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

Abstract

The invention discloses an intelligent antenna array control method, system and device and a storage medium, relates to the technical field of communication, and aims to solve the problems of insufficient beam forming 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 thermodynamic diagram, a channel interference matrix and a multipath feature vector; obtaining a phase and amplitude regulation and control instruction set of each antenna unit in the antenna array based on the dynamic data set and a preset beam adjustment parameter; based on the regulation and control instruction set and the current scene features, a beam template library is called to generate a radiation pattern control signal, the current scene features comprise the geographical environment type and the user distribution density, and the radiation pattern control signal is used for indicating adjustment of the radiation power level.

Description

Technical Field

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

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

[0003] Related technologies employ intelligent antenna array systems based on static or semi-dynamic strategies, adjusting the phase and amplitude distribution of each antenna element through preset parameters to achieve preliminary beam pointing control. However, these strategies rely solely on limited environmental perception parameters and are unable to respond in real time to dynamic changes such as sudden traffic flow or obstruction. This leads to fluctuations in beamforming accuracy. Furthermore, these strategies achieve coarse-grained control by switching only some antenna elements on and off, without considering the combined optimization of multiple parameters such as service type and channel quality. This results in low antenna array energy efficiency.

[0004] Therefore, there is an urgent need to design a solution 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 an intelligent antenna array control method, system, device and storage medium, aiming 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 solutions: The present application provides a smart antenna array control method, which includes: real-time collection of multi-dimensional parameters of the communication environment and generation of a dynamic data set, the dynamic data set including a user distribution heat map, a channel interference matrix, and a multipath eigenvector; based on the dynamic data set and preset beam adjustment parameters, obtaining a control instruction set for the phase and amplitude of each antenna unit in the antenna array, the preset beam adjustment parameters being predicted based on historical communication data; 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 the geographical environment type and the user distribution density, the radiation pattern control signal being used to indicate adjustment of the radiation power level.

[0007] The intelligent antenna array control method provided in the embodiment of the present application constructs a dynamic data set by real-time collection of user distribution heat maps, channel interference matrices and multipath eigenvectors to accurately capture the spatiotemporal dynamic characteristics of the communication environment, provide instant environmental perception capabilities for beam control, and avoid control deviations caused by data lags; combine preset beam adjustment parameters to generate phase and amplitude control instruction sets, and achieve coordinated optimization of historical experience and real-time status, thereby ensuring rapid convergence of beam pointing and enhancing the dynamic suppression capability of sudden interference; further integrate geographical environment type and user density characteristics to call pre-configured beam templates, so that the radiation pattern control signal is deeply matched with the specific scenario, while ensuring coverage continuity and reducing invalid power radiation, thereby achieving an improvement in the antenna array beamforming accuracy and system energy consumption ratio.

[0008] In some embodiments, the multidimensional parameters include terminal positioning data, signal status information, and electromagnetic environment data; the generation of a dynamic data set includes: parsing the terminal positioning data to generate a user distribution heat map, the terminal positioning data includes reference signal receiving power and arrival angle information; extracting channel status information to construct a channel interference matrix; detecting the time domain signal sequence in the electromagnetic environment data to generate a multipath feature vector; and constructing a dynamic data set based on the user distribution heat map, the channel interference matrix, and the multipath feature vector.

[0009] Based on this, the present application generates a user distribution heat map by analyzing the reference signal receiving power and arrival angle information in the terminal positioning data, and constructs a dynamic data set by combining the channel interference matrix and multipath eigenvector to improve the precision of beam steering.

[0010] In some embodiments, the above-mentioned phase and amplitude control instruction set of each antenna unit in the antenna array is obtained based on the dynamic data set and the preset beam adjustment parameters, including: time domain alignment of the dynamic data set and the preset beam adjustment parameters; according to the preset weight coefficients, the dynamic data set and the preset beam adjustment parameters after time domain alignment are integrated to generate an input set; the input set is input into the collaborative optimization engine, and the phase and amplitude control instruction set of each antenna unit is output.

[0011] Based on this, this application adopts time domain alignment to ensure the spatiotemporal synchronization of dynamic environment parameters and historical prediction parameters, and establishes a synergistic relationship between data and experience through a weight fusion mechanism, so that the input set contains both real-time features and historical laws, indirectly improving the beamforming accuracy.

[0012] In some embodiments, the above-mentioned inputting the input set into the collaborative optimization engine and outputting the control instruction set of the phase and amplitude of each antenna unit includes: constructing a beamforming optimization function with the goals of maximizing the signal-to-noise ratio and suppressing interference in the collaborative optimization engine; using the input set to iterate the beamforming optimization function to obtain an iterative result; and converting the iterative result into a control instruction set.

[0013] Based on this, this application transforms signal-to-noise ratio maximization and interference suppression into optimization objective functions, and achieves precise tuning of beamforming parameters through iterative solution.

[0014] In some embodiments, the above-mentioned calling of the beam template library to generate the radiation pattern control signal based on the control instruction set and the current scene characteristics 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 signal of each antenna unit based on the correction instruction set.

[0015] Based on this, this application introduces terrain diffraction compensation coefficients and multipath suppression parameters to perform scenario-based corrections to the control instruction set, and establishes a mapping relationship between geographical features and electromagnetic propagation models through the beam template library, thereby improving the effectiveness of beam adjustment in complex environments.

[0016] In some embodiments, the smart antenna array control method provided in the embodiments of the present application further includes: dividing the antenna array into active areas and inactive areas according to a user distribution heat map; and shutting down the power supply circuit of the antenna unit in the inactive area.

[0017] Based on this, this application dynamically divides the antenna array activation area and implements a differentiated power supply strategy. While ensuring coverage continuity, it turns off the power supply to the non-activated area, thereby reducing static power consumption.

[0018] In some embodiments, the smart antenna array control method provided in the embodiments of the present application further includes: configuring the radiation power of the antenna units in the activation area according to the service priority mapping table.

[0019] Based on this, the present application realizes on-demand allocation of power resources through service priority mapping, further reducing the power consumption of the antenna array.

[0020] The present application provides an intelligent antenna array system, which includes: an environmental perception unit, which is used to collect multi-dimensional parameters of the communication environment in real time and generate a dynamic data set, the dynamic data set including a user distribution heat map, a channel interference matrix and a multipath eigenvector; a parameter calculation unit, which is used to obtain a control instruction set for the phase and amplitude of each antenna unit in the antenna array based on the dynamic data set and preset beam adjustment parameters, the preset beam adjustment parameters are predicted based on historical communication data; a beam control unit, which is used to call a beam template library to generate a radiation pattern control signal based on the control instruction set and current scene characteristics, the current scene characteristics including the geographical environment type and user distribution density, and the radiation pattern control signal is used to indicate the adjustment of the radiation power level.

[0021] In some embodiments, the above-mentioned multidimensional parameters include terminal positioning data, signal status information and electromagnetic environment data; the above-mentioned environmental perception unit is specifically used to: parse the terminal positioning data to generate a user distribution heat map, the terminal positioning data includes reference signal receiving power and arrival angle information; extract channel status information to construct a channel interference matrix; detect the time domain signal sequence in the electromagnetic environment data to generate a multipath feature vector; and construct a dynamic data set based on the user distribution heat map, channel interference matrix and multipath feature vector.

[0022] In some embodiments, the parameter calculation unit is specifically used to: perform time domain alignment on the dynamic data set and the preset beam adjustment parameters; generate an input set by fusing the time domain aligned dynamic data set and the preset beam adjustment parameters according to preset weight coefficients; input the input set into the collaborative optimization engine, and output a control instruction set for the phase and amplitude of each antenna unit.

[0023] In some embodiments, the parameter calculation unit is specifically used to: construct a beamforming optimization function in the collaborative optimization engine with the goals of maximizing the 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.

[0024] In some embodiments, the above-mentioned 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 unit based on the correction instruction set.

[0025] In some embodiments, the intelligent antenna array system provided in the embodiments of the present application may also include: an energy consumption control unit, used to: divide the antenna array into active areas and inactive areas according to the user distribution heat map; and turn off the power supply circuit of the antenna unit in the inactive area.

[0026] In some embodiments, the energy consumption control unit is further configured to: configure the radiation power of the antenna units in the activation area according to the service priority mapping table.

[0027] The present application provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the smart antenna array control method described above.

[0028] The present application provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal, the terminal executes the smart antenna array control method described above.

[0029] The present application provides a computer program product comprising instructions, which, when executed by a computer, enables the computer to execute the smart antenna array control method described above.

[0030] The present application provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled, and the processor is used to run computer programs or instructions to implement the smart antenna array control method described above.

[0031] Specifically, the chip provided in the embodiment of the present application also includes a memory for storing computer programs or instructions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 A method flow chart of a smart antenna array control method provided in an embodiment of the present application; Figure 2 A diagram showing the structure of a smart antenna array system provided in an embodiment of the present application; Figure 3 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] In the description of this application, it should be understood that the terms "upper," "lower," "left," "right," "front," "back," "inner," "outer," and the like, indicating directions or positional relationships, are based on the directions or relative positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific direction, be constructed, or operate in a specific direction. Therefore, they should not be construed as limitations on this application. Unless otherwise specified, the above-mentioned directionality descriptions may be flexibly set in actual application, provided that the relative positional relationships shown in the accompanying drawings are met.

[0036] 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 the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. Throughout this application, unless otherwise specified, "plurality" means two or more.

[0037] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connected," and "connected" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections. They may be directly connected, indirectly connected through an intermediary, or internally connected between two components. Those skilled in the art will understand the specific meanings of these terms in this application based on the specific circumstances.

[0038] In some embodiments, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, article, or apparatus that includes the element.

[0039] In some embodiments, words such as "exemplary" or "for example" are used to indicate an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0040] In the description of this specification, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0041] Traditional antenna arrays are limited by their mechanical structure and fixed beamforming mechanism, and have gradually exposed the following technical bottlenecks in actual deployment: 1) Insufficient beamforming accuracy: Traditional arrays rely on preset static beam patterns and cannot dynamically track user locations or changes in service needs. This leads to beam pointing deviation and signal interference, significantly degrading communication quality, especially in scenarios with dense user distribution or high-speed mobility.

[0042] 2) Extensive energy management mechanism: All antenna units in the array operate at full power for a long time, lacking the ability to dynamically adjust power based on service load, resulting in unnecessary energy waste.

[0043] 3) Lack of scenario adaptability: Fixed parameter configurations are difficult to adapt to complex and changing wireless environments (such as densely populated urban areas and indoor-outdoor transition zones), resulting in coverage holes and signal attenuation, limiting the network's performance in differentiated scenarios.

[0044] To address these issues, related technologies have proposed antenna array systems based on static or semi-dynamic strategies, which adjust the phase and amplitude distribution of each antenna element through preset parameters to achieve preliminary beam pointing control. However, such solutions still have the following drawbacks: 1) Limited adaptability to dynamic environments: Beamforming strategies are based only on limited environmental perception parameters (such as a rough estimate of user location) and cannot respond in real time to dynamic changes such as sudden business traffic or obstacle obstruction, resulting in fluctuations in beamforming accuracy.

[0045] 2) Single dimension of energy efficiency optimization: Energy consumption management only achieves coarse-grained control by switching on and off some antenna units, without considering the joint optimization of multi-dimensional parameters such as service type and channel quality, which limits the room for improving energy efficiency.

[0046] 3) Insufficient scenario-based configuration capabilities: The lack of customized beamforming and resource allocation mechanisms for specific scenarios (such as high-speed rail lines and underground parking lots) makes it difficult to ensure communication stability and user experience in key scenarios.

[0047] In summary, the antenna array system in the related art still has problems such as insufficient beamforming accuracy, low energy efficiency and poor stability.

[0048] In this context, to address the issues of insufficient beamforming accuracy and low energy efficiency in traditional antenna arrays in related technologies, this application provides a smart antenna array control method, system, device, and storage medium. Through a three-level linkage mechanism of "real-time perception, prediction fusion, and scenario adaptation," this approach addresses the low accuracy and high energy consumption of traditional antennas in dynamic environments.

[0049] Refer to the following Figure 1 The smart antenna array control method provided in the embodiment of the present application is described.

[0050] Figure 1 The method flow chart of the intelligent antenna array control method provided in the embodiment of the present application, the subject executing the method can be the intelligent antenna array system, or it can be each device / module in the intelligent antenna array system, such as an integrated circuit or chip, and the embodiment of the present application does not make specific limitations on this.

[0051] For example, Figure 1 As shown, the smart antenna array control method provided in the embodiment of the present application may include the following S101 to S103: S101. Collect multi-dimensional parameters of the communication environment in real time and generate a dynamic data set.

[0052] In the embodiment of the present application, the communication environment refers to the physical space and electromagnetic field in which the wireless communication link between the base station and the user equipment is located.

[0053] Among them, multi-dimensional parameters include terminal positioning data, signal status information and electromagnetic environment data.

[0054] Exemplarily, the terminal positioning data includes reference signal received power (RSRP) and angle of arrival (AoA). The terminal positioning data may be a positioning frame structure with time-frequency synchronization (e.g., a positioning reference signal (PRS) containing orthogonal frequency division multiplexing (OFDM) symbols).

[0055] 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 a new / retransmission flag and a redundancy version number to ensure data transmission reliability. Signal status information reflects channel quality and traffic load characteristics.

[0056] For example, electromagnetic environment data can be a spectrum occupancy map and a Doppler shift sequence. The spectrum occupancy map has a resolution of 1 MHz and can reflect spectrum usage in real time. The Doppler shift is calculated using a fast Fourier transform (FFT) with an estimated frequency deviation error of ≤5 Hz, capturing the Doppler effect during electromagnetic wave propagation. Electromagnetic environment data is used to describe the propagation characteristics of electromagnetic waves in space.

[0057] In an embodiment of the present application, the dynamic data set includes a user distribution heat map, a channel interference matrix, and a multipath eigenvector.

[0058] In some embodiments, the user distribution heat map is generated based on a spatial interpolation algorithm (for example, 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).

[0059] For example, the terminal positioning data may be parsed to generate a user distribution heat map (for example, by using a kernel density estimation (KDE) algorithm (bandwidth parameter σ=2m) to achieve continuous density estimation of user locations and obtain a user distribution heat map).

[0060] In other embodiments, the channel interference matrix is ​​a two-dimensional array that quantifies the interference intensity between channels (e.g., the matrix element I(m,n) represents the interference coefficient of the mth antenna unit to the nth unit, with a value range of [0,1], providing an interference suppression basis for beamforming. The closer the interference coefficient is to 1, the stronger the interference).

[0061] For example, channel state information may be extracted to construct a channel interference matrix (eg, interference temperature is calculated based on a 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 is triggered).

[0062] In other embodiments, the multipath eigenvector is a set of vectors describing the delay and angle of the electromagnetic wave propagation path (such as including the main path delay τ0, the maximum extension delay Δτ and the angle extension Θ, providing parameter support for multipath suppression. The multipath effect will cause signal fading and interference).

[0063] For example, a time-domain signal sequence in electromagnetic environment data can be detected to generate a multipath feature vector (e.g., by using a space-alternating generalized expectation (SAGE) maximization algorithm with a delay resolution of 10 ns, which can accurately capture the multipath effect and provide accurate parameters for multipath suppression).

[0064] Furthermore, a dynamic dataset can be constructed based on the user distribution heat map, channel interference matrix and multipath eigenvector.

[0065] For example, principal component analysis (PCA) can be used to reduce the dimensions of the user distribution heat map, channel interference matrix, and multipath feature vector to 128-dimensional feature vectors, retaining key information to construct a dynamic data set.

[0066] In this way, the present application generates a user distribution heat map by analyzing the reference signal receiving power and arrival angle information in the terminal positioning data, and constructs a dynamic data set by combining the channel interference matrix and multipath eigenvector to improve the precision of beam steering.

[0067] S102: Based on the dynamic data set and the preset beam adjustment parameters, a control instruction set for the phase and amplitude of each antenna unit in the antenna array is obtained.

[0068] The preset beam adjustment parameters are predicted based on historical communication data, which includes historical user movement trajectories and traffic fluctuations. For example, historical trajectory data shows that users in a certain area gather at subway stations during the weekday morning rush hour (8:00-9:00 AM), while traffic data shows that video traffic accounts for 60% of traffic between 7:00 PM and 10:00 PM each night.

[0069] It should be noted that the above-mentioned preset beam adjustment parameters can use the existing neural network model to periodically identify historical user movement trajectories and business volume fluctuation information, and predict the preset beam adjustment parameters based on the identified periodic relationship. Compared with the embodiments of the present application, no specific limitations are made.

[0070] Optionally, the dynamic data set and the preset beam adjustment parameters may be aligned in time domain first, and then the time domain aligned dynamic data set and the preset beam adjustment parameters may be fused according to preset weight coefficients to generate an input set.

[0071] For example, the dynamic dataset and the preset beam adjustment parameters can be time-stamped and synchronized. For example, linear interpolation can be used to align the dynamic dataset (sampling interval 10ms) and the preset beam adjustment parameters (prediction interval 50ms) to the same time axis. The dynamic dataset and the preset beam adjustment parameters are then weighted and summed according to preset weight coefficients (e.g., α = 0.7 for the dynamic dataset and β = 0.3 for the preset beam adjustment parameters) to obtain the input set.

[0072] Optionally, the input set can be input into the collaborative optimization engine, which outputs a set of control instructions for the phase and amplitude of each antenna unit.

[0073] In this embodiment of the present application, the collaborative optimization engine refers to a parameter optimizer that integrates physical layer constraints with service quality (QoS) requirements. The collaborative optimization engine supports multiple objective constraints, such as beamwidth, sidelobe level, and bit error rate, to ensure that the optimization results meet actual requirements.

[0074] For example, the collaborative optimization engine can be a computing module that integrates nonlinear optimization algorithms (such as gradient descent and genetic algorithms) with machine learning models to solve beamforming optimization problems. For example, a deep learning-based neural network model can be used with 1024-dimensional input data and output phase (0-2π) and amplitude (0-1) control values ​​for 64 antenna elements.

[0075] In this way, this application uses time domain alignment to ensure the spatiotemporal synchronization of dynamic environment parameters and historical prediction parameters, and establishes a synergistic relationship between data and experience through a weight fusion mechanism, so that the input set contains both real-time features and historical laws, indirectly improving the beamforming accuracy.

[0076] In some embodiments, a beamforming optimization function targeting signal-to-noise ratio (SNR) maximization and interference suppression may be constructed in a collaborative optimization engine.

[0077] Exemplarily, the beamforming optimization function can be expressed by formula (1).

[0078] Formula (1) Where w is the weight vector of the antenna array (including phase and amplitude parameters); represents the maximum signal-to-noise ratio; represents the suppression of multi-user interference energy (i.e., interference suppression); λ is the interference suppression weight coefficient; and H represents the channel interference matrix.

[0079] Furthermore, the input set can be used to iterate the beamforming optimization function to obtain an iterative result; and the iterative result is converted into a control instruction set.

[0080] Exemplarily, 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 unit. Then, the initial parameter vector w0 is substituted into the above formula (1), and the parameter vector is updated using the gradient descent method with an adaptive step size until the convergence condition is met. The optimal parameter vector (i.e., the iterative result) is output.

[0081] Specifically, if the initial SNR is 15dB and the interference power is 20dBm, after the 10th iteration: SNR = 20dB (5dB improvement), interference power = 17dBm (3dB reduction); after the 30th iteration: SNR = 23dB (8dB cumulative improvement), interference power = 15dBm (5dB cumulative reduction); after the 50th iteration: SNR = 25dB (10dB cumulative improvement), interference power = 5dBm (15dB cumulative reduction), then the convergence condition is met and the parameter vector is output.

[0082] It should be noted that the mutual conversion between the parameter vector and the phase and amplitude control instruction set can adopt the existing conversion method, and the embodiment of the present application does not make any specific limitation on this.

[0083] In this way, this application transforms signal-to-noise ratio maximization and interference suppression into optimization objective functions, and achieves precise tuning of beamforming parameters through iterative solution.

[0084] S103: Based on the control instruction set and the current scene characteristics, call the beam template library to generate a radiation pattern control signal.

[0085] Among them, the current scene characteristics include the geographical environment type and the user distribution density; the radiation pattern control signal is used to instruct the adjustment of the radiation power level.

[0086] In the embodiment of the present application, the beam template library stores the terrain diffraction compensation coefficients and multipath suppression strategies for various scenarios. The terrain diffraction compensation coefficients and multipath suppression strategies for different scenarios are different. For example, the beam template library contains the terrain diffraction compensation coefficients and multipath suppression strategies for urban, suburban, indoor, high-density (e.g., >80 people / km) and high-density (e.g., >80 people / km) scenarios. 2 ), medium density (such as 20-80 people / km 2 ), low density (such as <20 people / km 2 ) and other scenarios.

[0087] In some embodiments, the geographical environment type and user distribution density may be input into a beam template library, and matching terrain diffraction compensation coefficients and multipath suppression parameters may be output.

[0088] 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 of terrain obstacles. The terrain diffraction compensation coefficient can compensate the beam accordingly according to the undulation and obstruction of the terrain to ensure the effective propagation of the signal.

[0089] The multipath suppression parameter refers to a threshold or weight used to identify and filter multipath signals. The multipath suppression parameter can combine or suppress multipath components according to the strength of the multipath effect, thereby improving communication quality.

[0090] For example, in a suburban environment with a low user density, the beam template library can be searched for terrain diffraction compensation coefficients and multipath mitigation strategies that meet both the suburban environment and the low user density. These terrain diffraction compensation coefficients and multipath mitigation strategies are then output.

[0091] Furthermore, the control instruction set, terrain diffraction compensation coefficient and multipath suppression parameter are integrated to generate a correction instruction set, and the radiation pattern control signal of each antenna unit is configured based on the correction instruction set.

[0092] For example, the phase in the control instruction set can be vector-superimposed with the diffraction compensation phase to form the compensated phase. The amplitude in the control instruction set is then combined with the diffraction compensation amplitude to obtain the compensated amplitude. Multipath mitigation parameters are then introduced to adjust the beamforming weights (i.e., modify the instruction set). Finally, the compensated phase, compensated amplitude, and beamforming weights are converted into data format to obtain the driving voltage (i.e., the radiation pattern control signal).

[0093] It should be noted that the above-mentioned data format conversion can adopt an existing achievable conversion method, and this application does not make any specific limitations on this.

[0094] In this way, this application introduces terrain diffraction compensation coefficients and multipath suppression parameters to perform scenario-based corrections to the control instruction set, establishes a mapping relationship between geographical features and electromagnetic propagation models through the beam template library, and improves the effectiveness of beam adjustment in complex environments.

[0095] In the intelligent antenna array control method provided in the embodiment of the present application, a dynamic data set is constructed by real-time collection of user distribution heat maps, channel interference matrices and multipath eigenvectors to accurately capture the spatiotemporal dynamic characteristics of the communication environment, provide instant environmental perception capabilities for beam control, and avoid control deviations caused by data lags; combine preset beam adjustment parameters to generate a phase and amplitude control instruction set to achieve coordinated optimization of historical experience and real-time status, thereby ensuring rapid convergence of beam pointing and enhancing the dynamic suppression capability of sudden interference; further integrate the geographical environment type and user density characteristics to call the pre-configured beam template, so that the radiation pattern control signal is deeply matched with the specific scenario, while ensuring coverage continuity and reducing invalid power radiation, thereby achieving an improvement in the antenna array beamforming accuracy and system energy consumption ratio.

[0096] Optionally, after the above S101, the smart antenna array control method provided in the embodiment of the present application may further include: dividing the antenna array into an active area and an inactive area according to a user distribution heat map.

[0097] The active area refers to the area composed of antenna units that need to transmit or receive signals at the current moment; the inactive area refers to the area composed of antenna units that do not need to transmit or receive signals at the current moment.

[0098] In some embodiments, the user distribution density can be determined based on the user distribution heat map, and the area composed of antenna units with a user distribution density greater than a preset density is determined as an activated area; the area composed of antenna units with a user distribution density less than or equal to the preset density is determined as an inactivated area.

[0099] 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 .

[0100] For example, the preset density is 10 people / 100m 2 For example, if the calculated user distribution density of area A composed of antenna units is 18 people / 100m 2 , area A can be determined as the activation area; if the user distribution density of area B composed of antenna units is calculated to be 9 people / 100m 2 , area B can be determined as an inactive area.

[0101] In an optional implementation, the power supply circuit of the antenna unit in the inactive area may be turned off.

[0102] For example, the power supply to the antenna in the inactive area may be disconnected by a relay.

[0103] In this way, the present application dynamically divides the antenna array activation area and implements a differentiated power supply strategy, thereby shutting down the power supply of the non-activated area while ensuring coverage continuity, thereby reducing static power consumption.

[0104] In another optional implementation, the radiation power of the antenna units in the activation area may be configured according to the service priority mapping table.

[0105] In the embodiments of the present application, the service priority mapping table is a table of predefined correspondences between service types and radiation power levels. For example, Table 1 shows the mapping relationships between some service types and radiation power levels. In actual scenarios, more or fewer service types and radiation power level mapping relationships may be included, and this application does not list them one by one.

[0106] Table 1 Business Type Priority Radiated power level HD video calls high 23dBm Web browsing middle 20dBm SMS service Low 17dBm For example, if the service type of the activated area is high-definition video calling as shown in Table 1, the radiation power of the antenna unit in the activated area may be configured to be 23 dBm.

[0107] In this way, the present application implements on-demand allocation of power resources through service priority mapping, further reducing the power consumption of the antenna array.

[0108] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, the smart antenna array system or electronic device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easy for those skilled in the art to realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0109] In the embodiment of the present application, the functional modules of the smart antenna array system or electronic device can be divided according to the above method. For example, the smart antenna array system or electronic device can include functional modules corresponding to the functional divisions, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or software functional modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0110] Figure 2 This is a structural diagram of a smart antenna array system provided in an embodiment of the present application. The smart antenna array system 200 includes: an environment sensing unit 201 , a parameter calculation unit 202 , and a beam control unit 203 .

[0111] Among them: the environmental perception unit 201 is used to collect multi-dimensional parameters of the communication environment in real time and generate a dynamic data set, which includes a user distribution heat map, a channel interference matrix and a multipath characteristic vector; the parameter calculation unit 202 is used to obtain a control instruction set for the phase and amplitude of each antenna unit in the antenna array based on the dynamic data set and preset beam adjustment parameters, and the preset beam adjustment parameters are predicted based on historical communication data; the beam control unit 203 is used to call the beam template library to generate a radiation pattern control signal based on the control instruction set and current scene characteristics, and the current scene characteristics include the geographical environment type and user distribution density. The radiation pattern control signal is used to indicate the adjustment of the radiation power level.

[0112] In some embodiments, the multidimensional parameters include terminal positioning data, signal status information, and electromagnetic environment data; the environment perception unit 201 is specifically used to: parse the terminal positioning data to generate a user distribution heat map, the terminal positioning data includes reference signal receiving power and arrival angle information; extract channel status information to construct a channel interference matrix; detect the time domain signal sequence in the electromagnetic environment data to generate a multipath feature vector; and construct a dynamic data set based on the user distribution heat map, the channel interference matrix, and the multipath feature vector.

[0113] In some embodiments, the parameter calculation unit 202 is specifically used to: perform time domain alignment on the dynamic data set and the preset beam adjustment parameters; generate an input set by fusing the time domain aligned dynamic data set and the preset beam adjustment parameters according to preset weight coefficients; input the input set into the collaborative optimization engine, and output a control instruction set for the phase and amplitude of each antenna unit.

[0114] In some embodiments, the parameter calculation unit 202 is specifically used to: construct a beamforming optimization function with the goals of maximizing the 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 a solution result; and use the solution result as a control instruction set for the phase and amplitude of each antenna unit.

[0115] In some embodiments, the above-mentioned 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 unit based on the correction instruction set.

[0116] In some embodiments, the intelligent antenna array system provided in the embodiments of the present application may also include: an energy consumption control unit, used to: divide the antenna array into active areas and inactive areas according to the user distribution heat map; and turn off the power supply circuit of the antenna unit in the inactive area.

[0117] In some embodiments, the energy consumption control unit is further configured to: configure the radiation power of the antenna units in the activation area according to the service priority mapping table.

[0118] In the intelligent antenna array system provided in the embodiment of the present application, a dynamic data set is constructed by real-time collection of user distribution heat maps, channel interference matrices and multipath eigenvectors, so that the system can accurately capture the spatiotemporal dynamic characteristics of the communication environment, provide instant environmental perception capabilities for beam control, and avoid control deviations caused by data lags; combine preset beam adjustment parameters to generate phase and amplitude control instruction sets, and realize the coordinated optimization of historical experience and real-time status, which not only ensures the rapid convergence of beam pointing, but also enhances the dynamic suppression capability of sudden interference; further integrates the geographical environment type and user density characteristics to call the pre-configured beam template, so that the radiation pattern control signal is deeply matched with the specific scenario, while ensuring coverage continuity and reducing invalid power radiation, thereby achieving the improvement of antenna array beamforming accuracy and system energy consumption ratio.

[0119] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0120] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 300 includes but is not limited to: a processor 301 and a memory 302 .

[0121] The memory 302 is configured to store 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 embodiment.

[0122] It should be noted that those skilled in the art can understand that Figure 3 The electronic device structure shown in the figure does not limit the electronic device, and the electronic device may include Figure 3 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0123] The processor 301 is the control center of the electronic device. It connects the various parts of the entire electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 302 and accessing data stored in the memory 302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 301 may include one or more processing units. Optionally, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly handles wireless communications. It is understood that the modem processor may not be integrated into the processor 301.

[0124] Memory 302 can be used to store software programs and various data. Memory 302 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and application programs required by at least one functional module (such as a determination unit, a processing unit, etc.). Furthermore, memory 302 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0125] In an exemplary embodiment, a computer-readable storage medium including instructions is further provided, such as a memory 302 including instructions. The above instructions can be executed by the processor 301 of the electronic device 300 to implement the smart antenna array control method in the above embodiment.

[0126] In actual implementation, Figure 2 The steps performed by the environment perception unit 201, the parameter calculation unit 202 and the beam control unit 203 in the embodiment can be represented by Figure 3 The processor 301 in the embodiment calls the computer program stored in the memory 302. The specific execution process can be referred to the description of the method part in the above embodiment, which will not be repeated here.

[0127] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0128] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, which can be executed by the processor 301 of the electronic device to implement the smart antenna array control method in the above embodiment.

[0129] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned method embodiment are implemented and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0130] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete the full classification or partial functions described above.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0133] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0134] If the integrated unit is implemented in the form of 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 embodiment of the present application, or the part that contributes to the prior art, or the entire classification part or part of the technical solution can be embodied in the form of a software product. The software product is stored in a storage medium and includes a number of instructions for enabling a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute the entire classification part or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or optical disk, etc., various media that can store program code.

[0135] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A smart antenna array control method, characterized in that: The method comprises: Collect multi-dimensional parameters of the communication environment in real time and generate a dynamic data set, the dynamic data set including a user distribution heat map, a channel interference matrix and a multipath eigenvector; Based on the dynamic data set and the preset beam adjustment parameters, a control instruction set 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, the beam template library is called to generate a radiation pattern control signal, the current scene characteristics include the geographical environment type and user distribution density, and the radiation pattern control signal is used to indicate the adjustment of the radiation power level.

2. The method according to claim 1, characterized in that The multi-dimensional parameters include terminal positioning data, signal status information, and electromagnetic environment data. Generating a dynamic data set includes: Parsing the terminal positioning data to generate the user distribution heat map, the terminal positioning data including reference signal received power and arrival angle information; Extracting the channel state information to construct the channel interference matrix; Detecting a time domain signal sequence in the electromagnetic environment data to generate the multipath feature vector; The dynamic data set is constructed according to the user distribution heat map, the channel interference matrix and the multipath eigenvector.

3. The method according to claim 1 or 2, characterized in that The obtaining, based on the dynamic data set and the preset beam adjustment parameters, a phase and amplitude control instruction set for each antenna unit in the antenna array includes: performing time domain alignment on the dynamic data set and the preset beam adjustment parameters; According to a preset weight coefficient, the dynamic data set after time domain alignment and the preset beam adjustment parameter are fused to generate an input set; The input set is input into the collaborative optimization engine, and a control instruction set for the phase and amplitude of each antenna unit is output.

4. The method according to claim 3, characterized in that Inputting the input set into the collaborative optimization engine and outputting a control instruction set for the phase and amplitude of each antenna unit includes: Constructing a beamforming optimization function in the collaborative optimization engine with the goals of maximizing signal-to-noise ratio and suppressing interference; Iterating the beamforming optimization function using the input set to obtain an iterative result; The iteration result is converted into the control instruction set.

5. The method according to claim 1, wherein The step of calling a beam template library to generate a radiation pattern control signal based on the control instruction set and current scene features includes: Inputting the geographical environment type and the user distribution density into a beam template library, and outputting a matching terrain diffraction compensation coefficient and multipath suppression parameter; fusing the control instruction set, the terrain diffraction compensation coefficient, and the multipath suppression parameter to generate a correction instruction set; The radiation pattern control signal of each antenna unit is configured based on the correction instruction set.

6. The method according to claim 1, characterized in that The method further comprises: Dividing the antenna array into an active area and an inactive area according to the user distribution heat map; The power supply circuit of the antenna unit in the inactive area is turned off.

7. The method according to claim 6, characterized in that The method further comprises: The radiation power of the antenna units in the activation area is configured according to the service priority mapping table.

8. A smart antenna array system, characterized in that: include: An environment sensing unit, configured to collect multi-dimensional parameters of the communication environment in real time and generate a dynamic data set, including a user distribution heat map, a channel interference matrix, and a multipath eigenvector; a parameter calculation unit, configured to obtain a control instruction set for the phase and amplitude of each antenna element in the antenna array based on the dynamic data set and preset beam adjustment parameters; A beam control unit is used to call a beam template library to generate a radiation pattern control signal based on the control instruction set and current scene characteristics, wherein the current scene characteristics include the geographical environment type and user distribution density, and the radiation pattern control signal is used to indicate the adjustment of the radiation power level.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing instructions, characterized in that: When a computer executes the instruction, the computer performs the method according to any one of claims 1 to 7.

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