Antenna optimization method and radio frequency terminal module

Abstract

The invention provides an antenna optimization method and a radio frequency terminal module, and the method comprises the following steps: carrying out the parameterization of the structure parameters of an antenna, and obtaining a parameter value; performing electromagnetic full-wave simulation on the parameter data to obtain a first function value containing return loss; judging whether the first function value accords with a preset function value containing return loss; if so, outputting structural parameters of the antenna according to the parameter data; and if the parameter data does not conform to the preset function value, substituting the parameter data into a particle swarm algorithm to set parameters of the particle swarm algorithm, repeatedly performing updating and electromagnetic full-wave simulation until a final function value conforming to the preset function value is obtained, and outputting structural parameters of the antenna according to the parameter data corresponding to the final function value. According to the invention, automatic optimization of the antenna can be realized, the optimization time is shortened, manual intervention is not needed, the design process of the antenna is simplified, and the design efficiency of the antenna can be improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to an antenna optimization method and a radio frequency terminal module. Background Art

[0002] The antenna is a trace printed on a printed circuit board. It can be designed as a straight line, an inverted F-shaped trace, a serpentine or circular trace, etc. An antenna with a length of one quarter of a wavelength can be formed for transmitting and receiving electrical signals.

[0003] In order to adapt to the compact design requirements of electronic devices such as mobile phones, tablets and laptops, the antennas of RF terminals need to be miniaturized, but this may sacrifice the performance of the antenna, such as gain and bandwidth. When the input impedance of the antenna does not match the characteristic impedance of the feeder, part of the energy will be reflected back, affecting the efficiency and performance of the antenna.

[0004] Antennas have limited bandwidth, which limits their ability to operate at different frequencies, especially in mobile communication systems, and their directivity and anti-interference capabilities are also limited, especially in multipath and interference environments.

[0005] There is no fixed rule for the bending of the snake antenna. It can be designed freely according to the needs. The purpose is to make the antenna smaller. There are many structures of snake antenna, such as A, B, C, and D, each with its own characteristics and applicable environment.

[0006] Due to its structural characteristics, the gain of the serpentine antenna may be lower than that of other types of antennas, such as the IFA antenna (inverted F-shaped antenna). Since the Class A serpentine antenna has no ground branch, it is not easy to adjust the input impedance of the antenna in the design, resulting in relatively poor return loss S11. The smaller the vertical segment spacing of the serpentine antenna, the worse the gain. Although reducing the vertical segment spacing can achieve antenna miniaturization, this is at the expense of gain. The Class C serpentine antenna cannot adjust the input impedance through the antenna itself, so the matching line is poor. Although a short-circuit branch can be added above the feed point to adjust the input impedance, this increases the complexity of the design. For some serpentine antenna structures, such as Class A and Class C, it is difficult to adjust the operating frequency because the resonant frequency band of the antenna is determined by the effective current path length of the antenna, and it needs to be adjusted starting from the physical length. For the Class B serpentine antenna, although the S11 parameter can be adjusted by adding a short-circuit branch, in actual design, the length of the short-circuit branch may need to be fine-tuned to obtain the best performance.

[0007] In the prior art, the design of the snake antenna mainly relies on experience and manual adjustment, that is, manual optimization design. This method requires human intervention, is relatively complex, takes a long time to optimize, and cannot achieve automatic optimization. Summary of the invention

[0008] In view of the above shortcomings of the prior art, the present invention proposes an antenna optimization method and a radio frequency terminal module to solve the problem that the design of the antenna in the prior art mainly relies on experience and manual adjustment. This optimization method not only requires human intervention, but is also relatively complex, takes a long time to optimize, and cannot achieve automatic optimization.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: In a first aspect, the present invention provides an antenna optimization method, wherein the antenna comprises a main line, a square waveform branch line connected to the main line, and a tail branch line connected to an end of the square waveform branch line away from the main line; the method comprises the following steps: Quantifying the structural parameters of the antenna to obtain parameter values; the structural parameters of the antenna include the length of the main line, the width of the main line, the width of the crest of the square-wave branch line on one side of its extension direction, the width of the trough of the square-wave branch line on the other side of its extension direction, the height from adjacent crests to troughs, the length of the tail branch line, and the width of the tail branch line; Performing electromagnetic full-wave simulation on the parameter data to obtain a first function value including return loss; Determine whether the first function value meets the preset function value including return loss: If it is in compliance, outputting the structural parameters of the antenna according to the parameter data; If it does not meet the requirements, the parameter data is brought into the particle swarm algorithm to set the parameters of the particle swarm algorithm, and then the update and electromagnetic full-wave simulation are repeatedly performed until the final function value that meets the preset function value is obtained, and the structural parameters of the antenna are output according to the parameter data corresponding to the final function value.

[0010] Preferably, the parameters of the particle swarm algorithm include particle swarm size, maximum number of iterations, inertia weight, individual learning factor and group learning factor; each particle represents a group of antenna structural parameters.

[0011] Preferably, each round of updating and electromagnetic full-wave simulation obtains an updated function value; repeatedly performing updating and electromagnetic full-wave simulation until a final function value that meets the preset function value is obtained comprises the following sub-steps: Compare the update function value obtained in each round of update and electromagnetic full-wave simulation with the update function value obtained in the previous round of update and electromagnetic full-wave simulation: If the updated function value obtained in the current round of updating and electromagnetic full-wave simulation is closer to the preset function value than the updated function value obtained in the previous round of updating and electromagnetic full-wave simulation, the next round of updating and electromagnetic full-wave simulation is performed after the individual optimal function value is updated; If the updated function value obtained in the current round of updating and electromagnetic full-wave simulation is further away from the preset function value than the updated function value obtained in the previous round of updating and electromagnetic full-wave simulation, directly proceed to the next round of updating and electromagnetic full-wave simulation; If the update function value obtained by the current round update and electromagnetic full-wave simulation meets the preset function value, then the update and electromagnetic full-wave simulation are terminated, and the update function value obtained by the current round update and electromagnetic full-wave simulation is used as the optimal function value; At the same time, before the final function value is obtained, the updated function value of each round of update and electromagnetic full-wave simulation is compared with the global optimal function value: If the update function value of the current round of update and electromagnetic full-wave simulation is closer to the preset function value than the global optimal function value, the global optimal function value is updated, and according to the particle speed and particle position formula of the particle swarm algorithm, combined with the update function value of the current round of update and electromagnetic full-wave simulation and the global optimal function value, the speed of each particle and the position of each particle of the particle swarm algorithm are updated, and then the next round of update and electromagnetic full-wave simulation is performed; If the updated function value of the current round of updating and electromagnetic full-wave simulation is further away from the preset function value than the global optimal function value, the next round of updating and electromagnetic full-wave simulation is directly performed.

[0012] Preferably, the step of repeatedly performing updating and electromagnetic full-wave simulation until a final function value that meets the preset function value is obtained comprises the following sub-steps: When the final function value is not obtained after reaching the maximum number of iterations, the structural parameters of the antenna and the parameters of the particle swarm algorithm are adjusted, and a new round of updating and electromagnetic full-wave simulation is performed until the final function value that meets the preset function value is obtained.

[0013] Preferably, the step of determining whether the first function value meets a preset function value including return loss comprises the following steps: Set the function difference range; Calculating a calculated difference between the first function value and the preset function value; It is determined whether the calculated difference is within the function difference range.

[0014] In a second aspect, the present invention provides a radio frequency terminal module, which includes a substrate and an antenna arranged on the substrate; the structural parameters of the antenna are obtained by the antenna optimization method as described above.

[0015] Compared with the prior art, the antenna optimization method of the present invention sequentially parameterizes the antenna's structural parameters to obtain parameter values; performs electromagnetic full-wave simulation on the parameter data to obtain a first function value including return loss; determines whether the first function value meets the preset function value including return loss: if it meets, the antenna's structural parameters are output according to the parameter data; if it does not meet, the parameter data is brought into the particle swarm algorithm to set the parameters of the particle swarm algorithm, and then repeatedly updates and performs electromagnetic full-wave simulation until a final function value that meets the preset function value is obtained, and the antenna's structural parameters are output according to the parameter data corresponding to the final function value. In this way, automatic optimization of the antenna can be achieved, the optimization time can be shortened, and no manual intervention is required, the antenna design process is simplified, and the antenna design efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings: Figure 1 A flowchart of the steps of the antenna optimization method provided by an embodiment of the present invention; Figure 2 A circuit structure diagram of an antenna in an antenna optimization method provided in an embodiment of the present invention; Figure 3 A top view of an antenna in the antenna optimization method provided by an embodiment of the present invention; Figure 4 A circuit layout diagram of an antenna in an antenna optimization method provided in an embodiment of the present invention; Figure 5 A simplified flow chart of an antenna optimization method provided by an embodiment of the present invention; Figure 6 A comparison diagram of the resonance point simulation results of the antenna designed in the prior art and the resonance point simulation results of the antenna designed in the embodiment of the present invention. DETAILED DESCRIPTION

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0018] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1 The embodiment of the present invention provides an antenna optimization method, such as Figure 1 As shown, it includes the following steps: S1. quantify the structural parameters of the antenna to obtain parameter values.

[0021] Among them, Figures 2 to 4 As shown, the antenna includes a main line 121, a square-wave branch line 122 connected to the main line 121, and a tail branch line 123 connected to one end of the square-wave branch line 122 away from the main line.

[0022] The structural parameters of the antenna include the length He of the main line, the width Ws of the main line, the width S1 of the crest of the square waveform branch line on one side of its extension direction, the width S2 of the trough of the square waveform branch line on the other side of its extension direction, the height L1 from adjacent crests to troughs, the length L2 of the tail branch line, and the width We of the tail branch line.

[0023] S2. Perform electromagnetic full-wave simulation on the parameter data to obtain a first function value including return loss.

[0024] Among them, the return loss is also called the S11 parameter.

[0025] S3. Determine whether the first function value meets a preset function value including return loss: S31, if it meets the conditions, output the structural parameters of the antenna according to the parameter data; S32. If it does not meet the requirements, the parameter data is brought into the particle swarm algorithm to set the parameters of the particle swarm algorithm, and then the update and electromagnetic full-wave simulation are repeatedly performed until a final function value that meets the preset function value is obtained, and the structural parameters of the antenna are output according to the parameter data corresponding to the final function value.

[0026] In this embodiment, the preset function value is that the echo loss is less than -10dB at 2.45GHz, which is equivalent to determining whether the first function value is less than -10dB at 2.45GHz. Of course, according to actual needs, the preset function value can also set the echo loss to be in other decibel ranges at other frequencies.

[0027] In the step S3, judging whether the first function value meets the preset function value including the return loss includes the following sub-steps: S301, setting the function difference range.

[0028] S302: Calculate a difference between the first function value and the preset function value.

[0029] S302: Determine whether the calculated difference is within the function difference range.

[0030] This step belongs to another judgment method, and is basically the same as or similar to the above step of judging whether the first function value is less than -10dB at 2.45GHz.

[0031] The parameters of the particle swarm algorithm include particle swarm size, maximum number of iterations, inertia weight, individual learning factor (C1) and group learning factor (C2); the particle swarm of the particle swarm algorithm is randomly initialized, and each particle represents a set of antenna structural parameters.

[0032] Each round of update and electromagnetic full-wave simulation obtains an update function value, and each update function value can represent a particle in the particle swarm algorithm. Specifically, after each round of update, an update data will be obtained, and the obtained update data will be used for electromagnetic full-wave simulation to obtain the update function value.

[0033] In step S32, repeatedly updating and performing electromagnetic full-wave simulation until a final function value that meets the preset function value is obtained includes the following sub-steps: S321, comparing the update function value obtained in each round of update and electromagnetic full-wave simulation with the update function value obtained in the previous round of update and electromagnetic full-wave simulation: S3211. If the updated function value obtained in the current round of updating and electromagnetic full-wave simulation is closer to the preset function value than the updated function value obtained in the previous round of updating and electromagnetic full-wave simulation, the next round of updating and electromagnetic full-wave simulation is performed after updating the individual optimal function value.

[0034] S3212. If the updated function value obtained in the current round of updating and electromagnetic full-wave simulation is further away from the preset function value than the updated function value obtained in the previous round of updating and electromagnetic full-wave simulation, directly proceed to the next round of updating and electromagnetic full-wave simulation.

[0035] S3213. If the updated function value obtained by the current round of updating and electromagnetic full-wave simulation meets the preset function value, the updating and electromagnetic full-wave simulation are terminated, and the updated function value obtained by the current round of updating and electromagnetic full-wave simulation is used as the optimal function value.

[0036] S322. At the same time, before the final function value is obtained, the updated function value of each round of updating and electromagnetic full-wave simulation is compared with the global optimal function value: S3221. If the updated function value of the current round of update and electromagnetic full-wave simulation is closer to the preset function value than the global optimal function value, the global optimal function value is updated, and according to the particle speed and particle position formula of the particle swarm algorithm, combined with the updated function value of the current round of update and electromagnetic full-wave simulation and the global optimal function value, the speed of each particle and the position of each particle of the particle swarm algorithm are updated, and then the next round of update and electromagnetic full-wave simulation is performed.

[0037] S3222. If the updated function value of the current round of updating and electromagnetic full-wave simulation is further away from the preset function value than the global optimal function value, directly proceed to the next round of updating and electromagnetic full-wave simulation.

[0038] In the step S32, the updating and electromagnetic full-wave simulation are repeatedly performed until a final function value that meets the preset function value is obtained, including the following sub-steps: S323, when the final function value has not been obtained after reaching the maximum number of iterations, the structural parameters of the antenna and the parameters of the particle swarm algorithm are adjusted, and a new round of updating and electromagnetic full-wave simulation is performed until a final function value that meets the preset function value is obtained.

[0039] In the step S323, a new round of updating and electromagnetic full-wave simulation is performed until a final function value that meets the preset function value is obtained. The steps adopted are the same or similar to the above steps S321 and S322.

[0040] The above individual optimal function value can be understood as the individual optimal position, and the global optimal function value can be understood as the global optimal position.

[0041] like Figure 5As shown, the antenna in the embodiment of the present invention belongs to a RF terminal serpentine antenna. The simplified process of the optimization method of the antenna is structural parameterization of the RF terminal serpentine antenna; electromagnetic full-wave simulation is performed to calculate the fitness function value; the fitness function value is converged, that is, compared with the preset function value: if it meets the preset function value, the optimal electromagnetic response and the corresponding structural parameters are output, that is, the structural parameters of the antenna are output; if it does not meet the preset function value, the particle velocity, the particle position and the external comparison set are updated in sequence, and then the electromagnetic full-wave simulation is performed to obtain the updated function value, and then the fitness function value is converged, and this is repeated until the final function value of the preset function value is obtained, and the optimal electromagnetic response and the corresponding structural parameters are output.

[0042] Updating the external comparison set is also called updating the external comparison set. The purpose is to track the performance of the particle swarm and ensure that the particle swarm can gradually approach the optimal solution. The external comparison set is usually used to store the best solution in history. Whenever a particle performs better than the currently recorded optimal solution, these particles will be added to the external comparison set to ensure that the particle swarm constantly adjusts itself through historical information. In the particle swarm algorithm, the "position" of a particle represents a solution in the solution space, while the "speed" determines the speed and direction of the particle's movement in the solution space. In each round of update iteration, the particle's speed and position will be updated according to its historical best solution and the best solution of the entire particle swarm. This update process can guide the particles to gradually approach the optimal solution, thereby optimizing the performance of the designed antenna. By continuously updating the position and speed of particles, the particle swarm can find better solutions in the search space.

[0043] Optimization algorithms aim to find global optimal solutions rather than local optimal solutions. They are suitable for complex, multi-peak search spaces. Such algorithms use random search to find the optimal solution in the solution space, and are parallel and robust. Optimization algorithms often imitate the behavior of groups in nature, such as bird flocks foraging and fish migration, and improve search efficiency through group collaboration. The particle swarm algorithm (PSO) has a simple principle, fewer parameters, and is easy to program and implement. At the same time, the particle swarm algorithm has good global search capabilities and is suitable for solving multi-peak or complex optimization problems. The particle swarm algorithm can quickly find the approximate optimal solution to the problem, especially in high-dimensional optimization problems, it converges to the optimal solution faster than the genetic algorithm. In addition, the particle swarm algorithm does not rely on the derivative information of the problem and is applicable to a variety of problem types.

[0044] Since the particle swarm algorithm has a powerful global search capability, it can effectively find the global optimal solution in a complex search space, which is particularly important for multi-peak and nonlinear problems in antenna design. In addition, the particle swarm algorithm is simple and easy to implement, with high computational efficiency, and is suitable for solving continuous feasible domain problems, especially for high-dimensional optimization problems, which can effectively shorten the calculation time. Some improved particle swarm algorithms can adaptively adjust parameters such as individual learning factors, global learning factors and inertia weights according to the dynamic changes of the search process, so that the algorithm can maintain high efficiency and effectiveness on different types of optimization problems. In the field of multi-objective optimization, the improvement of the particle swarm algorithm includes the development of more effective multi-objective search strategies and Pareto frontier update mechanisms, which helps to maintain the diversity of the solution set while finding the optimal solution. The improved particle swarm algorithm can handle multi-parameter collaborative optimization problems in complex antenna design, such as the shipborne conformal antenna optimization design of conformal RF performance. By adding repeated detection steps to the particle swarm algorithm, the number of simulation calculations is reduced, while the global optimization capability of the particle swarm algorithm is inherited, further accelerating the convergence or comparison speed.

[0045] like Figure 5 As shown, the antenna optimized by PSO can effectively improve the simulation efficiency and reduce the design complexity. At the same time, the PSO algorithm can better and more directly achieve the preset target resonance point requirements, which is of great significance for the requirements of RF terminals applied to different frequency bands. Compared with the manually optimized antenna in the prior art, it has better performance and function.

[0046] Compared with the prior art, the antenna optimization method in this embodiment successively parameterizes the antenna's structural parameters to obtain parameter values; performs electromagnetic full-wave simulation on the parameter data to obtain a first function value including return loss; determines whether the first function value meets the preset function value including return loss: if it meets, the antenna's structural parameters are output according to the parameter data; if it does not meet, the parameter data is brought into the particle swarm algorithm to set the parameters of the particle swarm algorithm, and then repeatedly updates and performs electromagnetic full-wave simulation until the final function value that meets the preset function value is obtained, and the antenna's structural parameters are output according to the parameter data corresponding to the final function value. In this way, the antenna can be automatically optimized, the optimization time can be shortened, and no manual intervention is required, the antenna design process can be simplified, and the antenna design efficiency can be improved; in addition, the preset target resonance point requirements can be better and more directly achieved through the particle swarm, and it can be better applied to RF terminals of different frequency bands.

[0047] Embodiment 2 The embodiment of the present invention provides a radio frequency terminal module 100, Figures 2 to 4 As shown, it includes a substrate 110 and an antenna 120 disposed on the substrate 110; the structural parameters of the antenna are obtained by the antenna optimization method in the above-mentioned embodiment 1.

[0048] in, Figure 2 In the figure, X indicates that the length of the substrate 110 is 14.15 mm, Y indicates that the width of the substrate 110 is 6 mm, and Z indicates that the thickness of the substrate 110 is 1.6 mm.

[0049] Since the structural parameters of the antenna 120 in this embodiment are obtained by the antenna optimization method in the above-mentioned embodiment 1, it can also achieve the technical effect achieved by the antenna optimization method in the above-mentioned embodiment 1, and will not be described in detail here.

[0050] It should be noted that the various embodiments described above with reference to the accompanying drawings are only used to illustrate the present invention rather than to limit the scope of the present invention. Those skilled in the art should understand that any modification or equivalent substitution of the present invention without departing from the spirit and scope of the present invention should be included within the scope of the present invention. In addition, unless otherwise indicated by the context, words appearing in the singular include the plural form, and vice versa. In addition, unless otherwise specified, all or part of any embodiment may be used in combination with all or part of any other embodiment.

Claims

1. A method for optimizing an antenna, the antenna comprising a main line, a square-wave branch line connected to the main line, and a tail branch line connected to an end of the square-wave branch line away from the main line; characterized in that: The antenna optimization method comprises the following steps: Quantifying the structural parameters of the antenna to obtain parameter values; the structural parameters of the antenna include the length of the main line, the width of the main line, the width of the crest of the square-wave branch line on one side of its extension direction, the width of the trough of the square-wave branch line on the other side of its extension direction, the height from adjacent crests to troughs, the length of the tail branch line, and the width of the tail branch line; Performing electromagnetic full-wave simulation on the parameter data to obtain a first function value including return loss; Determine whether the first function value meets the preset function value including return loss: If it is in compliance, outputting the structural parameters of the antenna according to the parameter data; If it does not meet the requirements, the parameter data is brought into the particle swarm algorithm to set the parameters of the particle swarm algorithm, and then the update and electromagnetic full-wave simulation are repeatedly performed until the final function value that meets the preset function value is obtained, and the structural parameters of the antenna are output according to the parameter data corresponding to the final function value.

2. The antenna optimization method according to claim 1, characterized in that: The parameters of the particle swarm algorithm include particle swarm size, maximum number of iterations, inertia weight, individual learning factor and group learning factor; each particle represents a group of antenna structural parameters.

3. The antenna optimization method according to claim 2, characterized in that: Each round of updating and electromagnetic full-wave simulation obtains an updated function value; repeatedly updating and electromagnetic full-wave simulation until a final function value that meets the preset function value is obtained includes the following sub-steps: Compare the update function value obtained in each round of update and electromagnetic full-wave simulation with the update function value obtained in the previous round of update and electromagnetic full-wave simulation: If the updated function value obtained in the current round of updating and electromagnetic full-wave simulation is closer to the preset function value than the updated function value obtained in the previous round of updating and electromagnetic full-wave simulation, the next round of updating and electromagnetic full-wave simulation is performed after the individual optimal function value is updated; If the updated function value obtained in the current round of updating and electromagnetic full-wave simulation is further away from the preset function value than the updated function value obtained in the previous round of updating and electromagnetic full-wave simulation, directly proceed to the next round of updating and electromagnetic full-wave simulation; If the update function value obtained by the current round update and electromagnetic full-wave simulation meets the preset function value, then the update and electromagnetic full-wave simulation are terminated, and the update function value obtained by the current round update and electromagnetic full-wave simulation is used as the optimal function value; At the same time, before the final function value is obtained, the updated function value of each round of update and electromagnetic full-wave simulation is compared with the global optimal function value: If the update function value of the current round of update and electromagnetic full-wave simulation is closer to the preset function value than the global optimal function value, the global optimal function value is updated, and according to the particle speed and particle position formula of the particle swarm algorithm, combined with the update function value of the current round of update and electromagnetic full-wave simulation and the global optimal function value, the speed of each particle and the position of each particle of the particle swarm algorithm are updated, and then the next round of update and electromagnetic full-wave simulation is performed; If the updated function value of the current round of updating and electromagnetic full-wave simulation is further away from the preset function value than the global optimal function value, the next round of updating and electromagnetic full-wave simulation is directly performed.

4. The antenna optimization method according to claim 3, characterized in that: The step of repeatedly performing updating and electromagnetic full-wave simulation until a final function value that meets the preset function value is obtained includes the following sub-steps: When the final function value is not obtained after reaching the maximum number of iterations, the structural parameters of the antenna and the parameters of the particle swarm algorithm are adjusted, and a new round of updating and electromagnetic full-wave simulation is performed until the final function value that meets the preset function value is obtained.

5. The antenna optimization method according to claim 1, characterized in that: The step of determining whether the first function value meets a preset function value including a return loss comprises the following steps: Set the function difference range; Calculating a calculated difference between the first function value and the preset function value; It is determined whether the calculated difference is within the function difference range.

6. A radio frequency terminal module, characterized in that: The radio frequency terminal module includes a substrate and an antenna arranged on the substrate; the structural parameters of the antenna are obtained by the antenna optimization method described in any one of claims 1 to 5.

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