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

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 in particular to a self-adaptive frequency point sampling frequency sweeping method, system and related equipment. [Background technology]

[0002] With the development of wireless communication technology, the demand for radio frequency devices in various home appliances is increasing. Accordingly, in the design process of radio frequency devices, designers' requirements for the frequency sweep efficiency and accuracy of simulation software are also increasing. Usually, dense frequency sweep points can improve the accuracy of the frequency sweep curve, but the time required for calculation also increases significantly. Therefore, a fast algorithm is needed to shorten the calculation time as much as possible while ensuring the calculation accuracy.

[0003] JPEG2025507427000002.jpg18170In the finite element method, the basic principle of the above method is to calculate the frequency response at some typical frequency points, and then approximately expand the entire matrix through mathematical methods such as Taylor series and Pade rational approximation to estimate the frequency response of adjacent frequency points with small computational overhead. The problem is that the Taylor series expansion based on methods such as AWE often only achieves high accuracy near the sampling frequency point, while the frequency response estimation accuracy is not high far from the sampling frequency point or where the frequency response changes rapidly, and the area where the frequency response changes rapidly is often a relatively concerned area during the sampling process.

[0004] In practical simulations, high accuracy of frequency response is often required only for some regions of the bandwidth, such as within the passband, and not so high for other regions. Conventional estimation methods still have a problem of low efficiency because they indiscriminately divide the entire bandwidth into several narrow frequency bands and then perform frequency sweep analysis for each bandwidth separately. Therefore, conventional estimation methods have the problem that they may not be accurate where the frequency response changes rapidly, while they also consume a large amount of computational resources for regions of no interest during sampling. In addition, 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 of the electric field value that changes with frequency. Such methods are not suitable for environments such as experimental measurements and machine learning where there are no explicit control equations. 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 apparatus that solve 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 a 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 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 is [x n-1 ,x n+1 ] in step S3; 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 have been obtained, each of the maximum error frequency points having an error value; and A step S5 of 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 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; Step S6 of stopping the iteration 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 the 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 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 a sigma to the iteratively used sampling points; 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 curve of the first S parameter and the second S parameter 11 (x p ) and F 21 (x p ) respectively, the evaluation index F of the first S parameter being 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 ), wherein 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 simulate 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 preset 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 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 n-th sampling point to obtain n-1 estimated curves, where the sampling point x 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 which is used to sequentially obtain n-1 of the maximum error frequency points, each of the maximum error frequency points having 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; and an output module for outputting all said sampled points as final sampled points.

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

[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 adapted to implement steps in a method for self-adaptive frequency point sampling as described in any one of the preceding embodiments when executed by a processor. Effect of the Invention

[0016] The beneficial effect achieved by the present invention is that a self-adaptive frequency point sampling method that does not rely on a specific numerical method is adopted, so that accurate simulations can be performed for points where the frequency response changes rapidly, and rough simulations can be performed in other areas of no interest, thereby improving the accuracy and efficiency of the design process of radio frequency devices; at the same time, different evaluation indexes are used for the characteristics of the Y parameters and S parameters in the frequency response, thereby reducing the sampling points required for frequency sweeping and improving the frequency sweeping speed. [Brief description of the drawings]

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

[0018] In order to make the object, technical means and advantages of the present invention clearer, the present invention will be described in more detail with reference to the drawings and examples below. 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, the frequency sweep method 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 nth 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 the sampling point x 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 n-th sampling point based on the first derivative and the second derivative, this is performed for every two adjacent sampling points, and can be further estimated by 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 have been obtained, each of which has an error value.

[0029] Specifically, for any range [x n ,x n+1 ] at point x 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 indexes and iterations, the embodiment of the present invention determines through data verification that the maximum error frequency point x p The value of x 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 of 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 the 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 the 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 maximum frequency point x at which the Y parameter value is maximum max and the 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 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 of the target, a 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: defining 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 curve 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: Ranking is performed based on the evaluation index, and the first m maximum error frequency points are selected 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 sample number of frequency points n', the iterations are stopped.

[0038] Specifically, in the embodiment of the present invention, a final sampling number n' of frequency points is set, and steps S2 to S6 are one complete iterative process, in which after each iteration, frequency estimation is performed again based on the initially divided sampling points, so that more sampling points for calculation are obtained than the number of sampling points used in the previous iteration, and 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: Output all the sampled points as final sampling points.

[0040] For example, refer to Fig. 2 and Fig. 3, which are schematic diagrams of sampling in S parameters and Y parameters by the frequency sweep method of self-adaptive frequency point sampling provided by the embodiment of the present invention, respectively, where for S parameters, the connecting line of sampling points in the passband has a high degree of conformity with the real curve, and for Y parameters, the connecting line of sampling points has a high degree of conformity with the whole real curve. The scattered points at the bottom of Fig. 2 and Fig. 3 are points selected for each sampling, and the sampling patches change hierarchically. From this, it can be seen that the sampling points of the first patch and the sampling points of the second patch both show uniform sampling, but the sampling points of the third patch and after are concentrated in a relatively concentrated area, and in Fig. 2 and Fig. 3, the sampling points of the third patch and after are all close to the in-band area of ​​the S parameters and the resonance point and anti-resonance point of the Y parameters.

[0041] The beneficial effect achieved by the present invention is that a self-adaptive frequency point sampling method that does not rely on a specific numerical method is adopted, so that accurate simulations can be performed for points where the frequency response changes rapidly, and rough simulations can be performed in other areas of no interest, thereby improving the accuracy and efficiency of the design process of radio frequency devices; at the same time, different evaluation indexes are used for the characteristics of the Y parameters and S parameters in the frequency response, thereby reducing the sampling points required for frequency sweeping and improving the frequency sweeping 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 a self-adaptive frequency point sampling frequency sweep system provided by an embodiment of the present invention, and 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 preset 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 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 n-th sampling point to obtain n-1 estimated curves, where the sampling point x 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 until n-1 of said maximum error frequency points are obtained, each of said maximum error frequency points having 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 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 206 for stopping iteration 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 similar technical effects, so please refer to the description in the above embodiment and will not repeat details 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 FIG. 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 a 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 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 is [xn-1 ,x n+1 ] in step S3; 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 have been obtained, each of the maximum error frequency points having an error value; and A step S5 of 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 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; Step S6 of stopping the iteration 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 the 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 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 a sigma to the iteratively used sampling points; 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 curve of the first S parameter and the second S parameter 11 (x p ) and F 21 (x p ) respectively, the evaluation index F of the first S parameter being 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 ), wherein 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 sweeping method of self-adaptive frequency point sampling in the above embodiment, and can achieve similar technical effects, so please refer to the descriptions in the above embodiment and will not repeat details here.

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

[0054] Those skilled in the art can 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 embodiments of the above methods. 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 "comprising," "including," 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 "comprising a" does not exclude the presence of additional identical elements in the 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 necessary 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, a computer, a server, an air conditioner, or a network device, 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 merely a preferred embodiment of the present invention. However, the present invention is not limited to the above specific embodiment. The above specific embodiment is not limiting but merely illustrative. Based on the teachings of the present invention, a person skilled in the art may make many equivalent modifications without departing from the spirit of the present invention and the scope protected by the claims, and all of them belong to the scope of protection of the present invention.

Claims

1. 1. A frequency sweep method of self-adaptive frequency point sampling applied to 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 ], sampling within the sampling range using a preset simulation method, and calculating a 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 is [x ｎ－１ , x ｎ＋１ ] in step S3; The adjacent estimated curve g ｘｎ (x) and g ｘｎ＋１ (x) and the maximum error frequency point x ｐ until n-1 maximum error frequency points have been obtained, each of the maximum error frequency points having an error value; and A step S5 of 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; performing a ranking based on the evaluation index and selecting 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, the process returns to step S2 and repeats the process. Step S6 of stopping the iteration when the cumulative number of sampled points reaches the final sampling number n' of frequency points; and outputting all the sampled points as final sampling points (S7).

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

3. 2. The frequency sweep method according to claim 1, wherein in step S2, the method used for calculating the first and second derivatives of the frequency response f(x) 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 relation (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 Y parameter value is maximum is the maximum value x ｍａｘ and the minimum frequency point x ｍｉｎ and select the corresponding maximum 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 estimated 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 to the iteratively used sampling points; The evaluation index of the maximum error frequency point is 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 a 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; Evaluation index F of the estimated curve of the first S parameter and the second S parameter １１ (x ｐ ) and F ２１ (x ｐ ) respectively, the evaluation index F of the first S parameter being 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 F(x ｐ ) wherein the evaluation index F(x ｐ )but, F(x ｐ )=F １１ (x ｐ )+F ２１ (x ｐ ) (7) and a step S53 of satisfying the following relational expression (7):

8. 1. 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 preset 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 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 which is used to sequentially obtain n-1 of the maximum error frequency points, each of the maximum error frequency points having 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 and iterate over the frequency response estimation module; an iterating module for stopping iteration when the cumulative number of sampled points reaches the final sampling number n′ of frequency points; and an output module for outputting all said sampled points as final sample points.

9. A computer apparatus comprising a memory, a processor and a computer program stored in said memory and executable by said 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 a computer program stored thereon, the computer program being adapted to implement the steps of the method for self-adaptive frequency point sampling according to any one of claims 1 to 7 when executed by a processor.

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