A Fast Impedance Matching Optimization Method for Mobile Phone Antennas Based on Genetic Algorithm

Abstract

The present invention discloses a fast impedance matching optimization method for mobile phone antennas based on genetic algorithms, comprising the following steps: S1, obtaining the initial parameters of the target antenna, constructing a multi-dimensional parameter space model and mapping it into a parameter matrix; S2, defining a multi-band timing matching function, dynamically allocating matching weights according to time periods and usage frequencies to optimize the band matching effect; S3, generating an initial solution set using the superposition state of qubits, expanding the solution space through quantum-inspired mutation operations and setting timing weights; S4, in the process of generating and evaluating the solution set, using the qubit phase adjustment mechanism to finely adjust the high-fitness solutions to meet the matching requirements of key frequency bands; S5, based on the progressive environmental feedback mechanism, adjusting the matching parameters in real time; S6, verifying the parameter stability through band switching simulation tests; S7, applying the optimized parameters to the target antenna to achieve adaptive impedance matching. The present invention improves the matching stability and dynamic adaptability of the antenna during multi-band switching.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a method for quickly optimizing impedance matching of a mobile phone antenna based on a genetic algorithm. Background Art

[0002] With the rapid development of wireless communication and mobile terminal devices, mobile phone antenna technology has become a core component for achieving efficient wireless signal transmission. However, with the increasingly complex and diverse wireless network environment, mobile phone antennas face the requirements of multi-band and broadband coverage, and at the same time, it is particularly important to maintain stable and efficient impedance matching in a changing working environment. In the prior art, traditional mobile phone antenna impedance matching methods often show obvious limitations in dynamic environments and scenarios of multi-band switching, making it difficult to achieve adaptive impedance matching, which affects the transmission efficiency and signal quality of the antenna.

[0003] Traditional mobile phone antenna impedance matching methods mostly rely on fixed matching networks or preset matching algorithms, and adjust the impedance of the antenna by adjusting matching components such as inductors and capacitors to meet the requirements of different frequency bands. This method can achieve a certain matching effect in fixed frequency bands and stable environments, but it has significant defects in the following aspects:

[0004] Lack of multi-band adaptability: The mobile phone antenna impedance matching schemes in the prior art usually can only be optimized in a single or limited number of frequency bands, and it is difficult to achieve efficient matching in scenarios where frequency bands are frequently switched or multi-bands coexist. Since the matching parameters need to be readjusted when switching frequency bands, the traditional method has poor adaptability between different frequency bands and often cannot meet the requirements of modern multi-band applications, resulting in increased signal loss during frequency band switching and affecting communication quality.

[0005] Poor adaptability to dynamic environments: The wireless communication environment is complex and changeable, and the working environment of mobile phone antennas will be affected by various dynamic factors such as temperature, humidity, and signal interference. Traditional matching methods usually rely on preset parameters or static matching networks and are difficult to respond to environmental changes in real time. When the external environmental parameters change, the impedance matching state of the antenna may deviate from the optimal value, resulting in a decrease in matching efficiency. There is a lack of an effective feedback mechanism in the prior art to monitor and adjust the matching parameters in real time, making the antenna less stable in complex environments.

[0006] Insufficient response speed and adjustment accuracy: In scenarios where the frequency band switches or the external environment changes drastically, the impedance matching of the antenna needs to respond quickly to ensure signal quality. However, most traditional impedance matching techniques rely on manual adjustment or fixed rules, making it difficult to meet the dynamic change requirements, with slow response speed and low adjustment accuracy. Especially when dealing with multi-band and multi-dimensional environments in complex scenarios, traditional matching methods are unable to cope effectively, unable to precisely control the matching effect under different frequency bands and environmental parameters, resulting in limited overall system performance.

[0007] Lack of adaptive adjustment mechanism: Most existing matching techniques rely on preset matching models and cannot be adaptively adjusted according to the actual working conditions. With the changes in the usage environment and frequency band requirements, fixed matching models are difficult to meet the requirements of multi-scenario applications. Traditional impedance matching techniques usually cannot autonomously identify and adapt to the priority requirements of different frequency bands, and it is difficult to preferentially match key frequency bands in high-frequency usage scenarios, affecting the high-frequency response effect of the antenna.

[0008] The optimization method is limited to fixed algorithms and lacks an intelligent optimization mechanism: In existing technologies, most use fixed impedance matching algorithms, such as a single optimization method based on genetic algorithms. These algorithms have low adaptability to multi-band and multi-environment requirements. With the complexity of antenna design, fixed algorithms are difficult to achieve precise optimization in multi-band requirements, and the feedback and processing of environmental changes are not flexible enough. Especially in the case of multi-variables and dynamic requirements, it is difficult for fixed algorithms to balance the relationship between antenna matching accuracy and response speed.

[0009] Therefore, how to provide a fast impedance matching optimization method for mobile phone antennas based on genetic algorithms is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0010] An object of the present invention is to propose a fast impedance matching optimization method for mobile phone antennas based on genetic algorithms. The present invention combines a quantum-inspired algorithm and a multi-band timing matching method. The present invention uses the superposition state of quantum bits to generate an initial solution set, and through quantum-inspired mutation operations and timing weight settings, dynamically optimizes the impedance matching of mobile phone antennas under multi-band and multi-environment conditions. The specific steps include obtaining the initial parameters of the antenna, defining the multi-band timing matching function, making progressive feedback adjustments based on real-time environmental changes, and finally verifying the stability of the optimized parameters. This method has the advantages of improving the multi-band adaptability of the antenna, enhancing the matching stability, and having a fast response speed.

[0011] A fast impedance matching optimization method for mobile phone antennas based on genetic algorithms according to an embodiment of the present invention includes the following steps:

[0012] Optionally, the S2 includes the following steps:

[0013] S21. Based on the constructed multi-dimensional parameter space model, extract data related to frequency bands, usage frequencies, and time periods from the parameter matrix, and generate an initial matching weight configuration to meet the matching optimization requirements for different time periods and frequency bands;

[0014] S22. Define a multi-band time sequence matching function F(t, f), where t represents the time period and f represents the target frequency band. The matching function dynamically allocates matching weights according to the changes in the time period t and the frequency band usage frequency, so that the frequency bands with higher frequency band usage frequencies obtain matching weights preferentially to optimize the frequency band matching effect within each time period;

[0015] S23. Map the frequency band usage frequencies of each time period to dynamic weight coefficients W(t, f), and establish a weight allocation matrix M. The matrix M is used to record the dynamic matching weights of each frequency band within different time periods to construct a matching structure that adapts to the multi-band time sequence requirements:

[0016]

[0017] Among them, W(t, f) represents the dynamic matching weight coefficient under the time period t and the frequency band f, θ(t) is the time decay factor, f usage (t, f) represents the usage frequency of the frequency band f in the time period t, is the total usage frequency of all frequency bands in the time period t;

[0018] S24. Based on the time series {t1, t2,..., t n} and the frequency band series {f1, f2,..., f m}, use the time sequence matching function F(t, f) to control the dynamic adjustment of the weight coefficient W(t i , f j ) in the matrix M, where each W(t i , f j ) represents the matching weight under the time period t i and the frequency band f j ;

[0019] S25. In the time period with frequent frequency band switching, by adjusting the weight coefficient W(t, f) in real time, the key frequency bands with high usage frequencies can obtain preferential matching in the time period with frequent switching, apply the preferential matching weight adjustment coefficient, and continuously optimize the weight allocation based on the feedback of the actual usage frequency:

[0020] W′(t, f) = W(t, f) · (1 + α(f) · δ(t));

[0021] Among them, W′(t, f) is the adjusted preferential matching weight, α(f) is the preferential coefficient reflecting the priority of the key frequency band, and δ(t) is the real-time factor adjusted according to the frequency band switching frequency within the current time period;

[0022] S26. Using the dynamic weight allocation mechanism of matrix M, apply the updated weight configuration to the multi-band time series matching function, so that the matching of frequency bands in different time periods meets the adaptive optimization requirements, enabling the antenna to achieve adaptive impedance matching.

[0023] Optionally, the said S3 includes the following steps:

[0024] S31. Generate an initial solution set using the superposition state of qubits, expressed as a set of state vectors Ψ = {ψ1, ψ2, …, ψ n}, where each state vector ψ i represents a candidate solution, and generate candidate solutions based on frequency bands, usage frequencies, time periods, and environmental information to expand the coverage of the solution space;

[0025] S32. Apply quantum-inspired mutation operations to each candidate solution ψ i in the initial solution set. By mutating the qubits in the state vector, generate new candidate solutions to expand the exploration range of the solution space, so that the generated candidate solutions meet the dynamic requirements of the time series matching function F(t, f);

[0026] S33. Apply the generated dynamic weight coefficient W(t, f) to each candidate solution ψ i in the initial solution set. According to the dynamic weight configuration of different time periods and frequency bands by the time series matching function F(t, f), assign the corresponding weight coefficient W(t, f) to optimize the matching effect;

[0027] S34. Apply the qubit phase adjustment mechanism. By finely adjusting the qubit phase of the candidate solution ψ i , make it adapt to the matching requirements of different time periods and frequency bands:

[0028]

[0029] where, is the phase value after qubit phase adjustment of the candidate solution ψ i under the time period t and frequency band f, represents the initial phase value, β is the phase adjustment coefficient used to control the adjustment amplitude, W(t, f) is the dynamic weight coefficient adjusted based on the frequency band usage frequency and time period decay factor, arctan(W(t, f)) introduces non-linear changes, making the phase more sensitive to weight changes, suitable for scenarios where the frequency band is used frequently or the time period requirements change significantly, and F(t, f) is the time series matching function;

[0030] S35. According to the fitness after dynamic weight and qubit phase adjustment, use composite weights to screen candidate solutions and generate an optimized solution set:

[0031]

[0032] Among them, ω i represents the composite fitness weight of the candidate solution ψ i for prioritizing candidate solutions during the screening process. γ is the weight balance coefficient for adjusting the dynamic weight W(t, f) and the fitness ratio after quantum phase adjustment. W(t, f) is used to reflect the dynamic weights of different time periods and frequency bands. Introduce the sine function and absolute value control of the quantum phase.

[0033] Optionally, S4 includes the following steps:

[0034] S41. During the generation and evaluation process of the candidate solution set Ψ = {ψ1, ψ2, …, ψ n}, screen out the candidate solutions that meet the preset fitness criteria to form a high-fitness solution set where each candidate solution ψ H ∈Ψ H is optimized based on the dynamic weight coefficient W(t, f);

[0035] S42. Apply the dynamic phase modulation mechanism to each candidate solution ψ H in the high-fitness solution set Ψ H and modulate it through the phase value to perform adaptive phase adjustment according to the priorities of different frequency bands:

[0036]

[0037] Among them, represents the phase modulation value of the candidate solution ψ H,i at time period t and frequency band f. is the initial phase value, β is the phase modulation coefficient to control the modulation amplitude, W(t, f) is the dynamic weight coefficient based on the frequency of use of the frequency band and the time period decay factor, F(t, f) is the time sequence matching function, α(f) is the frequency band priority coefficient, and ln(1 + W(t, f)·F(t, f)) introduces non-linear changes to make the phase more sensitive to the high-frequency band demand changes;

[0038] S43. Introduce a frequency band adaptive distribution matrix to adaptively adjust the priority weights of each candidate solution ψ H in the high-fitness solution set, and adjust the priority weights in real time based on the frequency of use of the current frequency band and the time period demand distribution to preferentially meet the matching requirements of the key frequency bands with high usage;

[0039] S44. Apply the memory feedback optimization mechanism. Based on the historical performance of high-fitness solutions, re-evaluate by preferentially selecting solutions with similar parameter characteristics to generate the final optimized solution set:

[0040]

[0041] Among them, Ω H,i represents the comprehensive weight value of the candidate solution ψ H,i , ω j is the fitness weight of the j-th historical solution, and d(ψ H,i , ψ H,j ) represents the similarity metric between the current candidate solution and the historical solution ψ H,j . γ is the feedback adjustment coefficient used to control the influence degree of memory feedback. The exponential decay exp(-γ·d(ψ H,i , ψ H,j )) makes similar historical solutions contribute more to the current fitness weight;

[0042] S45. Through a multi-layer progressive screening strategy, hierarchically screen and prioritize the final optimized solution set according to the time period and frequency band requirements in the time sequence matching function F(t, f) to preferentially meet the key frequency band matching requirements for high-frequency use, thereby optimizing the stability and dynamic adaptability of impedance matching during multi-band switching.

[0043] Optionally, the S5 includes the following steps:

[0044] S51. Establish a progressive environmental feedback mechanism. By real-time monitoring the changes in the working environment of the antenna, obtain the environmental parameter set E = {e1, e2, …, e k} including temperature, humidity, signal strength, and user location, and map the environmental parameters to the parameter matrix M env ;

[0045] S52. Introduce a multi-dimensional environmental response weighting mechanism. According to the change amplitude and change rate of each parameter in the environmental parameter set E, adjust the response coefficient of the matching weight to optimize the frequency band matching effect under complex environmental conditions:

[0046]

[0047] Among them, represents the environmental response weight of the environmental parameter e i in the current matching weight, σ(e i ) is the change amplitude of the environmental parameter e i , represents the change rate of the environmental parameter e i , and W(t, f) is the dynamic weight coefficient, reflecting the priority of frequency band matching in a specific time period and frequency band;

[0048] S53. During the environmental feedback process, apply the environmental disturbance balance mechanism to smooth the minor fluctuation data in the environmental parameter matrix M env and optimize the impact of slight environmental fluctuations on adjusting the matching weights;

[0049] S54. Define the multi-dimensional adaptive matching function F multi-adaptive (t, f, E), where t represents the time period, f represents the frequency band, and E is the current set of environmental parameters. Optimize the matching accuracy of high-priority frequency bands in multi-dimensional environmental changes by preferentially assigning weights to the frequency bands with greater environmental impact:

[0050]

[0051] where F multi-adaptive (t, f, E) is the multi-dimensional adaptive matching function, F(t, f) is the time-series matching function, is the environmental correction coefficient of the environmental parameter e i , and is the environmental response weight;

[0052] S55. Introduce a multi-band dynamic switching control mechanism. According to the current environment and the adjustment results of the multi-dimensional environmental response weighting mechanism, perform dynamic allocation and switching among frequency bands. Optimize the matching fitness and priority of each frequency band to achieve the best impedance matching effect under multi-band conditions;

[0053] S56. According to the output of the multi-dimensional adaptive matching function F multi-adaptive (t, f, E), re-screen the high-fitness solutions to generate the final optimized solution set Ψ env to meet the long-term impedance matching optimization requirements under multi-band conditions.

[0054] Optionally, S52 includes the following steps:

[0055] S521. Obtain the current value and its change rate of each environmental parameter e k from the set of environmental parameters E = {e1, e2,..., e i} and classify the change trend of each parameter e i according to the mutability, persistence, and change frequency of the change rate to identify the high-priority parameters that have an important impact on matching optimization;

[0056] S522. Generate an enhanced environmental response weight through an adaptive activation function to dynamically adjust the weight according to the priority and change rate of the environmental parameters:

[0057]

[0058] Among them, is the enhanced environmental response weight for the environmental parameter e, W(t, f) is the initial dynamic weight coefficient, and κ(e i ) is the priority weighting factor, which is dynamically set according to the historical influence degree of the environmental parameter e i . i The introduction of the hyperbolic tangent function enhances the response sensitivity to rapidly changing environmental parameters. Using the exponential decay term suppresses the influence in the case of small changes, enabling the system to stably handle low-impact changes;

[0059] S523. Integrate the environmental response weight R ei into the enhanced environmental weighting matrix Through the weight conversion mechanism, convert the weight from the original environmental parameter to the multi-band matching parameter, enabling the matrix to reflect the change trend of the high-priority frequency band in the dynamic environment;

[0060] S524. Apply the enhanced environmental weighting matrix to the multi-band matching basis, enabling it to adaptively adjust the priorities of different frequency bands in the dynamic environment, giving priority to meeting the band matching requirements of high-impact environmental parameters, and optimizing the matching stability and response speed during the multi-band switching process.

[0061] The beneficial effects of the present invention are as follows:

[0062] (1) By combining the quantum-inspired mutation search algorithm and the multi-band timing matching function, the present invention significantly improves the optimization efficiency and stability of the mobile phone antenna impedance matching. Especially in terms of multi-band adaptability and dynamic response to environmental changes, the present invention overcomes the deficiencies of traditional methods and can quickly and accurately adjust the matching parameters between different frequency bands and time periods, thereby improving the matching effect of the antenna in complex application scenarios.

[0063] (2) By introducing the progressive environmental feedback mechanism and the method of generating the initial solution set with qubit superposition states, the present invention optimizes the dynamic adjustment and real-time response in the antenna matching process, and improves the adaptability and matching accuracy of the system to environmental changes. The quantum-inspired mutation operation can efficiently handle the matching requirements of multiple frequency bands while expanding the solution space, ensuring the high efficiency of the antenna during frequency band switching.

[0064] (3) By comprehensively applying the quantum-inspired algorithm and the multi-band timing optimization technology, the present invention provides an efficient and adaptive antenna impedance matching optimization solution, enabling the system to automatically handle dynamic changes under multi-band conditions, optimize the matching performance of the antenna, reduce the complexity of manual debugging, and improve the applicability and stability of the mobile phone antenna in various environments.​ Description of the Drawings

[0065] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the drawings:

[0066] Figure 1 is a flowchart of a fast impedance matching optimization method for a mobile phone antenna based on a genetic algorithm proposed by the present invention;

[0067] Figure 2 is a schematic diagram of a quantum-inspired mutation operation proposed by the present invention;

[0068] Figure 3 is a schematic diagram of a progressive environmental feedback mechanism proposed by the present invention. Detailed Description of the Embodiments

[0069] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only showing the basic structure of the present invention in a schematic way, so they only show the components related to the present invention.

[0070] Refer to Figures 1-3 , a fast impedance matching optimization method for a mobile phone antenna based on a genetic algorithm, includes the following steps:

[0071] In this embodiment, the S2 includes the following steps:

[0072] S21. Based on the constructed multi-dimensional parameter space model, extract data related to frequency bands, usage frequencies, and time periods from the parameter matrix, and generate an initial matching weight configuration to meet the matching optimization requirements of different time periods and frequency bands;

[0073] S22. Define a multi-band time sequence matching function F(t, f), where t represents the time period and f represents the target frequency band. The matching function dynamically allocates matching weights according to the changes in the time period t and the frequency band usage frequency, so that the frequency bands with higher frequency band usage frequencies can obtain matching weights preferentially to optimize the frequency band matching effect within each time period;

[0074] S23. Map the frequency band usage frequencies of each time period to dynamic weight coefficients W(t, f), and establish a weight allocation matrix M. The matrix M is used to record the dynamic matching weights of each frequency band within different time periods to construct a matching structure that meets the multi-band time sequence requirements:

[0075]

[0076] where W(t, f) represents the dynamic matching weight coefficient under the time period t and the frequency band f, θ(t) is the time decay factor, f usage(t, f) represents the usage frequency of frequency band f in time period t, which is the total usage frequency of all frequency bands in time period t;

[0077] S24. Based on the time series {t1, t2, …, t n} and the frequency band series {f1, f2, …, f m}, use the time series matching function F(t, f) to control the dynamic adjustment of the weight coefficient W(t i , f j ) in matrix M, where each W(t i , f j ) represents the matching weight in time period t i and frequency band f j ;

[0078] S25. During the time period when the frequency band switching is frequent, by adjusting the weight coefficient W(t, f) in real time, the key frequency bands with high usage frequency can obtain priority matching during the frequent switching period, apply the priority matching weight adjustment coefficient, and continuously optimize the weight allocation based on the feedback of the actual usage frequency:

[0079] W′(t, f) = W(t, f) · (1 + α(f) · δ(t));

[0080] where W′(t, f) is the adjusted priority matching weight, α(f) is the priority coefficient reflecting the priority of the key frequency band, and δ(t) is the real-time factor adjusted according to the frequency band switching frequency in the current time period;

[0081] S26. Utilize the dynamic weight allocation mechanism of matrix M to apply the updated weight configuration to the multi-frequency band time series matching function, so that the matching of the frequency band in different time periods meets the adaptive optimization requirements, and enable the antenna to achieve adaptive impedance matching.

[0082] In this embodiment, the said S3 includes the following steps:

[0083] S31. Generate an initial solution set using the superposition state of qubits, expressed as the state vector set Ψ = {ψ1, ψ2, …, ψ n}, where each state vector ψ i represents a candidate solution, and generate candidate solutions based on frequency band, usage frequency, time period and environmental information to expand the coverage of the solution space;

[0084] S32. Apply the quantum-inspired mutation operation to each candidate solution ψ i in the initial solution set. By mutating the qubits in the state vector, generate new candidate solutions to expand the exploration scope of the solution space, so that the generated candidate solutions meet the dynamic requirements of the time series matching function F(t, f);

[0085] S33. Apply the generated dynamic weight coefficient W(t, f) to each candidate solution ψ in the initial solution set i , and according to the timing matching function F(t, f) for the dynamic weight configuration of different time periods and frequency bands, assign the corresponding weight coefficient W(t, f) to optimize the matching effect;

[0086] S34. Apply the qubit phase adjustment mechanism to fine-tune the qubit phase of the candidate solution ψ i to make it adapt to the matching requirements of different time periods and frequency bands:

[0087]

[0088] where is the phase value after the qubit phase adjustment of the candidate solution ψ i at the time period t and the frequency band f, represents the initial phase value, β is the phase adjustment coefficient used to control the adjustment amplitude, W(t, f) is the dynamic weight coefficient adjusted based on the frequency band usage frequency and the time period decay factor, arctan(W(t, f)) introduces non-linear changes to make the phase more sensitive to weight changes, suitable for scenarios where the frequency band is used frequently or the time period requirements change significantly, and F(t, f) is the timing matching function;

[0089] S35. According to the fitness after the dynamic weight and qubit phase adjustment, use the composite weight to screen the candidate solutions and generate the optimized solution set:

[0090]

[0091] where ω i represents the composite fitness weight of the candidate solution ψ i used to prioritize the candidate solutions during the screening process, γ is the weight balance coefficient used to adjust the ratio of the dynamic weight W(t, f) and the fitness after the quantum phase adjustment, and W(t, f) is used to reflect the dynamic weights of different time periods and frequency bands, introduce the sine function and absolute value control of the quantum phase.

[0092] In this embodiment, the S4 includes the following steps:

[0093] S41. During the generation and evaluation of the candidate solution set Ψ = {ψ1, ψ2,..., ψ n}, screen out the candidate solutions that meet the preset fitness criteria to form a high-fitness solution set where each candidate solution ψ H ∈ Ψ H is optimized based on the dynamic weight coefficient W(t, f);

[0094] S42. Apply the dynamic phase modulation mechanism to each candidate solution ψ H in the high fitness solution set Ψ H and modulate it through the phase value to perform adaptive phase adjustment according to the priorities of different frequency bands:

[0095]

[0096] where, represents the phase modulation value of the candidate solution ψ H,i at time period t and frequency band f, is the initial phase value, β is the phase modulation coefficient to control the modulation amplitude, W(t,f) is the dynamic weight coefficient based on the frequency band usage frequency and the time period decay factor, F(t,f) is the time series matching function, α(f) is the frequency band priority coefficient, and ln(1 + W(t,f)·F(t,f)) introduces non-linear changes to make the phase more sensitive to the changes in the high frequency band requirements;

[0097] S43. Introduce a frequency band adaptive distribution matrix to adaptively adjust the priority weights of each candidate solution ψ H in the high fitness solution set, and adjust the priority weights in real time based on the current frequency band usage frequency and the time period demand distribution to preferentially meet the key frequency band matching requirements with high usage;

[0098] S44. Apply the memory feedback optimization mechanism, and based on the historical performance of the high fitness solutions, generate the final optimized solution set by preferentially selecting solutions with similar parameter characteristics for re-evaluation:

[0099]

[0100] where, Ω H,i represents the comprehensive weight value of the candidate solution ψ H,i , ω j is the fitness weight of the j-th historical solution, d(ψ H,i , ψ H,j ) represents the similarity measure between the current candidate solution and the historical solution ψ H,j , γ is the feedback adjustment coefficient to control the influence degree of the memory feedback, and the exponential decay exp(-γ·d(ψ H,i , ψ H,j )) makes the similar historical solutions contribute more to the current fitness weight;

[0101] S45. Through a multi-layer progressive screening strategy, hierarchically screen and prioritize the final optimized solution set according to the time period and frequency band requirements in the time series matching function F(t,f) to preferentially meet the key frequency band matching requirements with high usage, so as to optimize the stability and dynamic adaptability of the impedance matching during the multi-frequency band switching process.

[0102] In this embodiment, S5 includes the following steps:

[0103] S51. Establish a progressive environmental feedback mechanism. By monitoring the changes in the working environment of the antenna in real time, obtain an environmental parameter set E = {e1, e2, …, e k}, which includes temperature, humidity, signal strength, and user location, and map the environmental parameters to the parameter matrix M env ;

[0104] S52. Introduce a multi-dimensional environmental response weighting mechanism. According to the change amplitude and change rate of each parameter in the environmental parameter set E, adjust the response coefficient of the matching weight to optimize the frequency band matching effect under complex environmental conditions:

[0105]

[0106] Among them, represents the environmental response weight of the environmental parameter e i in the current matching weight, σ(e i ) is the change amplitude of the environmental parameter e i , represents the change rate of the environmental parameter e i , W(t, f) is the dynamic weight coefficient, which reflects the priority of frequency band matching in a specific time period and frequency band;

[0107] S53. During the environmental feedback process, apply an environmental perturbation balance mechanism to smooth the small fluctuation data in the environmental parameter matrix M env and optimize the influence of slight fluctuations in the environment on the adjustment of the matching weight;

[0108] S54. Define a multi-dimensional adaptive matching function F multi-adaptive (t, f, E), where t represents the time period, f represents the frequency band, and E is the current environmental parameter set. By preferentially assigning weights to the frequency bands with greater environmental impact, optimize the matching accuracy of high-priority frequency bands in multi-dimensional environmental changes:

[0109]

[0110] Among them, F multi-adaptive (t, f, E) is the multi-dimensional adaptive matching function, F(t, f) is the time-series matching function, is the environmental correction coefficient of the environmental parameter e i , is the environmental response weight;

[0111] S55. Introduce a multi-band dynamic switching control mechanism. According to the current environment and the adjustment results of the multi-dimensional environment response weighting mechanism, dynamically allocate and switch between each frequency band. By optimizing the matching fitness and priority of each frequency band, achieve the best impedance matching effect under multi-band conditions;

[0112] S56. According to the output of the multi-dimensional adaptive matching function F multi-adaptive (t, f, E), re-screen the high-fitness solutions to generate the final optimized solution set Ψ env , to meet the long-term impedance matching optimization requirements under multi-band conditions.

[0113] In this embodiment, S52 includes the following steps:

[0114] S521. Obtain the current value and its change rate of each environmental parameter e k from the environmental parameter set E = {e1, e2,..., e i}, and classify the change trend of each parameter e according to the mutability, persistence, and change frequency of the change rate, so as to identify the high-priority parameters that have an important impact on the matching optimization; i

[0115] S522. Generate an enhanced environmental response weight through an adaptive activation function, so that the weight is dynamically adjusted according to the priority and change rate of the environmental parameters:

[0116]

[0117] where is the enhanced environmental response weight of the environmental parameter e i , W(t, f) is the initial dynamic weight coefficient, κ(e i ) is the priority weighting factor, which is dynamically set according to the historical influence degree of the environmental parameter e i i , introduce the hyperbolic tangent function to enhance the response sensitivity of rapidly changing environmental parameters, use the exponential decay term to suppress the influence in the case of small changes, so that the system can stably respond to low-impact changes;

[0118] S523. Integrate the environmental response weight into the enhanced environmental weighting matrix through a weight conversion mechanism, convert the weight from the original environmental parameters to multi-band matching parameters, so that the matrix can reflect the change trend of the frequency bands with high priority in the dynamic environment;

[0119] S524. The enhanced environmental weighting matrix Applied in the multi - band matching foundation, it can adaptively adjust the priorities of different bands in a dynamic environment, giving priority to meeting the matching requirements of bands with high - impact environmental parameters, and optimizing the matching stability and response speed during the multi - band switching process.

[0120] Embodiment 1:

[0121] To verify the feasibility of the present invention in implementation, the present invention is applied to a smartphone manufacturing company located in Shanghai to optimize the impedance matching process of its mobile phone antennas. The smartphones produced by this company support frequency bands including 2.4 GHz, 5 GHz, and the Sub6 band of 5G. The performance of the mobile phone antennas directly affects communication quality and signal stability. However, traditional antenna impedance matching methods rely on static rules and cannot fully adapt to complex and variable usage environments, resulting in low matching accuracy and large signal fluctuations during band switching, especially being more obvious in high - frequency bands and complex environments. Therefore, this company decides to introduce the fast impedance matching optimization method based on the quantum - inspired mutation algorithm and progressive environmental feedback mechanism in the present invention to improve the antenna performance.

[0122] During the implementation process, the company first sets initial antenna parameters according to different usage scenarios, including impedance, frequency band, antenna size, etc., and sets multiple key optimization goals for matching optimization, such as matching accuracy, signal stability, and band switching speed, etc. In this process, the impedance matching accuracy of the antenna is the most important optimization goal, so a relatively high weight coefficient is set for it.

[0123] Next, the company generates an initial solution set through the quantum - inspired mutation algorithm and uses quantum mutation operations to expand the search range of the solution space, making the solution set more in line with the matching requirements of different frequency bands and usage environments. At this time, the preliminary matching effect of the antenna is not yet perfect, but it lays the foundation for the subsequent progressive adjustment.

[0124] In practical applications, we selected three typical usage scenarios for verification. The first scenario is an indoor environment with a temperature of 22°C and a humidity of 60%, and a relatively high signal strength. Under the traditional method, the matching stability of the Wi-Fi band is only 85%. After adopting the quantum-inspired mutation algorithm, the signal stability of the Wi-Fi band is increased to 98%, and when the device moves, the signal fluctuation is significantly reduced. The second scenario is an outdoor environment with a temperature of 30°C and a humidity of 50%, and a strong 5G signal. At this time, the optimization method based on the quantum-inspired mutation algorithm enables the signal stability of the 5G band to reach 95%. Even during high-speed movement, the signal fluctuation remains within a small range. The third scenario is a basement environment with a temperature of 18°C and a relatively high humidity, where the 5G signal can hardly be received, mainly relying on the Wi-Fi signal. In this environment, the traditional method cannot stably access the Wi-Fi and LTE bands. After adopting the method of the present invention, the stability of the Wi-Fi signal reaches 90%, and the LTE signal is also optimized, and the signal access success rate is greatly improved.

[0125] To further improve the adaptability of the antenna, the company applies a progressive environmental feedback mechanism to adjust the antenna impedance in real time. By monitoring parameters such as the temperature, humidity, and signal strength of the environment, the feedback mechanism can adjust the matching parameters in real time, enabling the antenna to dynamically adapt to environmental changes. In actual operation, this mechanism can timely adjust the optimization strategy when the device enters different usage scenarios to ensure stable signal reception.

[0126] Finally, the company also applies a real-time feedback mechanism during the production process to monitor the changes in the antenna impedance in real time and make dynamic adjustments based on the feedback data. During the testing process, the feedback mechanism successfully identified and corrected the impedance fluctuation problems caused by environmental changes, enabling the impedance matching accuracy of the final antenna to meet the design requirements. Table 1: Comparison between the method of the present invention and the traditional method under different environmental conditions

[0127]

[0128]

[0129] Through the comparison of experimental data, the performance differences between the traditional static matching method and the optimization method of the present invention are very obvious. Specifically, the optimization method based on the quantum-inspired mutation algorithm has significant advantages in terms of signal stability, smoothness of frequency band switching, and matching accuracy. The experimental data shows that the matching stability of the Wi-Fi band is increased by 13%, the matching stability of the 5G band is increased by 25%, and the signal stability and frequency band switching stability in multiple different environments are significantly improved.

[0130] Through the above experiments, it is proved that the present invention has significant technical advantages in complex and variable actual application scenarios. The combination of the quantum-inspired mutation algorithm and the progressive environmental feedback mechanism enables the mobile phone antenna to achieve fast and accurate impedance matching in multiple frequency bands and multiple environments, significantly improving the signal reception quality and communication stability.

[0131] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered within the protection scope of the present invention.

Claims

1. A fast impedance matching optimization method for mobile phone antennas based on genetic algorithms, characterized in that, It includes the following steps: S1. Obtain the initial parameters of the target antenna, including impedance, frequency band, size, and usage environment information, construct a multi-dimensional parameter space model, and map this data into a parameter matrix for the input basis of the optimization process; S2. Based on the parameter matrix, define a multi-band timing matching function, establish a multi-band timing sequence, dynamically allocate matching weights according to different time periods and usage frequencies, and optimize the matching effect of different frequency bands in each time period; S3. Use the superposition state of qubits to generate an initial solution set, expand the exploration range of the solution space through quantum-inspired mutation operations, and use the timing weights of the multi-band timing matching function to make the initial solution set more suitable for the multi-band matching requirements of different time periods; S4. In the process of solution set generation and evaluation, the quantum-inspired mutation search algorithm fine-tunes the high-fitness solutions through the qubit phase adjustment mechanism, and combines the weight migration of the multi-band timing matching function to preferentially meet the key band matching requirements of high-frequency usage periods; S5. Apply a progressive environmental feedback mechanism to gradually adjust the matching parameters, and gradually optimize the matching parameters in small steps according to the real-time monitored environmental changes to cope with the dynamic changes under multi-band conditions; S6. After completing several generations of iterations and reaching the convergence criterion, based on the frequency band switching simulation test, optimize the stability of the parameters and verify the matching effect of each time period and frequency band; S7. Apply the finally optimized parameters that have passed the verification to the target antenna to complete the adaptive impedance matching optimization based on the multi-band timing matching function, so that the antenna maintains efficient matching during the frequency band switching in different time periods; The specific content of S2 includes: S21. Based on the constructed multi-dimensional parameter space model, extract the data related to the frequency band, usage frequency, and time period from the parameter matrix to generate an initial matching weight configuration to meet the matching optimization requirements of different time periods and frequency bands; S22. Define a multi-band timing matching function F(t,f), where t represents the time period and f represents the target frequency band. The matching function dynamically allocates matching weights according to the changes in the time period t and the frequency band usage frequency, so that the frequency bands with higher frequency band usage frequencies obtain matching weights first to optimize the frequency band matching effect within each time period; S23. Map the frequency band usage frequencies of each time period into dynamic weight coefficients W(t,f), establish a weight allocation matrix M, and the matrix M is used to record the dynamic matching weights of each frequency band within different time periods to construct a matching structure suitable for multi-band timing requirements: Among them, W(t,f) represents the dynamic matching weight coefficient at time period t and frequency band f, θ(t) is the time decay factor, and f usage (t,f) represents the usage frequency of frequency band f at time period t, is the total usage frequency of all frequency bands at time period t; S24. Based on the time series {t1, t2, …, t n} and the frequency band sequence {f1, f2, …, f m}, use the time series matching function F(t, f) to control the dynamic adjustment of the weight coefficient W(t i , f j ) in the matrix M, where each W(t i , f j ) represents the matching weight in the time period t i and the frequency band f j . S25. In the time period with frequent frequency band switching, by adjusting the weight coefficient W(t,f) in real time, the key frequency bands with high-frequency usage can obtain priority matching in the time period with frequent switching, apply the priority matching weight adjustment coefficient, and continuously optimize the weight allocation based on the feedback of the actual usage frequency: W′(t,f) = W(t,f)·(1 + α(f)·δ(t)); where, W'(t,f) is the adjusted priority matching weight, α(f) is the priority coefficient reflecting the priority of the key frequency band, and δ(t) is the real-time factor adjusted according to the frequency band switching frequency within the current time period; S26. Using the dynamic weight allocation mechanism of matrix M, apply the updated weight configuration to the multi-band timing matching function to make the matching of frequency bands in different time periods meet the adaptive optimization requirements.

2. A method for quickly optimizing impedance matching of a mobile phone antenna based on a genetic algorithm according to claim 1, characterized in that, The specific steps of S3 are as follows: S31. Generate an initial solution set using the superposition state of qubits, expressed as a set of state vectors Ψ = {ψ1, ψ2, …, ψ n}}, where each state vector ψ i represents a candidate solution, and candidate solutions are generated based on frequency bands, usage frequencies, time periods, and environmental information to expand the coverage of the solution space; S32. For each candidate solution ψ in the initial solution set i Apply a quantum-inspired mutation operation. By mutating the qubits in the state vector, generate new candidate solutions to expand the exploration scope of the solution space and make the generated candidate solutions meet the dynamic requirements of the timing matching function F(t, f). S33. Apply the generated dynamic weight coefficient W(t, f) to each candidate solution ψ in the initial solution set i , and according to the timing matching function F(t, f) for the dynamic weight configuration of different time periods and frequency bands, assign the corresponding weight coefficient W(t, f) to optimize the matching effect; S34. Apply the qubit phase adjustment mechanism to finely adjust the qubit phase of the candidate solution ψ i to adapt to the matching requirements of different time periods and frequency bands: Among them, is the candidate solution ψ i is the phase value after qubit phase adjustment at time period t and frequency band f, represents the initial phase value, β is the phase adjustment coefficient, W(t, f) is the dynamic weight coefficient, arctan(W(t, f)) is the non-linear variation, and F(t, f) is the timing matching function; S35. According to the fitness after adjusting the dynamic weight and qubit phase, use the composite weight to screen candidate solutions and generate an optimized solution set: Among them, ω i represents the composite fitness weight of the candidate solution ψ i , γ is the weight balance coefficient, which is used to adjust the dynamic weight W(t,f) and the fitness ratio after quantum phase adjustment. W(t,f) is used to reflect the dynamic weights of different time periods and frequency bands, introduce the sine function and absolute value control of the quantum phase.

3. A method for quickly optimizing impedance matching of a mobile phone antenna based on a genetic algorithm according to claim 1, characterized in that, The specific steps of S4 are as follows: S41. During the generation and evaluation process of the candidate solution set Ψ = {ψ1, ψ2, …, ψ n}, candidate solutions that meet the preset fitness criteria are screened out to form a high-fitness solution set where each candidate solution ψ H ∈Ψ H is optimized based on the dynamic weight coefficient W(t, f); S42. For each candidate solution ψ in the high fitness solution set Ψ H apply the dynamic phase modulation mechanism, modulate it through the phase value, and make it adaptively adjust the phase according to the priorities of different frequency bands: H ​ Among them, represents the candidate solution ψ H,i is the phase modulation value at time period t and frequency band f, is the initial phase value, β is the phase modulation coefficient, W(t,f) is the dynamic weight coefficient, based on the frequency usage frequency of the frequency band and the time period attenuation factor, F(t,f) is the time sequence matching function, α(f) is the frequency band priority coefficient, and ln(1 + W(t,f)·F(t,f)) introduces a non-linear change to make the phase more sensitive to the demand change of the high frequency band; S43. Introduce a frequency band adaptive distribution matrix to adaptively adjust the priority weights of each candidate solution ψ in the high fitness solution set, and adjust the priority weights in real time based on the usage frequency of the current frequency band and the time period demand distribution to preferentially meet the matching requirements of key frequency bands with high usage frequency; H ​ S44. Apply the memory feedback optimization mechanism. According to the historical performance of high-fitness solutions, re-evaluate by preferentially selecting solutions with similar parameter characteristics to generate the final optimized solution set: Among them, Ω H,i represents the comprehensive weight value of the candidate solution ψ H,i , ω j is the fitness weight of the j-th historical solution, d(ψ H,i , ψ H,j ) represents the similarity measure between the current candidate solution and the historical solution ψ H,j , γ is the feedback adjustment coefficient, used to control the influence degree of memory feedback, and the exponential decay exp(-γ·d(ψ H,i , ψ H,j )) makes the similar historical solutions contribute more to the current fitness weight; S45. Through a multi-layer progressive screening strategy, hierarchically screen and prioritize the final optimized solution set according to the time period and frequency band requirements in the timing matching function F(t, f) to preferentially meet the matching requirements of key frequency bands for high-frequency use, thereby optimizing the stability and dynamic adaptability of impedance matching during multi-band switching.

4. A method for quickly optimizing the impedance matching of a mobile phone antenna based on a genetic algorithm according to claim 1, characterized in that The specific steps of S5 are as follows: S51. Establish a progressive environmental feedback mechanism. By monitoring the changes in the working environment of the antenna in real time, obtain an environmental parameter set E = {e1, e2, …, e k}, which includes temperature, humidity, signal strength, and user location, and map the environmental parameters to a parameter matrix M env ; S52. Introduce a multi-dimensional environmental response weighting mechanism. According to the change amplitude and change rate of each parameter in the environmental parameter set E, adjust the response coefficient of the matching weight to optimize the frequency band matching effect under complex environmental conditions: Among them, represents the environmental parameter e i is the environmental response weight in the current matching weight, σ(e i ) is the change range of the environmental parameter e i , represents the change rate of the environmental parameter e i , and W(t, f) is the dynamic weight coefficient; S53. During the environmental feedback process, apply the environmental disturbance balance mechanism to smooth the minor fluctuation data in the environmental parameter matrix M env and optimize the impact of minor fluctuations in the environment on adjusting the matching weights; S54. Define a multi-dimensional adaptive matching function F multi-adaptive (t, f, E), where t represents the time period, f represents the frequency band, and E is the set of current environmental parameters. By preferentially assigning weights to the frequency bands with greater environmental impact, the matching accuracy of high-priority frequency bands in multi-dimensional environmental changes is optimized: Among them, F multi-adaptive (t, f, E) is a multi-dimensional adaptive matching function, and F(t, f) is a time-series matching function. is the environmental parameter e i 's environmental correction coefficient, is the environmental response weight; S55. Introduce a multi-band dynamic switching control mechanism. Based on the current environment and the adjustment results of the multi-dimensional environmental response weighting mechanism, perform dynamic allocation and switching among frequency bands. By optimizing the matching fitness and priority of each frequency band, achieve the best impedance matching effect under multi-band conditions; S56. Rescreen the high fitness solutions according to the output of the multi-dimensional adaptive matching function F multi-adaptive (t, f, E) to generate the final optimized solution set Ψ env to meet the long-term impedance matching optimization requirements under multi-band conditions.

5. A method for quickly optimizing impedance matching of a mobile phone antenna based on a genetic algorithm according to claim 4, characterized in that The specific steps of S52 are as follows: S521. Obtain the current value and its rate of change of each environmental parameter e from the set of environmental parameters E = {e1, e2, …, e k}, i and classify the trend of change of each parameter e according to the mutability, persistence, and change frequency of the rate of change, so as to identify high-priority parameters that have an important impact on matching optimization; i ​​ S522. Generate enhanced environment response weights Through an adaptive activation function, the weights are dynamically adjusted according to the priority and change rate of environmental parameters: Among them, is the enhanced environmental response weight for the environmental parameter e, i W(t, f) is the initial dynamic weight coefficient, and κ(e i ) is the priority weighting factor, which is dynamically set according to the historical influence degree of the environmental parameter e i ; is the hyperbolic tangent function, is the exponential decay term; S523. Integrate the environmental response weight into the enhanced environmental weighting matrix Through the weight conversion mechanism, convert the weight from the original environmental parameters to the multi-band matching parameters, so that the matrix can reflect the change trend of the high-priority frequency bands in the dynamic environment; S524. Apply the enhanced environmental weighting matrix to the multi-band matching basis, enabling it to adaptively adjust the priorities of different frequency bands in a dynamic environment, giving priority to meeting the matching requirements of frequency bands with high-impact environmental parameters, and optimizing the matching stability and response speed during multi-band switching.

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