Smart antenna array control method, system, device and storage medium

Abstract

The application discloses an intelligent antenna array control method, system, device and storage medium, relates to the technical field of communication, and aims to solve the problems of insufficient beamforming precision and low energy consumption ratio of a traditional antenna array. The method comprises the following steps: collecting multi-dimensional parameters of a communication environment in real time and generating a dynamic data set, wherein the dynamic data set comprises a user distribution heat map, a channel interference matrix and a multipath characteristic vector; obtaining a regulation and control instruction set of the phase and amplitude of each antenna unit in the antenna array based on the dynamic data set and preset beam adjustment parameters; calling a beam template library to generate a radiation pattern control signal based on the regulation and control instruction set and current scene characteristics, wherein the current scene characteristics comprise a geographical environment type and a user distribution density, and the radiation pattern control signal is used for indicating the adjustment of a radiation power level.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a smart antenna array control method, system, device and storage medium. Background Technology

[0002] In the evolution of communication technology, antenna arrays, as the core component for achieving high spectral efficiency and large-scale multiple-input multiple-output (MIMO), directly determine network coverage, data transmission rate, and system energy efficiency.

[0003] In related technologies, smart antenna array systems based on static or semi-dynamic strategies adjust the phase and amplitude distribution of each antenna element through preset parameters to achieve preliminary beam pointing control. However, static or semi-dynamic strategies only process limited environmental perception parameters and cannot respond in real time to dynamic changes such as sudden traffic surges or obstacle blockages, resulting in fluctuations in beamforming accuracy. Furthermore, they only achieve coarse-grained control by switching some antenna elements, without considering the joint optimization of multiple dimensions such as service type and channel quality, leading to low energy efficiency of the antenna array.

[0004] Therefore, there is an urgent need to design a scheme that can improve the beamforming accuracy and energy efficiency of antenna arrays. Summary of the Invention

[0005] The purpose of this application is to provide a smart antenna array control method, system, device and storage medium, which aims to solve the problems of insufficient beamforming accuracy and low energy efficiency of traditional antenna arrays.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] This application provides a smart antenna array control method, which includes: real-time acquisition of multi-dimensional parameters of the communication environment and generation of a dynamic dataset, the dynamic dataset including a user distribution heatmap, a channel interference matrix, and a multipath feature vector; based on the dynamic dataset and preset beam adjustment parameters, obtaining a set of phase and amplitude control instructions for each antenna element in the antenna array, the preset beam adjustment parameters being predicted based on historical communication data; and based on the control instruction set and current scene characteristics, calling a beam template library to generate a radiation pattern control signal, the current scene characteristics including geographical environment type and user distribution density, the radiation pattern control signal being used to indicate the adjustment of the radiation power level.

[0008] The intelligent antenna array control method provided in this application constructs a dynamic dataset by real-time acquisition of user distribution heatmaps, channel interference matrices, and multipath feature vectors. This dataset accurately captures the spatiotemporal dynamic characteristics of the communication environment, providing real-time environmental awareness for beamforming and avoiding control deviations caused by data lag. It combines preset beam adjustment parameters to generate a phase amplitude control instruction set, achieving synergistic optimization of historical experience and real-time status. This ensures rapid beam convergence and enhances dynamic suppression of sudden interference. Furthermore, it integrates geographical environment type and user density characteristics to invoke pre-configured beam templates, enabling deep matching between the radiation pattern control signal and the specific scene. This ensures coverage continuity while reducing ineffective power radiation, thereby improving antenna array beamforming accuracy and system energy efficiency.

[0009] In some embodiments, the multidimensional parameters include terminal positioning data, signal state information, and electromagnetic environment data; the generation of the dynamic dataset includes: parsing terminal positioning data to generate a user distribution heatmap, wherein the terminal positioning data includes reference signal received power and angle of arrival information; extracting channel state information to construct a channel interference matrix; detecting time-domain signal sequences in electromagnetic environment data to generate multipath feature vectors; and constructing a dynamic dataset based on the user distribution heatmap, the channel interference matrix, and the multipath feature vectors.

[0010] Based on this, this application generates a user distribution heatmap by analyzing the reference signal received power and angle of arrival information in the terminal positioning data, and constructs a dynamic dataset by combining the channel interference matrix and multipath feature vector, thereby improving the precision of beam control.

[0011] In some embodiments, obtaining the phase and amplitude control instruction set of each antenna element in the antenna array based on the dynamic dataset and preset beam adjustment parameters includes: performing time-domain alignment on the dynamic dataset and preset beam adjustment parameters; generating an input set by fusing the time-domain aligned dynamic dataset and preset beam adjustment parameters according to preset weighting coefficients; inputting the input set into the collaborative optimization engine and outputting the phase and amplitude control instruction set of each antenna element.

[0012] Based on this, this application adopts temporal alignment to ensure the spatiotemporal synchronization of dynamic environmental parameters and historical prediction parameters, and establishes a collaborative relationship between data and experience through a weighted fusion mechanism, so that the input set contains both real-time features and historical patterns, thereby indirectly improving beamforming accuracy.

[0013] In some embodiments, the above-mentioned input set is input into the collaborative optimization engine, and the output is a set of control instructions for the phase and amplitude of each antenna element, including: constructing a beamforming optimization function in the collaborative optimization engine with the goal of maximizing the signal-to-noise ratio and suppressing interference; iterating the beamforming optimization function using the input set to obtain the iteration result; and converting the iteration result into a set of control instructions.

[0014] Based on this, this application transforms the maximization of signal-to-noise ratio and interference suppression into optimization objective functions, and achieves precise tuning of beamforming parameters through iterative solution.

[0015] In some embodiments, the above-mentioned generation of radiation pattern control signals based on the control instruction set and current scene characteristics by calling the beam template library includes: inputting the geographical environment type and user distribution density into the beam template library, and outputting the matching terrain diffraction compensation coefficient and multipath suppression parameter; fusing the control instruction set, terrain diffraction compensation coefficient and multipath suppression parameter to generate a correction instruction set; and configuring the radiation pattern control signals of each antenna element based on the correction instruction set.

[0016] Based on this, this application introduces terrain diffraction compensation coefficient and multipath suppression parameter to modify the control command set in a scenario-based manner, and establishes a mapping relationship between geographical features and electromagnetic propagation model through beam template library, thereby improving the effectiveness of beam adjustment in complex environments.

[0017] In some embodiments, the smart antenna array control method provided in this application further includes: dividing the antenna array into active and inactive regions according to a user distribution heatmap; and turning off the power supply circuit of the antenna unit in the inactive region.

[0018] Based on this, this application dynamically divides the active area of ​​the antenna array and implements a differentiated power supply strategy to reduce static power consumption by shutting down the power supply to the inactive area while ensuring coverage continuity.

[0019] In some embodiments, the smart antenna array control method provided in this application further includes: configuring the radiated power of the antenna elements in the active region according to a service priority mapping table.

[0020] Based on this, this application achieves on-demand allocation of power resources through service priority mapping, further reducing the power consumption of the antenna array.

[0021] This application provides an intelligent antenna array system, comprising: an environment sensing unit for real-time acquisition of multi-dimensional parameters of the communication environment and generation of a dynamic dataset, the dynamic dataset including a user distribution heatmap, a channel interference matrix, and a multipath feature vector; a parameter calculation unit for obtaining a set of control instructions for the phase and amplitude of each antenna element in the antenna array based on the dynamic dataset and preset beam adjustment parameters, the preset beam adjustment parameters being predicted based on historical communication data; and a beam control unit for generating a radiation pattern control signal by calling a beam template library based on the control instruction set and current scene characteristics, the current scene characteristics including geographical environment type and user distribution density, the radiation pattern control signal being used to indicate the adjustment of the radiation power level.

[0022] In some embodiments, the aforementioned multidimensional parameters include terminal positioning data, signal state information, and electromagnetic environment data; the aforementioned environmental sensing unit is specifically used for: parsing terminal positioning data to generate a user distribution heatmap, wherein the terminal positioning data includes reference signal received power and angle of arrival information; extracting channel state information to construct a channel interference matrix; detecting time-domain signal sequences in electromagnetic environment data to generate multipath feature vectors; and constructing a dynamic dataset based on the user distribution heatmap, the channel interference matrix, and the multipath feature vectors.

[0023] In some embodiments, the parameter calculation unit is specifically used for: performing time-domain alignment of the dynamic dataset and preset beam adjustment parameters; generating an input set by fusing the time-domain aligned dynamic dataset and preset beam adjustment parameters according to preset weighting coefficients; inputting the input set into the collaborative optimization engine and outputting a set of phase and amplitude control instructions for each antenna element.

[0024] In some embodiments, the parameter calculation unit is specifically used to: construct a beamforming optimization function in the collaborative optimization engine with the objectives of maximizing signal-to-noise ratio and suppressing interference; iterate the beamforming optimization function using an input set to obtain an iterative result; and convert the iterative result into a control instruction set.

[0025] In some embodiments, the beam control unit is specifically used to: input the geographical environment type and user distribution density into the beam template library, and output the matching terrain diffraction compensation coefficient and multipath suppression parameter; fuse the control instruction set, terrain diffraction compensation coefficient and multipath suppression parameter to generate a correction instruction set; and configure the radiation pattern control signal of each antenna element based on the correction instruction set.

[0026] In some embodiments, the smart antenna array system provided in this application may further include: an energy consumption control unit, used to: divide the antenna array into active and inactive regions according to a user distribution heatmap; and turn off the power supply circuit of the antenna units in the inactive region.

[0027] In some embodiments, the energy consumption control unit is further configured to: configure the radiated power of the antenna unit in the active area according to the service priority mapping table.

[0028] This application provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the smart antenna array control method described above.

[0029] This application provides a computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform the smart antenna array control method described above.

[0030] This application provides a computer program product containing instructions that, when executed by a computer, cause the computer to perform the smart antenna array control method described above.

[0031] This application provides a chip including a processor and a communication interface, the communication interface and the processor being coupled together. The processor is used to run computer programs or instructions to implement the smart antenna array control method described above.

[0032] Specifically, the chip provided in this application embodiment also includes a memory for storing computer programs or instructions. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating a smart antenna array control method provided in this application embodiment;

[0035] Figure 2 A structural diagram of a smart antenna array system provided in this application embodiment;

[0036] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] In the description of this application, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or relative positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and for simplification, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Unless otherwise specified, the above-mentioned orientational descriptions can be flexibly set in practical applications, provided that the relative positional relationships shown in the accompanying drawings are satisfied.

[0039] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0040] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "communication" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection. They can refer to a direct connection or an indirect connection through an intermediate medium, or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0041] In some embodiments, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, article, or apparatus that includes that element.

[0042] In some embodiments, the words "exemplary" or "for example" are used to indicate that something is an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0043] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0044] Traditional antenna arrays, limited by their mechanical structure and fixed beamforming mechanisms, have gradually revealed the following technical bottlenecks in practical deployments:

[0045] 1) Insufficient beamforming accuracy: Traditional arrays rely on preset static beam patterns, which cannot dynamically track changes in user location or service requirements, resulting in beam pointing deviation and signal interference problems. Especially in scenarios with dense user distribution or high-speed movement, communication quality is significantly reduced.

[0046] 2) Inefficient energy management mechanism: All antenna elements in the array operate at full power for extended periods, lacking dynamic power allocation capabilities based on service load, resulting in unnecessary energy waste.

[0047] 3) Lack of scene adaptability: Fixed parameter configuration is difficult to adapt to complex and ever-changing wireless environments (such as dense urban areas and indoor-outdoor transition areas), resulting in coverage gaps and signal attenuation problems, which limits the network's performance in different scenarios.

[0048] To address the aforementioned issues, related technologies propose antenna array systems based on static or semi-dynamic strategies. These systems adjust the phase and amplitude distribution of each antenna element using preset parameters to achieve preliminary beam pointing control. However, such solutions still suffer from the following drawbacks:

[0049] 1) Limited adaptability to dynamic environments: Beamforming strategies are based on limited environmental perception parameters (such as a rough estimate of user location), which cannot respond in real time to dynamic changes such as sudden traffic surges or obstacle obstruction, resulting in fluctuations in beamforming accuracy.

[0050] 2) Single dimension of energy efficiency optimization: Energy consumption management only achieves coarse-grained control by switching some antenna units, without considering the joint optimization of multiple dimensions of parameters such as service type and channel quality, thus limiting the room for improvement in energy efficiency ratio.

[0051] 3) Insufficient scenario-based configuration capabilities: The lack of customized beamforming and resource allocation mechanisms for specific scenarios (such as along high-speed rail lines and underground parking lots) makes it difficult to guarantee communication stability and user experience in critical scenarios.

[0052] In summary, antenna array systems in related technologies still suffer from problems such as insufficient beamforming accuracy, low energy efficiency, and poor stability.

[0053] Against this backdrop, in order to address the problems of insufficient beamforming accuracy and low energy efficiency in traditional antenna arrays, this application provides a smart antenna array control method, system, device, and storage medium. Through a three-level linkage mechanism of "real-time perception, predictive fusion, and scene adaptation," the shortcomings of traditional antennas in dynamic environments, such as low accuracy and high energy consumption, are resolved.

[0054] The following is a reference. Figure 1 The intelligent antenna array control method provided in the embodiments of this application is described.

[0055] Figure 1 The flowchart of the intelligent antenna array control method provided in this application embodiment can be implemented by an intelligent antenna array system or by various devices / modules in the intelligent antenna array system, such as integrated circuits or chips. This application embodiment does not specifically limit the implementation of this method.

[0056] For example, such as Figure 1 As shown, the smart antenna array control method provided in this application embodiment may include the following S101 to S103:

[0057] S101: Real-time acquisition of multi-dimensional parameters of the communication environment and generation of dynamic datasets.

[0058] In this embodiment of the application, the communication environment refers to the physical space and electromagnetic field where the wireless communication link between the base station and the user equipment is located.

[0059] The multidimensional parameters include terminal positioning data, signal status information, and electromagnetic environment data.

[0060] For example, the terminal positioning data includes reference signal received power (RSRP) and angle of arrival (AoA) information. The terminal positioning data can be a time-frequency synchronized positioning frame structure (such as a positioning reference signal (PRS) containing orthogonal frequency division multiplexing (OFDM) symbols).

[0061] For example, signal status information can be channel quality indicator (CQI) and hybrid automatic repeat request (HARQ) feedback. CQI uses 4-bit quantization to reflect channel quality in real time; HARQ feedback includes new / retransmission identifiers and redundancy version numbers, ensuring data transmission reliability. Signal status information is used to reflect channel quality and service load characteristics.

[0062] For example, electromagnetic environment data can be a spectrum occupancy map and a Doppler frequency shift sequence. The spectrum occupancy map has a resolution of 1 MHz, reflecting spectrum usage in real time; the Doppler frequency shift is calculated using a fast Fourier transform (FFT), with a frequency offset estimation error ≤ 5 Hz, capturing the Doppler effect during electromagnetic wave propagation. Electromagnetic environment data is used to describe the characteristics of electromagnetic wave propagation in space.

[0063] In this embodiment, the dynamic dataset includes a user distribution heatmap, a channel interference matrix, and a multipath feature vector.

[0064] In some embodiments, the user distribution heatmap is generated based on a spatial interpolation algorithm (e.g., using the Kriging algorithm with a spatial resolution of 0.5m × 0.5m, which can intuitively display the user distribution and provide user density information for beamforming).

[0065] For example, user distribution heatmaps can be generated by parsing terminal location data (e.g., by using a kernel density estimation (KDE) algorithm (bandwidth parameter σ=2m) to estimate the continuous density of user locations and obtain user distribution heatmaps).

[0066] In other embodiments, the channel interference matrix is ​​a two-dimensional array that quantifies the inter-channel interference intensity (e.g., matrix element I(m,n) represents the interference coefficient of the m-th antenna element to the n-th element, with a value range of [0,1], providing a basis for interference suppression in beamforming. The closer the interference coefficient is to 1, the stronger the interference).

[0067] For example, channel state information can be extracted to construct a channel interference matrix (such as calculating the interference temperature based on the minimum mean square error (MMSE) algorithm, with a threshold set to -95dBm to ensure channel quality. When the interference temperature exceeds the threshold, an interference suppression mechanism will be triggered).

[0068] In other embodiments, the multipath feature vector is a set of vectors describing the propagation path delay and angle of electromagnetic waves (such as including the main path delay τ0, the maximum spread delay Δτ, and the angle spread Θ, providing parameter support for multipath suppression. Multipath effects can lead to signal fading and interference).

[0069] For example, multipath feature vectors can be generated by detecting time-domain signal sequences in electromagnetic environment data (e.g., by using the space-alternating generalized expectation (SAGE) maximization algorithm with a time delay resolution of 10 ns, which can accurately capture multipath effects and provide accurate parameters for multipath suppression).

[0070] Furthermore, a dynamic dataset can be constructed based on user distribution heatmaps, channel interference matrices, and multipath feature vectors.

[0071] For example, principal component analysis (PCA) can be used to reduce the dimensionality of the user distribution heatmap, channel interference matrix, and multipath feature vector to 128-dimensional feature vectors, while retaining key information to construct a dynamic dataset.

[0072] Thus, this application generates a user distribution heatmap by analyzing the reference signal received power and angle of arrival information in the terminal positioning data, and constructs a dynamic dataset by combining the channel interference matrix and multipath feature vector, thereby improving the precision of beam control.

[0073] S102. Based on the dynamic dataset and preset beam adjustment parameters, obtain the control command set for the phase and amplitude of each antenna element in the antenna array.

[0074] The preset beam adjustment parameters are predicted based on historical communication data, which includes historical user movement trajectories and traffic fluctuation information. For example, historical trajectory data shows the pattern of users in a certain area gathering at subway stations during weekday morning rush hour (8:00-9:00), and traffic data shows that video traffic accounts for 60% every evening from 7:00-10:00.

[0075] It should be noted that the aforementioned preset beam adjustment parameters can be obtained by using existing neural network models to periodically identify historical user movement trajectories and traffic fluctuation information, and predict the preset beam adjustment parameters based on the identified periodic relationships. This application does not impose any specific limitations on the embodiments described above.

[0076] Optionally, the dynamic dataset and preset beam adjustment parameters can be aligned in the time domain first, and then the time-aligned dynamic dataset and preset beam adjustment parameters can be fused according to preset weight coefficients to generate an input set.

[0077] For example, the dynamic dataset and the preset beam adjustment parameters can be timestamped first. For instance, the dynamic dataset (sampling interval 10ms) and the preset beam adjustment parameters (prediction interval 50ms) can be aligned to the same time axis using linear interpolation; then, the dynamic dataset and the preset beam adjustment parameters can be weighted and summed according to preset weight coefficients (e.g., dynamic dataset α=0.7, preset beam adjustment parameter β=0.3) to obtain the input set.

[0078] Optionally, the input set can be fed into the collaborative optimization engine to output a set of phase and amplitude control instructions for each antenna element.

[0079] In this embodiment, the collaborative optimization engine refers to a parameter optimizer that integrates physical layer constraints and service quality of service (QoS) requirements. Specifically, the collaborative optimization engine supports multiple objective constraints such as beamwidth, sidelobe level, and bit error rate, ensuring that the optimization results meet actual requirements.

[0080] For example, the collaborative optimization engine can be a computational module that integrates nonlinear optimization algorithms (such as gradient descent and genetic algorithms) and machine learning models to solve beamforming optimization problems. For instance, it can use a deep learning-based neural network model with 1024-dimensional input data to output phase (0-2π) and amplitude (0-1) control values ​​for 64 antenna elements.

[0081] Thus, this application employs temporal alignment to ensure the spatiotemporal synchronization of dynamic environmental parameters and historical prediction parameters, and establishes a collaborative relationship between data and experience through a weighted fusion mechanism, so that the input set simultaneously contains real-time features and historical patterns, thereby indirectly improving beamforming accuracy.

[0082] In some embodiments, a beamforming optimization function aimed at maximizing signal-to-noise ratio (SNR) and suppressing interference can be constructed in the collaborative optimization engine.

[0083] For example, the beamforming optimization function can be represented by formula (I).

[0084] Formula (1)

[0085] Where w is the weighting vector of the antenna array (including phase and amplitude parameters); This indicates maximizing the signal-to-noise ratio; λ represents the energy for suppressing multi-user interference (i.e., interference suppression); λ is the interference suppression weighting coefficient; H represents the channel interference matrix.

[0086] Furthermore, the beamforming optimization function can be iterated using the input set to obtain the iterative results; the iterative results can then be converted into a control instruction set.

[0087] For example, the input set can be mapped to the initial parameter vector w0 of the beamforming optimization function through a fully connected layer, including the initial phase and amplitude values ​​of each antenna element. Then, the initial parameter vector w0 is substituted into the above formula (I), and the parameter vector is updated by the gradient descent method with an adaptive step size until the convergence condition is met. Then, the optimal parameter vector (i.e., the iteration result) is output.

[0088] Specifically, if the initial state has an SNR of 15dB and an interference power of 20dBm, after the 10th iteration: SNR = 20dB (an improvement of 5dB), interference power = 17dBm (a decrease of 3dB); after the 30th iteration: SNR = 23dB (a cumulative improvement of 8dB), interference power = 15dBm (a cumulative decrease of 5dB); after the 50th iteration: SNR = 25dB (a cumulative improvement of 10dB), interference power = 5dBm (a cumulative decrease of 15dB), then the convergence condition is met, and the parameter vector is output.

[0089] It should be noted that the conversion between parameter vectors and phase and amplitude control command sets can be performed using existing conversion methods, and this application does not impose specific limitations on this.

[0090] Thus, this application transforms the maximization of signal-to-noise ratio and interference suppression into optimization objective functions, and achieves precise tuning of beamforming parameters through iterative solution.

[0091] S103. Based on the control instruction set and current scene characteristics, call the beam template library to generate radiation pattern control signals.

[0092] The current scene features include geographical environment type and user distribution density; the radiation pattern control signal is used to indicate the adjustment of radiation power level.

[0093] In this embodiment, the beam template library stores terrain diffraction compensation coefficients and multipath suppression strategies for various scenarios. The terrain diffraction compensation coefficients and multipath suppression strategies differ across different scenarios. For example, the beam template library includes urban areas, suburbs, indoor areas, and high-density areas (e.g., >80 people / km). 2 Medium density (e.g., 20-80 people / km) 2 Low density (e.g., <20 people / km) 2 Terrain diffraction compensation coefficient and multipath suppression strategy in scenarios such as ( ).

[0094] In some embodiments, the geographic environment type and user distribution density can be input into the beam template library, and the output can be the matching terrain diffraction compensation coefficient and multipath suppression parameter.

[0095] Among them, the terrain diffraction compensation coefficient refers to the coefficient used to correct the energy attenuation and phase shift of the signal caused by diffraction due to terrain obstacles. The terrain diffraction compensation coefficient can compensate the beam accordingly based on the undulation and obstruction of the terrain to ensure the effective propagation of the signal.

[0096] Among them, the multipath suppression parameter refers to the threshold or weight used to identify and filter multipath signals. The multipath suppression parameter can merge or suppress multipath components according to the strength of the multipath effect, thereby improving communication quality.

[0097] For example, consider a suburban geographic environment with a low user density. The beam template library can be used to filter for terrain diffraction compensation coefficients and multipath suppression strategies that satisfy both the suburban geographic environment type and the low user density type. Then, the terrain diffraction compensation coefficients and multipath suppression parameters can be output.

[0098] Furthermore, the control instruction set, terrain diffraction compensation coefficient, and multipath suppression parameters are fused to generate a correction instruction set, and the radiation pattern control signal of each antenna element is configured based on the correction instruction set.

[0099] For example, the phase in the control command set can be vector-superimposed with the diffraction compensation phase to obtain the compensated phase; then the amplitude in the control command set can be combined with the diffraction compensation amplitude to obtain the compensated amplitude; then a multipath suppression parameter can be introduced to adjust the beamforming weight (i.e., correct the command set). Finally, the compensated phase, compensated amplitude, and beamforming weight are converted into data formats to obtain the driving voltage (i.e., the radiation pattern control signal).

[0100] It should be noted that the above data format conversion can adopt existing feasible conversion methods, and this application does not make specific limitations on this.

[0101] Thus, this application introduces terrain diffraction compensation coefficient and multipath suppression parameter to modify the control command set in a scenario-based manner, and establishes a mapping relationship between geographical features and electromagnetic propagation model through beam template library, thereby improving the effectiveness of beam adjustment in complex environments.

[0102] In the intelligent antenna array control method provided in this application embodiment, a dynamic dataset is constructed by real-time acquisition of user distribution heatmaps, channel interference matrices, and multipath feature vectors to accurately capture the spatiotemporal dynamic characteristics of the communication environment, providing real-time environmental perception capabilities for beamforming and avoiding control deviations caused by data lag. A phase amplitude control instruction set is generated by combining preset beam adjustment parameters to achieve synergistic optimization of historical experience and real-time status, ensuring rapid convergence of beam pointing and enhancing dynamic suppression capabilities against sudden interference. Furthermore, the method integrates geographical environment type and user density characteristics to invoke pre-configured beam templates, enabling deep matching between the radiation pattern control signal and the specific scene. This ensures coverage continuity while reducing ineffective power radiation, thereby improving the antenna array beamforming accuracy and system energy efficiency.

[0103] Optionally, after S101 above, the smart antenna array control method provided in this application embodiment may further include: dividing the antenna array into active and inactive regions according to the user distribution heat map.

[0104] The active region refers to the region composed of antenna elements that need to transmit or receive signals at the current moment; the inactive region refers to the region composed of antenna elements that do not need to transmit or receive signals at the current moment.

[0105] In some embodiments, the user distribution density can be determined based on the user distribution heatmap, and the area composed of antenna elements with a user distribution density greater than a preset density can be determined as an active area; the area composed of antenna elements with a user distribution density less than or equal to the preset density can be determined as an inactive area.

[0106] The preset density can be a manually set value, which can be flexibly adjusted according to the actual scenario. For example, the preset density can be 10 people / 100m². 2 .

[0107] For example, with a preset density of 10 people / 100m 2 For example, if the calculated user distribution density in region A, composed of antenna elements, is 18 people / 100m... 2 If the calculated user distribution density of region B, composed of antenna elements, is 9 people / 100m, then region A can be identified as the active region; 2 Then region B can be identified as an inactive region.

[0108] In one alternative implementation, the power supply circuit of the antenna unit in the inactive area can be turned off.

[0109] For example, the power supply to the antenna in the inactive area can be disconnected via a relay.

[0110] Thus, this application reduces static power consumption by dynamically dividing the active area of ​​the antenna array and implementing a differentiated power supply strategy, thereby shutting off the power supply to the inactive area while ensuring coverage continuity.

[0111] In another optional implementation, the radiated power of the antenna elements in the active area can be configured according to the service priority mapping table.

[0112] In this embodiment, the service priority mapping table is a predefined correspondence table between service types and radiation power levels. For example, Table 1 shows the mapping relationship between some service types and radiation power levels. In actual scenarios, there may be more or fewer mapping relationships between service types and radiation power levels, which are not listed in this application.

[0113] Table 1

[0114] Business type Priority Radiated power level High-definition video call high 23dBm Web browsing middle 20dBm SMS service Low 17dBm

[0115] For example, if the service type of the activated area is high-definition video call as shown in Table 1, the radiated power of the antenna unit of the activated area can be configured to 23dBm.

[0116] Thus, this application achieves on-demand allocation of power resources through service priority mapping, further reducing the power consumption of the antenna array.

[0117] The foregoing primarily describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the aforementioned functions, the smart antenna array system or electronic device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0118] This application embodiment can, based on the above method, exemplarily divide a smart antenna array system or electronic device into functional modules. For example, the smart antenna array system or electronic device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0119] Figure 2 This is a structural diagram of an intelligent antenna array system provided in an embodiment of this application. The intelligent antenna array system 200 includes: an environment sensing unit 201, a parameter calculation unit 202, and a beam control unit 203.

[0120] The system includes: an environment sensing unit 201, which collects multi-dimensional parameters of the communication environment in real time and generates a dynamic dataset, including a user distribution heatmap, a channel interference matrix, and a multipath feature vector; a parameter calculation unit 202, which, based on the dynamic dataset and preset beam adjustment parameters, obtains a set of control instructions for the phase and amplitude of each antenna element in the antenna array, which is predicted based on historical communication data; and a beam control unit 203, which, based on the control instruction set and current scene characteristics, calls a beam template library to generate a radiation pattern control signal, including geographical environment type and user distribution density, and the radiation pattern control signal is used to indicate the adjustment of the radiation power level.

[0121] In some embodiments, the aforementioned multidimensional parameters include terminal positioning data, signal state information, and electromagnetic environment data; the aforementioned environmental sensing unit 201 is specifically used for: parsing terminal positioning data to generate a user distribution heatmap, wherein the terminal positioning data includes reference signal received power and angle of arrival information; extracting channel state information to construct a channel interference matrix; detecting time-domain signal sequences in electromagnetic environment data to generate multipath feature vectors; and constructing a dynamic dataset based on the user distribution heatmap, the channel interference matrix, and the multipath feature vectors.

[0122] In some embodiments, the parameter calculation unit 202 is specifically used for: performing time-domain alignment of the dynamic dataset and preset beam adjustment parameters; generating an input set by fusing the time-domain aligned dynamic dataset and preset beam adjustment parameters according to preset weighting coefficients; inputting the input set into the collaborative optimization engine and outputting a set of phase and amplitude control instructions for each antenna element.

[0123] In some embodiments, the parameter calculation unit 202 is specifically used to: construct a beamforming optimization function with the objectives of maximizing signal-to-noise ratio and suppressing interference in the collaborative optimization engine; iteratively solve the input set as the parameter space of the beamforming optimization function to obtain the solution result; and use the solution result as the control instruction set for the phase and amplitude of each antenna element.

[0124] In some embodiments, the beam control unit 203 is specifically used to: input the geographical environment type and user distribution density into the beam template library, and output the matching terrain diffraction compensation coefficient and multipath suppression parameter; fuse the control instruction set, terrain diffraction compensation coefficient and multipath suppression parameter to generate a correction instruction set; and configure the radiation pattern control signal of each antenna element based on the correction instruction set.

[0125] In some embodiments, the smart antenna array system provided in this application may further include: an energy consumption control unit, used to: divide the antenna array into active and inactive regions according to a user distribution heatmap; and turn off the power supply circuit of the antenna units in the inactive region.

[0126] In some embodiments, the energy consumption control unit is further configured to: configure the radiated power of the antenna unit in the active area according to the service priority mapping table.

[0127] In the intelligent antenna array system provided in this application embodiment, a dynamic dataset is constructed by real-time acquisition of user distribution heatmaps, channel interference matrices, and multipath feature vectors. This enables the system to accurately capture the spatiotemporal dynamic characteristics of the communication environment, providing real-time environmental awareness for beamforming and avoiding control deviations caused by data lag. A phase amplitude control instruction set is generated by combining preset beam adjustment parameters, achieving synergistic optimization of historical experience and real-time status. This ensures rapid beam convergence and enhances the dynamic suppression capability against sudden interference. Furthermore, by integrating geographical environment types and user density characteristics to invoke pre-configured beam templates, the radiation pattern control signal achieves deep matching with the specific scene, reducing ineffective power radiation while ensuring coverage continuity. This improves the antenna array beamforming accuracy and system energy efficiency.

[0128] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0129] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes, but is not limited to, a processor 301 and a memory 302.

[0130] The memory 302 described above is used to store the executable instructions of the processor 301. It is understood that the processor 301 is configured to execute instructions to implement the smart antenna array control method in the above embodiments.

[0131] It should be noted that those skilled in the art will understand that Figure 3 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 3 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0132] Processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in memory 302, and by calling data stored in memory 302, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Processor 301 may include one or more processing units. Optionally, processor 301 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 301.

[0133] The memory 302 can be used to store software programs and various data. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, a processing unit, etc.), etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0134] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 302 including instructions, which can be executed by a processor 301 of an electronic device 300 to implement the smart antenna array control method in the above embodiments.

[0135] In actual implementation, Figure 2 The steps performed by the environment sensing unit 201, parameter calculation unit 202, and beam control unit 203 can all be performed by... Figure 3 The processor 301 calls the computer program stored in the memory 302 to implement the process. The specific execution process can be found in the method section of the previous embodiment, and will not be repeated here.

[0136] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0137] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 301 of an electronic device to complete the smart antenna array control method in the above embodiments.

[0138] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of an electronic device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0141] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0144] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling an intelligent antenna array, characterized in that, The method includes: Real-time acquisition of multi-dimensional parameters of the communication environment and generation of dynamic dataset, which includes user distribution heatmap, channel interference matrix and multipath feature vector; Based on the dynamic dataset and preset beam adjustment parameters, a set of control instructions for the phase and amplitude of each antenna element in the antenna array is obtained; Based on the control instruction set and current scene characteristics, a radiation pattern control signal is generated by calling the beam template library. The current scene characteristics include geographical environment type and user distribution density. The radiation pattern control signal is used to indicate the adjustment of radiation power level. The multidimensional parameters include terminal positioning data, channel state information, and electromagnetic environment data. The generation of the dynamic dataset includes: The user distribution heatmap is generated by parsing the terminal positioning data, wherein the terminal positioning data includes reference signal received power and angle of arrival information; Extract the channel state information to construct the channel interference matrix; The multipath feature vector is generated by detecting the time-domain signal sequence in the electromagnetic environment data. The dynamic dataset is constructed based on the user distribution heatmap, the channel interference matrix, and the multipath feature vector; Also includes: Based on the user distribution heatmap, the active and inactive regions of the antenna array are divided. Turn off the power supply circuit of the antenna unit in the inactive area.

2. The method according to claim 1, characterized in that, The step of obtaining the phase and amplitude control command set for each antenna element in the antenna array based on the dynamic dataset and preset beam adjustment parameters includes: The dynamic dataset and the preset beam adjustment parameters are time-domain aligned; According to preset weighting coefficients, the dynamic dataset after time-domain alignment and the preset beam adjustment parameters are fused to generate an input set; The input set is fed into the collaborative optimization engine, which outputs a set of control instructions for the phase and amplitude of each antenna element.

3. The method according to claim 2, characterized in that, The step of inputting the input set into the collaborative optimization engine and outputting a set of phase and amplitude control instructions for each antenna element includes: A beamforming optimization function with the objectives of maximizing signal-to-noise ratio and suppressing interference is constructed in the collaborative optimization engine; The beamforming optimization function is iterated using the input set to obtain the iteration result; The iteration results are converted into the control instruction set.

4. The method according to claim 1, characterized in that, The step of generating a radiation pattern control signal by calling the beam template library based on the control instruction set and current scene characteristics includes: Input the geographic environment type and the user distribution density into the beam template library, and output the matching terrain diffraction compensation coefficient and multipath suppression parameter; The control instruction set, the terrain diffraction compensation coefficient, and the multipath suppression parameter are fused to generate a correction instruction set; Configure the radiation pattern control signals of each antenna element based on the modified instruction set.

5. The method according to claim 1, characterized in that, The method further includes: Configure the radiated power of the antenna units in the activated area according to the service priority mapping table.

6. A smart antenna array system, characterized in that, include: An environment sensing unit is used to collect multi-dimensional parameters of the communication environment in real time and generate a dynamic dataset, which includes a user distribution heatmap, a channel interference matrix, and a multipath feature vector. The parameter calculation unit is used to obtain the phase and amplitude control command set of each antenna element in the antenna array based on the dynamic dataset and preset beam adjustment parameters. The beam control unit is used to generate a radiation pattern control signal by calling the beam template library based on the control instruction set and the current scene characteristics. The current scene characteristics include geographical environment type and user distribution density. The radiation pattern control signal is used to indicate the adjustment of radiation power level. The aforementioned multidimensional parameters include terminal positioning data, channel state information, and electromagnetic environment data. The aforementioned environmental sensing unit is specifically used for: parsing terminal positioning data to generate a user distribution heatmap, the terminal positioning data including reference signal received power and angle of arrival information; extracting channel state information to construct a channel interference matrix; detecting time-domain signal sequences in the electromagnetic environment data to generate multipath feature vectors; and constructing a dynamic dataset based on the user distribution heatmap, channel interference matrix, and multipath feature vectors. The aforementioned parameter calculation unit is specifically used for: constructing a beamforming optimization function in the collaborative optimization engine with the objectives of maximizing the signal-to-noise ratio and suppressing interference; iterating the beamforming optimization function using the input set to obtain the iteration result; and converting the iteration result into a control instruction set.

7. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method described in any one of claims 1 to 5.

8. A computer-readable storage medium storing instructions, characterized in that, When the computer executes the instruction, the computer performs the method described in any one of claims 1 to 5.

Images