5g / 6g base station antenna system and intelligent coverage optimization method

Abstract

The present application relates to the field of communication technology, especially to a 5G / 6G base station antenna system and intelligent coverage optimization method, comprising: collecting coverage perception data of a target coverage area; determining signal coverage strength, signal interference coefficient and dynamic environment stability after preprocessing the collected data to obtain a comprehensive coverage quality index; when the current coverage is unqualified according to the comprehensive coverage quality index, identifying the coverage abnormal type and executing a multi-actuator collaborative adjustment strategy; determining the conflict type based on the change direction of the coverage perception data and executing the corresponding conflict processing strategy; recalculating the comprehensive coverage quality index according to the re-collected coverage perception data after adjustment, determining the adjustment unqualified, and determining the adjustment strategy of the next iteration based on the change direction and change amount of the comprehensive coverage quality index before and after this adjustment. The present application improves the coverage optimization efficiency of the base station in complex environment.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a 5G / 6G base station antenna system and a smart coverage optimization method. Background Technology

[0002] As mobile communication technology evolves from 5G to 6G, base station antenna systems face increasingly complex coverage optimization challenges. In the 5G era, the application of massive MIMO and beamforming technologies has significantly improved system capacity, but it has also brought problems such as complex beam parameter configuration and frequent dynamic changes in coverage. For future 6G networks, the use of higher frequency bands (such as millimeter waves and terahertz) makes signal propagation more sensitive to obstruction and environmental changes, making it difficult for traditional static or semi-static coverage optimization methods to meet real-time requirements. Existing base station antenna coverage optimization technologies suffer from the following shortcomings: reliance on manual experience and drive test data leads to long response cycles, high labor costs, and difficulty in adapting to dynamic changes; static beam configuration lacks flexibility, failing to dynamically adjust beam pointing, width, and power allocation based on real-time channel conditions and service requirements, resulting in both coverage holes and overlapping coverage; insufficient multi-objective optimization capabilities make it difficult to achieve true collaborative optimization among mutually constraining objectives such as signal strength, interference levels, user capacity, and energy efficiency; furthermore, most existing systems employ open-loop or semi-open-loop control architectures, lacking a complete closed-loop self-optimization mechanism encompassing real-time perception, intelligent decision-making, dynamic execution, and effect evaluation, thus hindering continuous learning and evolution during network operation. Summary of the Invention

[0003] To address this, the present invention provides a 5G / 6G base station antenna system and a smart coverage optimization method to overcome the problems in the prior art where the interaction between the base station antenna and the smart metasurface does not consider the effect on coverage quality, resulting in coverage cancellation, interference introduction, and multi-target conflict during multi-actuator coordinated adjustment, and the inability to adaptively iteratively correct these issues.

[0004] To achieve the above objectives, the present invention provides a method for intelligent coverage optimization of 5G / 6G base station antennas, comprising: Step S1: Collect coverage sensing data of the target coverage area, including base station operating parameters, channel implicit parameters, terminal feedback parameters, and environmental parameters; Step S2: After preprocessing the collected data, determine the signal coverage strength, signal interference coefficient, and dynamic environmental stability to obtain the comprehensive coverage quality index; Step S3: Determine the qualification of the current coverage based on the comparison result between the comprehensive coverage quality index and the preset quality index. If the coverage is not qualified, identify the coverage anomaly type and execute the multi-actuator collaborative adjustment strategy corresponding to the anomaly type. The collaborative adjustment strategy includes a variable step size approximation strategy, a sequential serial strategy, and a conflict backoff strategy. Step S4: During the execution of the variable step size approximation strategy or the sequential serial strategy, the change direction of the base station operating parameters, channel implicit parameters, and terminal feedback parameters is monitored in real time to determine the conflict type in the multi-actuator coordinated adjustment process and execute the corresponding conflict handling strategy. The conflict handling strategy includes freezing the smart metasurface, adjusting the antenna polarization mode and the smart metasurface reflection angle, or executing a backoff strategy with phase conjugate compensation. Step S5: Recalculate the comprehensive coverage quality index based on the newly collected coverage sensing data after adjustment. If the adjustment is deemed unqualified based on the comprehensive coverage quality index, determine the adjustment strategy for the next iteration based on the direction and amount of change of the comprehensive coverage quality index before and after this adjustment, including switching the multi-actuator collaborative adjustment strategy or changing the adjustment step size.

[0005] Furthermore, in step S1, the base station operating parameters include: effective radiated power of the antenna; The channel implicit parameters include: multipath signal coherence coefficient, neighboring cell signal interference ratio, and smart metasurface reflection phase offset; The terminal feedback parameters include: the terminal received signal-to-noise ratio and the dynamic change rate of the terminal position; The environmental parameters include: the elevation angle fluctuation value of low-orbit satellite signals.

[0006] Furthermore, in step S2, the signal coverage strength is determined by the terminal's received signal-to-noise ratio and the antenna's effective radiated power; The signal interference coefficient is determined by the multipath signal coherence coefficient and the neighboring cell signal interference ratio; The dynamic environment stability is determined by the dynamic change rate of the terminal position and the elevation angle fluctuation value of the low-orbit satellite signal. The comprehensive coverage quality index is determined by signal coverage strength, signal interference coefficient, and dynamic environmental stability.

[0007] Furthermore, in step S3, in response to the comprehensive coverage quality index being less than the preset quality index, the current coverage is determined to be unqualified, and a multi-executor collaborative adjustment strategy corresponding to the coverage anomaly type is executed.

[0008] Furthermore, the step S3, which covers the exception types and corresponding multi-executor collaborative adjustment strategies, includes: When the signal coverage strength is less than the preset coverage strength, it is identified as a weak signal coverage anomaly. The corresponding collaborative adjustment strategy is a variable step size approximation strategy, which dynamically adjusts the antenna beam direction and the intelligent metasurface reflection phase according to the deviation between the current comprehensive coverage quality index and the preset quality index. When the signal interference coefficient is greater than the preset interference coefficient, it is identified as an abnormal signal interference. The corresponding collaborative adjustment strategy is a sequential strategy, that is, first adjust the antenna polarization mode, and then adjust the intelligent metasurface reflection angle. When the dynamic environment stability is less than the preset stability, it is identified as a dynamic coverage imbalance anomaly. The corresponding collaborative adjustment strategy is a conflict rollback strategy, which means pausing the current adjustment and replanning the adjustment scheme based on the terminal's movement trajectory and the transit time of low-orbit satellites.

[0009] Furthermore, in step S4, the process of determining the conflict type and the corresponding conflict resolution strategy is as follows: During the execution of the variable step size approximation strategy or the sequential serial strategy, the changes in the terminal's received signal-to-noise ratio, multipath signal coherence coefficient, and effective radiated power of the antenna are monitored in real time. If the terminal receives a signal-to-noise ratio that decreases while the antenna’s effective radiated power increases, it is determined to be a coverage cancellation conflict. The smart metasurface adjustment is frozen, and only the base station antenna performs beam scanning optimization. The smart metasurface is gradually unfrozen after the signal-to-noise ratio recovers and increases. If the terminal receives a signal-to-noise ratio that increases while the coherence coefficient of the multipath signal increases, it is determined that interference has introduced a conflict. The antenna polarization mode is adjusted and the feed line impedance is calibrated. At the same time, the intelligent metasurface reflection angle is adjusted. The interference coefficient is re-evaluated after each adjustment. If both the signal-to-noise ratio and the coherence coefficient of the multipath signal decrease at the terminal, it is determined to be a multi-target conflict. All adjustments are rolled back, the intelligent metasurface reflection phase offset is set to the conjugate value of the antenna beam phase, and the adjustment step size is reduced by a preset ratio before re-execution.

[0010] Furthermore, in step S5, in response to the adjusted comprehensive coverage quality index being less than the preset quality index, the adjustment is deemed unqualified, and the adjustment strategy for the next iteration is determined based on the direction and amount of change of the comprehensive coverage quality index before and after this adjustment.

[0011] Furthermore, in step S5, the specific method for determining the next iteration adjustment strategy based on the direction and amount of change in the comprehensive coverage quality index before and after this adjustment is as follows: When the improvement in the overall coverage quality index is greater than or equal to the first preset threshold, maintain the current strategy and increase the adjustment step size by the first preset ratio. When the increase is less than the first preset threshold but greater than zero, the current strategy is maintained and the step size remains unchanged. When the overall coverage quality index declines, switching the adjustment strategy may change the adjustment step size.

[0012] Furthermore, when the overall coverage quality index declines, the switching adjustment strategy includes: If a variable step size approximation strategy is currently being used, then switch to a sequential serial strategy. If the current sequential serial strategy is used, switch to the conflict rollback strategy; If the current conflict rollback strategy is adopted, the conflict rollback strategy will be maintained and the adjustment step size will be reduced by the second preset ratio.

[0013] The present invention also provides a 5G / 6G base station antenna system, comprising: The data acquisition module is used to collect coverage sensing data of the target coverage area; The data processing module, which is connected to the data acquisition module, is used to preprocess the acquired data, determine the signal coverage strength, signal interference coefficient and dynamic environmental stability, and calculate the comprehensive coverage quality index. The judgment and strategy generation module, which is connected to the data processing module, is used to determine whether the current coverage is qualified based on the comprehensive coverage quality index, identify the coverage anomaly type under the condition of non-qualification, and generate a multi-actuator collaborative adjustment strategy corresponding to the anomaly type. A multi-executor coordination module, which is connected to the determination and strategy generation module, is used to determine the type of coordination conflict based on the direction of change of the coverage sensing data during the execution of the coordination adjustment strategy, and to execute the corresponding conflict handling strategy according to the conflict type. The iterative optimization module, which is connected to the data processing module and the multi-executor collaboration module respectively, is used to determine whether the adjustment is qualified based on the adjusted comprehensive coverage quality index. If it is not qualified, the adjustment strategy for the next iteration is determined based on the direction and amount of change of the comprehensive coverage quality index before and after the current adjustment. An actuator unit, which is connected to the multi-actuator coordination module, includes a base station antenna and a smart metasurface, and is used to receive and execute adjustment commands issued by the multi-actuator coordination module.

[0014] Compared with existing technologies, the beneficial effects of this invention are that it achieves a multi-dimensional comprehensive evaluation of coverage quality by collecting base station operating parameters, channel implicit parameters, terminal feedback parameters, and environmental parameters, and determining signal coverage strength, signal interference coefficient, and dynamic environmental stability. Existing technologies typically judge coverage quality based on only a single indicator (such as signal-to-noise ratio or received power), which is insufficient to reflect the true coverage status under complex environments. This invention solves the problem of large evaluation bias caused by a single indicator by weighted fusion of coverage strength, interference level, and environmental stability, thus improving the accuracy and robustness of coverage anomaly detection.

[0015] Furthermore, this invention identifies three types of anomalies: weak signal coverage anomalies, signal interference anomalies, and dynamic coverage imbalance anomalies. It then applies a variable step-size approximation strategy, a sequential serial strategy, and a conflict backoff strategy, respectively, achieving precise matching between anomaly types and adjustment strategies. Existing technologies often employ uniform adjustment strategies, failing to adapt to the differentiated needs of various anomaly types. This invention selects suitable collaborative adjustment strategies based on the essential characteristics of each anomaly type, improving the targeting and effectiveness of the adjustments.

[0016] Furthermore, in the process of multi-actuator coordinated adjustment, this invention determines the conflict type based on the changing direction of the terminal received signal-to-noise ratio, multipath signal coherence coefficient, and effective radiated power of the antenna, and executes corresponding conflict handling strategies accordingly. Existing technologies lack a detection mechanism for the mutual influence between the base station antenna and the smart metasurface, which can easily lead to opposite adjustment directions, mutually canceling effects, or the introduction of new interference. This invention, by monitoring the changing direction of key parameters in real time, can promptly detect and classify coordinated conflicts, avoiding negative optimization phenomena during multi-actuator adjustment.

[0017] Furthermore, this invention recalculates the comprehensive coverage quality index based on the adjusted and re-acquired coverage sensing data, and determines the adjustment strategy for the next iteration based on the direction and magnitude of change in the comprehensive coverage quality index before and after the adjustment. When the quality index increases, the step size is maintained or increased to accelerate convergence; when the quality index decreases, the strategy is switched and the step size is reduced to suppress oscillations; when there are two consecutive periods of no change, a conflict backoff strategy is triggered. Existing technologies often use fixed step sizes or single-strategy iterations, making it difficult to achieve a balance between convergence speed and adjustment accuracy. This invention improves iterative convergence efficiency and reduces the number of invalid adjustments by adaptively adjusting the strategy and step size. Attached Figure Description

[0018] Figure 1 This is a flowchart of the intelligent coverage optimization method for 5G / 6G base station antennas according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating how to determine whether coverage is qualified based on a comprehensive coverage quality index, according to an embodiment of the present invention. Figure 3 A flowchart for determining the conflict type in an embodiment of the present invention; Figure 4 This is a schematic diagram of the module connection of a 5G / 6G base station antenna system according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0020] Those skilled in the art will understand that the method for determining the above-mentioned parameters for a single item in this invention can be as follows: selecting the value with the highest proportion based on the data distribution as the preset standard parameter; using weighted summation to use the obtained value as the preset standard parameter; substituting each historical data into a specific formula and using the value obtained by the formula as the preset standard parameter; or other selection methods, as long as the present invention can clearly define different specific situations in the single item determination process through the obtained values.

[0021] Please see Figure 1 As shown, the intelligent coverage optimization method for 5G / 6G base station antennas in this embodiment of the invention includes: Step S1: Collect coverage sensing data of the target coverage area, including base station operating parameters, channel implicit parameters, terminal feedback parameters, and environmental parameters; Step S2: After preprocessing the collected data, determine the signal coverage strength, signal interference coefficient, and dynamic environmental stability to obtain the comprehensive coverage quality index; Step S3: Determine the qualification of the current coverage based on the comparison result between the comprehensive coverage quality index and the preset quality index. If the coverage is not qualified, identify the coverage anomaly type and execute the multi-actuator collaborative adjustment strategy corresponding to the anomaly type. The collaborative adjustment strategy includes a variable step size approximation strategy, a sequential serial strategy, and a conflict backoff strategy. Step S4: During the execution of the variable step size approximation strategy or the sequential serial strategy, the change direction of the base station operating parameters, channel implicit parameters, and terminal feedback parameters is monitored in real time to determine the conflict type in the multi-actuator coordinated adjustment process and execute the corresponding conflict handling strategy. The conflict handling strategy includes freezing the smart metasurface, adjusting the antenna polarization mode and the smart metasurface reflection angle, or executing a backoff strategy with phase conjugate compensation. Step S5: Recalculate the comprehensive coverage quality index based on the newly collected coverage sensing data after adjustment. If the adjustment is deemed unqualified based on the comprehensive coverage quality index, determine the adjustment strategy for the next iteration based on the direction and amount of change of the comprehensive coverage quality index before and after this adjustment, including switching the multi-actuator collaborative adjustment strategy or changing the adjustment step size.

[0022] Specifically, in step S1, the base station operating parameters include: effective radiated power of the antenna; The channel implicit parameters include: multipath signal coherence coefficient, neighboring cell signal interference ratio, and smart metasurface reflection phase offset; The terminal feedback parameters include: the terminal received signal-to-noise ratio and the dynamic change rate of the terminal position; The environmental parameters include: the elevation angle fluctuation value of low-orbit satellite signals.

[0023] Specifically, the base station operating parameters are obtained through the base station's built-in radio frequency monitoring unit and baseband signal processing unit. The effective radiated power of the antenna is calculated by the base station power calibration unit based on the power amplifier output power, feeder loss, and antenna gain. The effective radiated power of the antenna = power amplifier output power - feeder loss + antenna gain.

[0024] The channel implicit parameters are jointly obtained by the base station channel detection module and the intelligent metasurface control unit. Among them, the multipath signal coherence coefficient is calculated by the base station through the uplink detection signal and the channel estimation module to calculate the correlation of the multipath components of the received signal; the neighbor cell signal interference ratio is calculated by the base station neighbor cell measurement module to calculate the ratio of the serving cell signal power to the neighbor cell interference signal power; and the intelligent metasurface reflection phase offset is obtained by the intelligent metasurface controller directly reading the phase parameters of its currently configured reflection unit.

[0025] The terminal feedback parameters are obtained through the terminal measurement and reporting mechanism in the mobile communication air interface protocol. The terminal's received signal-to-noise ratio is measured by the terminal's physical layer and then periodically reported to the base station through the uplink control channel. The dynamic change rate of the terminal's position is calculated by the base station based on the continuously reported positioning information or the time difference of the reference signal received by the terminal, using a motion estimation algorithm.

[0026] The environmental parameters are collected in real time by a low-orbit satellite signal monitoring device. The low-orbit satellite signal elevation angle fluctuation value is obtained by continuously monitoring the incident elevation angle of the passing low-orbit satellite signal and calculating the fluctuation amplitude through satellite signal receiving sensors deployed at the edge of the target coverage area.

[0027] Specifically, in step S2, the signal coverage strength is determined by the terminal's received signal-to-noise ratio and the antenna's effective radiated power. The signal interference coefficient is determined by the multipath signal coherence coefficient and the neighboring cell signal interference ratio; The dynamic environment stability is determined by the dynamic change rate of the terminal position and the elevation angle fluctuation value of the low-orbit satellite signal. The comprehensive coverage quality index is determined by signal coverage strength, signal interference coefficient, and dynamic environmental stability.

[0028] Specifically, to ensure the stability and real-time nature of the coverage assessment results, the aforementioned base station operating parameters, channel implicit parameters, terminal feedback parameters, and environmental parameters are all statistical values ​​that have been continuously collected and smoothed within a preset time window. In this embodiment, the preset time window is set to 500ms, meaning that the above parameters are the average or valid values ​​collected by the system within a continuous 500ms period, in order to avoid instantaneous fluctuations causing erroneous triggering of coverage determination and adjustment strategies.

[0029] Specifically, signal coverage strength = normalized terminal received signal-to-noise ratio × first weighting coefficient + normalized antenna effective radiated power × second weighting coefficient; signal interference coefficient = normalized multipath signal coherence coefficient × third weighting coefficient + normalized neighboring cell signal interference ratio × fourth weighting coefficient; dynamic environment stability = (1 (normalized terminal position dynamic change rate) × fifth weighting coefficient + (1 The normalized low-orbit satellite signal elevation angle fluctuation value) × the sixth weighting coefficient; the comprehensive coverage quality index = signal coverage strength × the seventh weighting coefficient + (1 - signal interference coefficient) × the eighth weighting coefficient + dynamic environmental stability × the ninth weighting coefficient.

[0030] In this embodiment of the invention, the first weighting coefficient is 0.6, the second weighting coefficient is 0.4, the third weighting coefficient is 0.5, the fourth weighting coefficient is 0.5, the fifth weighting coefficient is 0.6, the sixth weighting coefficient is 0.4, the seventh weighting coefficient is 0.5, the eighth weighting coefficient is 0.3, and the ninth weighting coefficient is 0.2.

[0031] Please see Figure 2 As shown, it is a flowchart of an embodiment of the present invention for determining whether coverage is qualified based on the comprehensive coverage quality index; Specifically, in step S3, if the overall coverage quality index is less than the preset quality index, the current coverage is determined to be unqualified, and a multi-executor collaborative adjustment strategy corresponding to the coverage anomaly type is executed; if the overall coverage quality index is greater than or equal to the preset quality index, the current coverage is determined to be qualified.

[0032] In this embodiment of the invention, the preset quality index value is 0.75, but this value is not limited to this. Those skilled in the art can adjust this value according to actual needs.

[0033] Specifically, step S3, which covers the exception types and corresponding multi-executor collaborative adjustment strategies, includes: When the signal coverage strength is less than the preset coverage strength, it is identified as a weak signal coverage anomaly. The corresponding collaborative adjustment strategy is a variable step size approximation strategy, which dynamically adjusts the antenna beam direction and the intelligent metasurface reflection phase according to the deviation between the current comprehensive coverage quality index and the preset quality index. When the signal interference coefficient is greater than the preset interference coefficient, it is identified as an abnormal signal interference. The corresponding collaborative adjustment strategy is a sequential strategy, that is, first adjust the antenna polarization mode, and then adjust the intelligent metasurface reflection angle. When the dynamic environment stability is less than the preset stability, it is identified as a dynamic coverage imbalance anomaly. The corresponding collaborative adjustment strategy is a conflict rollback strategy, which means pausing the current adjustment and replanning the adjustment scheme based on the terminal's movement trajectory and the transit time of low-orbit satellites.

[0034] In this embodiment of the invention, the preset coverage strength is 0.70, the preset interference coefficient is 0.35, and the preset stability is 0.65. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the above values ​​according to actual needs.

[0035] Specifically, the variable step size approximation strategy is implemented as follows: First, the comprehensive coverage quality index deviation is calculated, which is the difference between the preset quality index and the current comprehensive coverage quality index. Based on this deviation, the adjustment step size is dynamically determined, and the variable step size adjustment is achieved by using the method of "maximum step size × deviation". The maximum adjustment step size of the antenna beam azimuth angle is set as the first adjustment step size, the maximum adjustment step size of the downtilt angle is set as the second adjustment step size, and the maximum adjustment step size of the intelligent metasurface reflection phase is set as the third adjustment step size. In this embodiment of the invention, the first adjustment step size is 5°, the second adjustment step size is 3°, and the third adjustment step size is 20°. The specific adjustment step sizes are the product of the first adjustment step size and the deviation, the product of the second adjustment step size and the deviation, and the product of the third adjustment step size and the deviation, respectively. During the adjustment process, the azimuth and downtilt angles of the antenna beam are adaptively adjusted according to the magnitude of the deviation, and the reflection phase of each reflection unit of the smart metasurface is corrected simultaneously, so that the energy of the main beam is concentrated in the weak coverage area. After each round of adjustment, the coverage sensing data is re-acquired and the comprehensive coverage quality index is calculated until the signal coverage strength reaches or exceeds the preset coverage strength, so as to achieve the gradual approach optimization of the coverage state.

[0036] The specific implementation of the sequential strategy is as follows: Antenna polarization is switched step-by-step according to a fixed priority of vertical polarization, horizontal polarization, and ±45° dual polarization. Each time a polarization state is switched, the signal interference coefficient is collected and calculated in real time. The change in interference coefficient before and after the switch is compared, and the polarization state that causes the most significant decrease in signal interference coefficient is locked and maintained. The intelligent metasurface reflection angle adjustment is calculated based on the interference coefficient deviation. This interference coefficient deviation is the difference between the current signal interference coefficient and the preset interference coefficient. The intelligent metasurface reflection angle adjustment = angle conversion factor × interference coefficient deviation. In this embodiment, the angle conversion factor is 12° / unit deviation. The adjustment direction is to reflect neighboring multipath interference signals and coherent interference signals to non-target coverage areas. After each adjustment, the signal interference coefficient is recalculated until the interference coefficient falls below the preset interference coefficient, thus completing the optimization of interference anomalies.

[0037] The specific implementation of the conflict rollback strategy is as follows: First, the dynamic environment stability deviation is calculated. This deviation is the difference between the preset stability and the current dynamic environment stability. When the deviation is greater than 0.2, all current antenna beam adjustments and intelligent metasurface parameter adjustments are immediately suspended to avoid adjustment failure or coverage oscillation due to environmental instability. Subsequently, based on the dynamic change rate of the terminal position fed back by the terminal, the terminal's movement trajectory within the next 3 seconds is predicted using a motion estimation algorithm to determine the terminal's movement direction and speed. Simultaneously, combined with the low-orbit satellite signal elevation angle fluctuation value in the environmental parameters, the low-orbit satellite transit pattern is analyzed to predict periods of strong satellite signal interference and stable periods. During the low-orbit satellite interference gaps and the stable range of low-speed terminal movement, the combination scheme of antenna beam parameters (azimuth angle, downtilt angle) and intelligent metasurface reflection parameters (reflection phase, reflection angle) is replanned. After the re-acquired dynamic environment stability rises above the preset stability, the adjustment process is resumed to ensure the stability and reliability of the optimization process.

[0038] Please see Figure 3 As shown, it is a flowchart for determining the conflict type in an embodiment of the present invention; Specifically, in step S4, the process of determining the conflict type and the corresponding conflict resolution strategy is as follows: During the execution of the variable step size approximation strategy or the sequential serial strategy, the changes in the terminal's received signal-to-noise ratio, multipath signal coherence coefficient, and effective radiated power of the antenna are monitored in real time. If the terminal receives a signal-to-noise ratio that decreases while the antenna’s effective radiated power increases, it is determined to be a coverage cancellation conflict. The smart metasurface adjustment is frozen, and only the base station antenna performs beam scanning optimization. The smart metasurface is gradually unfrozen after the signal-to-noise ratio recovers and increases. If the terminal receives a signal-to-noise ratio that increases while the coherence coefficient of the multipath signal increases, it is determined that interference has introduced a conflict. The antenna polarization mode is adjusted and the feed line impedance is calibrated. At the same time, the intelligent metasurface reflection angle is adjusted. The interference coefficient is re-evaluated after each adjustment. If both the signal-to-noise ratio and the coherence coefficient of the multipath signal decrease at the terminal, it is determined to be a multi-target conflict. All adjustments are rolled back, the intelligent metasurface reflection phase offset is set to the conjugate value of the antenna beam phase, and the adjustment step size is reduced by a preset ratio before re-execution.

[0039] Specifically, the gradual unfreezing of the smart metasurface involves the following steps: after the terminal receives three consecutive samples with an increasing signal-to-noise ratio, the reflection phase adjustment step size of the smart metasurface is gradually restored according to a preset ratio. The first step size is restored to 30% of the normal step size. After this step size is maintained for two adjustments and the signal-to-noise ratio does not decrease, the second step size is restored to 60% of the normal step size. After two more adjustments and the signal-to-noise ratio continues to improve, the third step size is fully restored to the normal step size.

[0040] Specifically, the base station antenna uses a dual-polarization antenna array, supporting both vertical and horizontal polarization modes, as well as ±45° slant polarization. When interference is detected, the system first reads the current antenna polarization. If it is currently vertically polarized, it switches to horizontal polarization; if it is currently horizontally polarized, it switches to vertical polarization; if it is currently ±45° slant polarization, it switches to orthogonal polarization. 45° slant polarization. Polarization switching is achieved by controlling the feed phase of the antenna array through the baseband signal processing unit. Specifically, the feed phase difference of each antenna element is adjusted to rotate the electric field direction of the radiated electromagnetic wave by 90° or 45° to an orthogonal polarization state. The switching process is completed within a single time slot (1ms).

[0041] After completing the polarization switch, the system performs feeder impedance calibration. The base station power calibration unit reads the forward and reverse power of the feeder in real time through the built-in VSWR monitoring module and calculates the current VSWR. If the VSWR is greater than 1.5, it indicates that the feeder impedance is mismatched with the antenna port impedance, resulting in reflection loss. During calibration, the system adjusts the adjustable capacitor and adjustable inductor in the matching network at the end of the feeder to gradually bring the feeder input impedance closer to the 50Ω standard value. The specific adjustment method is as follows: first, fine-tune the parallel capacitor, changing it by 1pF in each step, and observe the trend of VSWR change. If the VSWR decreases, continue adjusting in the same direction; if the VSWR increases, adjust in the opposite direction. When the VSWR drops below 1.2, calibration stops. If the VSWR is still higher than 1.5 after three consecutive adjustments, it is determined to be a feeder hardware fault and an alarm is reported.

[0042] After completing the first two adjustments, the system adjusts the reflection angle of the smart metasurface. The smart metasurface consists of M×N adjustable reflection units, each integrating a variable capacitor diode. The reflection phase of the unit is controlled by changing the bias voltage. Adjusting the reflection angle essentially involves configuring the reflection phase distribution of each unit to make the metasurface exhibit a specific beam direction. Based on the current spatial orientation of the terminal relative to the smart metasurface and the beam direction of the base station antenna, the system dynamically calculates the optimal target reflection angle. Based on this angle and the coordinate positions of each unit on the metasurface plane, it calculates the required compensation phase for each unit according to the principle of array antennas, ensuring that the reflected wavefronts of each unit are in phase and superimposed in the desired direction. Then, a digital-to-analog converter converts the phase values ​​into corresponding bias voltages, which are applied to the varactor diodes of each unit. The initial adjustment step size for the reflection angle is set to 2°, with the adjustment range limited to ±30°. After each reflection angle adjustment, the system re-acquires the multipath signal coherence coefficient and the terminal's received signal-to-noise ratio, recalculates the interference coefficient, and compares it with a preset interference coefficient threshold. If the interference coefficient drops below the threshold, the conflict resolution is complete; if the interference coefficient still exceeds the limit, continue to adjust the metasurface reflection angle at the same time length until the maximum number of adjustments (set to 5 times) is reached or the adjustment range is exceeded.

[0043] Specifically, the system restores all adjustable parameters of the base station antenna to their state before the adjustment began, including the antenna beam direction angle, effective radiated power, and polarization. Simultaneously, it restores the reflection phase offset of the smart metasurface to its pre-adjustment value and resets all reflective elements of the smart metasurface to their initial phase state. After the rollback operation is complete, the system waits for a feedback period (100 milliseconds) until the channel state stabilizes before executing subsequent steps.

[0044] After the channel stabilizes, the system obtains the phase distribution characteristics of the current base station antenna beam through the baseband signal processing unit. For each reflecting element on the smart metasurface, the system sets its reflection phase to the opposite of the spatial phase of the incident signal at that element, making the overall reflection phase response of the metasurface equal to the complex conjugate of the antenna beam phase. Through this phase conjugate configuration, the smart metasurface accurately reflects the incident signal to the direction of the antenna main beam, achieving in-phase superposition of the reflected signal and the direct signal at the terminal position, thereby maximizing the receiving gain. The calculation and configuration of the phase conjugate value is completed within 10 milliseconds. The system converts the calculated phase value into a bias voltage through a digital-to-analog converter and applies it to the varactor diodes of each reflecting element.

[0045] In this embodiment of the invention, the preset ratio for step size reduction is 50%, that is, the new step size is equal to 50% of the original step size. If multi-target conflict is triggered twice consecutively within the same optimization cycle, the step size is further reduced to 25% of the original step size; if it is triggered a third time, it is reduced to 10% and a manual intervention alarm is triggered.

[0046] Specifically, in step S5, if the adjusted comprehensive coverage quality index is less than the preset quality index, the adjustment is deemed unqualified, and the adjustment strategy for the next iteration is determined based on the direction and amount of change of the comprehensive coverage quality index before and after this adjustment; if the adjusted comprehensive coverage quality index is greater than or equal to the preset quality index, the adjustment is deemed qualified.

[0047] Specifically, in step S5, the method for determining the next iteration adjustment strategy based on the direction and amount of change in the comprehensive coverage quality index before and after this adjustment is as follows: When the improvement in the overall coverage quality index is greater than or equal to the first preset threshold, maintain the current strategy and increase the adjustment step size by the first preset ratio. When the increase is less than the first preset threshold but greater than zero, the current strategy is maintained and the step size remains unchanged. When the overall coverage quality index declines, switch the adjustment strategy or change the adjustment step size.

[0048] In this embodiment of the invention, the first preset threshold is 0.05 and the first preset ratio is 20%, that is, the new step size = the original step size × 1.2. However, the above values ​​are not limited to these. Those skilled in the art can adjust the above values ​​according to actual needs.

[0049] Specifically, when the overall coverage quality index declines, the switching adjustment strategy includes: If a variable step size approximation strategy is currently being used, then switch to a sequential serial strategy. If the current sequential serial strategy is used, switch to the conflict rollback strategy; If the current conflict rollback strategy is adopted, the conflict rollback strategy will be maintained and the adjustment step size will be reduced by the second preset ratio.

[0050] In this embodiment of the invention, the second preset ratio is 50%, that is, the new step size = the original step size × 0.5. However, this value is not limited to this, and those skilled in the art can adjust the value according to actual needs.

[0051] Understandably, when the overall coverage quality index improves significantly, it indicates that the current adjustment direction is correct and the effect is significant. In this case, maintaining the current strategy and appropriately increasing the step size can accelerate the approach to the optimal coverage state and improve adjustment efficiency. When the improvement is small but still positive, it indicates that the adjustment direction is correct but has approached the optimal area. In this case, maintaining the step size unchanged can prevent quality fluctuations caused by exceeding the optimal point due to an excessively large step size. When the overall coverage quality index declines, it indicates that the current adjustment direction is incorrect or the environment has changed. In this case, it is necessary to switch the adjustment strategy to try a new adjustment direction or reduce the step size for a more refined search to avoid continuous deviation in the wrong direction.

[0052] In designing the strategy switching path, the adjustment mechanisms are progressively designed from low to high complexity: the variable step size approximation strategy focuses on rapid optimization, the sequential strategy focuses on eliminating interfering factors one by one, and the conflict rollback strategy focuses on responding to drastic environmental changes. When the overall coverage quality index declines, switching from the current strategy to the next strategy can gradually introduce a more robust adjustment mechanism. If the quality declines even under the conflict rollback strategy, it indicates that the current environment is extremely unstable or the step size is still too large. In this case, the conflict rollback strategy is maintained, and the step size is further reduced to make fine adjustments in a more conservative manner until the coverage quality recovers and improves.

[0053] Please see Figure 4 As shown, the 5G / 6G base station antenna system of this invention includes: The data acquisition module is used to collect coverage sensing data of the target coverage area; The data processing module, which is connected to the data acquisition module, is used to preprocess the acquired data, determine the signal coverage strength, signal interference coefficient and dynamic environmental stability, and calculate the comprehensive coverage quality index. The judgment and strategy generation module, which is connected to the data processing module, is used to determine whether the current coverage is qualified based on the comprehensive coverage quality index, identify the coverage anomaly type under the condition of non-qualification, and generate a multi-actuator collaborative adjustment strategy corresponding to the anomaly type. A multi-executor coordination module, which is connected to the determination and strategy generation module, is used to determine the type of coordination conflict based on the direction of change of the coverage sensing data during the execution of the coordination adjustment strategy, and to execute the corresponding conflict handling strategy according to the conflict type. The iterative optimization module, which is connected to the data processing module and the multi-executor collaboration module respectively, is used to determine whether the adjustment is qualified based on the adjusted comprehensive coverage quality index. If it is not qualified, the adjustment strategy for the next iteration is determined based on the direction and amount of change of the comprehensive coverage quality index before and after the current adjustment. An actuator unit, which is connected to the multi-actuator coordination module, includes a base station antenna and a smart metasurface, and is used to receive and execute adjustment commands issued by the multi-actuator coordination module.

[0054] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in the details for the sake of brevity.

[0055] This invention is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for intelligent coverage optimization of 5G / 6G base station antennas, characterized in that, include: Step S1: Collect coverage sensing data of the target coverage area, including base station operating parameters, channel implicit parameters, terminal feedback parameters, and environmental parameters; Step S2: After preprocessing the collected data, determine the signal coverage strength, signal interference coefficient, and dynamic environmental stability to obtain the comprehensive coverage quality index; Step S3: Determine the qualification of the current coverage based on the comparison result between the comprehensive coverage quality index and the preset quality index. If the coverage is not qualified, identify the coverage anomaly type and execute the multi-actuator collaborative adjustment strategy corresponding to the anomaly type. The collaborative adjustment strategy includes a variable step size approximation strategy, a sequential serial strategy, and a conflict backoff strategy. Step S4: During the execution of the variable step size approximation strategy or the sequential serial strategy, the change direction of the base station operating parameters, channel implicit parameters, and terminal feedback parameters is monitored in real time to determine the conflict type in the multi-actuator coordinated adjustment process and execute the corresponding conflict handling strategy. The conflict handling strategy includes freezing the smart metasurface, adjusting the antenna polarization mode and the smart metasurface reflection angle, or executing a backoff strategy with phase conjugate compensation. Step S5: Recalculate the comprehensive coverage quality index based on the newly collected coverage perception data after adjustment. If the adjustment is deemed unqualified based on the comprehensive coverage quality index, determine the adjustment strategy for the next iteration based on the direction and amount of change of the comprehensive coverage quality index before and after this adjustment, including switching the multi-actuator collaborative adjustment strategy or changing the adjustment step size. In step S3, in response to the comprehensive coverage quality index being less than the preset quality index, the current coverage is determined to be unqualified, and a multi-actuator collaborative adjustment strategy corresponding to the coverage anomaly type is executed. The exception types and corresponding multi-executor collaborative adjustment strategies covered in step S3 include: When the signal coverage strength is less than the preset coverage strength, it is identified as a weak signal coverage anomaly. The corresponding collaborative adjustment strategy is a variable step size approximation strategy, which dynamically adjusts the antenna beam direction and the intelligent metasurface reflection phase according to the deviation between the current comprehensive coverage quality index and the preset quality index. When the signal interference coefficient is greater than the preset interference coefficient, it is identified as an abnormal signal interference. The corresponding collaborative adjustment strategy is a sequential strategy, that is, first adjust the antenna polarization mode, and then adjust the intelligent metasurface reflection angle. When the dynamic environment stability is less than the preset stability, it is identified as a dynamic coverage imbalance anomaly. The corresponding collaborative adjustment strategy is a conflict rollback strategy, that is, suspend the current adjustment and re-plan the adjustment scheme based on the terminal's movement trajectory and the transit time of low-orbit satellites. In step S4, the process of determining the conflict type and the corresponding conflict resolution strategy is as follows: During the execution of the variable step size approximation strategy or the sequential serial strategy, the changes in the terminal's received signal-to-noise ratio, multipath signal coherence coefficient, and effective radiated power of the antenna are monitored in real time. If the terminal receives a signal-to-noise ratio that decreases while the antenna’s effective radiated power increases, it is determined to be a coverage cancellation conflict. The smart metasurface adjustment is frozen, and only the base station antenna performs beam scanning optimization. The smart metasurface is gradually unfrozen after the signal-to-noise ratio recovers and increases. If the terminal receives a signal-to-noise ratio that increases while the coherence coefficient of the multipath signal increases, it is determined that interference has introduced a conflict. The antenna polarization mode is adjusted and the feed line impedance is calibrated. At the same time, the intelligent metasurface reflection angle is adjusted. The interference coefficient is re-evaluated after each adjustment. If both the signal-to-noise ratio and the coherence coefficient of the multipath signal decrease at the terminal, it is determined to be a multi-target conflict. All adjustments are rolled back, the intelligent metasurface reflection phase offset is set to the conjugate value of the antenna beam phase, and the adjustment step size is reduced by a preset ratio before re-execution.

2. The 5G / 6G base station antenna intelligent coverage optimization method according to claim 1, characterized in that, In step S1, the base station operating parameters include: effective radiated power of the antenna; The channel implicit parameters include: multipath signal coherence coefficient, neighboring cell signal interference ratio, and smart metasurface reflection phase offset; The terminal feedback parameters include: the terminal received signal-to-noise ratio and the dynamic change rate of the terminal position; The environmental parameters include: the elevation angle fluctuation value of low-orbit satellite signals.

3. The intelligent coverage optimization method for 5G / 6G base station antennas according to claim 2, characterized in that, In step S2, the signal coverage strength is determined by the terminal receiving signal-to-noise ratio and the effective radiated power of the antenna. The signal interference coefficient is determined by the multipath signal coherence coefficient and the neighboring cell signal interference ratio; The dynamic environment stability is determined by the dynamic change rate of the terminal position and the elevation angle fluctuation value of the low-orbit satellite signal. The comprehensive coverage quality index is determined by signal coverage strength, signal interference coefficient, and dynamic environmental stability.

4. The intelligent coverage optimization method for 5G / 6G base station antennas according to claim 3, characterized in that, Step S5 responds to the fact that the adjusted comprehensive coverage quality index is less than the preset quality index, determines that the adjustment is unqualified, and determines the adjustment strategy for the next iteration based on the direction and amount of change of the comprehensive coverage quality index before and after this adjustment.

5. The intelligent coverage optimization method for 5G / 6G base station antennas according to claim 4, characterized in that, In step S5, the specific method for determining the next iteration adjustment strategy based on the direction and amount of change in the comprehensive coverage quality index before and after this adjustment is as follows: When the improvement in the overall coverage quality index is greater than or equal to the first preset threshold, maintain the current strategy and increase the adjustment step size by the first preset ratio. When the increase is less than the first preset threshold but greater than zero, the current strategy is maintained and the step size remains unchanged. When the overall coverage quality index declines, switch the adjustment strategy or change the adjustment step size.

6. The intelligent coverage optimization method for 5G / 6G base station antennas according to claim 5, characterized in that, When the overall coverage quality index declines, the switching adjustment strategy includes: If a variable step size approximation strategy is currently being used, then switch to a sequential serial strategy. If the current sequential serial strategy is used, switch to the conflict rollback strategy; If the current conflict rollback strategy is adopted, the conflict rollback strategy will be maintained and the adjustment step size will be reduced by the second preset ratio.

7. A 5G / 6G base station antenna system, based on the intelligent coverage optimization method for 5G / 6G base station antennas according to any one of claims 1-6, characterized in that, include: The data acquisition module is used to collect coverage sensing data of the target coverage area; The data processing module, which is connected to the data acquisition module, is used to preprocess the acquired data, determine the signal coverage strength, signal interference coefficient and dynamic environmental stability, and calculate the comprehensive coverage quality index. The judgment and strategy generation module, which is connected to the data processing module, is used to determine whether the current coverage is qualified based on the comprehensive coverage quality index, identify the coverage anomaly type under the condition of non-qualification, and generate a multi-actuator collaborative adjustment strategy corresponding to the anomaly type. The covered exception types and corresponding multi-executor collaborative adjustment strategies include: When the signal coverage strength is less than the preset coverage strength, it is identified as a weak signal coverage anomaly. The corresponding collaborative adjustment strategy is a variable step size approximation strategy, which dynamically adjusts the antenna beam direction and the intelligent metasurface reflection phase according to the deviation between the current comprehensive coverage quality index and the preset quality index. When the signal interference coefficient is greater than the preset interference coefficient, it is identified as an abnormal signal interference. The corresponding collaborative adjustment strategy is a sequential strategy, that is, first adjust the antenna polarization mode, and then adjust the intelligent metasurface reflection angle. When the dynamic environment stability is less than the preset stability, it is identified as a dynamic coverage imbalance anomaly. The corresponding collaborative adjustment strategy is a conflict rollback strategy, that is, suspend the current adjustment and re-plan the adjustment scheme based on the terminal's movement trajectory and the transit time of low-orbit satellites. A multi-executor coordination module, which is connected to the determination and strategy generation module, is used to determine the type of coordination conflict based on the direction of change of the coverage sensing data during the execution of the coordination adjustment strategy, and to execute the corresponding conflict handling strategy according to the conflict type. The process of determining the type of conflict and the corresponding conflict resolution strategy is as follows: During the execution of the variable step size approximation strategy or the sequential serial strategy, the changes in the terminal's received signal-to-noise ratio, multipath signal coherence coefficient, and effective radiated power of the antenna are monitored in real time. If the terminal receives a signal-to-noise ratio that decreases while the antenna’s effective radiated power increases, it is determined to be a coverage cancellation conflict. The smart metasurface adjustment is frozen, and only the base station antenna performs beam scanning optimization. The smart metasurface is gradually unfrozen after the signal-to-noise ratio recovers and increases. If the terminal receives a signal-to-noise ratio that increases while the coherence coefficient of the multipath signal increases, it is determined that interference has introduced a conflict. The antenna polarization mode is adjusted and the feed line impedance is calibrated. At the same time, the intelligent metasurface reflection angle is adjusted. The interference coefficient is re-evaluated after each adjustment. If both the signal-to-noise ratio and the coherence coefficient of the multipath signal decrease at the terminal, it is determined to be a multi-target conflict. All adjustments are rolled back, the phase offset of the intelligent metasurface reflection is set to the conjugate value of the antenna beam phase, and the adjustment step size is reduced by a preset ratio before re-execution. The iterative optimization module, which is connected to the data processing module and the multi-executor collaboration module respectively, is used to determine whether the adjustment is qualified based on the adjusted comprehensive coverage quality index. If it is not qualified, the adjustment strategy for the next iteration is determined based on the direction and amount of change of the comprehensive coverage quality index before and after the current adjustment. An actuator unit, which is connected to the multi-actuator coordination module, includes a base station antenna and a smart metasurface, and is used to receive and execute adjustment commands issued by the multi-actuator coordination module.

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