of the frequency response f(x) is the central difference method.

[0048] Furthermore, in step S3, the estimated curve g xn (x) is g xn (x)=f(x n )+f'(x n )*(xx n )+1 / 2*f”(x n )*(xxn ) 2 (1) The relation (1) is satisfied.

[0049] Furthermore, in step S4, the maximum error frequency point x p satisfies the following relation (2), x p =(x n +x n+1 ) / twenty two) The error value is Abs[g xn (x p )-g xn+1 (x p )] (3) The relation (3) is satisfied.

[0050] Furthermore, in step S5, if the type of the frequency response is a Y parameter, the preset evaluation method is Among the sampling points, the frequency point where the Y parameter value is maximum is the maximum value x max and frequency point minimum x min and the corresponding maximum estimation curve g xmax (x) and minimum value estimated curve g xmin Step S51 of calculating (x); The maximum value estimation curve g xmax (x) and the minimum value estimation curve g xmin (x) based on the resonance point x r and anti-resonance point x a and obtain the resonance point x r and the anti-resonance point x a a step S52 of adding the value of the sample points to be repeatedly used; The evaluation index of the maximum error frequency point is expressed as F(x p ) and the evaluation index F(x p )but, F(x p )=Abs[g xn (x p )-g xn+1 (x p )] (4) and step S53, which satisfies the relational expression (4).

[0051] Furthermore, in step S5, if the type of the frequency response is an S parameter, the preset evaluation method is Step S51 of defining the S-parameters of the frequency response to include a first S-parameter and a second S-parameter; Evaluation index F of the estimated curves of the first S parameter and the second S parameter 11 (x p ) and F 21 (x p ) and the evaluation index F of the first S parameter is calculated. 11 (x p ) satisfies the following relation (5), F 11 (x p )=Abs[g xn (x p )-g xn+1 (x p )] / Abs[(g xn (x p )+g xn+1 (x p ))] 2 (5) The evaluation index F of the second S parameter 21 (x p )but, F 21 (x p )=Abs[g xn (x p )-g xn+1 (x p )]*Abs[(g xn (x p )+g xn+1 (x p ))] 2 (6) Step S52, which satisfies the relational expression (6), The evaluation index of the maximum error frequency point is expressed as F(x p ) and the evaluation index F(x p )but, F(x p )=F 11 (x p )+F 21 (xp ) (7) and step S53, which satisfies the relational expression (7).

[0052] The computer equipment 300 provided by the embodiment of the present invention can realize the steps in the frequency sweep method of self-adaptive frequency point sampling in the above embodiment, and can achieve the same technical effects. Please refer to the description in the above embodiment, and the details will not be repeated here.

[0053] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it realizes each process and step in the frequency sweep method for self-adaptive frequency point sampling provided by the embodiment of the present invention, and can achieve similar technical effects. In order to avoid repetition, details will not be repeated here.

[0054] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be realized by hardware associated with computer program instructions. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the above method embodiments. Here, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), etc.

[0055] It should be noted that the terms "comprise," "include," and other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus comprising a set of elements includes not only those elements but also other elements not expressly listed or inherent in the process, method, product, or apparatus. Absent more limitations, an element qualified by the term "comprises a" does not exclude the presence of additional identical elements in a process, method, product, or apparatus that includes the element.

[0056] From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be realized by software and a required general hardware platform, and of course can also be executed by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the essential part of the technical solution of the present invention or the part that contributes to the prior art can be realized in the form of a software product, and this computer software product is stored in a storage medium (e.g., ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, network equipment, etc.) to execute the methods described in each embodiment of the present invention.

[0057] Although the embodiments of the present invention have been described above with reference to the drawings, what has been disclosed is only a preferred embodiment of the present invention. However, the present invention is not limited to the above-described specific embodiments. The above-described specific embodiments are not limiting but merely illustrative. Based on the teachings of the present invention, those skilled in the art may make many modifications in various forms and equivalents without departing from the spirit of the present invention and the scope protected by the claims, and all of them fall within the scope of protection of the present invention.

Claims

1. 1. A frequency sweep method of self-adaptive frequency point sampling applied to the simulation of a radio frequency device, comprising: The frequency sweep method includes: The number of frequency points sampled n, the final number of frequency points sampled n′, and the sampling range [f ｍｉｎ , f ｍａｘ ], and sampling within the sampling range using a preset simulation method to calculate the frequency response of the n-th sampling point, ｎ Step S1, where the frequency response at is f(x); a step S2 of calculating the first and second derivatives of the frequency response f(x); performing a frequency response curve estimation for the n-th sampling point based on the first derivative and the second derivative to obtain n-1 estimated curves, ｎ The estimated curve corresponding to g ｘｎ (x), and the scope of application is [x ｎ－１ , x ｎ＋１ Step S3: The adjacent estimated curve g ｘｎ (x) and g ｘｎ＋１ (x) and the maximum error frequency point x ｐ Sequentially obtaining n-1 maximum error frequency points, each having an error value; Step S5: calculating the error value of each of the maximum error frequency points according to a preset evaluation method to obtain an evaluation index for each of the maximum error frequency points; performing a ranking based on the evaluation index and selecting the first m maximum error frequency points as sampled points, wherein m is less than n, and both m and n are integers greater than 0; If the cumulative number of sampled points has not reached the final sampling number n' of frequency points, return to step S2 and repeat the process. Step S6: stopping the repetition when the cumulative number of sampled points reaches the final sampling number n' of frequency points; and step S7 of outputting all the sampled points as final sampling points.

2. 2. The frequency sweep method according to claim 1, wherein in step S1, the sampling patterns within the sampling range according to the preset simulation method are all uniform sampling.

3. 2. The frequency sweep method according to claim 1, wherein the method used to calculate the first and second derivatives of the frequency response f(x) in step S2 is the central difference method.

4. In step S3, the estimated curve g ｘｎ (x) is g ｘｎ (x)=f(x ｎ )+f’(x ｎ )*(x-x ｎ )+1 / 2*f”(x ｎ )*(x-x ｎ ) ２ (1) 2. The frequency sweep method according to claim 1, wherein the following relational expression (1) is satisfied:

5. In step S4, the maximum error frequency point x ｐ satisfies the following relational expression (2), x ｐ =(x ｎ +x ｎ＋１ ) / 2 (2) The error value is Abs[g ｘｎ (x ｐ )-g ｘｎ＋１ (x ｐ )] (3) 5. The frequency sweep method according to claim 4, wherein the following relational expression (3) is satisfied:

6. In step S5, if the type of the frequency response is a Y parameter, the preset evaluation method is Among the sampling points, the frequency point where the value of the Y parameter is maximum is the maximum value x ｍａｘ and frequency point minimum x ｍｉｎ and the corresponding maximum value estimation curve g ｘｍａｘ (x) and the minimum value estimated curve g ｘｍｉｎ Step S51 of calculating (x); The maximum value estimation curve g ｘｍａｘ (x) and the minimum value estimation curve g ｘｍｉｎ Based on (x), the resonance point x ｒ and anti-resonance point x ａ and obtain the resonance point x ｒ and the anti-resonance point x ａ a step S52 of adding the value of the sample points to be iterated; The evaluation index of the maximum error frequency point is expressed as F(x ｐ ) and the evaluation index F(x ｐ )but, F(x ｐ )=Abs[g ｘｎ (x ｐ )-g ｘｎ＋１ (x ｐ )] (4) and step S53 of satisfying the following relational expression (4):

7. In step S5, if the type of the frequency response is an S parameter, the preset evaluation method is Step S51 of defining the S-parameters of the frequency response to include a first S-parameter and a second S-parameter; An evaluation index F of the estimated curves of the first S parameter and the second S parameter １１ (x ｐ ) and F ２１ (x ｐ ) and the evaluation index F of the first S parameter is calculated. １１ (x ｐ ) satisfies the following relational expression (5), F １１ (x ｐ )=Abs[g ｘｎ (x ｐ )-g ｘｎ＋１ (x ｐ )] / Abs[(g ｘｎ (x ｐ )+g ｘｎ＋１ (x ｐ ))] ２ (5) The evaluation index F of the second S parameter ２１ (x ｐ )but, F ２１ (x ｐ )=Abs[g ｘｎ (x ｐ )-g ｘｎ＋１ (x ｐ )]*Abs[(g ｘｎ (x ｐ )+g ｘｎ＋１ (x ｐ ))] ２ (6) Step S52, which satisfies the relational expression (6): The evaluation index of the maximum error frequency point is expressed as F(x ｐ ) and the evaluation index F(x ｐ )but, F(x ｐ )=F １１ (x ｐ )+F ２１ (x ｐ ) (7) and step S53 of satisfying the following relational expression (7):

8. 1. A self-adaptive frequency point sampling frequency sweep system applied to the simulation of a radio frequency device, comprising: The number of frequency points sampled n, the final number of frequency points sampled n′, and the sampling range [f ｍｉｎ , f ｍａｘ ] is determined, and a predetermined simulation method is used to sample within the sampling range and calculate the frequency response of the nth sampling point, and the sampling point x ｎ an initialization module where the frequency response at is f(x); a derivative finding module for calculating first and second derivatives of the frequency response f(x); Based on the first derivative and the second derivative, a frequency response curve estimation is performed for the nth sampling point, which is used to obtain n-1 estimated curves, where the sampling point x ｎ The estimated curve corresponding to g ｘｎ (x) and the scope of application is [x ｎ－１ , x ｎ＋１ ]; and a frequency response estimation module, The adjacent estimated curve g ｘｎ (x) and g ｘｎ＋１ (x) and the maximum error frequency point x ｐ a maximum error calculation module used to sequentially obtain n-1 of the maximum error frequency points, each of which has an error value; an evaluation module for calculating the error value of each of the maximum error frequency points according to a preset evaluation method to obtain an evaluation index of each of the maximum error frequency points; ranking the first m maximum error frequency points based on the evaluation index and selecting them as sampled points, where m is less than n, and both m and n are integers greater than 0; If the cumulative number of sampled points has not reached the final sampling number n' of frequency points, return to the frequency response estimation module and execute iteratively; an iterative module for stopping iteration when the cumulative number of sampled points reaches the final sampling number n' of frequency points; an output module for outputting all the sampled points as final sampling points.

9. 8. A computer apparatus comprising: a memory; a processor; and a computer program stored in the memory and executable by the processor, the computer apparatus implementing the steps of the method for self-adaptive frequency point sampling according to any one of claims 1 to 7 when the processor executes the computer program.

10. 8. A computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of the method for self-adaptive frequency point sampling according to any one of claims 1 to 7.

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