Rapid impedance matching optimization method for mobile phone antenna based on genetic algorithm

Abstract

The invention discloses a mobile phone antenna rapid impedance matching optimization method based on a genetic algorithm, and the method comprises the following steps: S1, obtaining an initial parameter of a target antenna, constructing a multi-dimensional parameter space model, and mapping the multi-dimensional parameter space model into a parameter matrix; s2, defining a multi-band time sequence matching function, dynamically distributing matching weights according to time periods and use frequencies, and optimizing a band matching effect; s3, generating an initial solution set by using a quantum bit superposition state, expanding a solution space through quantum heuristic mutation operation, and setting a time sequence weight; s4, in the generation and evaluation solution set, finely adjusting the high-fitness solution by using a quantum bit phase adjustment mechanism to meet the key frequency band matching requirement; s5, adjusting matching parameters in real time based on a progressive environment feedback mechanism; s6, verifying parameter stability through a frequency band switching simulation test; and S7, applying the optimized parameters to the target antenna to realize adaptive impedance matching. According to the invention, the matching stability and dynamic adaptability of the antenna in multi-band switching are improved.

Description

Technical Field

[0001] The invention relates to the technical field of wireless communications, and in particular to a method for optimizing fast impedance matching of a mobile phone antenna based on a genetic algorithm. Background Art

[0002] With the rapid development of wireless communications and mobile terminal devices, mobile phone antenna technology has become a core component for achieving efficient wireless signal transmission. However, as wireless network environments become increasingly complex and diverse, mobile phone antennas are facing the need for multi-band and broadband coverage. 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 multi-band switching scenarios, 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 antenna impedance by adjusting matching elements such as inductors and capacitors to meet the needs of different frequency bands. This method can achieve a certain matching effect in a fixed frequency band and a stable environment, but it has significant defects in the following aspects:

[0004] Lack of multi-band adaptive capability: The existing mobile phone antenna impedance matching solutions can usually only be optimized on a single or limited frequency band, and it is difficult to achieve efficient matching in scenarios where the frequency bands are frequently switched or multiple frequency bands coexist. Since the matching parameters need to be readjusted when switching frequency bands, the traditional methods have poor adaptability between different frequency bands and often cannot meet the needs of modern multi-band applications, resulting in increased signal loss during frequency band switching, 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 a variety of dynamic factors, such as temperature, humidity, signal interference, etc. Traditional matching methods are usually based on preset parameters or static matching networks, which 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. The existing technology lacks an effective feedback mechanism to monitor and adjust the matching parameters in real time, resulting in poor stability of the antenna in complex environments.

[0006] Insufficient response speed and adjustment accuracy: In scenarios where frequency bands are switched or the external environment changes dramatically, the impedance matching of the antenna needs to respond quickly to ensure signal quality. However, traditional impedance matching technologies mostly rely on manual adjustment or fixed rules, which are difficult to meet the needs of dynamic changes, have slow response speeds, and low adjustment accuracy. Especially when dealing with multi-band and multi-dimensional environments in complex scenarios, traditional matching methods are unable to cope with the situation and cannot precisely control the matching effects under different frequency bands and environmental parameters, resulting in limited overall system performance.

[0007] Lack of adaptive adjustment mechanism: Most existing matching technologies rely on preset matching models and cannot be adaptively adjusted according to actual working conditions. As the use environment and frequency band requirements change, fixed matching models are difficult to meet the requirements of multi-scenario applications. Traditional impedance matching technology is usually unable to autonomously identify and adapt to the priority requirements of different frequency bands, making it difficult to prioritize 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 intelligent optimization mechanisms: In the existing technology, most of them use fixed impedance matching algorithms, such as single optimization methods based on genetic algorithms. These algorithms are not highly adaptable to multi-band and multi-environment requirements. With the complexity of antenna design, it is difficult for fixed algorithms to achieve precise optimization in multi-band requirements, and the feedback and processing of environmental changes are not flexible enough. Especially under multi-variable 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 method for fast impedance matching optimization of 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] One purpose of the present invention is to propose a method for fast impedance matching optimization of mobile phone antennas based on genetic algorithms. The present invention combines quantum heuristic algorithms and multi-band timing matching methods. The present invention uses quantum bit superposition states to generate initial solution sets, and dynamically optimizes the impedance matching of mobile phone antennas under multi-band and multi-environment conditions through quantum heuristic mutation operations and timing weight settings. The specific steps include obtaining the initial parameters of the antenna, defining a multi-band timing matching function, performing 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 matching stability, and fast response speed.

[0011] A method for optimizing fast impedance matching of a mobile phone antenna based on a genetic algorithm according to an embodiment of the present invention comprises the following steps:

[0012] Optionally, S2 includes the following steps:

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

[0014] S22, defining a multi-band time series matching function F(t,f), where t represents a time period, and f represents a target frequency band, and the matching function dynamically allocates matching weights according to changes in the time period t and the frequency of frequency band use, so that frequency bands with higher frequency of use are given priority to obtain matching weights, so as to optimize the frequency band matching effect in each time period;

[0015] S23, mapping the frequency of use of the frequency bands in each time period to a dynamic weight coefficient W(t,f), and establishing a weight allocation matrix M, which is used to record the dynamic matching weights of each frequency band in different time periods, so as to construct a matching structure that adapts to the timing requirements of multiple frequency bands:

[0016]

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

[0018] S24, based on the time series {t1, t2, …, t n} and the frequency band sequence {f1,f2,…,f m}, using the timing matching function F(t,f) to control the weight coefficient W(t i ,f j ) is dynamically adjusted, where each W(t i ,f j ) represents the period t i and frequency band f j The matching weight under ;

[0019] S25. During the period of frequent frequency switching, the weight coefficient W(t,f) is adjusted in real time to make the key frequency bands with high frequency use get priority matching during the period of frequent switching. The priority matching weight adjustment coefficient is applied, and the weight distribution is continuously optimized based on the feedback of the actual frequency of use:

[0020] W′(t,f)=W(t,f)·(1+α(f)·δ(t));

[0021] Among them, 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 switching frequency of the frequency band in the current time period;

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

[0023] Optionally, S3 includes the following steps:

[0024] S31. Generate an initial solution set using the quantum bit superposition state, represented by the state vector set Ψ={ψ1,ψ2,…,ψ n}, where each state vector ψ i Represents a candidate solution, generating candidate solutions based on frequency band, usage frequency, time period and environmental information to expand the coverage of the solution space;

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

[0026] S33, applying the generated dynamic weight coefficient W(t,f) to each candidate solution ψ in the initial solution set i , according to the timing matching function F(t,f), the dynamic weight configuration of different time periods and frequency bands is assigned with the corresponding weight coefficient W(t,f) to optimize the matching effect;

[0027] S34, using the quantum bit phase adjustment mechanism, by adjusting the candidate solution ψ i The quantum bit phase is fine-tuned to adapt to the matching requirements of different time periods and frequency bands:

[0028]

[0029] in, is the candidate solution ψ i The phase value of the qubit after phase adjustment in time period t and frequency band f, represents the initial phase value, β is the phase adjustment coefficient, which is used to control the adjustment amplitude, W(t,f) is the dynamic weight coefficient, which is adjusted based on the frequency of frequency band use and the time period attenuation factor, arctan(W(t,f)) introduces nonlinear changes to make the phase more sensitive to weight changes, which is suitable for scenarios where frequency bands are frequently used or time period requirements change significantly, and F(t,f) is the timing matching function;

[0030] S35. Based on the fitness after dynamic weight and qubit phase adjustment, composite weight is used to screen candidate solutions and generate an optimized solution set:

[0031]

[0032] Among them, ω i Denotes the candidate solution ψ i The composite fitness weight is used to prioritize candidate solutions during the screening process. γ is the weight balance coefficient, which is used to adjust the fitness ratio after the dynamic weight W(t,f) and quantum phase adjustment. W(t,f) is used to reflect the dynamic weights of different time periods and frequency bands. Sine function and absolute value control of quantum phase are introduced.

[0033] Optionally, S4 includes the following steps:

[0034] S41, in the candidate solution set Ψ={ψ1,ψ2,…,ψ n In the process of generating and evaluating the solution set, candidate solutions that meet the preset fitness standards are selected to form a high fitness solution set. where each candidate solution ψ H ∈Ψ H Optimize based on dynamic weight coefficient W(t,f);

[0035] S42, for high fitness solution set Ψ H Each candidate solution ψ in H The dynamic phase modulation mechanism is applied to modulate the phase value so that it can perform adaptive phase adjustment according to the priority of different frequency bands:

[0036]

[0037] in, Denotes the candidate solution ψ H,i The phase modulation value in time period t and frequency band f is: is the initial phase value, β is the phase modulation coefficient, which controls the amplitude of the modulation, W(t,f) is the dynamic weight coefficient, which is based on the frequency of frequency band use and the time period attenuation factor, F(t,f) is the timing matching function, α(f) is the frequency band priority coefficient, and ln(1+W(t,f)·F(t,f)) introduces nonlinear changes, making the phase more sensitive to changes in high-frequency band demand;

[0038] S43, introduce the frequency band adaptive distribution matrix, for each candidate solution ψ in the high fitness solution set H The priority weight is adaptively adjusted, and the priority weight is adjusted in real time based on the usage frequency and time demand distribution of the current frequency band to give priority to meeting the matching needs of key frequency bands with high frequency use;

[0039] S44, applying the memory feedback optimization mechanism, based on the historical performance of the high fitness solution, by giving priority to solutions with similar parameter characteristics for re-evaluation, the final optimization solution set is generated:

[0040]

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

[0042] S45. Through a multi-layer progressive screening strategy, the final optimized solution set is hierarchically screened and prioritized according to the time period and frequency band requirements in the timing matching function F(t,f) to give priority to meeting 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, S5 includes the following steps:

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

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

[0046]

[0047] in, Indicates environmental parameter e i The environmental response weight in the current matching weight, σ(e i ) is the environmental parameter e i The range of change, Indicates environmental parameter e i W(t,f) is the rate of change of frequency band, W(t,f) is the dynamic weight coefficient, which reflects the priority of frequency band matching in a specific time period and frequency band;

[0048] S53, in the environmental feedback process, the environmental disturbance balance mechanism is applied to the environmental parameter matrix M env Smooth the small fluctuations in the data, and optimize the impact of slight fluctuations in the environment on the adjustment of matching weights;

[0049] 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 represents the current environmental parameter set. By giving priority to assigning weights to frequency bands with greater environmental impact, the matching accuracy of high-priority frequency bands in multi-dimensional environmental changes is optimized:

[0050]

[0051] Among them, F multi-adaptive (t,f,E) is a multi-dimensional adaptive matching function, F(t,f) is a temporal matching function, is the environmental parameter e i The environmental correction factor, is the environmental response weight;

[0052] S55, introduce a multi-band dynamic switching control mechanism, dynamically allocate and switch between frequency bands according to the adjustment results of the current environment and the multi-dimensional environment response weighting mechanism, and 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 multi-dimensional adaptive matching function F multi-adaptive The output of (t,f,E) is used to re-screen the high fitness solutions and generate the final optimized solution set Ψ env , to meet the needs of long-term impedance matching optimization under multi-band conditions.

[0054] Optionally, the S52 includes the following steps:

[0055] S521, from the environmental parameter set E = {e1, e2, ..., e k} to get each environment parameter e i The current value and its rate of change And according to the mutation, persistence and frequency of the change rate, each parameter e i Classify the changing trends of the matching parameters to identify the high-priority parameters that have a significant impact on matching optimization;

[0056] S522. Generate enhanced environmental response weights Through the adaptive activation function, the weights are dynamically adjusted according to the priority and change rate of environmental parameters:

[0057]

[0058] in, is the environmental parameter e i The enhanced environmental response weight, W(t,f) is the initial dynamic weight coefficient, κ(e i ) is the priority weighting factor, according to the environmental parameter e i Dynamic setting of historical influence, The hyperbolic tangent function is introduced to enhance the response sensitivity of rapidly changing environmental parameters. Use exponential decay terms to suppress the impact of small changes, so that the system can be stable in response to low-impact changes;

[0059] S523, the environmental response weight R ei Integration into the enhanced environmental weighting matrix Through the weight conversion mechanism, the weights are converted from the original environmental parameters to the multi-band matching parameters, so that the matrix can reflect the changing trend of the high-priority frequency bands in a dynamic environment;

[0060] S524, the enhanced environment weighted matrix Applied to the multi-band matching basis, it can adaptively adjust the priority of different frequency bands in a dynamic environment, give priority to meeting the matching requirements of frequency bands with high impact environmental parameters, and optimize the matching stability and response speed during multi-band switching.

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

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

[0063] (2) The present invention optimizes the dynamic adjustment and real-time response in the antenna matching process by introducing a progressive environmental feedback mechanism and a method for generating an initial solution set using a quantum bit superposition state, thereby improving the system's adaptability to environmental changes and matching accuracy. While expanding the solution space, the quantum-inspired mutation operation can efficiently handle the matching requirements of multiple frequency bands, ensuring the efficient performance of the antenna in frequency band switching.

[0064] (3) The present invention provides an efficient and adaptive antenna impedance matching optimization solution by comprehensively using quantum heuristic algorithms and multi-band timing optimization technology, 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 mobile phone antennas in various environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying 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 and do not constitute a limitation of the present invention. In the accompanying drawings:

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

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

[0068] Figure 3 A schematic diagram of a progressive environmental feedback mechanism proposed by the present invention. DETAILED DESCRIPTION

[0069] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0070] refer to Figure 1-3 , a mobile phone antenna fast impedance matching optimization method based on genetic algorithm, comprising the following steps:

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

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

[0073] S22, defining a multi-band time series matching function F(t,f), where t represents a time period, and f represents a target frequency band, and the matching function dynamically allocates matching weights according to changes in the time period t and the frequency of frequency band use, so that frequency bands with higher frequency of use are given priority to obtain matching weights, so as to optimize the frequency band matching effect in each time period;

[0074] S23, mapping the frequency of use of the frequency bands in each time period to a dynamic weight coefficient W(t,f), and establishing a weight allocation matrix M, which is used to record the dynamic matching weights of each frequency band in different time periods, so as to construct a matching structure that adapts to the timing requirements of multiple frequency bands:

[0075]

[0076] Where W(t,f) represents the dynamic matching weight coefficient in time period t and frequency band f, θ(t) is the time attenuation factor, and f usage(t,f) represents the frequency of use of frequency band f in time period t, 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 sequence {f1,f2,…,f m}, using the timing matching function F(t,f) to control the weight coefficient W(t i ,f j ) is dynamically adjusted, where each W(t i ,f j ) represents the period t i and frequency band f j The matching weight under ;

[0078] S25. During the period of frequent frequency switching, the weight coefficient W(t,f) is adjusted in real time to make the key frequency bands with high frequency use get priority matching during the period of frequent switching. The priority matching weight adjustment coefficient is applied, and the weight distribution is continuously optimized based on the feedback of the actual frequency of use:

[0079] W′(t,f)=W(t,f)·(1+α(f)·δ(t));

[0080] Among them, 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 switching frequency of the frequency band in the current time period;

[0081] S26. Using the dynamic weight allocation mechanism of the matrix M, the updated weight configuration is applied to the multi-band timing matching function, so that the matching of the frequency bands in different time periods meets the adaptive optimization requirements, so that the antenna can achieve adaptive impedance matching.

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

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

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

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

[0086] S34, using the quantum bit phase adjustment mechanism, by adjusting the candidate solution ψ i The quantum bit phase is fine-tuned to adapt to the matching requirements of different time periods and frequency bands:

[0087]

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

[0089] S35. Based on the fitness after dynamic weight and qubit phase adjustment, composite weight is used to screen candidate solutions and generate an optimized solution set:

[0090]

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

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

[0093] S41, in the candidate solution set Ψ={ψ1,ψ2,…,ψ n In the process of generating and evaluating the solution set, candidate solutions that meet the preset fitness standards are selected to form a high fitness solution set. where each candidate solution ψ H ∈Ψ H Optimize based on dynamic weight coefficient W(t,f);

[0094] S42, for high fitness solution set Ψ H Each candidate solution ψ in H The dynamic phase modulation mechanism is applied to modulate the phase value so that it can perform adaptive phase adjustment according to the priority of different frequency bands:

[0095]

[0096] in, Denotes the candidate solution ψ H,i The phase modulation value in time period t and frequency band f is: is the initial phase value, β is the phase modulation coefficient, which controls the amplitude of the modulation, W(t,f) is the dynamic weight coefficient, which is based on the frequency of frequency band use and the time period attenuation factor, F(t,f) is the timing matching function, α(f) is the frequency band priority coefficient, and ln(1+W(t,f)·F(t,f)) introduces nonlinear changes, making the phase more sensitive to changes in high-frequency band demand;

[0097] S43, introduce the frequency band adaptive distribution matrix, for each candidate solution ψ in the high fitness solution set H The priority weight is adaptively adjusted, and the priority weight is adjusted in real time based on the usage frequency and time demand distribution of the current frequency band to give priority to meeting the matching needs of key frequency bands with high frequency use;

[0098] S44, applying the memory feedback optimization mechanism, based on the historical performance of the high fitness solution, by giving priority to solutions with similar parameter characteristics for re-evaluation, the final optimization solution set is generated:

[0099]

[0100] Among them, Ω H,i Denotes the candidate solution ψ H,i The comprehensive weight value, ω j is the fitness weight of the jth historical solution, d(ψ H,i ,ψ H,j ) represents the current candidate solution and the historical solution ψ H,j The similarity measure between them, γ is the feedback adjustment coefficient, which is used to control the influence of memory feedback, and the exponential decay exp(-γ·d(ψ H,i ,ψ H,j )) Make similar historical solutions contribute more to the current fitness weight;

[0101] S45. Through a multi-layer progressive screening strategy, the final optimized solution set is hierarchically screened and prioritized according to the time period and frequency band requirements in the timing matching function F(t,f) to give priority to meeting the key frequency band matching requirements for high-frequency use, thereby optimizing the stability and dynamic adaptability of impedance matching during multi-band switching.

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

[0103] S51. Establish a progressive environmental feedback mechanism to obtain a set of environmental parameters including temperature, humidity, signal strength and user location E = {e1, e2, …, e k}, and map the environmental parameters to the parameter matrix M env ;

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

[0105]

[0106] in, Indicates environmental parameter e i The environmental response weight in the current matching weight, σ(e i ) is the environmental parameter e i The range of change, Indicates environmental parameter e i W(t,f) is the rate of change of frequency band, 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, in the environmental feedback process, the environmental disturbance balance mechanism is applied to the environmental parameter matrix M env Smooth the small fluctuations in the data, and optimize the impact of slight fluctuations in the environment on the adjustment of matching weights;

[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 represents the current environmental parameter set. By giving priority to assigning weights to frequency bands with greater environmental impact, the matching accuracy of high-priority frequency bands in multi-dimensional environmental changes is optimized:

[0109]

[0110] Among them, F multi-adaptive (t,f,E) is a multi-dimensional adaptive matching function, F(t,f) is a temporal matching function, is the environmental parameter e i The environmental correction factor, is the environmental response weight;

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

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

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

[0114] S521, from the environmental parameter set E = {e1, e2, ..., e k} to get each environment parameter e i The current value and its rate of change And according to the mutation, persistence and frequency of the change rate, each parameter e i Classify the changing trends of the matching parameters to identify the high-priority parameters that have a significant impact on matching optimization;

[0115] S522. Generate enhanced environmental response weights Through the adaptive activation function, the weights are dynamically adjusted according to the priority and change rate of environmental parameters:

[0116]

[0117] in, is the environmental parameter e i The enhanced environmental response weight, W(t,f) is the initial dynamic weight coefficient, κ(e i ) is the priority weighting factor, according to the environmental parameter e i Dynamic setting of historical influence, The hyperbolic tangent function is introduced to enhance the response sensitivity of rapidly changing environmental parameters. Use exponential decay terms to suppress the impact of small changes, so that the system can be stable in response to low-impact changes;

[0118] S523, weight the environmental response Integration into the enhanced environmental weighting matrix Through the weight conversion mechanism, the weights are converted from the original environmental parameters to the multi-band matching parameters, so that the matrix can reflect the changing trend of the high-priority frequency bands in a dynamic environment;

[0119] S524, the enhanced environment weighted matrix Applied to the multi-band matching basis, it can adaptively adjust the priority of different frequency bands in a dynamic environment, give priority to meeting the matching requirements of frequency bands with high impact environmental parameters, and optimize the matching stability and response speed during multi-band switching.

[0120] Embodiment 1:

[0121] In order to verify the feasibility of the present invention in implementation, the present invention was applied to a smartphone manufacturing company located in Shanghai to optimize the impedance matching process of its mobile phone antenna. The smartphones produced by the company support frequency bands including 2.4GHz, 5GHz and 5G Sub6 bands. The performance of the mobile phone antenna directly affects the communication quality and signal stability. However, the traditional antenna impedance matching method relies on static rules and cannot fully adapt to the complex and changeable usage environment, resulting in low matching accuracy and large signal fluctuations when switching frequency bands, especially in high frequency bands and complex environments. For this reason, the company decided 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 antenna performance.

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

[0123] Next, the company generated an initial solution set through a quantum-inspired mutation algorithm and used 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 point, the initial matching effect of the antenna is not perfect, but it lays the foundation for the subsequent gradual 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, a humidity of 60%, and a high signal strength. Under the traditional method, the matching stability of the Wi-Fi band is only 85%, but after adopting the quantum-inspired mutation algorithm, the signal stability of the Wi-Fi band is increased to 98%, and the signal fluctuation is significantly reduced when the device moves. The second scenario is an outdoor environment with a temperature of 30°C, a humidity of 50%, and a strong 5G signal. At this time, the optimization method based on the quantum-inspired mutation algorithm makes the signal stability of the 5G band reach 95%, and even during high-speed movement, the signal fluctuation is maintained within a small range. The third scenario is a basement environment with a temperature of 18°C ​​and high humidity. The 5G signal is almost unreceivable and mainly relies 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%, the LTE signal is also optimized, and the signal access success rate is greatly improved.

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

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

[0127]

[0128]

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

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

[0131] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for fast impedance matching optimization of mobile phone antenna based on genetic algorithm, characterized in that: The steps include: S1. Obtain the initial parameters of the target antenna, including impedance, frequency band, size and use environment information, build a multi-dimensional parameter space model, and map these data into a parameter matrix for the input basis of the optimization process; S2. Based on the parameter matrix, define the multi-band timing matching function, establish the multi-band timing sequence, dynamically allocate matching weights according to different time periods and usage frequencies, and optimize the matching effects of different frequency bands in each time period; S3. Generate an initial solution set using the superposition state of quantum bits, expand the exploration range of the solution space through quantum-inspired mutation operations, and use the timing weight setting of the multi-band timing matching function to make the initial solution set more suitable for the multi-band matching requirements of different time segments; S4. During the solution set generation and evaluation process, the quantum-inspired mutation search algorithm fine-tunes the high-fitness solution through the quantum bit phase adjustment mechanism, and combines the weight migration of the multi-band timing matching function to give priority to meeting the key frequency band matching requirements during high-frequency usage periods; S5. Use a progressive environmental feedback mechanism to gradually adjust the matching parameters. According to the real-time monitored environmental changes, the matching parameters are gradually optimized in small steps to cope with dynamic changes under multi-band conditions. S6. After completing several iterations and reaching the convergence standard, the stability of the optimization parameters is tested based on the frequency band switching simulation to verify the matching effect of each time period and frequency band; S7. Apply the verified final optimization parameters 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 frequency band switching in different time segments.

2. According to the genetic algorithm-based fast impedance matching optimization method for mobile phone antennas according to claim 1, it is characterized in that: The S2 specifically includes: S21. Based on the constructed multi-dimensional parameter space model, data related to frequency bands, usage frequency and time periods are extracted from the parameter matrix to generate initial matching weight configurations to meet the matching optimization requirements of different time periods and frequency bands; S22, defining a multi-band time series matching function F(t,f), where t represents a time period, and f represents a target frequency band, and the matching function dynamically allocates matching weights according to changes in the time period t and the frequency of frequency band use, so that frequency bands with higher frequency of use are given priority to obtain matching weights, so as to optimize the frequency band matching effect in each time period; S23, mapping the frequency of use of the frequency bands in each time period to a dynamic weight coefficient W(t,f), and establishing a weight allocation matrix M, which is used to record the dynamic matching weights of each frequency band in different time periods, so as to construct a matching structure that adapts to the timing requirements of multiple frequency bands: Where W(t,f) represents the dynamic matching weight coefficient in time period t and frequency band f, θ(t) is the time attenuation factor, and f usage (t,f) represents the frequency of use of frequency band f in time period t, is the total usage frequency of all frequency bands in time period t; S24, based on the time series {t1, t2, …, t n } and the frequency band sequence {f1,f2,…,f m }, using the timing matching function F(t,f) to control the weight coefficient W(t i ,f j ) is dynamically adjusted, where each W(t i ,f j ) represents the period t i and frequency band f j The matching weight under ; S25. During the period of frequent frequency switching, the weight coefficient W(t,f) is adjusted in real time to make the key frequency bands with high frequency use get priority matching during the period of frequent switching. The priority matching weight adjustment coefficient is applied, and the weight distribution is continuously optimized based on the feedback of the actual frequency of use: W′(t,f)=W(t,f)(1+α(f)·δ(t)); Among them, 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 switching frequency of the frequency band in the current time period; S26. Using the dynamic weight allocation mechanism of the matrix M, the updated weight configuration is applied to the multi-band timing matching function, so that the matching of the frequency bands in different time periods meets the adaptive optimization requirements.

3. According to the genetic algorithm-based fast impedance matching optimization method for mobile phone antennas in claim 1, it is characterized in that: The S3 specifically includes: S31. Generate an initial solution set using the quantum bit superposition state, represented by the state vector set Ψ={ψ1,ψ2,…,ψ n }, where each state vector ψ i Represents a candidate solution, generating candidate solutions based on frequency band, usage frequency, time period and environmental information to expand the coverage of the solution space; S32, for each candidate solution ψ in the initial solution set i Apply quantum-inspired mutation operations to generate new candidate solutions by mutating the qubits in the state vector to expand the exploration range of the solution space so that the generated candidate solutions meet the dynamic requirements of the timing matching function F(t,f); S33, applying the generated dynamic weight coefficient W(t,f) to each candidate solution ψ in the initial solution set i , according to the timing matching function F(t,f), the dynamic weight configuration of different time periods and frequency bands is assigned with the corresponding weight coefficient W(t,f) to optimize the matching effect; S34, using the quantum bit phase adjustment mechanism, by adjusting the candidate solution ψ i The quantum bit phase is fine-tuned to adapt to the matching requirements of different time periods and frequency bands: in, is the candidate solution ψ i The phase value of the qubit after phase adjustment in 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 nonlinear change, and F(t,f) is the timing matching function; S35. Based on the fitness after dynamic weight and qubit phase adjustment, composite weight is used to screen candidate solutions and generate an optimized solution set: Among them, ω i Denotes the candidate solution ψ i The composite fitness weight, γ is the weight balance coefficient, which is used to adjust the fitness ratio after the dynamic weight W(t,f) and quantum phase adjustment. W(t,f) is used to reflect the dynamic weight of different time periods and frequency bands. Sine function and absolute value control of quantum phase are introduced.

4. According to the genetic algorithm-based fast impedance matching optimization method for mobile phone antennas in claim 1, it is characterized in that: The S4 specifically includes: S41, in the candidate solution set Ψ={ψ1,ψ2,…,ψ n In the process of generating and evaluating the solution set, candidate solutions that meet the preset fitness standards are selected to form a high fitness solution set. where each candidate solution ψ H ∈Ψ H Optimize based on dynamic weight coefficient W(t,f); S42, for high fitness solution set Ψ H Each candidate solution ψ in H The dynamic phase modulation mechanism is applied to modulate the phase value so that it can perform adaptive phase adjustment according to the priority of different frequency bands: in, Denotes the candidate solution ψ H,i The phase modulation value in time period t and frequency band f is: is the initial phase value, β is the phase modulation coefficient, W(t,f) is the dynamic weight coefficient based on the frequency of frequency band use and the time period attenuation factor, F(t,f) is the timing matching function, α(f) is the frequency band priority coefficient, and ln(1+W(t,f)·F(t,f)) introduces nonlinear changes, making the phase more sensitive to changes in high-frequency band demand; S43, introduce the frequency band adaptive distribution matrix, for each candidate solution ψ in the high fitness solution set H The priority weight is adaptively adjusted, and the priority weight is adjusted in real time based on the usage frequency and time demand distribution of the current frequency band to give priority to meeting the matching needs of key frequency bands with high frequency use; S44, applying the memory feedback optimization mechanism, based on the historical performance of the high fitness solution, by giving priority to solutions with similar parameter characteristics for re-evaluation, the final optimization solution set is generated: Among them, Ω H,i Denotes the candidate solution ψ H,i The comprehensive weight value, ω j is the fitness weight of the jth historical solution, d(ψ H,i ,ψ H,j ) represents the current candidate solution and the historical solution ψ H,j The similarity measure between them, γ is the feedback adjustment coefficient, which is used to control the influence of memory feedback, and the exponential decay exp(-γ·d(ψ H,i ,ψ H,j )) Make similar historical solutions contribute more to the current fitness weight; S45. Through a multi-layer progressive screening strategy, the final optimized solution set is hierarchically screened and prioritized according to the time period and frequency band requirements in the timing matching function F(t,f) to give priority to meeting the key frequency band matching requirements for high-frequency use, thereby optimizing the stability and dynamic adaptability of impedance matching during multi-band switching.

5. The method for fast impedance matching optimization of mobile phone antenna based on genetic algorithm according to claim 1, characterized in that: The S5 specifically includes: S51. Establish a progressive environmental feedback mechanism to obtain a set of environmental parameters including temperature, humidity, signal strength and user location E = {e1, e2, …, e k }, and map the environmental parameters to the parameter matrix M env ; S52. Introduce a multi-dimensional environmental response weighting mechanism, adjust the response coefficient of the matching weight according to the change amplitude and change rate of each parameter in the environmental parameter set E, and optimize the frequency band matching effect under complex environmental conditions: in, Indicates environmental parameter e i The environmental response weight in the current matching weight, σ(e i ) is the environmental parameter e i The range of change, Indicates environmental parameter e i The rate of change of , W(t,f) is the dynamic weight coefficient; S53, in the environmental feedback process, the environmental disturbance balance mechanism is applied to the environmental parameter matrix M env Smooth the small fluctuations in the data, and optimize the impact of slight fluctuations in the environment on the adjustment of 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 represents the current environmental parameter set. By giving priority to assigning weights to 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, F(t,f) is a temporal matching function, is the environmental parameter e i The environmental correction factor, is the environmental response weight; S55, introduce a multi-band dynamic switching control mechanism, dynamically allocate and switch between frequency bands according to the adjustment results of the current environment and the multi-dimensional environment response weighting mechanism, and optimize the matching fitness and priority of each frequency band to achieve the best impedance matching effect under multi-band conditions; S56, according to the multi-dimensional adaptive matching function F multi-adaptive The output of (t,f,E) is used to re-screen the high fitness solutions and generate the final optimized solution set Ψ env , to meet the needs of long-term impedance matching optimization under multi-band conditions.

6. The method for fast impedance matching optimization of mobile phone antenna based on genetic algorithm according to claim 5, characterized in that: The S52 specifically includes: S521, from the environmental parameter set E = {e1, e2, ..., e k } to get each environment parameter e i The current value and its rate of change And according to the mutation, persistence and frequency of the change rate, each parameter e i Classify the changing trends of the matching parameters to identify the high-priority parameters that have a significant impact on matching optimization; S522. Generate enhanced environmental response weights Through the adaptive activation function, the weights are dynamically adjusted according to the priority and change rate of environmental parameters: in, is the environmental parameter e i The enhanced environmental response weight, W(t,f) is the initial dynamic weight coefficient, κ(e i ) is the priority weighting factor, according to the environmental parameter e i Dynamic setting of historical influence, is the hyperbolic tangent function, is the exponential decay term; S523, weight the environmental response Integration into the enhanced environmental weighting matrix Through the weight conversion mechanism, the weights are converted from the original environmental parameters to the multi-band matching parameters, so that the matrix can reflect the changing trend of the high-priority frequency bands in a dynamic environment; S524, the enhanced environment weighted matrix Applied to the multi-band matching basis, it can adaptively adjust the priority of different frequency bands in a dynamic environment, give priority to meeting the matching requirements of frequency bands with high impact environmental parameters, and optimize the matching stability and response speed during multi-band switching.

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