Antenna size optimization method and apparatus, and terminal device

Abstract

The invention provides an antenna size optimization method and device and terminal equipment, and is suitable for the technical field of wireless communication, and the method comprises the steps: obtaining a plurality of pieces of initial antenna size parameter information; according to the multiple pieces of initial antenna size parameter information and a preset antenna performance characterization parameter calculation model, multiple pieces of antenna performance characterization parameter information are obtained through calculation; and based on a preset antenna size optimization model, according to the multiple pieces of antenna performance characterization parameter information, screening and optimizing the multiple pieces of initial antenna size parameter information to obtain multiple pieces of target antenna size parameter information. The method is used for solving the problem that the multi-parameter joint optimization efficiency of the metasurface circularly polarized antenna is low, and the optimization time is shortened while the antenna size optimization precision is ensured, so that the multi-parameter optimization efficiency of the metasurface circularly polarized antenna is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of wireless communication, and particularly relates to an antenna size optimization method and device and a terminal device. BACKGROUND

[0002] As a new technology, a metasurface is applied to the field of antennas, and the characteristics of radiated electromagnetic waves can be flexibly adjusted by means of the artificial structure arrangement of the electromagnetic metasurface. For a metasurface antenna with a multi-layer structure, the relative position of a metasurface layer relative to an antenna below is usually rotated to realize the reconstruction of the polarization characteristics and the directional pattern characteristics of the antenna.

[0003] In the prior art, for a metasurface antenna with a multi-layer structure, a full-wave simulation software, such as HFSS, is usually used for design and optimization. A three-dimensional model of the antenna is manually constructed, size parameters are set, the electromagnetic performance of the antenna is simulated by using the simulation software, and performance indicators such as impedance, axial ratio and gain are obtained. For multi-parameter joint optimization, parameter scanning is usually performed in the simulation software, and size parameters are constantly adjusted to find the optimal combination.

[0004] However, when a metasurface antenna is optimized by using a full-wave simulation software, a large amount of computing resources and time are consumed in the parameter scanning process, and joint simulation usually takes several hours, which is difficult to complete efficiently. For a metasurface antenna with a multi-layer structure, there are many size parameters involved, and there is a complex nonlinear relationship between the parameters. The efficiency of the traditional optimization method in processing multi-parameter joint optimization is low, and it is difficult to quickly obtain the optimal size combination that meets the performance requirements. SUMMARY

[0005] Therefore, the embodiments of the present application provide an antenna size optimization method and device and a terminal device, aiming to solve the problems in the prior art that the efficiency is low, a large amount of computing resources and time are consumed, and it is difficult to quickly process the complex nonlinear relationship between the parameters to obtain the optimal size combination when a metasurface circularly polarized antenna is optimized.

[0006] The first aspect of the embodiments of the present application provides an antenna size optimization method, comprising: obtaining a plurality of initial antenna size parameter information; calculating a plurality of antenna performance characterization parameter information according to the plurality of initial antenna size parameter information and a preset antenna performance characterization parameter calculation model; performing screening and optimization processing on the plurality of initial antenna size parameter information according to the plurality of antenna performance characterization parameter information based on a preset antenna size optimization model, to obtain a plurality of target antenna size parameter information.

[0007] The second aspect of the embodiments of the present application provides an antenna size optimization device, comprising: An initial antenna size parameter information acquisition module is configured to acquire a plurality of initial antenna size parameter information. An antenna performance characterization parameter information calculation module is configured to calculate a plurality of antenna performance characterization parameter information according to the plurality of initial antenna size parameter information and a preset antenna performance characterization parameter calculation model. A target antenna size parameter information generation module is configured to perform screening and optimization processing on the plurality of initial antenna size parameter information according to the plurality of antenna performance characterization parameter information based on a preset antenna size optimization model, to obtain a plurality of target antenna size parameter information.

[0008] A third aspect of the embodiments of the present application provides a terminal device, which comprises a memory and a processor, the memory stores a computer program capable of running on the processor, and the processor implements the steps of the antenna size optimization method as described in the first aspect above when executing the computer program.

[0009] A fourth aspect of the embodiments of the present application provides a computer readable storage medium, which comprises a computer program stored therein, and the computer program is executed by a processor to implement the steps of the antenna size optimization method as described in the first aspect above.

[0010] Compared with the prior art, the embodiments of the present application have the beneficial effects that the present application is used to solve the problem of low efficiency of super surface circularly polarized antenna multi-parameter joint optimization, compared with the traditional optimization method using commercial electromagnetic simulation software in the prior art, the overall optimization time is greatly reduced while ensuring the optimization accuracy, the efficiency of super surface circularly polarized antenna multi-parameter optimization is significantly improved, the potential solution of various antenna sizes is explored and identified through the preset antenna size optimization model, and the effectiveness of antenna performance optimization is improved. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 is an implementation flowchart of the antenna size optimization method provided by the first embodiment of the present application; Figure 2 is an implementation flowchart of the antenna size optimization method provided by the second embodiment of the present application; Figure 3 is an implementation flowchart of the antenna size optimization method provided by the third embodiment of the present application; Figure 4is a schematic diagram of an implementation process of the antenna size optimization method provided in Embodiment Four of the present application; Figure 5 is a schematic diagram of an implementation process of the antenna size optimization method provided in Embodiment Five of the present application; Figure 6 is a schematic diagram of an implementation process of the antenna size optimization method provided in Embodiment Six of the present application; Figure 7 is a schematic diagram of the structure of the antenna size optimization apparatus provided in the present application; Figure 8 is a schematic diagram of the terminal device provided in the present application. Figure 9 is a schematic diagram of the slot parameter of the antenna provided in the present application. DETAILED DESCRIPTION

[0013] In the following description, for the purpose of explanation and not limitation, specific details are set forth, such as specific system structures, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. However, it should be apparent to those skilled in the art that the present application can be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0014] In order to illustrate the technical solutions described in the present application, the following will be described through specific embodiments.

[0015] Figure 1 The implementation flowchart of the antenna size optimization method provided in Embodiment One of the present application is shown, and the details are as follows: Step S101, obtaining a plurality of initial antenna size parameter information.

[0016] In the embodiment, the initial antenna size parameter information can refer to specific values of 9 key size parameters in the four-layer structure (metasurface layer, radiation patch layer, feed network layer, and reflector plate layer) of the metasurface circularly polarized antenna, which have a significant impact on the electromagnetic performance (such as impedance, axial ratio, and gain) of the antenna, including the side length of the metasurface layer (Wc), the cut angle size of the metasurface layer (a), the side length of the radiation patch layer (W2), the length of the feed line extending out of the gap (Ta) of the feed network layer, the gap parameters (Da, La, Wa, Sa) between the four-layer structure, and the distance (H2) from the radiation patch layer to the feed network layer. The initial antenna size parameter information can be obtained by manually constructing a three-dimensional model of the antenna in the CST simulator, recording the VBA script of each modeling step in the history simulation history tree, writing code in Matlab based on the VBA script of CST, and realizing automatic construction of the antenna model, parameter modification, and automatic export of electromagnetic performance parameters through Matlab-CST-API joint simulation. An orthogonal experiment table is designed for the 9 key size parameters to explore the influence of multiple parameters and their interactions on the performance of the antenna in the least number of experiments, so as to determine the value range of each parameter, and finally obtain multiple sets of initial size parameter combinations covering the parameter range to form a data set. The value range of each parameter is as follows: the value range of Ta can be 2.6-4mm, the value range of W2 can be 29.55-30.95mm, the value range of H2 can be 7.4-8.45mm, the value range of Wc can be 36.4-37.45mm, the value range of a can be 21.4-22.45mm, the value range of Da can be 0.25-1.3mm, the value range of La can be 13.75-16.375mm, the value range of Wa can be 0.55-1.6mm, and the value range of Sa can be 5.3-6.7mm. A specific step size can be set to intercept parameters within a specific value range, thereby obtaining multiple values within a specific value range for constructing a data set.

[0017] In step S102, multiple antenna performance characterization parameter information is calculated according to the multiple initial antenna size parameter information and a preset antenna performance characterization parameter calculation model.

[0018] In the embodiment, the preset antenna performance characterization parameter calculation model can be a trained MLP model, which can be a three-layer network structure, the number of input layer neurons is consistent with the number of initial antenna size parameters, the number of hidden layer neurons is determined as 2n+1 (n is the number of input layer neurons) according to the Hecht-Nelson method, the activation function adopts the Sigmod function, and the data set obtained through Matlab-CST-API joint simulation has been trained, which can learn the nonlinear relationship between the 9 size parameters and the electromagnetic performance parameters. It can be that the 9 initial antenna size parameter information of the edge length Wc of the metasurface layer, the cut angle size a, the edge length W2 of the radiation patch layer, the feed line length Ta of the feed network layer, the slot parameters Da, La, Wa, Sa, and the distance H2 from the radiation patch layer to the feed network layer are processed, which is converted into data with a mean value of 0 and a standard deviation of 1 through Z-Score standardization to eliminate the dimensional differences between the features, and then the processed initial antenna size parameter information is input into the preset antenna performance characterization parameter calculation model, i.e. the multilayer perceptron (MLP) model, and the reflection coefficient, axial ratio, gain and other antenna performance characterization parameter information are obtained through the multilayer perceptron (MLP) model, wherein the amplitude of the S parameter derived from CST is in dB, and then converted into the reflection coefficient through the formula and mapped to the interval of 0 to 1, and the axial ratio and gain are directly output by the MLP model.

[0019] In the embodiment, the antenna performance characterization parameter information includes antenna impedance bandwidth information, antenna axial ratio bandwidth information, and antenna gain information. The antenna impedance bandwidth information can refer to a frequency range in which the impedance characteristics of the antenna meet the design requirements, and is usually defined as a frequency interval in which the S parameter (reflection coefficient) is less than -10 dB. The antenna axial ratio bandwidth information can refer to a frequency range in which the axial ratio parameter is less than 3 dB when the antenna radiates circularly polarized waves, and is represented by the frequency difference of the intersection points of the axial ratio curve and the 3 dB line, and is used to measure the purity of circular polarization. The antenna gain information can refer to the ability of the antenna to concentrate the input power for radiation, and is expressed in dBi, which can be used to reflect the strength of the antenna radiation signal. In the present application, the optimization target is to have a gain of not less than 13 dBi. The antenna impedance bandwidth information, the antenna axial ratio bandwidth information, and the antenna gain information can be calculated by constructing an antenna three-dimensional model using a CST simulator, or can be a data set obtained by joint simulation using Matlab-CST-API. After the MLP model learns the non-linear relationship between the 9 size parameters and the electromagnetic performance parameters, the S parameter, the axial ratio, and the gain data can be predicted by inputting the initial antenna size parameters. The S parameter and the axial ratio data predicted by the MLP can also be fitted by a polynomial curve to find the intersection points of -10 dB (impedance) and 3 dB (axial ratio), respectively, and the impedance bandwidth and the axial ratio bandwidth can be calculated by the intersection frequency values. The gain information can be directly output by the MLP model or extracted from the simulation data.

[0020] In step S103, based on the preset antenna size optimization model, the multiple initial antenna size parameter information is filtered and optimized according to the multiple antenna performance characterization parameter information, to obtain multiple target antenna size parameter information.

[0021] In the embodiment, the preset antenna size optimization model can be designed based on a genetic algorithm. The population can be initialized first, 200 individuals are selected from the population in each generation, and 10 parent individuals are selected each time. The selection strategy can be the roulette method, and the selection probability is proportional to the fitness of the individual. Then, the crossover operation and the mutation operation are performed, wherein the crossover probability can be set to 0.8, and the mutation operation can use the mutationgaussian function to randomly extract a number from a Gaussian distribution with a mean of 0 and a standard deviation of 1 and add it to each gene of the parent individual. The mutation amplitude gradually decreases to 0 with evolution. In each round of optimization, the fitness is calculated in combination with the antenna performance characterization parameter information, wherein the fitness function is fitness=a1*w1+b1*w2+c1*w3-penalty (a1, b1, and c1 are the impedance bandwidth, the axial ratio bandwidth, and the gain, respectively, w1, w2, and w3 are weight coefficients, which can be set artificially, and penalty is a penalty function, i.e., penalty=λ1*max(0,a−a1) 2+ λ2 * max(0, b - b1) 2 + λ3 * max(0, c - c1) 2 λ1, λ2, λ3 are penalty coefficients, which can be artificially set), and the fitness of an individual that does not meet the set conditions (impedance bandwidth ≥ 11%, axial ratio bandwidth ≥ 0.55, gain ≥ 13dBi) is reduced; the algorithm is iterated for 1000 generations until the conditions are met, and the final output of the individual corresponding to the size parameter is the size parameter information of the plurality of target antennas.

[0022] The antenna size optimization method provided by the embodiments of the present application is used to solve the problem of low efficiency of multi-parameter joint optimization of metasurface circularly polarized antennas. Compared with the traditional optimization method using commercial electromagnetic simulation software in the prior art, the overall optimization time is greatly reduced while ensuring the optimization accuracy, the efficiency of multi-parameter optimization of metasurface circularly polarized antennas is significantly improved, and the effectiveness of antenna performance optimization is improved by exploring and identifying potential solutions of various antenna sizes through the preset antenna size optimization model.

[0023] Figure 2 The implementation flowchart of the antenna size optimization method provided by the second embodiment of the present application is shown, which is different from the first embodiment described above in that the step S103 specifically includes: In step S201, based on the preset polynomial fitting coordinate system, the antenna impedance bandwidth optimization characteristic variable is calculated according to the antenna impedance bandwidth information, the preset antenna impedance bandwidth reference straight line, the preset antenna center frequency, the preset antenna impedance bandwidth optimization threshold value and the preset antenna impedance bandwidth optimization weight information.

[0024] In this embodiment, the preset polynomial fitting coordinate system can be artificially set, the frequency can be taken as the horizontal axis and the S parameter (reflection coefficient) can be taken as the vertical axis, the preset antenna impedance bandwidth reference straight line is y=-10dB, the preset antenna center frequency can be artificially set and can be determined by the hardware of the antenna, which can be 3.5GHz, the preset antenna impedance bandwidth optimization threshold value is 11%, and the preset antenna impedance bandwidth optimization weight information can be artificially set and can be w1=3. Through polynomial curve fitting of the S parameter data corresponding to the antenna impedance bandwidth information, two intersection points x1 and x2 of the fitting curve and the antenna impedance bandwidth reference straight line are found, the impedance bandwidth is calculated as (x2-x1) / 3.5x100%, and then the impedance bandwidth is compared with the antenna impedance bandwidth optimization threshold value to obtain the antenna impedance bandwidth optimization characteristic variable, which is the product of the impedance bandwidth and the weight w1. It can be understood that if the impedance bandwidth is lower than the threshold value, the penalty term needs to be calculated in combination with the penalty coefficient before participating in the variable calculation.

[0025] In step S202, based on the preset polynomial fitting coordinate system, the antenna axial ratio bandwidth optimization characteristic variable is calculated according to the plurality of antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth reference straight line, the preset antenna axial ratio bandwidth optimization calibration threshold and the preset antenna axial ratio bandwidth optimization weight information.

[0026] In the embodiment, the preset polynomial fitting coordinate system takes frequency as the horizontal axis and axial ratio as the vertical axis, the preset antenna axial ratio bandwidth reference straight line is y=-3dB, the preset antenna axial ratio bandwidth optimization calibration threshold is 0.55, and the preset antenna axial ratio bandwidth optimization weight information is w2=2. By performing polynomial curve fitting on the axial ratio data corresponding to the plurality of antenna axial ratio bandwidth information, two intersection points x1 and x2 of the fitting curve and the antenna axial ratio bandwidth reference straight line are found, the axial ratio bandwidth is calculated as x2-x1, the axial ratio bandwidth is compared with the antenna axial ratio bandwidth optimization calibration threshold, and the antenna axial ratio bandwidth optimization characteristic variable, that is, the product of the axial ratio bandwidth and the weight w2, is obtained. If the axial ratio bandwidth is lower than the threshold, a penalty term is calculated after combining a penalty coefficient, and then the variable calculation is performed.

[0027] In step S203, the antenna gain optimization characteristic variable is calculated according to the antenna gain information, the preset antenna gain threshold and the preset antenna gain optimization weight information.

[0028] In the embodiment, the preset antenna gain threshold can be 13dBi, and the preset antenna gain optimization weight information is w3=1. The antenna gain information is compared with the antenna gain threshold, and the antenna gain optimization characteristic variable, that is, the product of the gain value and the weight w3, is obtained.

[0029] In step S204, the antenna performance optimization characteristic information is calculated according to the antenna impedance bandwidth optimization characteristic variable, the antenna axial ratio bandwidth optimization characteristic variable and the antenna gain optimization characteristic variable.

[0030] In the embodiment, the antenna performance optimization characteristic information is obtained by adding the antenna impedance bandwidth optimization characteristic variable, the antenna axial ratio bandwidth optimization characteristic variable and the antenna gain optimization characteristic variable, and subtracting a penalty term calculated according to a penalty function, that is, antenna performance optimization characteristic information=antenna impedance bandwidth optimization characteristic variable+antenna axial ratio bandwidth optimization characteristic variable+antenna gain optimization characteristic variable-penalty, where penalty=λ1·max(0,a-a1)2+λ2·max(0,b-b1)2+λ3·max(0,c-c1)2, λ1=2, λ2=2, λ3=1, a, b and c are respectively the thresholds of the antenna impedance bandwidth, the axial ratio bandwidth and the gain, and a1, b1 and c1 are respectively the actually calculated impedance bandwidth, axial ratio bandwidth and gain.

[0031] Step S205, based on the preset antenna size optimization model, filtering and optimizing the plurality of initial antenna size parameter information according to the antenna performance optimization characterization information, to obtain a plurality of target antenna size parameter information.

[0032] In this embodiment, the preset antenna size optimization model is a genetic algorithm, and the plurality of initial antenna size parameter information is filtered and optimized based on the antenna performance optimization characterization information. It can be an initialization population (200 individuals per generation), 10 parent individuals are selected by roulette selection method (selection probability is proportional to the antenna performance optimization characterization information), cross operation is performed with a cross probability of 0.8, mutation operation is performed using the mutation gaussian function (the mutation amplitude gradually decreases to 0 with evolution), and after 1000 iterations, until the antenna performance optimization characterization information meets the preset condition, the corresponding size parameter is finally output as the plurality of target antenna size parameter information.

[0033] The antenna size optimization method provided by the embodiment of the application can accurately capture the nonlinear relationship between electromagnetic performance and size parameters by polynomial curve fitting of impedance and axial ratio data, can ensure that key indicators are preferentially met by combining a penalty function to forcibly constrain parameter thresholds according to the weight of different performance indicators, and can ensure that the preset antenna size optimization model can more accurately filter and optimize the initial antenna size parameter information, thereby improving the efficiency and accuracy of the multi-parameter joint optimization of the metasurface circularly polarized antenna under the premise of ensuring accuracy.

[0034] Figure 3 An implementation flowchart of the antenna size optimization method provided by the third embodiment of the application is shown, which is different from the second embodiment described above in that the step S201 specifically includes: Step S301, fitting processing is performed on the plurality of antenna impedance bandwidth information based on a preset polynomial fitting coordinate system, to generate an antenna impedance bandwidth curve.

[0035] In this embodiment, the preset polynomial fitting coordinate system takes frequency as the horizontal axis and S parameter (reflection coefficient) as the vertical axis, polynomial curve fitting processing is performed on the S parameter data corresponding to the plurality of antenna impedance bandwidth information, and an antenna impedance bandwidth curve that can reflect the trend of the change of the S parameter with frequency is generated.

[0036] Step S302, obtaining a plurality of antenna impedance bandwidth curve reference intersection point information according to the antenna impedance bandwidth curve and a preset antenna impedance bandwidth reference straight line.

[0037] In this embodiment, the preset antenna impedance bandwidth reference straight line is a straight line with a reflection coefficient equal to -10 dB, the intersection point obtained by intersecting the antenna impedance bandwidth curve with the reference straight line is the frequency information corresponding to the plurality of antenna impedance bandwidth curve reference intersection point information.

[0038] Step S303, the difference value of the plurality of antenna impedance bandwidth curve reference intersection information is calculated to obtain an antenna impedance bandwidth reference variable.

[0039] In this embodiment, the difference value of the frequency values corresponding to the obtained plurality of antenna impedance bandwidth curve reference intersection information is calculated, that is, the larger frequency value in the two intersection points is subtracted from the smaller frequency value, and the result is the antenna impedance bandwidth reference variable.

[0040] Step S304, according to the antenna impedance bandwidth reference variable, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold and the preset antenna impedance bandwidth optimization weight information, an antenna impedance bandwidth optimization characteristic variable is calculated.

[0041] In this embodiment, the preset antenna center frequency is 3.5 GHz, the preset antenna impedance bandwidth optimization calibration threshold is 11%, and the preset antenna impedance bandwidth optimization weight information is 3; the actual antenna impedance bandwidth is obtained by dividing the antenna impedance bandwidth reference variable by the antenna center frequency and then multiplying by 100%; the impedance bandwidth is compared with the optimization calibration threshold, if the threshold requirement is met, the impedance bandwidth is multiplied by the weight information to obtain the antenna impedance bandwidth optimization characteristic variable, if not, the penalty term is calculated combined with the penalty coefficient and then multiplied by the weight information to obtain the antenna impedance bandwidth optimization characteristic variable.

[0042] The antenna size optimization method provided by the embodiment of the application accurately captures the characteristics of the antenna impedance bandwidth, thereby improving the accuracy of the antenna size optimization model in screening and optimizing the initial antenna size parameter information, ensuring the optimization effect, and significantly improving the accuracy and efficiency of the multi-parameter joint optimization of the metasurface circularly polarized antenna.

[0043] Figure 4 The implementation flowchart of the antenna size optimization method provided by the fourth embodiment of the application is shown, which is different from the third embodiment described above in that the step S304 specifically includes: Step S401, according to the antenna impedance bandwidth reference variable and the preset antenna center frequency, an antenna impedance bandwidth optimization variable is obtained.

[0044] In this embodiment, the preset antenna center frequency is 3.5 GHz, and the antenna impedance bandwidth optimization variable in the form of percentage is obtained by dividing the antenna impedance bandwidth reference variable by the antenna center frequency and then multiplying by 100%.

[0045] Step S402, it is judged whether the antenna impedance bandwidth optimization variable is greater than or equal to the preset antenna impedance bandwidth optimization calibration threshold, if yes, step S403 is entered; if not, step S404 is entered.

[0046] In the embodiment, the preset antenna impedance bandwidth optimization calibration threshold is 11%, the calculated antenna impedance bandwidth optimization variable is compared with the threshold to determine whether the antenna impedance bandwidth meets the optimization requirement.

[0047] In step S403, the antenna impedance bandwidth information is weighted according to the preset antenna impedance bandwidth optimization weight information, and an antenna impedance bandwidth optimization characteristic variable is calculated.

[0048] In the embodiment, the preset antenna impedance bandwidth optimization weight information is 3, and when the antenna impedance bandwidth optimization variable meets the preset antenna impedance bandwidth optimization calibration threshold, the antenna impedance bandwidth optimization variable is directly multiplied by the weight information to obtain the antenna impedance bandwidth optimization characteristic variable, so as to highlight the importance of the antenna impedance bandwidth meeting the requirement in the overall optimization.

[0049] In step S404, an antenna impedance bandwidth penalty value is calculated according to the antenna impedance bandwidth information, a preset antenna impedance bandwidth threshold and a preset antenna impedance bandwidth optimization penalty coefficient.

[0050] In the embodiment, the preset antenna impedance bandwidth threshold is 11%, and the preset antenna impedance bandwidth optimization penalty coefficient is 2. When the antenna impedance bandwidth optimization variable does not meet the preset antenna impedance bandwidth optimization calibration threshold, the difference between the antenna impedance bandwidth information and the antenna impedance bandwidth threshold is calculated, the square of the difference is multiplied by the penalty coefficient to obtain the antenna impedance bandwidth penalty value, so as to punish the antenna impedance bandwidth that does not meet the requirement.

[0051] In step S405, an antenna impedance bandwidth optimization characteristic variable is calculated according to the antenna impedance bandwidth information, the preset antenna impedance bandwidth optimization weight information and the antenna impedance bandwidth penalty value.

[0052] In the embodiment, the antenna impedance bandwidth optimization variable is multiplied by the preset antenna impedance bandwidth optimization weight information, and then the calculated antenna impedance bandwidth penalty value is subtracted to obtain the antenna impedance bandwidth optimization characteristic variable, so as to comprehensively consider the optimization effect and the penalty factor of the antenna impedance bandwidth.

[0053] The antenna size optimization method provided by the embodiment enhances the pertinence and accuracy of the antenna impedance bandwidth optimization, can more effectively screen out antenna size parameters meeting the requirement, and reasonably punishes the parameters not meeting the requirement, so as to improve the efficiency and accuracy of the super surface circularly polarized antenna multi-parameter joint optimization under the premise of ensuring the optimization accuracy.

[0054] Figure 5An implementation flowchart of the antenna size optimization method provided by Embodiment Five of the present application is shown, which is different from Embodiment Two described above in that the step S202 specifically comprises: In step S501, the multiple antenna axial ratio bandwidth information is fitted based on a preset polynomial fitting coordinate system to generate an antenna axial ratio bandwidth curve.

[0055] In this embodiment, the preset polynomial fitting coordinate system takes frequency as the horizontal axis and axial ratio as the vertical axis, and performs polynomial curve fitting processing on the axial ratio data corresponding to the multiple antenna axial ratio bandwidth information to generate an antenna axial ratio bandwidth curve that can reflect the trend of the axial ratio changing with frequency.

[0056] In step S502, multiple antenna axial ratio bandwidth curve reference intersection information is obtained according to the antenna axial ratio bandwidth curve and a preset antenna axial ratio bandwidth reference straight line.

[0057] In this embodiment, the preset antenna axial ratio bandwidth reference straight line is a straight line with an axial ratio equal to 3dB, and the frequency information corresponding to the intersection obtained by intersecting the antenna axial ratio bandwidth curve with the reference straight line is the multiple antenna axial ratio bandwidth curve reference intersection information.

[0058] In step S503, the difference of the multiple antenna axial ratio bandwidth curve reference intersection information is calculated to obtain an antenna axial ratio bandwidth reference variable.

[0059] In this embodiment, the difference of the frequency values corresponding to the obtained multiple antenna axial ratio bandwidth curve reference intersection information is calculated, that is, the larger frequency value is subtracted from the smaller frequency value in the two intersections, and the result is the antenna axial ratio bandwidth reference variable.

[0060] In step S504, an antenna axial ratio bandwidth optimization representation variable is calculated according to the antenna axial ratio bandwidth reference variable, a preset antenna axial ratio bandwidth optimization calibration threshold, and preset antenna axial ratio bandwidth optimization weight information.

[0061] In this embodiment, the preset antenna axial ratio bandwidth optimization calibration threshold is 0.55, and the preset antenna axial ratio bandwidth optimization weight information is 2; the antenna axial ratio bandwidth reference variable is compared with the optimization calibration threshold, if the threshold requirement is met, the antenna axial ratio bandwidth reference variable is multiplied by the weight information to obtain the antenna axial ratio bandwidth optimization representation variable, if not, the penalty term is calculated combined with the penalty coefficient and then multiplied by the weight information to obtain the antenna axial ratio bandwidth optimization representation variable.

[0062] The antenna size optimization method provided in the embodiments of the present application accurately captures the characteristics of the axial ratio bandwidth, makes the calculation of the antenna axial ratio bandwidth optimization characterization variable more reliable, and further improves the precision of the antenna size optimization model in filtering and optimizing the initial antenna size parameter information, while ensuring the optimization effect, and improves the accuracy and efficiency of the multi-parameter joint optimization of the metasurface circularly polarized antenna.

[0063] Figure 6 An implementation flowchart of the antenna size optimization method provided in Embodiment Six of the present application is shown, which is different from Embodiment Five described above in that the step S504 specifically includes: Step S601: Determine whether the antenna axial ratio bandwidth reference variable is greater than or equal to the preset antenna axial ratio bandwidth optimization calibration threshold value. If yes, go to step S602; if no, go to step S603.

[0064] In this embodiment, the preset antenna axial ratio bandwidth optimization calibration threshold value is 0.55. The antenna axial ratio bandwidth reference variable is compared with the antenna axial ratio bandwidth optimization calibration threshold value to determine whether the antenna axial ratio bandwidth reference variable is greater than or equal to the antenna axial ratio bandwidth optimization calibration threshold value, so as to determine whether the antenna axial ratio bandwidth meets the optimization requirement.

[0065] Step S602: According to the preset antenna axial ratio bandwidth optimization weight information, the antenna axial ratio bandwidth information is weighted processed to calculate the antenna axial ratio bandwidth optimization characterization variable.

[0066] In this embodiment, the preset antenna axial ratio bandwidth optimization weight information is 2. When the antenna axial ratio bandwidth reference variable meets the preset antenna axial ratio bandwidth optimization calibration threshold value, the antenna axial ratio bandwidth reference variable is directly multiplied by the weight information to obtain the antenna axial ratio bandwidth optimization characterization variable, so as to highlight the importance of the antenna axial ratio bandwidth meeting the requirement in the overall optimization.

[0067] Step S603: According to the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth threshold value, and the preset antenna axial ratio bandwidth optimization penalty coefficient, the antenna axial ratio bandwidth penalty value is calculated.

[0068] In this embodiment, the preset antenna axial ratio bandwidth threshold value is 0.55, and the preset antenna axial ratio bandwidth optimization penalty coefficient is 2. When the antenna axial ratio bandwidth reference variable does not meet the preset antenna axial ratio bandwidth optimization calibration threshold value, the difference between the antenna axial ratio bandwidth information and the antenna axial ratio bandwidth threshold value is calculated, the square of the difference is multiplied by the penalty coefficient to obtain the antenna axial ratio bandwidth penalty value, so as to punish the antenna axial ratio bandwidth that does not meet the requirement.

[0069] In step S604, the antenna axial ratio bandwidth optimization characteristic variable is calculated according to the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth optimization weight information, and the antenna axial ratio bandwidth penalty value.

[0070] In this embodiment, the antenna axial ratio bandwidth reference variable is multiplied by the preset antenna axial ratio bandwidth optimization weight information, and then the calculated antenna axial ratio bandwidth penalty value is subtracted to obtain the antenna axial ratio bandwidth optimization characteristic variable, so as to comprehensively consider the optimization effect and the penalty factor of the antenna axial ratio bandwidth.

[0071] The antenna size optimization method provided in the embodiments of the present application enhances the pertinence and accuracy of the antenna axial ratio bandwidth optimization, effectively screens the antenna size parameters meeting the requirements, and reasonably punishes the parameters not meeting the requirements, so as to improve the efficiency and accuracy of the multi-parameter joint optimization of the metasurface circularly polarized antenna on the premise of ensuring the optimization accuracy.

[0072] Corresponding to the method of the above embodiment, Figure 7 The structure block diagram of the antenna size optimization device provided in the embodiments of the present application is shown, and only the parts related to the embodiments of the present application are shown for ease of description. Figure 7 The example antenna size optimization device can be the execution subject of the antenna size optimization method provided in the first embodiment.

[0073] Reference is made to Figure 7 The antenna size optimization device includes: An initial antenna size parameter information acquisition module 710 is configured to acquire a plurality of initial antenna size parameter information. An antenna performance characteristic parameter information calculation module 720 is configured to calculate a plurality of antenna performance characteristic parameter information according to the plurality of initial antenna size parameter information and a preset antenna performance characteristic parameter calculation model. A target antenna size parameter information generation module 730 is configured to perform screening and optimization processing on the plurality of initial antenna size parameter information according to the plurality of antenna performance characteristic parameter information based on a preset antenna size optimization model, to obtain a plurality of target antenna size parameter information.

[0074] The processes in which the modules in the antenna size optimization device provided in the embodiments of the present application realize their respective functions can be referred to the descriptions of the first embodiment shown in the foregoing Figure 1 The descriptions of the first embodiment shown in the foregoing

[0075] It should be understood that the size of the serial number of each step in the above embodiments does not mean the execution order, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0076] It will be understood that the term “includes,” “comprises,” “comprising,” “has,” “having,” “includes,” “including,” or other variants thereof when used in this application and / or the claims are intended to cover the presence of one or more features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0077] It will also be understood that the term “and / or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term “at least one of’ denotes one, or a combination of two or more items.

[0078] As used in this application and the appended claims, the term “if’ can be construed to mean “when” or “once” or “in response to determining” or “in response to detecting” depending on the context. Similarly, the phrase “if it is determined” or “if [a described condition or event] is detected” can be construed to mean “once it is determined” or “in response to determining” or “once [the described condition or event] is detected” or “in response to detecting [the described condition or event],” depending on the context.

[0079] In addition, the description in the specification of this application and the appended claims uses the term “first,” “second,” “third,” and the like to refer to various elements, but these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first table could be termed a second table, and, similarly, a second table could be termed a first table, without departing from the scope of various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0080] The description in the specification of this application uses references “one embodiment” or “some embodiments” among others to convey the fact that a particular feature, structure, or characteristic described in one or more embodiments is included in at least one embodiment. Thus, the use of the term “in one embodiment” or “in some embodiments” or “in other embodiments” or “in still other embodiments” or “in yet other embodiments” or “in various embodiments” or “in other various embodiments” or the like, in various places in the specification of this application does not necessarily refer to the same embodiment, although it can. The terms “including,” “comprising,” “having,” and variations thereof, mean “including but not limited to,” unless expressly specified otherwise.

[0081] The antenna size optimization method provided by the embodiments of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, notebook computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), and the like. The embodiments of the present application do not make any limitation on the specific type of terminal device.

[0082] For example, the terminal device can be a station (STATION, ST) in a WLAN, and can be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication function, a computing device, or other processing device connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite radio device, a wireless modem card, a television set top box (STB), a customer premise equipment (CPE), and / or other devices for communicating over a wireless system, and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network.

[0083] By way of example and not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for smart design and development of daily wear by applying wearable technology, such as glasses, gloves, watches, clothing, and shoes, etc. The wearable device is a portable device that is directly worn on the body or integrated into the clothes or accessories of the user. The wearable device is not only a hardware device, but also a powerful function realized through software support and data interaction and cloud interaction. The general wearable smart device includes a full function, a large size, and can realize complete or partial functions without relying on a smart phone, such as a smart watch or smart glasses, and focuses on only one type of application function, and needs to be used in cooperation with other devices such as a smart phone, such as various types of smart wristbands, smart jewelry, and the like.

[0084] Figure 8This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 8 As shown, the terminal device 8 of this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the diagram), and a memory 81 is stored in which a computer program 82 can be run on the processor 80. When the processor 80 executes the computer program 82, it implements the steps in the various antenna size optimization method embodiments described above, for example... Figure 1 Steps S101 to S103 are shown. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 7 The functions of modules 710 to 730 are shown.

[0085] The terminal device 8 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of terminal device 8 and does not constitute a limitation on terminal device 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input transmission devices, network access devices, buses, etc.

[0086] The processor 80 may be a Central Processing Unit (CPU), or other general-purpose processors, 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, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0087] The memory 81 can be an internal storage unit of the terminal device 8 in some embodiments, for example, a hard disk or a memory of the terminal device 8. The memory 81 can also be an external storage device of the terminal device 8, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the terminal device 8. Further, the memory 81 can also include both the internal storage unit and the external storage device of the terminal device 8. The memory 81 is used to store an operating system, an application program, a BootLoader, data, and other programs, etc., for example, program codes of the computer program, etc. The memory 81 can also be used to temporarily store data that has been transmitted or is to be transmitted.

[0088] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0089] The embodiments of the present application also provide a terminal device, which comprises at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor, and the processor executes the computer program to enable the terminal device to implement the steps in any of the above method embodiments.

[0090] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps in any of the above method embodiments.

[0091] The embodiments of the present application provide a computer program product, which, when executed on a terminal device, enables the terminal device to implement the steps in any of the above method embodiments.

[0092] The integrated modules / units, if implemented in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code.

[0093] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0094] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0095] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment according to actual needs.

[0096] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for optimizing antenna size, characterized in that, include: Obtain multiple initial antenna size parameter information; Based on the multiple initial antenna size parameters and the preset antenna performance characterization parameter calculation model, multiple antenna performance characterization parameters are calculated. Based on a preset antenna size optimization model, the initial antenna size parameters are filtered and optimized according to the multiple antenna performance characterization parameters to obtain multiple target antenna size parameters.

2. The antenna size optimization method as described in claim 1, characterized in that, The antenna performance characterization parameters include antenna impedance bandwidth, antenna axial ratio bandwidth, and antenna gain.

3. The antenna size optimization method as described in claim 2, characterized in that, The step of filtering and optimizing multiple initial antenna size parameters based on a preset antenna size optimization model to obtain multiple target antenna size parameters, specifically includes: Based on a preset polynomial fitting coordinate system, and according to the antenna impedance bandwidth information, the preset antenna impedance bandwidth reference line, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information, the antenna impedance bandwidth optimization characterization variables are calculated. Based on a preset polynomial fitting coordinate system, and according to the multiple antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth reference line, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information, the antenna axial ratio bandwidth optimization characterization variables are calculated. Based on the antenna gain information, the preset antenna gain threshold, and the preset antenna gain optimization weight information, the antenna gain optimization characterization variables are calculated. Antenna performance optimization characterization information is calculated based on the antenna impedance bandwidth optimization characterization variables, antenna axial ratio bandwidth optimization characterization variables, and antenna gain optimization characterization variables. Based on the preset antenna size optimization model, and according to the antenna performance optimization characterization information, the multiple initial antenna size parameter information is filtered and optimized to obtain multiple target antenna size parameter information.

4. The antenna size optimization method as described in claim 3, characterized in that, The step of calculating the antenna impedance bandwidth optimization characterization variables based on a preset polynomial fitting coordinate system, according to the antenna impedance bandwidth information, a preset antenna impedance bandwidth reference line, a preset antenna center frequency, a preset antenna impedance bandwidth optimization calibration threshold, and preset antenna impedance bandwidth optimization weight information, specifically includes: Based on a preset polynomial fitting coordinate system, the multiple antenna impedance bandwidth information are fitted to generate an antenna impedance bandwidth curve. Based on the antenna impedance bandwidth curve and the preset antenna impedance bandwidth reference line, multiple antenna impedance bandwidth curve reference intersection point information are obtained. Calculate the difference in the reference intersection information of the multiple antenna impedance bandwidth curves to obtain the antenna impedance bandwidth reference variable; The antenna impedance bandwidth optimization characteristic variable is calculated based on the antenna impedance bandwidth reference variable, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information.

5. The antenna size optimization method as described in claim 4, characterized in that, The step of calculating the antenna impedance bandwidth optimization characteristic variable based on the antenna impedance bandwidth reference variable, the preset antenna center frequency, the preset antenna impedance bandwidth optimization calibration threshold, and the preset antenna impedance bandwidth optimization weight information specifically includes: Based on the antenna impedance bandwidth reference variable and the preset antenna center frequency, the antenna impedance bandwidth optimization variable is obtained; Determine whether the antenna impedance bandwidth optimization variable is greater than or equal to a preset antenna impedance bandwidth optimization calibration threshold; If so, the antenna impedance bandwidth information is weighted according to the preset antenna impedance bandwidth optimization weight information to calculate the antenna impedance bandwidth optimization characterization variable. If not, the antenna impedance bandwidth penalty value is calculated based on the antenna impedance bandwidth information, the preset antenna impedance bandwidth threshold, and the preset antenna impedance bandwidth optimization penalty coefficient. Based on the antenna impedance bandwidth information, the preset antenna impedance bandwidth optimization weight information, and the antenna impedance bandwidth penalty value, the antenna impedance bandwidth optimization characterization variables are calculated.

6. The antenna size optimization method as described in claim 3, characterized in that, The step of calculating the antenna axial ratio bandwidth optimization characterization variables based on a preset polynomial fitting coordinate system, according to the multiple antenna axial ratio bandwidth information, a preset antenna axial ratio bandwidth reference line, a preset antenna axial ratio bandwidth optimization calibration threshold, and preset antenna axial ratio bandwidth optimization weight information, specifically includes: Based on a preset polynomial fitting coordinate system, the axial ratio bandwidth information of the multiple antennas is fitted to generate an antenna axial ratio bandwidth curve. Based on the antenna axial ratio bandwidth curve and the preset antenna axial ratio bandwidth reference line, multiple antenna axial ratio bandwidth curve reference intersection point information are obtained; Calculate the difference in the reference intersection information of the multiple antenna axial ratio bandwidth curves to obtain the antenna axial ratio bandwidth reference variable; The antenna axial ratio bandwidth optimization characteristic variable is calculated based on the antenna axial ratio bandwidth reference variable, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information.

7. The antenna size optimization method as described in claim 6, characterized in that, The step of calculating the antenna axial ratio bandwidth optimization characterization variable based on the antenna axial ratio bandwidth reference variable, the preset antenna axial ratio bandwidth optimization calibration threshold, and the preset antenna axial ratio bandwidth optimization weight information specifically includes: Determine whether the antenna axial ratio bandwidth reference variable is greater than or equal to a preset antenna axial ratio bandwidth optimization calibration threshold; If so, the antenna axial ratio bandwidth information is weighted according to the preset antenna axial ratio bandwidth optimization weight information to calculate the antenna axial ratio bandwidth optimization characterization variable; If not, the antenna axial ratio bandwidth penalty value is calculated based on the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth threshold, and the preset antenna axial ratio bandwidth optimization penalty coefficient. The antenna axial ratio bandwidth optimization characteristic variable is calculated based on the antenna axial ratio bandwidth information, the preset antenna axial ratio bandwidth optimization weight information, and the antenna axial ratio bandwidth penalty value.

8. An antenna size optimization device, characterized in that, include: The initial antenna size parameter information acquisition module is used to acquire multiple initial antenna size parameter information. The antenna performance characterization parameter information calculation module is used to calculate multiple antenna performance characterization parameter information based on the multiple initial antenna size parameter information and the preset antenna performance characterization parameter calculation model. The target antenna size parameter information generation module is used to filter and optimize the multiple initial antenna size parameter information based on a preset antenna size optimization model and the multiple antenna performance characterization parameter information to obtain multiple target antenna size parameter information.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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