Intelligent antenna control method based on microsystem

Abstract

The invention discloses an intelligent antenna control method based on a microsystem, and the method comprises the steps: S1, setting system control parameters in the microsystem, generating a wave body, and distributing a discrete phase value and initial energy; s2, collecting and storing communication performance indexes of the antenna array; s3, calling an improved water wave optimization algorithm to execute phase optimization when the communication performance index declines and exceeds a threshold value or the silence time reaches a set threshold value; s4, discrete disturbance is carried out on the wave individuals, and communication performance indexes are calculated; s5, updating the phase according to the difference between the communication performance indexes before and after disturbance, performing rupture operation on the wave individuals with reduced performance, and dynamically adjusting the disturbance step length; s6, when the energy of the wave individual is lower than an energy threshold value, a phase vector is regenerated with the global optimal wave individual phase as the center, and energy is recovered; and S7, finally outputting a phase control signal to realize array adaptive adjustment. According to the invention, the antenna pattern control precision and the communication stability are improved.

Description

Technical Field

[0001] This invention relates to the field of antenna array control and microsystem control technology, and in particular to a smart antenna control method based on a microsystem. Background Technology

[0002] With the rapid development of mobile communication systems and satellite communication technologies, smart antenna arrays are playing an increasingly important role in high-frequency and high-speed communication. Smart antenna systems control the phase of each subarray or element within the array to achieve adjustable beam direction and optimized signal gain, thereby improving the system's signal coverage quality and anti-interference capabilities. Traditional antenna control methods often rely on centralized signal processing units and fixed algorithm models, employing periodic scanning or phase control methods based on static weights. These methods are complex in structure, slow in response, and difficult to achieve real-time adaptive control in multi-user, multi-path, and dynamic channel environments, leading to unstable communication performance. Especially in miniaturized and distributed application scenarios, the power consumption and latency issues of centralized control are more pronounced, limiting the development of smart antennas in microsystem integration.

[0003] Existing smart antenna phase control schemes primarily achieve beam pattern adjustment through optimization mechanisms based on particle swarm optimization, genetic algorithms, or fuzzy control algorithms. While these algorithms can improve beam pattern performance to some extent, they generally suffer from slow convergence speed, susceptibility to local optima, and complex parameter tuning. In high-dimensional array systems, the algorithms need to simultaneously handle a large number of phase variables and nonlinear constraints. The insufficient global search capability of traditional optimization algorithms leads to an inability to respond in real time to changes in communication performance under dynamic channel conditions. Furthermore, most existing schemes rely on external computing platforms or cloud-based collaborative optimization, lacking independent computational and adaptive decision-making capabilities within the microsystem itself, making stable operation in low-power, high-time-efficiency embedded environments impossible.

[0004] At the hardware level, existing antenna control systems typically employ a multi-module distributed architecture, with signal acquisition, data processing, and phase control operating independently, resulting in significant data transmission delays between modules. Communication performance metrics such as received signal strength, signal-to-noise ratio, and error vector amplitude often require multiple sampling and averaging processes before being used for control decisions, leading to insufficient real-time performance. Furthermore, traditional systems lack energy feedback and self-recovery mechanisms. When the control process is affected by external disturbances or optimization interruptions, the system struggles to automatically recover to a stable state, resulting in antenna pattern shifts and degraded communication quality.

[0005] Therefore, how to provide a smart antenna control method based on microsystems is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] One objective of this invention is to propose a microsystem-based smart antenna control method. This invention combines microsystem control with an improved water wave optimization algorithm to address the phase adjustment hysteresis and performance fluctuations of smart antennas in dynamic channels. It introduces energy feedback and step-size self-adjustment mechanisms to achieve adaptive group optimization and stable convergence. A globally optimal phase is determined through comprehensive evaluation of multiple indicators, generating a phase control signal to drive synchronous array adjustment, thereby improving communication performance stability and pattern control accuracy.

[0007] A smart antenna control method based on a microsystem according to an embodiment of the present invention includes the following steps:

[0008] S1. Set system control parameters in the microsystem, generate multiple wave individuals, assign discrete phase values ​​to each wave individual and set initial energy;

[0009] S2. The microsystem collects and stores the communication performance indicators of the antenna array;

[0010] S3. When the communication performance index drops below a preset threshold or the silence time reaches a set threshold, the microsystem calls the improved water wave optimization algorithm to perform phase optimization.

[0011] S4. In the improved water wave optimization algorithm, several subarrays are selected for discrete perturbation of the individual waves, the perturbed phase is limited to the phase quantization set, and the corresponding communication performance index is calculated.

[0012] S5. Update the phase of the wave individual according to the difference in communication performance indicators before and after the disturbance, perform the break operation on the wave individual with degraded performance according to the preset break step size, and dynamically adjust the disturbance step size according to the amplitude of communication performance fluctuation.

[0013] S6. Adjust the energy value based on the optimization results of the wave individual. When the energy of the wave individual is lower than the energy threshold, regenerate the phase vector and restore the energy with the currently recorded global best wave individual phase as the center.

[0014] S7. Select the wave individual with the highest comprehensive score and stable energy state as the global optimal result, and output the corresponding phase control signal from the microsystem to the antenna array to perform phase adjustment.

[0015] Optionally, step S1 includes:

[0016] S11. The microsystem uses an internal clock initialization control module to perform power-on detection on the storage unit, signal acquisition unit, and phase control unit, and establish a communication connection with the antenna array.

[0017] S12. The system control parameters are a set of operating parameters set by the microsystem during the initialization phase, including the number of wave groups, disturbance step size, break step size, number of subarrays, signal change threshold, silence time threshold, communication performance fluctuation threshold, and energy threshold.

[0018] S13. Set the wave group number parameter in the storage unit to determine the number of wave individuals participating in the phase search at the same time;

[0019] S14. Set the perturbation step size parameter to limit the discrete increment range of each phase adjustment;

[0020] S15. Set the number of subarrays according to the structural information of the antenna array. Each subarray contains several array elements and corresponds to the phase control unit.

[0021] S16. Set the signal change threshold, silence time threshold, communication performance fluctuation threshold, and energy threshold in the processing unit;

[0022] S17. Generate a corresponding number of wave individuals according to the set number of wave groups and subarrays, assign an initial phase value to each wave individual within the discrete phase range according to the preset perturbation step size, and determine the initial energy level of each wave individual in combination with the energy threshold.

[0023] S18. The phase values ​​of each subarray of the wave individual are used to form a discrete phase vector, thus forming a set of wave individuals.

[0024] Optionally, step S2 includes:

[0025] S21. The microsystem sets the sampling period and sampling duration, and establishes a data buffer and a sliding window. The sliding window is a circular storage structure that stores multiple consecutive sampling records.

[0026] S22. Determine the set of communication performance index fields, which includes received signal strength, signal-to-noise ratio and error vector amplitude, and set the data recording format and measurement unit for each index.

[0027] S23. Read the original measurement values ​​from the RF front-end and baseband processing unit according to the sampling period, add a timestamp and channel identifier to each measurement value, generate a single sampling record and write it into the data buffer area;

[0028] S24. Perform preprocessing on the single sampling record, including jitter removal, amplitude limiting, and threshold-based outlier masking, and write the processed record into the sliding window;

[0029] S25. Update the interval statistics field within the sliding window. The interval statistics field includes the average, range, and number of samples, and establishes an index relationship with the most recent sampling record.

[0030] S26. Synchronize the current content of the sliding window and the interval statistics field to the running cache area, and write it to the persistent storage unit, recording the writing sequence number and time information;

[0031] S27. Generate a data availability flag and set the corresponding status to valid.

[0032] Optionally, step S3 includes:

[0033] S31. The latest statistical value of the microsystem detection communication performance index field set is compared with the preset signal change threshold and silent time threshold in the system control parameters;

[0034] S32. When the change in the communication performance index exceeds the signal change threshold or the continuous silence time reaches the silence time threshold, the microsystem generation algorithm triggers an instruction and writes it into the running buffer.

[0035] S33. Detect the status of the data availability flag. When the data availability flag is detected to be valid, the microsystem enters the execution stage of the improved water wave optimization algorithm.

[0036] S34. Read the set of communication performance index fields and corresponding time indexes from the running buffer, and load the set of wave individuals formed in the storage unit;

[0037] S35. Establish an optimization task queue based on the read set of communication performance index fields, wherein the optimization task queue corresponds to the phase adjustment target of the antenna array;

[0038] S36. During the process of establishing the optimized task queue, the microsystem records the trigger time and the current communication performance status, and writes the trigger information into the operation log unit;

[0039] S37. After the optimized task queue is established, the microsystem maintains the current launch status and locks the communication channel.

[0040] Optionally, step S4 includes:

[0041] S41. After the microsystem enters the execution stage of the improved water wave optimization algorithm, it selects several wave individuals to participate in the initial optimization according to the preset phase adjustment target in the optimization task queue, and assigns an independent optimization channel to each wave individual.

[0042] S42. Perform a discrete perturbation operation on the individual waves participating in the optimization. The discrete perturbation operation adjusts the phase of a portion of the subarray within the phase quantization set according to a preset perturbation step size. The perturbed phase value is limited to the discrete set.

[0043] S43. Calculate the difference in communication performance indicators of the wave individuals participating in the optimization before and after the disturbance, and update the energy state record of the corresponding wave individuals.

[0044] S44. Mark the wave individuals with improved communication performance as effective wave individuals, and record the corresponding phase values ​​and communication performance results;

[0045] S45. Mark wave individuals with degraded communication performance as invalid wave individuals and maintain the phase state before the disturbance;

[0046] S46. After each iteration, the current global optimal wave individual is determined based on the comprehensive score of the communication performance index, and the corresponding phase vector is written into the running buffer.

[0047] S47. When all optimized channels have completed the predetermined number of iterations or the communication performance indicators have reached the convergence condition, the microsystem records the current optimization results and enters the parameter adjustment stage.

[0048] Optionally, step S5 includes:

[0049] S51. Based on the optimization results of the improved water wave optimization algorithm, the microsystem reads the communication performance indicators of each wave before and after the disturbance in the previous iteration period.

[0050] S52. The microsystem maintains the original phase vector of the effective wave individual and updates the energy state;

[0051] S53. The microsystem performs a breakup operation on the invalid wave individual and redistributes the phase values ​​of some subarrays within the phase quantization range according to the preset breakup amplitude.

[0052] S54. Perform quantization constraint processing on the redistributed phase values, limit the adjustment results to the phase quantization set, and generate a new phase vector;

[0053] S55. After each iteration, the microsystem calculates the disturbance step size adjustment coefficient based on the communication performance fluctuation amplitude of multiple consecutive iterations.

[0054] S56. When the communication performance fluctuation amplitude is higher than the preset communication performance fluctuation threshold, the disturbance step size is reduced; when the communication performance fluctuation amplitude is lower than the preset communication performance fluctuation threshold, the disturbance step size is increased.

[0055] S57. Update the phase vector and perturbation step size parameters of each wave individual to form a new wave group state.

[0056] Optionally, step S6 includes:

[0057] S61. After the microsystem completes the iteration of the improved water wave optimization algorithm, it detects the energy value of each wave individual and compares it with the energy threshold to determine whether the wave individual meets the regeneration condition.

[0058] S62. When the energy value of a wave individual is lower than the energy threshold, the microsystem selects the phase vector of the current globally optimal wave individual as the regeneration center.

[0059] S63. Regenerate multiple discrete phase vectors near the regeneration center phase according to a preset perturbation step size to form a new set of wave individuals;

[0060] S64. Assign initial energy values ​​to the regenerated wave individuals and update the wave group energy distribution table;

[0061] S65. Add the set of wave individuals after energy recovery to the current wave group and activate the corresponding optimization channel.

[0062] Optionally, step S7 includes:

[0063] S71. Read the communication performance indicators and corresponding phase vectors of each wave individual from the running buffer;

[0064] S72. Using the weighted result of received signal strength, signal-to-noise ratio, and error vector amplitude as the evaluation basis, a comprehensive score is given to each individual wave.

[0065] S73. The wave individual with the highest comprehensive score and stable energy state is determined as the global optimal wave individual;

[0066] S74. The microsystem generates a phase control signal based on the phase vector of the global optimal wave individual, and the phase control signal includes the phase setting value and synchronization trigger command of each subarray.

[0067] S75. Perform data packaging and timestamp marking on the phase control signal, and send it to the antenna array control module via the communication interface;

[0068] S76. The antenna array control module synchronously adjusts the phase of each subarray according to the received phase control signal to complete the array pattern update.

[0069] S77. The microsystem records the phase adjustment completion status and corresponding communication performance indicators, and updates the operation log and system status table.

[0070] The beneficial effects of this invention are:

[0071] This invention addresses the challenges of large communication performance fluctuations, phase adjustment hysteresis, and local optima trapping in smart antenna arrays under dynamic channel conditions by deeply integrating a microsystem adaptive control architecture with an improved water wave optimization algorithm. It constructs a phase optimization control method with real-time feedback and energy self-recovery mechanisms. By setting system control parameters within the microsystem and combining multi-channel signal acquisition with a sliding window statistical structure, continuous monitoring and dynamic storage of communication performance indicators such as received signal strength, signal-to-noise ratio, and error vector amplitude are achieved. During the algorithm triggering phase, the microsystem automatically initiates the improved water wave optimization algorithm based on signal change thresholds and silence time thresholds. This algorithm performs perturbation, fragmentation, and regeneration operations in the discrete phase space, maintaining stable wave group evolution through an energy feedback control mechanism. To address the low search efficiency and fixed parameters of traditional optimization algorithms, this invention introduces a step-size self-adjustment mechanism driven by communication performance fluctuation amplitude. This enables dynamic updates of the perturbation step size during iteration, thereby improving global optimization capability and convergence speed. By comprehensively evaluating the weighted results of communication performance indicators, the globally optimal wave individual is determined, and a phase control signal is generated to drive the antenna array to perform synchronous phase adjustment and update the radiation pattern distribution in real time. This invention achieves autonomous phase optimization and robust control in a microsystem environment, significantly improving the communication stability, energy utilization efficiency, and array radiation pattern control accuracy of smart antennas under complex channel conditions. Attached Figure Description

[0072] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0073] Figure 1 This is a schematic diagram of the overall process of a smart antenna control method based on a microsystem proposed in this invention;

[0074] Figure 2 This is a schematic diagram of the microsystem structure in this invention;

[0075] Figure 3 This is a flowchart illustrating the operation of the improved water wave optimization algorithm in antenna phase control in this invention. Detailed Implementation

[0076] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0077] refer to Figure 1-3 A smart antenna control method based on a microsystem includes the following steps:

[0078] S1. Set system control parameters in the microsystem, generate multiple wave individuals, assign discrete phase values ​​to each wave individual and set initial energy;

[0079] S2. The microsystem collects and stores the communication performance indicators of the antenna array;

[0080] S3. When the communication performance index drops below a preset threshold or the silence time reaches a set threshold, the microsystem calls the improved water wave optimization algorithm to perform phase optimization.

[0081] S4. In the improved water wave optimization algorithm, several subarrays are selected for discrete perturbation of the individual waves, the perturbed phase is limited to the phase quantization set, and the corresponding communication performance index is calculated.

[0082] S5. Update the phase of the wave individual according to the difference in communication performance indicators before and after the disturbance, perform the break operation on the wave individual with degraded performance according to the preset break step size, and dynamically adjust the disturbance step size according to the amplitude of communication performance fluctuation.

[0083] S6. Adjust the energy value based on the optimization results of the wave individual. When the energy of the wave individual is lower than the energy threshold, regenerate the phase vector and restore the energy with the currently recorded global best wave individual phase as the center.

[0084] S7. Select the wave individual with the highest comprehensive score and stable energy state as the global optimal result, and output the corresponding phase control signal from the microsystem to the antenna array to perform phase adjustment.

[0085] In this invention, the communication performance indicators are collected by the signal detection module within the microsystem, including received signal strength or signal-to-noise ratio data. When the processing unit within the microsystem detects a performance degradation or a silence time reaching a threshold, it automatically invokes the improved water wave optimization algorithm module to perform phase optimization. The improved water wave optimization algorithm determines the optimal phase combination in the discrete phase space through a process of perturbation, comparison, and energy update, and outputs the phase control signal to the antenna array control unit to achieve dynamic adjustment of beam direction and gain.

[0086] In this embodiment, step S1 includes:

[0087] S11. The microsystem uses an internal clock initialization control module to perform power-on detection on the storage unit, signal acquisition unit, and phase control unit, and establish a communication connection with the antenna array.

[0088] S12. The system control parameters are a set of operating parameters set by the microsystem during the initialization phase, including the number of wave groups, disturbance step size, break step size, number of subarrays, signal change threshold, silence time threshold, communication performance fluctuation threshold, and energy threshold.

[0089] S13. Set the wave group number parameter in the storage unit to determine the number of wave individuals participating in the phase search at the same time;

[0090] S14. Set the perturbation step size parameter to limit the discrete increment range of each phase adjustment;

[0091] S15. Set the number of subarrays according to the structural information of the antenna array. Each subarray contains several array elements and corresponds to the phase control unit.

[0092] S16. Set the signal change threshold, silence time threshold, communication performance fluctuation threshold, and energy threshold in the processing unit;

[0093] S17. Generate a corresponding number of wave individuals according to the set number of wave groups and subarrays, assign an initial phase value to each wave individual within the discrete phase range according to the preset perturbation step size, and determine the initial energy level of each wave individual in combination with the energy threshold.

[0094] S18. The phase values ​​of each subarray of the wave individual are used to form a discrete phase vector, thus forming a set of wave individuals.

[0095] In this invention, the microsystem loads and sets system control parameters through a control module. These parameters can be manually set or dynamically updated using historical operating data. The system control parameters include the number of wave groups, perturbation step size, number of subarrays, signal change threshold, silence time threshold, energy threshold, and communication performance index threshold. Multiple wave individuals are generated based on the set parameters. Each wave individual consists of a discrete phase vector composed of the initial phases of several subarrays. These initial phases are distributed within a preset range according to the perturbation step size to form diverse initial states. The energy threshold is used to limit the activity range of the wave individuals in subsequent optimization processes, and the communication performance index threshold is used to determine the system performance benchmark, together completing the initial energy level classification of the wave individuals.

[0096] In this embodiment, step S2 includes:

[0097] S21. The microsystem sets the sampling period and sampling duration, and establishes a data buffer and a sliding window. The sliding window is a circular storage structure that stores multiple consecutive sampling records.

[0098] S22. Determine the set of communication performance index fields, which includes received signal strength, signal-to-noise ratio and error vector amplitude, and set the data recording format and measurement unit for each index.

[0099] The set of communication performance index fields has an extensible structure. In addition to received signal strength, signal-to-noise ratio and error vector amplitude, parameters such as bit error rate, array pattern deviation and carrier phase stability can be added according to application requirements to support communication performance evaluation in different scenarios.

[0100] S23. Read the original measurement values ​​from the RF front-end and baseband processing unit according to the sampling period, add a timestamp and channel identifier to each measurement value, generate a single sampling record and write it into the data buffer area;

[0101] S24. Perform preprocessing on the single sampling record, including jitter removal, amplitude limiting, and threshold-based outlier masking, and write the processed record into the sliding window;

[0102] S25. Update the interval statistics field within the sliding window. The interval statistics field includes the average, range, and number of samples, and establishes an index relationship with the most recent sampling record.

[0103] The sliding window is updated according to the sampling period. Each update removes the oldest record and adds the latest record, thus maintaining a fixed capacity. The interval statistics field is automatically recalculated after each window update to reflect the dynamic changes in communication performance indicators within the current time period.

[0104] S26. Synchronize the current content of the sliding window and the interval statistics field to the running cache area, and write it to the persistent storage unit, recording the writing sequence number and time information;

[0105] S27. Generate a data availability flag and set the corresponding status to valid.

[0106] The data availability flag is generated after the sliding window update, interval statistical field synchronization, and data writing to the persistent storage unit are completed. It is used to indicate that the current batch of communication performance index data has been collected completely and has not been overwritten. When the flag is detected as valid, the subsequent trigger judgment module can call the corresponding data.

[0107] In this embodiment, step S3 includes:

[0108] S31. The latest statistical value of the microsystem detection communication performance index field set is compared with the preset signal change threshold and silent time threshold in the system control parameters;

[0109] The signal change threshold is determined based on the long-term average fluctuation range of the communication performance index, and the silence time threshold is set based on the working cycle of the antenna array and the stability of environmental noise. Both can be dynamically adjusted through the system control parameter table to adapt to the working state under different scenarios.

[0110] S32. When the change in the communication performance index exceeds the signal change threshold or the continuous silence time reaches the silence time threshold, the microsystem generation algorithm triggers an instruction and writes it into the running buffer.

[0111] The algorithm trigger command is a status identifier signal, which is used to mark in the running buffer that the current communication performance index has met the optimization conditions. When the microsystem detects that the identifier is valid, the control flow switches to the execution stage of the improved water wave optimization algorithm.

[0112] S33. Detect the status of the data availability flag. When the data availability flag is detected to be valid, the microsystem enters the execution stage of the improved water wave optimization algorithm.

[0113] S34. Read the set of communication performance index fields and corresponding time indexes from the running buffer, and load the set of wave individuals formed in the storage unit;

[0114] S35. Establish an optimization task queue based on the read set of communication performance index fields, wherein the optimization task queue corresponds to the phase adjustment target of the antenna array;

[0115] The optimization task queue includes multiple task units, each task unit corresponds to a subarray of the antenna array, and each task unit records the phase adjustment target, energy level and execution priority. The microsystem allocates computing resources according to priority order to complete the optimization task in parallel.

[0116] S36. During the process of establishing the optimized task queue, the microsystem records the trigger time and the current communication performance status, and writes the trigger information into the operation log unit;

[0117] S37. After the optimized task queue is established, the microsystem maintains the current launch status and locks the communication channel.

[0118] In this embodiment, step S4 includes:

[0119] S41. After the microsystem enters the execution stage of the improved water wave optimization algorithm, it selects several wave individuals to participate in the initial optimization according to the preset phase adjustment target in the optimization task queue, and assigns an independent optimization channel to each wave individual.

[0120] The optimization channel is a parallel execution thread in the microsystem. Each channel independently processes the disturbance, performance calculation and energy recording process of the corresponding wave individual under the scheduling of the control unit. The channels share a communication performance index database to achieve parallel optimization of multiple wave individuals.

[0121] S42. Perform a discrete perturbation operation on the individual waves participating in the optimization. The discrete perturbation operation adjusts the phase of a portion of the subarray within the phase quantization range according to a preset perturbation step size. The perturbed phase value is limited to the discrete set.

[0122] The phase quantization range is determined based on the phase control resolution of the antenna array hardware, and the discrete set consists of integer multiples of the phase step. During the perturbation process, the microsystem selects discrete phase combinations of different subarrays to form a search path, ensuring that the optimization process conforms to hardware constraints and has full-space search capability.

[0123] S43. Calculate the difference in communication performance indicators of the wave individuals participating in the optimization before and after the disturbance, and update the energy state record of the corresponding wave individuals.

[0124] The microsystem records the difference in communication performance indicators before and after the disturbance for each wavelet, and updates the energy state according to the performance change trend. When communication performance improves, the energy of the wavelet increases; when communication performance decreases, the energy of the wavelet decreases. This energy change is used for wavelet screening and regeneration determination in subsequent stages.

[0125] S44. Mark the wave individuals with improved communication performance as effective wave individuals, and record the corresponding phase values ​​and communication performance results;

[0126] S45. Mark wave individuals with degraded communication performance as invalid wave individuals and maintain the phase state before the disturbance;

[0127] S46. After each iteration, the current global optimal wave individual is determined based on the comprehensive score of the communication performance index, and the corresponding phase vector is written into the running buffer.

[0128] The comprehensive score of the communication performance index is obtained by weighted fusion of three indicators: received signal strength, signal-to-noise ratio, and error vector amplitude. The microsystem standardizes each indicator through normalization processing and performs linear combination according to the preset weight coefficients in the system control parameters. The comprehensive score reflects the communication quality of the current wave individual and is used to select the globally optimal individual.

[0129] S47. When all optimized channels have completed the predetermined number of iterations or the communication performance indicators have reached the convergence condition, the microsystem records the current optimization results and enters the parameter adjustment stage.

[0130] In this embodiment, step S5 includes:

[0131] S51. Based on the optimization results of the improved water wave optimization algorithm, the microsystem reads the communication performance indicators of each wave before and after the disturbance in the previous iteration period.

[0132] S52. The microsystem maintains the original phase vector of the effective wave individual and updates the energy state;

[0133] S53. The microsystem performs a breakup operation on the invalid wave individual and redistributes the phase values ​​of some subarrays within the phase quantization range according to the preset breakup amplitude.

[0134] S54. Perform quantization constraint processing on the redistributed phase values, limit the adjustment results to the phase quantization set, and generate a new phase vector;

[0135] S55. After each iteration, the microsystem calculates the disturbance step size adjustment coefficient based on the communication performance fluctuation amplitude of multiple consecutive iterations.

[0136] S56. When the communication performance fluctuation amplitude is higher than the preset communication performance fluctuation threshold, the disturbance step size is reduced; when the communication performance fluctuation amplitude is lower than the preset communication performance fluctuation threshold, the disturbance step size is increased.

[0137] S57. Update the phase vector and perturbation step size parameters of each wave individual to form a new wave group state.

[0138] In the operation of the improved water wave optimization algorithm, the microsystem performs statistical analysis on the communication performance indicators of each wave individual after each iteration, calculating the communication performance fluctuation amplitude. This fluctuation amplitude reflects the stability of the communication performance changes of each wave individual within a continuous iteration cycle. Based on the difference between the communication performance fluctuation amplitude and a preset communication performance fluctuation threshold, the microsystem calculates a perturbation step size adjustment coefficient. When the communication performance fluctuation amplitude is higher than the fluctuation threshold, the microsystem sets the perturbation step size adjustment coefficient to a scaling factor less than one to reduce the perturbation step size, thereby enhancing local search accuracy. When the communication performance fluctuation amplitude is lower than the fluctuation threshold, the microsystem sets the perturbation step size adjustment coefficient to a scaling factor greater than one to increase the perturbation step size, thereby expanding the global search range. Through this dynamic adjustment, the microsystem can adaptively adjust the perturbation step size according to the communication performance change trend at different iteration stages, achieving a balance between search range and convergence accuracy, thereby improving the optimization efficiency and stability of antenna array phase control.

[0139] In this embodiment, step S6 includes:

[0140] S61. After the microsystem completes the iteration of the improved water wave optimization algorithm, it detects the energy value of each wave individual and compares it with the energy threshold to determine whether the wave individual meets the regeneration condition.

[0141] S62. When the energy value of a wave individual is lower than the energy threshold, the microsystem selects the phase vector of the current globally optimal wave individual as the regeneration center.

[0142] S63. Regenerate multiple discrete phase vectors near the regeneration center phase according to a preset perturbation step size to form a new set of wave individuals;

[0143] S64. Assign initial energy values ​​to the regenerated wave individuals and update the wave group energy distribution table;

[0144] S65. Add the set of wave individuals after energy recovery to the current wave group and activate the corresponding optimization channel.

[0145] In this invention, the microsystem dynamically adjusts the energy value based on changes in the communication performance of individual wavelets. The energy value reflects the activity and optimization contribution of the wavelet. When the energy value of an individual wavelet falls below a set threshold, the system determines that the wavelet has lost its optimization capability and needs to be regenerated. The microsystem uses the phase vector of the currently globally optimal wavelet as the regeneration center, generating new wavelets near its phase to maintain consistency in the search direction and global convergence of the algorithm. After regeneration, the system allocates initial energy to the new wavelets and updates the wave group energy distribution table to ensure overall energy balance, thereby enabling the optimization process to have continuous evolutionary capabilities.

[0146] In this embodiment, step S7 includes:

[0147] S71. Read the communication performance indicators and corresponding phase vectors of each wave individual from the running buffer;

[0148] S72. Using the weighted result of received signal strength, signal-to-noise ratio, and error vector amplitude as the evaluation basis, a comprehensive score is given to each individual wave.

[0149] S73. The wave individual with the highest comprehensive score and stable energy state is determined as the global optimal wave individual;

[0150] S74. The microsystem generates a phase control signal based on the phase vector of the global optimal wave individual, and the phase control signal includes the phase setting value and synchronization trigger command of each subarray.

[0151] S75. Perform data packaging and timestamp marking on the phase control signal, and send it to the antenna array control module via the communication interface;

[0152] S76. The antenna array control module synchronously adjusts the phase of each subarray according to the received phase control signal to complete the array pattern update.

[0153] S77. The microsystem records the phase adjustment completion status and corresponding communication performance indicators, and updates the operation log and system status table.

[0154] In this invention, after the improved water wave optimization algorithm is iterated, the microsystem reads the communication performance indicators and corresponding phase vectors of each wave individual from the running buffer, and performs a comprehensive score according to a preset performance evaluation model. The performance evaluation model uses received signal strength, signal-to-noise ratio, and error vector amplitude as the main evaluation parameters, and determines the comprehensive score of each wave individual through weighted calculation. The microsystem selects the wave individual with the highest comprehensive score and stable energy state as the globally optimal wave individual, and generates a phase control signal based on its phase vector. The phase control signal contains the phase setpoint and synchronization trigger command for each subarray, and is sent to the antenna array control module via the communication interface to perform phase synchronization adjustment and complete the array pattern update. After the phase adjustment is completed, the microsystem records the new communication performance indicators and execution status, providing feedback data for subsequent optimization.

[0155] Example 1:

[0156] To verify the feasibility and performance improvement of this invention in a practical communication system, it was applied to an adaptive control system for an intelligent antenna array of a 5G base station cluster in a city. This base station cluster uses a 32-element phased array antenna, each element with independent phase adjustment capability. The array operates at a frequency of 3.5 GHz and has a coverage radius of approximately 500 meters. The base station control unit embeds a microsystem module for real-time monitoring and phase optimization of the antenna array. This embodiment compares the traditional fixed-step phase optimization algorithm with the improved water wave optimization algorithm based on the microsystem proposed in this invention to verify the improvements in signal stability, pattern control accuracy, and optimized convergence efficiency.

[0157] During system initialization, the microsystem sets system control parameters, including 15 wave groups, 8 subarrays, an initial perturbation step size of 3°, a break step size of 10°, an energy threshold of 0.25, a signal variation threshold of 2.0dB, a silence time threshold of 100ms, and a communication performance fluctuation threshold of 1.5dB. The signal variation threshold is used to monitor significant fluctuations in communication performance indicators within a short period. When the signal strength drops below this threshold for two consecutive samplings or the silence time exceeds the set threshold, the microsystem automatically triggers an improved water wave optimization algorithm to perform a phase optimization process.

[0158] During operation, the microsystem acquires received signal strength, signal-to-noise ratio, and error vector amplitude at a sampling period of 100ms, and establishes a sliding window for dynamic statistics. After triggering the optimization phase, the algorithm performs discrete perturbation operations on a portion of the subarray for each wave group, with the perturbation range controlled by the perturbation step size. If the communication performance improves after perturbation, the phase is retained; if it decreases, a breakup operation is performed. The breakup operation redistributes phase values ​​within the quantized phase range according to the breakup step size to escape local optima. When the wave group energy is below the energy threshold, the microsystem regenerates the phase vector centered on the current globally optimal wave group phase and restores the energy, achieving wave group regeneration.

[0159] To verify the beneficial effects of this invention, a comparative test was conducted with the traditional particle swarm optimization algorithm during the experimental phase. Both algorithms were run under the same channel model, noisy environment (signal-to-noise ratio 10dB), and array structure. Each optimization round lasted for 300 iterations. The experimental results are shown in Table 1.

[0160] Table 1. Performance Comparison of the Invention Method and Traditional Methods in Antenna Control Systems

[0161] Comparison indicators Method of the present invention Traditional Particle Swarm Optimization Algorithm Final main lobe gain (dB) 17.8 14.6 Sidelobe level (dB) -13.7 -9.3 Average signal-to-noise ratio improvement (dB) 4.3 2.6 Mean convergence time (ms) 71.8 112.4 Coverage radius increased (%) 12 9.2 Radiation pattern offset angle (°) 0.8 1.9

[0162] As shown in Table 1, the present invention exhibits higher stability and faster convergence speed during phase optimization. Regarding average computation delay, the traditional algorithm is approximately 112.4 milliseconds, while the improved water wave optimization algorithm of the present invention requires only 71.8 milliseconds on average, a reduction of approximately 36%. In terms of final communication performance, the main lobe gain of the antenna array increased from 14.6 dB in the original system to 17.8 dB, the sidelobe level decreased from -9.3 dB to -13.7 dB, and the signal coverage radius increased by approximately 12%. Furthermore, to further evaluate the system's adaptive capability under dynamic channel conditions, a continuous 10-minute test was conducted under simulated multipath and interference conditions, measuring signal-to-noise ratio (SNR) fluctuations and pattern stability. The results show that the system of the present invention exhibits an SNR decrease of no more than 1.2 dB and a pattern shift angle of less than 0.8° under strong interference, while the traditional algorithm shows an SNR decrease of up to 3.5 dB and a shift angle of up to 1.9° under the same conditions, indicating that the present invention has stronger robustness under complex channel conditions.

[0163] In summary, this invention introduces a dual-parameter adaptive control mechanism at the microsystem level, involving both signal variation threshold and break step size, enabling the improved water wave optimization algorithm to achieve efficient phase optimization and energy self-recovery in dynamic channel environments. This mechanism significantly improves the pattern control accuracy and communication stability of smart antennas.

[0164] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A smart antenna control method based on a microsystem, characterized in that, Includes the following steps: S1. Set system control parameters in the microsystem, generate multiple wave individuals, assign discrete phase values ​​to each wave individual and set initial energy; S2. The microsystem collects and stores the communication performance indicators of the antenna array; S3. When the communication performance index drops below a preset threshold or the silence time reaches a set threshold, the microsystem calls the improved water wave optimization algorithm to perform phase optimization. S4. In the improved water wave optimization algorithm, several subarrays are selected for discrete perturbation of the individual waves, the perturbed phase is limited to the phase quantization set, and the corresponding communication performance index is calculated. S5. Update the phase of the wave individual according to the difference in communication performance indicators before and after the disturbance, perform the break operation on the wave individual with degraded performance according to the preset break step size, and dynamically adjust the disturbance step size according to the amplitude of communication performance fluctuation. S6. Adjust the energy value based on the optimization results of the wave individual. When the energy of the wave individual is lower than the energy threshold, regenerate the phase vector and restore the energy with the currently recorded global optimal wave individual phase as the center. S7. Select the wave individual with the highest comprehensive score and stable energy state as the global optimal result, and output the corresponding phase control signal from the microsystem to the antenna array to perform phase adjustment.

2. The intelligent antenna control method based on a microsystem according to claim 1, characterized in that, Step S1 includes: S11. The microsystem uses an internal clock initialization control module to perform power-on detection on the storage unit, signal acquisition unit, and phase control unit, and establish a communication connection with the antenna array. S12. The system control parameters are a set of operating parameters set by the microsystem during the initialization phase, including the number of wave groups, disturbance step size, break step size, number of subarrays, signal change threshold, silence time threshold, communication performance fluctuation threshold, and energy threshold. S13. Set the wave group number parameter in the storage unit to determine the number of wave individuals participating in the phase search at the same time; S14. Set the perturbation step size parameter to limit the discrete increment range of each phase adjustment; S15. Set the number of subarrays according to the structural information of the antenna array. Each subarray contains several array elements and corresponds to the phase control unit. S16. Set the signal change threshold, silence time threshold, communication performance fluctuation threshold, and energy threshold in the processing unit; S17. Generate a corresponding number of wave individuals according to the set number of wave groups and subarrays, assign an initial phase value to each wave individual within the discrete phase range according to the preset perturbation step size, and determine the initial energy level of each wave individual in combination with the energy threshold. S18. The phase values ​​of each subarray of the wave individual are used to form a discrete phase vector, thus forming a set of wave individuals.

3. The intelligent antenna control method based on a microsystem according to claim 1, characterized in that, Step S2 includes: S21. The microsystem sets the sampling period and sampling duration, and establishes a data buffer and a sliding window. The sliding window is a circular storage structure that stores multiple consecutive sampling records. S22. Determine the set of communication performance index fields, which includes received signal strength, signal-to-noise ratio and error vector amplitude, and set the data recording format and measurement unit for each index. S23. Read the original measurement values ​​from the RF front-end and baseband processing unit according to the sampling period, add a timestamp and channel identifier to each measurement value, generate a single sampling record and write it into the data buffer area; S24. Perform preprocessing on the single sampling record, including jitter removal, amplitude limiting, and threshold-based outlier masking, and write the processed record into the sliding window; S25. Update the interval statistics field within the sliding window. The interval statistics field includes the average, range, and number of samples, and establishes an index relationship with the most recent sampling record. S26. Synchronize the current content of the sliding window and the interval statistics field to the running cache area, and write it to the persistent storage unit, recording the writing sequence number and time information; S27. Generate a data availability flag and set the corresponding status to valid.

4. The intelligent antenna control method based on a microsystem according to claim 3, characterized in that, Step S3 includes: S31. The latest statistical value of the microsystem detection communication performance index field set is compared with the preset signal change threshold and silent time threshold in the system control parameters; S32. When the change in the communication performance index exceeds the signal change threshold or the continuous silence time reaches the silence time threshold, the microsystem generation algorithm triggers an instruction and writes it into the running buffer. S33. Detect the status of the data availability flag. When the data availability flag is detected to be valid, the microsystem enters the execution stage of the improved water wave optimization algorithm. S34. Read the set of communication performance index fields and corresponding time indexes from the running buffer, and load the set of wave individuals formed in the storage unit; S35. Establish an optimization task queue based on the read set of communication performance index fields, wherein the optimization task queue corresponds to the phase adjustment target of the antenna array; S36. During the process of establishing the optimized task queue, the microsystem records the trigger time and the current communication performance status, and writes the trigger information into the operation log unit; S37. After the optimized task queue is established, the microsystem maintains the current launch status and locks the communication channel.

5. The intelligent antenna control method based on a microsystem according to claim 4, characterized in that, Step S4 includes: S41. After the microsystem enters the execution stage of the improved water wave optimization algorithm, it selects several wave individuals to participate in the initial optimization according to the preset phase adjustment target in the optimization task queue, and assigns an independent optimization channel to each wave individual. S42. Perform a discrete perturbation operation on the individual waves participating in the optimization. The discrete perturbation operation adjusts the phase of a portion of the subarray within the phase quantization set according to a preset perturbation step size. The perturbed phase value is limited to the discrete set. S43. Calculate the difference in communication performance indicators of the wave individuals participating in the optimization before and after the disturbance, and update the energy state record of the corresponding wave individuals. S44. Mark the wave individuals with improved communication performance as effective wave individuals, and record the corresponding phase values ​​and communication performance results; S45. Mark wave individuals with degraded communication performance as invalid wave individuals and maintain the phase state before the disturbance; S46. After each iteration, the current global optimal wave individual is determined based on the comprehensive score of the communication performance index, and the corresponding phase vector is written into the running buffer. S47. When all optimized channels have completed the predetermined number of iterations or the communication performance indicators have reached the convergence condition, the microsystem records the current optimization results and enters the parameter adjustment stage.

6. The intelligent antenna control method based on a microsystem according to claim 5, characterized in that, Step S5 includes: S51. Based on the optimization results of the improved water wave optimization algorithm, the microsystem reads the communication performance indicators of each wave before and after the disturbance in the previous iteration period. S52. The microsystem maintains the original phase vector of the effective wave individual and updates the energy state; S53. The microsystem performs a breakup operation on the invalid wave individual and redistributes the phase values ​​of some subarrays within the phase quantization range according to the preset breakup amplitude. S54. Perform quantization constraint processing on the redistributed phase values, limit the adjustment results to the phase quantization set, and generate a new phase vector; S55. After each iteration, the microsystem calculates the disturbance step size adjustment coefficient based on the communication performance fluctuation amplitude of multiple consecutive iterations. S56. When the communication performance fluctuation amplitude is higher than the preset communication performance fluctuation threshold, the disturbance step size is reduced; when the communication performance fluctuation amplitude is lower than the preset communication performance fluctuation threshold, the disturbance step size is increased. S57. Update the phase vector and perturbation step size parameters of each wave individual to form a new wave group state.

7. The intelligent antenna control method based on a microsystem according to claim 1, characterized in that, Step S6 includes: S61. After the microsystem completes the iteration of the improved water wave optimization algorithm, it detects the energy value of each wave individual and compares it with the energy threshold to determine whether the wave individual meets the regeneration condition. S62. When the energy value of a wave individual is lower than the energy threshold, the microsystem selects the phase vector of the current globally optimal wave individual as the regeneration center. S63. Regenerate multiple discrete phase vectors near the regeneration center phase according to a preset perturbation step size to form a new set of wave individuals; S64. Assign initial energy values ​​to the regenerated wave individuals and update the wave group energy distribution table; S65. Add the set of wave individuals after energy recovery to the current wave group and activate the corresponding optimization channel.

8. The intelligent antenna control method based on a microsystem according to claim 1, characterized in that, Step S7 includes: S71. Read the communication performance indicators and corresponding phase vectors of each wave individual from the running buffer; S72. Using the weighted result of received signal strength, signal-to-noise ratio, and error vector amplitude as the evaluation basis, a comprehensive score is given to each individual wave. S73. The wave individual with the highest comprehensive score and stable energy state is determined as the global optimal wave individual; S74. The microsystem generates a phase control signal based on the phase vector of the global optimal wave individual, and the phase control signal includes the phase setting value and synchronization trigger command of each subarray. S75. Perform data packaging and timestamp marking on the phase control signal, and send it to the antenna array control module via the communication interface; S76. The antenna array control module synchronously adjusts the phase of each subarray according to the received phase control signal to complete the array pattern update. S77. The microsystem records the phase adjustment completion status and corresponding communication performance indicators, and updates the operation log and system status table.

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