Antenna monomer space coverage scheduling method and system based on compound eye bionic structure

Abstract

The invention provides an antenna monomer space coverage scheduling method and system based on a compound eye bionic structure, and relates to the technical field of antenna engineering, and the method comprises the steps: dividing an antenna radiation space into N space sub-sectors, and configuring N bionic sub-coverage units; carrying out distributed communication scene perception to obtain N pieces of communication scene information; performing two-dimensional quantization to obtain N space demand intensity values and N interference risk level values; performing dynamic weighting priority calculation to obtain a scheduling priority sequence; carrying out self-adaptive cooperative scheduling, and generating N antenna control instructions; and after issuing, sensing feedback based on the communication scene, and executing sub-sector-level closed-loop updating. According to the method and the device, the technical problems that in antenna scheduling in the prior art, a scheduling strategy is often carried out based on a fixed rule or a preset parameter, dynamic response to real-time communication requirements and interference conditions is lacked, resources cannot be optimally allocated in time, and the overall communication quality is influenced are solved.

Description

Technical Field

[0001] This invention relates to the field of antenna engineering technology, specifically to a method and system for spatial coverage scheduling of individual antenna units based on a compound eye bionic structure. Background Technology

[0002] With the rapid development of wireless communication technology, the bandwidth and coverage requirements of communication networks are increasing. To meet these requirements, the performance of antenna systems has become one of the key factors in network performance. Antenna systems not only need to cover a wide area, but also need to be flexible, adaptable, and efficient to cope with different environmental conditions and ever-changing communication needs.

[0003] In traditional antenna scheduling methods, scheduling strategies are often based on fixed rules or preset parameters, lacking dynamic response to real-time communication needs and interference. This static scheduling method often fails to effectively adjust various antenna parameters, resulting in resources not being optimally allocated in a timely manner in areas with large variations in user distribution density or strong interference. For example, in high-traffic areas, traditional antenna scheduling cannot promptly increase transmission power or adjust beams, making it impossible to fully meet the communication needs of these areas, thus causing insufficient network capacity and affecting overall communication quality. Summary of the Invention

[0004] This application provides a spatial coverage scheduling method and system for antenna units based on a compound eye bionic structure. It aims to solve the technical problem that in the antenna scheduling of the prior art, the scheduling strategy is often based on fixed rules or preset parameters, which lacks dynamic response to real-time communication needs and interference, resulting in the inability to optimize resource allocation in a timely manner and affecting the overall communication quality.

[0005] The first aspect disclosed in this application provides a spatial coverage scheduling method for antenna units based on a compound eye bionic structure. The method includes: dividing the antenna radiation space into N spatial sub-sectors; dynamically grouping radiating elements in the compound eye bionic device to configure N bionic sub-coverage units for the N spatial sub-sectors; wherein the N bionic sub-coverage units are connected to a communication neural network via a bus-type control interface; performing distributed communication scene perception on the N bionic sub-coverage units through the communication neural network to obtain N communication scene information; performing dual-dimensional quantization based on the N communication scene information to obtain N spatial demand intensity values ​​and N interference risk level values; performing dynamic weighted priority calculation based on the N spatial demand intensity values ​​and N interference risk level values ​​to obtain a scheduling priority sequence; performing adaptive collaborative scheduling of the N bionic sub-coverage units based on the scheduling priority sequence to generate N antenna control commands; after the communication neural network sends the N antenna control commands to the compound eye bionic device, performing sub-sector-level closed-loop updates on the antenna radiation space based on the communication scene perception feedback uploaded by the N bionic sub-coverage units.

[0006] The second aspect of this application discloses an antenna unit spatial coverage scheduling system based on a compound eye bionic structure. The system is used in the aforementioned antenna unit spatial coverage scheduling method based on a compound eye bionic structure. The system includes: a sub-coverage unit configuration module, used to divide the antenna radiation space into N spatial sub-sectors and then dynamically group the radiating elements in the compound eye bionic device to configure N bionic sub-coverage units for the N spatial sub-sectors, wherein the N bionic sub-coverage units are connected to a communication neural network via a bus-type control interface; a communication scene perception module, used to perform distributed communication scene perception of the N bionic sub-coverage units through the communication neural network to obtain N communication scene information; and a two-dimensional quantization module. The system includes a first, a second, and a third, a third, a fourth, and a fifth, a fifth, a sixth, and a sixth, a fifth, a sixth, and a seventh, a fifth, a sixth, and a sixth, a seventh, a fifth, and a sixth, a seventh, a sixth, and a seventh, a fifth, a sixth, and a sixth, a seventh, a sixth, and a seventh, a sixth, a seventh, and a sixth, a seventh, a seventh, and a sixth, a seventh, a seventh, and a sixth, a seventh, a seventh, and a sixth, a seventh, a seventh, and a sixth, a seventh, a seventh, and a sixth, a seventh, a seventh, and a sixth, a seventh, a ninth ...

[0007] One or more technical solutions provided in this application have at least the following beneficial effects: By dividing the antenna radiation space into N spatial sub-sectors and configuring corresponding bionic sub-coverage units for each sub-sector, the radiation pattern of each area can be precisely controlled to achieve directional coverage, avoiding the global configuration limitations of traditional antennas and improving the utilization efficiency of space resources. Through distributed communication scene perception, each bionic sub-coverage unit can perceive the environment based on its location and actual communication needs, acquiring N communication scene information. This distributed perception mechanism can capture the communication status, user needs, and interference information of each area in real time and accurately, ensuring that the antenna adapts to different communication needs in a dynamically changing network environment. By quantifying the communication scene information in two dimensions, N spatial demand intensity values ​​and N interference risk level values ​​are obtained, enabling precise assessment of the communication needs and interference risks of each area. This two-dimensional assessment method enhances the comprehensive understanding of multi-dimensional resources and risks, laying the foundation for subsequent... The scheduling decision provides a scientific basis; by dynamically weighting N spatial demand intensity values ​​and N interference risk level values, a scheduling priority sequence is obtained. This priority calculation mechanism can dynamically adjust resource allocation based on real-time data, avoiding the inefficiency of traditional static scheduling methods and improving the utilization rate of communication resources and the system response speed. Based on the scheduling priority sequence, adaptive collaborative scheduling is performed, allowing antennas to be adjusted according to the specific needs of each sub-region, avoiding the over-concentration or resource waste of traditional antennas, and improving the system's flexibility and intelligence. After the communication neural network issues control commands, based on the communication scene perception feedback uploaded by the bionic sub-coverage unit, the antenna radiation space is updated in a sub-sector-level closed loop. This closed-loop feedback mechanism enables the system to continuously optimize its configuration in a dynamically changing environment, ensuring that coverage and resource allocation are always kept in the optimal state.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic flowchart of a spatial coverage scheduling method for an antenna unit based on a compound eye bionic structure, provided in an embodiment of this application.

[0010] Figure 2 A schematic diagram of the spatial coverage scheduling system for an antenna unit based on a compound eye bionic structure provided in this application embodiment.

[0011] Explanation of reference numerals in the attached diagram: Sub-coverage unit configuration module 10, communication scene perception module 20, dual-dimensional quantization module 30, weighted priority calculation module 40, adaptive collaborative scheduling module 50, and closed-loop update module 60. Detailed Implementation

[0012] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structure, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0013] Example 1, as Figure 1 As shown in the embodiments of this application, a spatial coverage scheduling method for antenna units based on a compound eye bionic structure is provided, the method comprising: A100: After dividing the antenna radiation space into N spatial sub-sectors, the radiation elements in the compound eye bionic device are dynamically grouped to configure N bionic sub-coverage units for the N spatial sub-sectors. The N bionic sub-coverage units are connected to the communication neural network through a bus-type control interface.

[0014] The antenna radiation space is divided into N spatial sub-sectors, each representing an independent region to optimize spatial coverage and signal processing. This division is based on the uniformity of antenna radiation power density, with each region's radiation power within a specified range. For each spatial sub-sector, a corresponding bionic sub-coverage unit is configured. This technology mimics the structure of a biological compound eye, and each sub-coverage unit possesses independent beam control capabilities. Its task is to adjust the beam direction and intensity according to its spatial location and communication requirements. In the compound eye bionic device, radiating elements are dynamically grouped; each radiating element is the basic radiating unit of the antenna, and each group corresponds to a bionic sub-coverage unit. This grouping method is adjusted according to the actual needs and scenarios of each spatial sub-sector, ensuring that each radiating element can effectively control the beam for different sub-sectors. All N bionic sub-coverage units are connected to a communication neural network via a bus-type control interface. This network coordinates communication and information exchange between the sub-coverage units, ensuring that each sub-unit can adjust its radiation parameters according to network commands and feedback information.

[0015] A200: The distributed communication scene perception of the N bionic sub-covering units is performed through the communication neural network to obtain N communication scene information.

[0016] Through a communication neural network, each bionic sub-coverage unit performs its communication scene perception task. Each sub-coverage unit scans the communication environment within its corresponding spatial sub-sector. Each bionic sub-coverage unit performs radio frequency signal scanning based on specific perception parameters, such as scanning frequency band range, sampling time window, and signal detection threshold. During the scanning process, it captures current communication scene information, including signal strength, interference information, user distribution, and channel status.

[0017] A300: Based on the N communication scenario information, perform dual-dimensional quantization to obtain N spatial demand intensity values ​​and N interference risk level values.

[0018] A two-dimensional quantification approach is employed. The spatial demand intensity value measures the communication demand of each spatial sub-sector, reflecting user density, communication load, and data transmission needs. The interference risk level value measures the interference risk faced by each sub-sector, originating from other users, neighboring sectors, or the external environment; the interference risk level reflects the magnitude of this risk. The quantification process is as follows: Data such as user distribution density and service demand level are extracted from communication scenario information and weighted to obtain the spatial demand intensity value for each sub-sector, reflecting the degree of communication demand within that area. Data such as interference signal strength and channel delay spread are also extracted from communication scenario information. This data is used to assess the interference intensity within the area and its impact on communication. A joint mapping evaluation method is used to combine the interference signal strength and delay spread data to obtain the interference risk level value for each sub-sector, reflecting the degree of interference risk within that area.

[0019] A400: Dynamically weighted priority calculation is performed based on the N spatial demand intensity values ​​and N interference risk level values ​​to obtain the scheduling priority sequence.

[0020] Based on the spatial demand intensity value and interference risk level value of each sub-sector, the priority of each sub-sector is calculated through weighted decision-making. The weights of the spatial demand intensity value and interference risk level value are adjusted according to preset decision rules or strategies, giving priority to areas with high demand and low interference. According to the calculated priority scores, all N spatial sub-sectors are sorted to obtain a scheduling priority sequence, where sub-sectors with higher priority scores are scheduled first to ensure that these areas receive sufficient resource support.

[0021] A500: Based on the scheduling priority sequence, perform adaptive cooperative scheduling of the N bionic sub-coverage units to generate N antenna control commands.

[0022] Based on the scheduling priority sequence, adaptive cooperative scheduling of N bionic sub-coverage units is performed. The goal is to optimize antenna resource allocation and improve the communication quality of the entire system. Specifically, the scheduling order of each bionic sub-coverage unit is determined according to the priority of each sub-sector. Areas with higher priority will receive more resources or better beam pointing, while areas with lower priority will receive resource allocation in subsequent scheduling cycles. For each bionic sub-coverage unit, N antenna control commands are generated based on its scheduling priority and resource allocation. These commands cover parameters such as beam pointing, width, and power to ensure that the antenna can operate in the optimal configuration.

[0023] A600: After the communication neural network sends the N antenna control commands to the compound eye bionic device, it performs sub-sector-level closed-loop update of the antenna radiation space based on the communication scene perception feedback uploaded by the N bionic sub-coverage units.

[0024] The communication neural network sends N antenna control commands to the compound eye bionic device. Upon receiving the control commands, each bionic sub-coverage unit of the compound eye bionic device adjusts its beam parameters, such as beam pointing, width, and transmit power. After the compound eye bionic device completes its adjustments according to the commands, each bionic sub-coverage unit re-perceives the current communication scenario. Based on the updated perception feedback information, it performs a closed-loop update. This process ensures that the system can adjust and optimize its scheme according to the actual communication environment. If there are communication quality problems, interference, or insufficient coverage, dynamic adjustments are made to optimize antenna parameters or scheduling strategies.

[0025] Furthermore, after dividing the antenna radiation space into N spatial sub-sectors, N bionic sub-coverage units are configured for the N spatial sub-sectors through dynamic grouping of radiating elements in the compound eye bionic device. The method includes: A110: Establish a spherical coordinate system with the phase center of the antenna unit as the origin, and then spatially discretize the antenna radiation space based on the uniformity of radiated power density to obtain the N spatial sub-sectors; A120: Extract the N geometric center direction vectors of the N spatial sub-sectors; A130: Extract multiple radiation pattern functions of multiple radiating elements in the compound eye bionic device, and then iterate through and calculate the N sets of direction matching degrees between the multiple radiation pattern functions and the N center direction vectors; A140: Using the minimum radiating element group size as the grouping constraint, perform grouping optimization solution of the multiple radiating elements based on the N sets of direction matching degrees to obtain N sets of radiating elements; A150: Connect the N sets of radiating elements to N bus-type control interfaces to obtain the N bionic sub-covering units.

[0026] A spherical coordinate system is established in three-dimensional space with the phase center of the antenna unit as the origin. This system includes: radius *r*, the distance from the phase center to a point; polar angle *θ*, the angle downwards from the z-axis; and azimuth angle *φ*, the rotation angle around the z-axis. To optimize the antenna's spatial coverage, the radiated power density in each direction needs to be uniformly distributed. Therefore, during spatial discretization, the uniformity of the radiated power density is ensured, meaning the antenna's radiation intensity in different directions should remain as consistent as possible. By uniformly distributing the antenna's radiation space, N spatial sub-sectors are created. Each sub-sector corresponds to a relatively independent region with specific directionality and radiation characteristics. This partitioning method ensures that each sub-sector achieves relatively balanced coverage and resource allocation in actual communication.

[0027] Each spatial sub-sector has a geometric center, which is the directional representation of the geometric center point of the region. The geometric center direction vector is a vector pointing from the antenna phase center (the origin of the spherical coordinate system) to the geometric center of each sub-sector. This vector defines the direction of the sub-sector and is used to guide subsequent radiator allocation and beam adjustment.

[0028] A radiating element is the basic building block of an antenna. Each radiating element has a specific radiation pattern, which is a function of its radiation intensity as a function of direction. The radiation pattern of each radiating element represents its radiation capability in different directions. To ensure that each radiating element can effectively cover the corresponding spatial sub-sector, the matching degree between the radiation pattern function of the radiating element and the geometric center direction vector of each sub-sector is calculated. The matching degree is evaluated by calculating the angular difference or correlation between the radiation pattern of the radiating element and the target direction vector. By iterating through and calculating the matching degree between the radiation pattern function of each radiating element and N geometric center direction vectors, N sets of directional matching degrees are obtained.

[0029] During the grouping process, a constraint on the minimum size of radiating elements is followed. This constraint is set based on hardware specifications or other system requirements to ensure that the number of radiating elements in each group meets coverage needs and avoids over-grouping. Based on the N groups of direction matching degrees, an optimization algorithm is used to group multiple radiating elements, aiming to match the radiation pattern of each group of radiating elements with the direction vector of the geometric center of the corresponding spatial sub-sector as closely as possible, thereby improving the system's radiation efficiency. Through optimization, N groups of radiating elements are finally obtained, each group matching a specific spatial sub-sector, ensuring efficient utilization of the radiation space and balanced coverage.

[0030] Each radiating element is connected to a bus-type control interface, enabling it to effectively connect to the communication neural network. The bus-type control interface serves as a communication channel for transmitting instructions and information among multiple bionic sub-covering units. It is responsible for sending antenna control commands to the radiating elements and reporting the communication scene perception feedback from the radiating elements to the neural network. Ultimately, N bionic sub-covering units are obtained, each with independent control capabilities, capable of flexibly adjusting parameters such as beam and power to ensure that communication requirements within its covered area are met.

[0031] Furthermore, using the minimum radiative element size as the grouping constraint, the grouping optimization solution of the multiple radiative elements is performed based on the N groups of direction matching degrees to obtain N groups of radiative elements. The method includes: A141: After extracting the maximum effective radiation power of a single radiating element from the hardware specifications, calculate the minimum radiating element size based on the radiation coverage distance requirement; A142: Using the minimum radiating element size as a rigid grouping constraint, perform initial grouping of radiating elements based on the direction matching degree of N groups. After obtaining the N initial radiating element affiliations, perform iterative weak coverage compensation by exchanging radiating elements of adjacent units until the direction matching degree of the N spatial sub-sectors reaches the preset threshold, and output the N groups of radiating elements.

[0032] The radiation performance of each individual radiator is limited by its maximum effective radiated power, a parameter extracted from the hardware specifications that represents the maximum radiated power achievable by the radiator. The minimum radiator array size is calculated based on the radiation coverage distance requirements of each spatial sub-sector, taking into account the signal coverage requirements of each spatial region and the power and effective range of the radiators. The radiation coverage distance requirements are determined based on the size of each spatial sub-sector, signal attenuation characteristics, and quality of service requirements; for example, more distant areas require more radiators or higher power output. The minimum radiator array size ensures that each array of radiators meets coverage requirements while avoiding over-allocation.

[0033] The initial grouping of radiating elements is based on N sets of directional matching degrees. The goal of this initial grouping is to divide the radiating elements into appropriate groups according to their directional matching degrees, ensuring that each group of radiating elements covers its corresponding spatial sub-sector as much as possible with a high matching degree. After the initial grouping, some areas may experience insufficient coverage or uneven distribution of radiating elements. To compensate for this weak coverage, radiating element exchange between adjacent units is used. Radiating element exchange refers to adjusting the radiating element configuration between adjacent spatial units, allowing areas with insufficient coverage to be supplemented by radiating elements from neighboring areas. This exchange is iterative, meaning it is adjusted multiple times until the coverage requirements are met and the directional matching degree of each spatial sub-sector reaches a preset threshold. After radiating element exchange and compensation, N sets of radiating elements are finally obtained, which can meet the communication needs of each spatial sub-sector and guarantee optimal directional matching degree.

[0034] Furthermore, the method involves using the communication neural network to perform distributed communication scene perception of the N bionic sub-covering units, thereby obtaining N communication scene information. A210: The communication neural network sends sensing parameters to the N bionic sub-covering units through the N bus-type control interfaces, wherein the sensing parameters include the scanning frequency band range, sampling time window, and signal detection threshold; A220: The N bionic sub-covering units perform radio frequency signal scanning in the corresponding N spatial sub-sectors according to the sensing parameters to capture the N communication scene information, and then encapsulate and feed back the structured data to the communication neural network.

[0035] Perception parameters refer to the configuration parameters required by the communication neural network to control the bionic sub-covering unit to perceive the communication scene. These parameters define how the bionic sub-covering unit performs radio frequency signal scanning and the perception process of the communication environment. Specifically, the scanning frequency band range determines the frequency range selected by the bionic sub-covering unit during radio frequency signal scanning. Signals in different frequency bands have different propagation characteristics, and the scanning frequency band range can be flexibly set according to communication requirements, environmental conditions, and spectrum availability. The sampling time window refers to the time period during which the bionic sub-covering unit collects data during signal scanning. The length of the sampling time directly affects the evaluation of signal quality; a longer time window can capture more comprehensive signal characteristics but may also introduce more noise or errors. The signal detection threshold defines the threshold at which the bionic sub-covering unit determines whether a signal is valid during signal scanning. The signal detection threshold is adjusted based on signal strength and noise level. A threshold that is too high may cause the loss of some weak signals, while a threshold that is too low may introduce excessive noise and invalid signals.

[0036] Based on the received sensing parameters, each bionic sub-coverage unit performs radio frequency signal scanning in its corresponding spatial sub-sector. During the scanning process, the bionic sub-coverage unit scans the radio frequency signal according to a given scanning frequency band range, covering multiple frequency bands to capture the required communication information. It also judges the validity of the signal based on signal strength and noise levels, and filters out unwanted interference signals or noise according to a set signal detection threshold, ultimately capturing N communication scene information. The N communication scene information are then encapsulated into structured data. Structured data encapsulation means organizing the captured information according to a preset format, facilitating subsequent data transmission and processing. The structured communication scene information is fed back to the communication neural network through a bus-type control interface. The neural network receives and processes this information, performing analysis, decision-making, and adjustments, thereby achieving dynamic optimization of the entire system.

[0037] Furthermore, based on the N communication scenario information, a two-dimensional quantization is performed to obtain N spatial demand intensity values ​​and N interference risk level values. The method includes: A310: Extract user distribution density data, service demand level data, interference signal strength data, and channel delay spread data from the first communication scenario information; A320: Perform coupled weighted calculation on the user distribution density data and service demand level data to obtain a first spatial demand strength value, wherein the spatial demand strength value carries a first service demand weight identifier; A330: Perform joint mapping evaluation on the interference signal strength data and channel delay spread data to obtain a first interference risk level value, wherein the first interference risk level value carries a first delay risk factor identifier.

[0038] The first communication scenario information is any one of N communication scenario information and is used as the current analysis object. Relevant data is extracted from the first communication scenario information. Among them, the user distribution density data represents the distribution of users in the first spatial sub-sector, and this data is expressed as the number of users per unit area or user density; the service demand level data represents the degree of service demand of users in the area, which is affected by multiple factors, such as users' communication requirements, service types, and time period requirements. A higher service demand level means that the area has a greater demand for network resources; the interference signal strength data represents the strength of interference signals from other sources in the area. These signals come from other wireless devices, neighboring sub-sectors, or external interference sources. The higher the interference signal strength, the worse the communication quality in the area; the channel delay spread data reflects the delay spread during signal transmission. Delay spread refers to the extension of signal arrival time caused by factors such as multipath effects, reflection, or refraction during signal transmission. A higher delay spread may lead to increased communication delay and affect system performance.

[0039] Coupled weighted calculation means that the two sets of data are combined and weighted according to different weights. The weight coefficients are flexibly adjusted according to actual needs and system design. The calculated first spatial demand intensity value reflects the total communication demand of the first spatial sub-sector. The first spatial demand intensity value carries the first service demand weight identifier, which indicates the weight of the value in the spatial demand and reflects the degree of influence of the service demand in the whole calculation.

[0040] Interference signal strength data and channel delay spread data affect the communication quality and stability of the affected area. Combining these two data points for joint mapping assessment yields the first interference risk level value for the first spatial sub-sector. A higher interference risk level value indicates that the area faces significant communication interference and delay issues. The calculated first interference risk level value is labeled with a first delay risk factor, indicating the contribution of channel delay spread to the interference risk assessment and reflecting the impact of delay on the overall risk level of the communication environment.

[0041] Furthermore, a first spatial demand intensity value is obtained by coupling and weighting the user distribution density data and business demand level data. The method includes: A321: Based on the business demand level data, the user distribution density data is weighted and corrected to obtain a weighted user density. Then, the first spatial demand intensity value is calculated based on the weighted user density. A322: The priority distribution characteristics of the business demand level data are extracted as the first business demand weight to identify the first spatial demand intensity value.

[0042] User distribution density data reflects the distribution of users within a specific spatial sub-sector. To more accurately reflect the actual communication needs of a region, it is necessary to combine service demand level data to weight and correct the user distribution density data. The service demand level serves as the weight to adjust the user density, and the resulting weighted user density reflects the actual communication load of each region under different service demands. Based on the weighted user density, the first spatial demand intensity value is further calculated using the following formula: First spatial demand intensity value = a × weighted user density + b × service demand level, where a and b are weighting coefficients used to adjust the relative contributions of weighted user density and service demand level to the spatial demand intensity value.

[0043] The service demand level data not only reflects the intensity of resource demand from users within a region but also includes the priority distribution characteristics of these demands. The priority distribution characteristics reflect whether users' demand for network resources within a region is concentrated, and can be represented as a hierarchical structure of service demands. For example, some regions may have demand primarily focused on low-bandwidth or low-latency services, while other regions may have higher bandwidth demands. The extracted priority distribution characteristics are used as the first service demand weight. This identifier indicates the degree of influence of service demands on the spatial demand intensity value. Specifically, high-priority service demands will be assigned a higher weight because these regions have a more urgent need for network resources; low-priority service demands will have their weight reduced in the total spatial demand.

[0044] Furthermore, a joint mapping assessment is performed on the interference signal strength data and channel delay spread data to obtain a first interference risk level value. The method includes: A331: Convert the interference signal strength data into the first interference risk level value through a piecewise linear mapping function; A332: Calculate the delay confidence factor using the channel delay spread data, correct the first interference risk level value with confidence, generate the first delay risk factor based on the confidence correction result, and identify the data of the first interference risk level value.

[0045] Interference signal strength data refers to the intensity of interference signals from other devices or sources within a spatial sub-sector. These interference signals affect communication quality and therefore need to be quantified for interference risk assessment. A piecewise linear mapping function is a tool used to convert continuous data into graded values. It divides the input data (interference signal strength) into multiple intervals according to different intensity ranges. The interference signal strength within each interval is mapped to an interference risk level value through a linear function. For example, low interference signal strength is mapped to a low interference risk level; medium interference signal strength to a medium interference risk level; and high interference signal strength to a high interference risk level. This mapping method reflects the impact of interference signal strength on communication quality; the greater the interference signal strength, the greater the impact on communication quality, and therefore the higher the corresponding first interference risk level value.

[0046] The delay confidence factor is used to measure the impact of channel delay spread on communication performance. The delay confidence factor is defined as follows: ,in, For channel delay spread, This is the latency threshold, i.e., the pre-set maximum acceptable latency spread. is the delay confidence factor, which represents the degree to which delay affects signal quality.

[0047] The delay confidence factor, reflecting the impact of delay spread on communication, corrects the confidence level of the previously calculated first interference risk level. Specifically, a higher delay indicates greater interference, requiring an increase in the interference risk level for that area to more accurately reflect the actual risk. The first delay risk factor is the corrected interference risk level value, reflecting the degree of impact of delay spread on interference risk. This factor provides an important basis for subsequent system scheduling and optimization. In the final data identifier, the first interference risk level value carries the first delay risk factor identifier, indicating that the value has been corrected for interference risk based on delay spread.

[0048] Furthermore, a scheduling priority sequence is obtained by dynamically weighting the N spatial demand intensity values ​​and N interference risk level values, wherein the method includes: A410: Using the first business demand weight and the first delay risk factor as two-dimensional retrieval keys, match the first weight coefficient combination in the dynamic weight decision table, and dynamically weight the first spatial demand intensity value and the first interference risk level value to obtain the first priority score; A420: Similarly, calculate the N priority scores of the N spatial demand intensity values ​​and the N interference risk level values; A430: Arrange the N spatial sub-sectors in descending order according to the N priority scores, and output the scheduling priority sequence.

[0049] The first service demand weight identifier indicates the weight of the impact of service demand on the spatial demand intensity value, reflecting the service priority within the area. The first delay risk factor reflects the impact of channel delay spread on the interference risk level value, indicating the degree of delay's impact on interference. These two values ​​are used as two-dimensional retrieval keys to find the corresponding weight coefficient combination from the dynamic weight decision table. The dynamic weight decision table contains weight coefficient combinations for different service demands and delay risks, used to weight the spatial demand intensity value and interference risk level value under specific conditions. The corresponding first weight coefficient combination is found through the two-dimensional retrieval key, used for dynamic weighting calculation to ensure the reasonable contribution of different service demands and delay risks in the overall priority calculation. The matched first weight coefficient combination is used to dynamically weight the first spatial demand intensity value and the first interference risk level value. In this way, the spatial demand intensity value and the interference risk level value are weighted according to their service demands and delay risks, thus obtaining a total first priority score.

[0050] Similarly, the same calculation is performed on all N spatial sub-sectors to obtain the priority score for each sub-sector.

[0051] All N priority scores are sorted in descending order. Regions with higher priority scores have higher priority in resource allocation and scheduling. Based on the descending order, a scheduling priority sequence is generated, which is the order from the highest priority to the lowest priority. This sequence reflects the resource scheduling order of each spatial sub-sector. Regions with higher priority will receive resource allocation first.

[0052] Furthermore, based on the scheduling priority sequence, adaptive cooperative scheduling of the N bionic sub-coverage units is performed to generate N antenna control commands. The method includes: A510: Calculate N initial beam pointing angles and N initial beamwidths based on the N spatial demand intensity values; A520: Allocate N initial transmit powers based on the N interference risk level values; A530: Based on the geometric position distribution relationship of the N spatial sub-sectors, perform three-dimensional parameter conflict detection on the N initial beam pointing angles, N initial beamwidths, and N initial transmit powers, and then perform parameter coordinating re-optimization of the conflicting sectors according to the scheduling priority sequence to obtain the N antenna control commands, wherein the antenna control commands include optimized beam pointing angle parameters, optimized beamwidth parameters, and optimized transmit power parameters.

[0053] The initial beam pointing angle refers to the direction of the antenna beam in space, which determines the signal coverage area. Based on the requirements of each spatial sub-sector, the beam pointing angle should ensure signal coverage to the center of that area. For high-demand areas, a more precise beam pointing is needed to ensure resources are concentrated in that area; for low-demand areas, a wider beam pointing is used to avoid over-concentration of resources. The calculation method involves converting the geometric center direction vector of each spatial sub-sector into the corresponding beam pointing angle, ensuring coverage of the area with the highest demand. The initial beamwidth determines the beam's coverage range; a wider beamwidth covers a wider area, but signal strength may decrease. The initial beamwidth is calculated based on the spatial demand strength value and the actual coverage requirements of the spatial sub-sectors. For high-demand areas, the beam can be appropriately narrowed to concentrate resources; while for low-demand areas, the beamwidth will be moderately widened to reduce interference to surrounding areas.

[0054] The initial transmit power is allocated based on the interference risk level of each spatial sub-sector. Areas with higher interference risk require higher transmit power to ensure effective signal propagation and overcome interference; areas with lower interference risk can have their transmit power appropriately reduced to avoid unnecessary interference to other areas and conserve resources. The initial transmit power is allocated based on the interference risk level using a preset adjustment coefficient.

[0055] After assigning initial beam pointing angle, initial beamwidth, and initial transmit power to each spatial sub-sector, collision detection of three-dimensional parameters is performed. Collisions occur in the following situations: beam pointing angle collision, when the beam pointing angles of two or more spatial sub-sectors are too close, they will interfere with each other; beamwidth collision, when the beamwidth is too large, it will cause unnecessary interference, especially when the beams of multiple spatial sub-sectors overlap; transmit power collision, excessively high transmit power will cause interference in other areas, especially when there is high demand in neighboring areas.

[0056] The parameters of conflicting sectors are re-optimized collaboratively. Specifically, for high-priority spatial sub-sectors, their beam pointing angle, beamwidth, and transmit power remain unchanged; for low-priority spatial sub-sectors, interference with other areas is reduced by adjusting the beam pointing angle or narrowing the beamwidth. Narrowing the beamwidth can effectively reduce unnecessary signal coverage areas, thereby reducing interference; for spatial sub-sectors with high interference risk, transmit power should be prioritized and the transmit power of these areas should be appropriately increased to ensure effective signal transmission and reduce the impact of interference on communication quality.

[0057] After collision detection and collaborative re-optimization, the parameters of each spatial sub-sector are finally optimized, generating N antenna control commands. Each command contains optimized beam pointing angle, optimized beamwidth, and optimized transmit power. These control commands will be sent to the corresponding bionic sub-coverage units to ensure that the network's resource allocation and interference control are optimal.

[0058] Example 2, based on the same inventive concept as the antenna unit spatial coverage scheduling method based on the compound eye bionic structure in the previous examples, such as... Figure 2 As shown, this application provides an antenna unit spatial coverage scheduling system based on a compound eye bionic structure, the system comprising: The sub-coverage unit configuration module 10 is used to divide the antenna radiation space into N spatial sub-sectors and then configure N bionic sub-coverage units for the N spatial sub-sectors through dynamic grouping of radiating elements in the compound eye bionic device. The N bionic sub-coverage units are connected to a communication neural network via a bus-type control interface. The communication scene perception module 20 is used to perform distributed communication scene perception of the N bionic sub-coverage units through the communication neural network to obtain N communication scene information. The dual-dimensional quantization module 30 is used to perform dual-dimensional quantization based on the N communication scene information to obtain N spatial demand intensity values ​​and N... Interference risk level value; weighted priority calculation module 40, used to perform dynamic weighted priority calculation based on the N spatial demand intensity values ​​and N interference risk level values ​​to obtain a scheduling priority sequence; adaptive cooperative scheduling module 50, used to perform adaptive cooperative scheduling of the N bionic sub-coverage units based on the scheduling priority sequence to generate N antenna control commands; closed-loop update module 60, used to perform sub-sector-level closed-loop update of the antenna radiation space based on the communication scene perception feedback uploaded by the N bionic sub-coverage units after the communication neural network sends the N antenna control commands to the compound eye bionic device.

[0059] Furthermore, the sub-coverage unit configuration module 10 is used to perform the following operation steps: After establishing a spherical coordinate system with the phase center of the antenna unit as the origin, the antenna radiation space is spatially discretized based on the uniformity of radiated power density to obtain the N spatial sub-sectors; N geometric center direction vectors of the N spatial sub-sectors are extracted; after extracting multiple radiation pattern functions of multiple radiating elements in the compound eye bionic device, the N sets of direction matching degrees of the multiple radiation pattern functions and the N center direction vectors are traversed and calculated; with the minimum size of the radiating element group as the grouping constraint, the grouping optimization solution of the multiple radiating elements is performed based on the N sets of direction matching degrees to obtain N sets of radiating elements; N bus-type control interfaces are connected to the N sets of radiating elements to obtain the N bionic sub-coverage units.

[0060] Furthermore, the sub-coverage unit configuration module 10 is used to perform the following operation steps: After extracting the maximum effective radiated power of a single radiating element from the hardware specifications, the minimum radiating element size is calculated in combination with the radiation coverage distance requirement. Using the minimum radiating element size as a rigid grouping constraint, the radiating elements are initially grouped based on the direction matching degree of N groups. After obtaining the N initial radiating element affiliations, iterative weak coverage compensation is performed by exchanging radiating elements of adjacent units until the direction matching degree of the N spatial sub-sectors reaches a preset threshold, and the N groups of radiating elements are output.

[0061] Furthermore, the communication scene perception module 20 is used to perform the following operation steps: The communication neural network sends sensing parameters to the N bionic sub-covering units through the N bus-type control interfaces. The sensing parameters include the scanning frequency band range, sampling time window, and signal detection threshold. The N bionic sub-covering units scan radio frequency signals in their corresponding N spatial sub-sectors according to the sensing parameters to capture the N communication scene information, and then encapsulate and feed the structured data back to the communication neural network.

[0062] Furthermore, the two-dimensional quantization module 30 is used to perform the following operation steps: User distribution density data, service demand level data, interference signal strength data, and channel delay spread data are extracted from the first communication scenario information. The user distribution density data and service demand level data are coupled and weighted to obtain a first spatial demand strength value, wherein the spatial demand strength value carries a first service demand weight identifier. The interference signal strength data and channel delay spread data are jointly mapped and evaluated to obtain a first interference risk level value, wherein the first interference risk level value carries a first delay risk factor identifier.

[0063] Furthermore, the two-dimensional quantization module 30 is used to perform the following operation steps: The user distribution density data is weighted and corrected based on the business demand level data to obtain a weighted user density. The first spatial demand intensity value is then calculated based on the weighted user density. The priority distribution features of the business demand level data are extracted as the first business demand weight to identify the first spatial demand intensity value.

[0064] Furthermore, the two-dimensional quantization module 30 is used to perform the following operation steps: The interference signal strength data is converted into the first interference risk level value through a piecewise linear mapping function; the delay confidence factor is calculated using the channel delay spread data, and the first interference risk level value is corrected for confidence level. Based on the confidence correction result, the first delay risk factor is generated, and the data of the first interference risk level value is identified.

[0065] Furthermore, the weighted priority calculation module 40 is used to perform the following operation steps: Using the first business demand weight and the first latency risk factor as two-dimensional retrieval keys, the first weight coefficient combination is matched in the dynamic weight decision table, and the first spatial demand intensity value and the first interference risk level value are dynamically weighted to obtain the first priority score; and so on, the N priority scores of the N spatial demand intensity values ​​and the N interference risk level values ​​are calculated; the N spatial sub-sectors are arranged in descending order according to the N priority scores, and the scheduling priority sequence is output.

[0066] Furthermore, the adaptive cooperative scheduling module 50 is used to perform the following operation steps: Calculate N initial beam pointing angles and N initial beamwidths based on the N spatial demand intensity values; allocate N initial transmit powers based on the N interference risk level values; perform three-dimensional parameter conflict detection on the N initial beam pointing angles, N initial beamwidths, and N initial transmit powers based on the geometric position distribution relationship of the N spatial sub-sectors; and then perform parameter coordinating re-optimization of the conflicting sectors according to the scheduling priority sequence to obtain the N antenna control commands, wherein the antenna control commands include optimized beam pointing angle parameters, optimized beamwidth parameters, and optimized transmit power parameters.

[0067] Through the foregoing detailed description of the antenna unit spatial coverage scheduling method based on the compound eye bionic structure, those skilled in the art can clearly understand the antenna unit spatial coverage scheduling system based on the compound eye bionic structure in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A spatial coverage scheduling method for antenna units based on a compound eye bionic structure, characterized in that, The method includes: After dividing the antenna radiation space into N spatial sub-sectors, the radiation elements in the compound eye bionic device are dynamically grouped to configure N bionic sub-coverage units for the N spatial sub-sectors. The N bionic sub-coverage units are connected to the communication neural network through a bus-type control interface. The distributed communication scene perception of the N bionic sub-covering units is performed through the communication neural network to obtain N communication scene information; Based on the N communication scenario information, a two-dimensional quantification is performed to obtain N spatial demand intensity values ​​and N interference risk level values; A scheduling priority sequence is obtained by dynamically weighting the N spatial demand intensity values ​​and N interference risk level values. Based on the scheduling priority sequence, adaptive cooperative scheduling of the N bionic sub-coverage units is performed to generate N antenna control commands; After the communication neural network sends the N antenna control commands to the compound eye bionic device, it performs sub-sector-level closed-loop updates on the antenna radiation space based on the communication scene perception feedback uploaded by the N bionic sub-coverage units.

2. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 1, characterized in that, After dividing the antenna radiation space into N spatial sub-sectors, N bionic sub-coverage units are configured for the N spatial sub-sectors through dynamic grouping of radiating elements in the compound eye bionic device. The method includes: After establishing a spherical coordinate system with the phase center of the antenna unit as the origin, the antenna radiation space is spatially discretized based on the uniformity of the radiated power density to obtain the N spatial sub-sectors. Extract the N geometric center direction vectors of the N spatial sub-sectors; After extracting multiple radiation pattern functions from multiple radiating elements in the compound eye bionic device, the N sets of direction matching degrees between the multiple radiation pattern functions and the N central direction vectors are calculated traversally. Using the minimum size of the radiative element as the grouping constraint, the grouping optimization of the multiple radiative elements is performed based on the N groups of direction matching degrees to obtain N groups of radiative elements; Connect the N groups of radiating elements to N bus-type control interfaces to obtain the N bionic sub-covering units.

3. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 2, characterized in that, Using the minimum radiative element size as the grouping constraint, and based on the N groups of direction matching degrees, the method optimizes the grouping of the multiple radiative elements to obtain N groups of radiative elements. After extracting the maximum effective radiant power of a single radiant element from the hardware specifications, the minimum radiant element size is calculated in combination with the radiation coverage distance requirement. Using the minimum radiative element size as a rigid grouping constraint, radiative elements are initially grouped based on the direction matching degree of N groups. After obtaining the N initial radiative element affiliations, iterative weak coverage compensation is performed by exchanging radiative elements of adjacent units until the direction matching degree of the N spatial sub-sectors reaches a preset threshold, and the N groups of radiative elements are output.

4. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 2, characterized in that, The method involves using the communication neural network to perform distributed communication scene perception of the N bionic sub-covering units, thereby obtaining N communication scene information. The communication neural network sends sensing parameters to the N bionic sub-covering units through the N bus-type control interfaces. The sensing parameters include the scanning frequency band range, sampling time window, and signal detection threshold. The N bionic sub-covering units scan radio frequency signals in their corresponding N spatial sub-sectors according to the sensing parameters to capture the N communication scene information, and then encapsulate and feed back structured data to the communication neural network.

5. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 4, characterized in that, Based on the N communication scenario information, a two-dimensional quantization is performed to obtain N spatial demand intensity values ​​and N interference risk level values. The method includes: Extract user distribution density data, service demand level data, interference signal strength data, and channel delay spread data from the first communication scenario information; The user distribution density data and business demand level data are coupled and weighted to obtain a first spatial demand intensity value, wherein the spatial demand intensity value carries a first business demand weight identifier; The interference signal strength data and channel delay spread data are jointly mapped and evaluated to obtain a first interference risk level value, wherein the first interference risk level value is labeled with a first delay risk factor.

6. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 5, characterized in that, The method involves coupling and weighting the user distribution density data and business demand level data to obtain a first spatial demand intensity value. The user distribution density data is weighted and corrected based on the business demand level data to obtain the weighted user density. Then, the first spatial demand intensity value is calculated based on the weighted user density. The priority distribution features of the business demand level data are extracted and used as the first business demand weight identifier for the first spatial demand intensity value.

7. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 5, characterized in that, The method involves jointly mapping and evaluating the interference signal strength data and channel delay spread data to obtain a first interference risk level value. The interference signal strength data is converted into the first interference risk level value through a piecewise linear mapping function; The delay confidence factor is calculated using the channel delay spread data. After the confidence level of the first interference risk level value is corrected, the first delay risk factor is generated based on the confidence correction result, and the data of the first interference risk level value is identified.

8. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 7, characterized in that, A scheduling priority sequence is obtained by dynamically weighting the N spatial demand intensity values ​​and N interference risk level values. The method includes: Using the first business demand weight and the first time delay risk factor as two-dimensional search keys, the first weight coefficient combination is matched in the dynamic weight decision table, and the first spatial demand intensity value and the first interference risk level value are dynamically weighted to obtain the first priority score. Similarly, calculate the N priority scores for the N spatial demand intensity values ​​and the N interference risk level values; Arrange the N spatial sub-sectors in descending order according to the N priority scores, and output the scheduling priority sequence.

9. The antenna unit spatial coverage scheduling method based on compound eye bionic structure as described in claim 7, characterized in that, Based on the scheduling priority sequence, adaptive cooperative scheduling of the N bionic sub-coverage units is performed to generate N antenna control commands. The method includes: Calculate N initial beam pointing angles and N initial beamwidths based on the N spatial demand intensity values; N initial transmit powers are allocated based on N interference risk level values; Based on the geometric position distribution of the N spatial sub-sectors, after performing three-dimensional parameter conflict detection on the N initial beam pointing angles, N initial beamwidths, and N initial transmit powers, the parameters of the conflicting sectors are re-optimized collaboratively according to the scheduling priority sequence to obtain the N antenna control commands. The antenna control commands include optimized beam pointing angle parameters, optimized beamwidth parameters, and optimized transmit power parameters.

10. A spatial coverage scheduling system for a single antenna unit based on a compound eye bionic structure, characterized in that, The system is used to implement the antenna unit spatial coverage scheduling method based on the compound eye bionic structure according to any one of claims 1-9, the system comprising: The sub-coverage unit configuration module is used to divide the antenna radiation space into N spatial sub-sectors and then configure N bionic sub-coverage units for the N spatial sub-sectors through dynamic grouping of radiating elements in the compound eye bionic device. The N bionic sub-coverage units are connected to the communication neural network through a bus-type control interface. The communication scene perception module is used to perform distributed communication scene perception of the N bionic sub-covering units through the communication neural network, and obtain N communication scene information. The dual-dimensional quantization module is used to perform dual-dimensional quantization based on the N communication scenario information to obtain N spatial demand intensity values ​​and N interference risk level values; The weighted priority calculation module is used to perform dynamic weighted priority calculation based on the N spatial demand intensity values ​​and N interference risk level values ​​to obtain a scheduling priority sequence; An adaptive cooperative scheduling module is used to perform adaptive cooperative scheduling of the N bionic sub-coverage units according to the scheduling priority sequence, and generate N antenna control commands. The closed-loop update module is used to perform sub-sector-level closed-loop updates on the antenna radiation space based on the communication scene perception feedback uploaded by the N bionic sub-coverage units after the communication neural network sends the N antenna control commands to the compound eye bionic device.

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