A 5G antenna adaptive beam adjustment method for complex construction sites
By constructing a business- and scenario-driven decision-making framework and a monitoring-decision-execution feedback loop, the problem of insufficient adaptive capability of 5G antenna adaptive beam adjustment in complex construction sites was solved, achieving precise allocation of network resources and improvement of service experience, as well as enhancing system capacity and communication assurance capabilities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA CONSTR FOURTH ENG DIV CORP LTD
- Filing Date
- 2026-02-25
- Publication Date
- 2026-06-05
AI Technical Summary
Existing technologies have limited adaptive capabilities in 5G antenna adaptive beam adjustment at complex construction sites, making it difficult to achieve optimal matching between network resources and service experience. This is especially true when facing rapid changes in service types, terminal distribution, and environmental obstruction, resulting in a disconnect between resource allocation and service experience.
By constructing a business and scenario-driven decision-making framework, using array antennas for spatial environment perception and business demand perception, generating joint status reports, constructing an optimization model based on the quality of service utility function, calculating optimal beam parameters, and scheduling beams through time-division multiplexing, a monitoring-decision-execution feedback closed loop is established to achieve precise and dynamic allocation of network resources.
It enables precise and efficient allocation of network resources in complex construction sites, improves beam adaptability and cross-layer optimization efficiency, directly guarantees the actual experience quality of terminal services, reduces manual intervention, and improves system capacity and communication guarantee capabilities for high-value services.
Smart Images
Figure CN122160785A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wireless communication technology, and in particular to a method for adaptive beam adjustment of 5G antennas for use in complex construction sites. Background Technology
[0002] In the field of wireless communication, employing array antennas and adaptive beamforming is a key method to improve network performance in complex environments. Existing related technologies can be mainly divided into three categories: Method 1: Based on fixed or switchable precoded beams. This method presets a limited number of beam patterns and switches based on simple signal measurements. Because of its simple principle of preset and switching, Method 1 cannot continuously and finely perceive changes in the environment and services, thus limiting its optimization capabilities.
[0003] Method 2: Beamforming based on real-time signal optimization. This method dynamically calculates beam weights to improve the signal-to-interference-plus-noise ratio (SINR) through algorithms such as minimum mean square error (LMS) and minimum variance distortionless response (MVDR). Its performance is superior to Method 1. Although the core algorithm of Method 2 can optimize physical layer signals, its design follows the traditional communication layering theory, and the optimization target is always limited to physical layer signal indicators, failing to be directly related to the quality of service (QoS) requirements of specific upper-layer services.
[0004] Method 3: Intelligent Beamforming Based on Machine Learning. This method utilizes models such as neural networks to learn the mapping relationship between channels and beams from data. While it possesses a certain level of intelligence, it typically operates as a "black box." A "black box" refers to the mapping relationship between the model's (e.g., neural network) inputs (such as channel state and service requirements) and outputs (e.g., beam weights and beam direction), which is obtained through training with massive amounts of data. However, the intermediate decision-making process and internal logic are opaque and cannot be intuitively understood, explained, or traced by humans. This "black box" characteristic leads to an opaque decision-making process, poor interpretability, and the model's performance is highly dependent on massive training data for specific scenarios. Its optimization objective is fixed during the training phase, and it lacks an interpretable framework to directly translate high-level service semantics into beam strategies, making it difficult to flexibly adapt to dynamically changing multi-service priority requirements.
[0005] These fundamental reasons make it difficult for existing technologies to achieve optimal matching between network resources and service experience when facing complex and dynamic scenarios with rapidly changing service types, terminal distribution, and environmental obstructions. Summary of the Invention
[0006] In view of this, the purpose of this invention is to propose a 5G antenna adaptive beam adjustment method for complex construction sites. By constructing a service and scenario-driven decision-making framework, it can achieve precise and dynamic allocation of network resources, realize intelligent management of construction progress, improve beam adaptability, and improve cross-layer optimization efficiency, thereby directly ensuring and improving the actual experience quality of terminal services.
[0007] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows: This invention provides a 5G antenna adaptive beam adjustment method for complex construction sites, comprising the following steps: Step 1: Install the array antenna at a high point on the construction site, connect it to the bearer network, import the regional digital map and divide the key protection areas, and set the basic system parameters. Step 2: Perform spatial environment perception and service requirement perception in parallel, obtain the direction of arrival of the wireless signal corresponding to the wireless communication terminal and its service quality requirement information, and generate a joint status report; Step 3: Based on the joint state report, cluster wireless communication terminals that are spatially adjacent and have similar service quality requirements into the same logical service group, and define a service quality utility function for each logical service group; construct an optimization model based on the service quality utility function with the goal of maximizing the total weighted utility of the system. Step 4: Solve the optimization model to obtain the optimal beam parameters corresponding to each logical service group; Step 5: Calculate the complex weighting coefficients of each element of the array antenna based on the optimal beam parameters, and schedule the transmission of multiple beams in a time-division multiplexing manner. Step 6: Monitor the actual performance indicators of each logical service group in real time and determine whether they meet the standards. If the performance meets the standards, maintain the current beam configuration; if the performance does not meet the standards or environmental changes are detected, return to step 2.
[0008] Furthermore, step 1 specifically includes: Step 11: Install the array antenna at a high point in the coverage area, wherein the high point includes the top of the tower crane cab or a temporary communication pole; Step 12: Connect the array antenna to an industrial power supply via a power cord; Step 13: Connect the array antenna to the bearer network at the construction site via optical fiber or network cable to connect to the network management system, including the local edge computing server or central network management platform; Step 14: Import the regional digital map through the backend of the network management system. The regional digital map includes building outlines and fixed location information of large equipment. Step 15: Divide key protection areas on the regional digital map according to business importance, wherein the key protection areas include at least one of video surveillance area, tower crane operation area and material entrance / exit area; Step 16: Associate the key protection areas with the corresponding business types and initial priorities; Step 17: Set the basic system parameters, including optimization period, maximum rated transmit power, maximum number of concurrent beams, interference threshold, minimum guaranteed bandwidth, and maximum tolerable latency.
[0009] Furthermore, step 2 specifically includes: Step 21: Start the 5G antenna according to the set optimization cycle and execute spatial environment perception and service demand perception in parallel. Step 22: In the spatial environment perception, the array antenna receives the wireless signal from the wireless communication terminal, uses a direction-finding algorithm to calculate the spatial spectrum of the wireless signal, and identifies the direction and power value corresponding to the spectral peak with power higher than the environmental noise, as the direction of arrival and intensity of the wireless signal. Step 23: In the business demand perception, the data packets corresponding to the current business are obtained in real time through the network optical mirroring or the network management system interface, the header and payload characteristics of the data packets are parsed, the business type is identified, and the service quality requirement information of the current business is mapped, including the minimum guaranteed bandwidth, the maximum tolerable latency and the priority. Step 24: Bind the origin of the wireless signal corresponding to each wireless communication terminal to its quality of service requirement information, and generate a joint status report.
[0010] Furthermore, step 3 specifically includes: Step 31: Receive the joint status report, which includes the origin of the wireless signal and its quality of service requirements for each wireless communication terminal. Step 32: Based on the direction of the wireless signal and the quality of service requirement information, classify wireless communication terminals that are spatially adjacent and have similar demand types and levels into the same logical service group. Step 33: Define a service quality utility function for each logical business group to quantify the degree of satisfaction of its business experience; Step 34: Construct an optimization model based on the service quality utility function. The optimization model aims to maximize the total weighted utility of the system and includes multiple constraints. Step 35: Set the transmit power, beam pointing angle, and beam width of the beam corresponding to each logical service group as the optimization variables to be solved.
[0011] Furthermore, the expression for the service quality utility function is:
[0012] in, k Indexes representing logical business groups Represents the service quality utility function. The average throughput actually achieved by this logical business group This represents the average latency actually achieved by this logical business group. This is the minimum guaranteed bandwidth for this logical service group. This represents the maximum tolerable latency for this logical business group. and These are weighting coefficients determined based on the business type. Furthermore, the objective function of the optimization model is:
[0013] in, For logical business group k Priority weights, K This represents the total number of logical business groups. Several constraints include: total transmit power constraint, inter-beam interference constraint, and quality of service constraint. (1) The total transmit power constraint is:
[0014] in, To be assigned to the logical business group k Corresponding beam transmit power, This is the system's rated maximum transmit power; (2) The inter-beam interference constraint is: for any two different logical service groups, the radiation gain of their corresponding beams in the main service direction of the other party must be lower than the preset interference threshold. (3) The service quality constraint is as follows: For high-priority logical business groups, the following must be met. and .
[0015] Furthermore, step 4 specifically includes: Step 41: Use the direction of arrival of the wireless signal corresponding to each wireless communication terminal as the initial search direction or spatial constraint for determining the optimal beam pointing angle of each logical service group. Step 42: Solve the optimization model using a numerical algorithm. The optimization model is a constrained nonlinear optimization problem. Step 43: Execute the solution process according to the time interval synchronized with the optimization cycle; Step 44: After the solution is completed, extract the optimal solution corresponding to each logical service group and parse out the optimal beam parameters, including the transmit power of the optimal beam, the pointing angle of the optimal beam, and the width of the optimal beam. Step 45: Generate a structured beam control command set based on the optimal beam parameters obtained from the analysis.
[0016] Furthermore, step 5 specifically includes: Step 51: The beamforming module receives a structured beam control instruction set, which includes the pointing angle of the beam corresponding to at least one logical service group. Step 52: For each beam control command, use a beamforming algorithm to calculate the complex weighting coefficients required for each element of the array antenna, taking the beam pointing angle in the beam control command as the desired direction. Step 53: The scheduler uses a time-division multiplexing strategy to divide consecutive radio frames into multiple time slots; the number of time slots is less than or equal to the preset maximum number of concurrent beams. Step 54: Arrange the calculated complex weighting coefficient groups corresponding to different beams into each time slot in sequence; Step 55: The RF front end loads different complex weighted coefficient groups according to the scale timing, and uses a beamforming processor based on field programmable gate array to drive the array antenna to generate multiple directional beams in a time-division multiplexing manner.
[0017] Furthermore, step 6 specifically includes: Step 61: Continuously obtain the actual performance metrics of each logical business group, including average throughput. and average delay ; Step 62: Compare the actual performance indicators with the preset service quality requirements, which include minimum guaranteed bandwidth. and maximum tolerance latency ; Step 63: Determine whether the current performance meets the preset compliance criteria. like and If the current performance is deemed satisfactory, the current beam configuration will be maintained; among which, and This is an adjustable coefficient that can be dynamically configured based on the service quality requirements of different business types. like < or > If the current performance is deemed unsatisfactory, and if the performance of any logical service group is unsatisfactory or a new strong interference source is detected by the 5G antenna, the process will automatically return to step 2 to start a new round of optimization, forming an adaptive closed loop.
[0018] Furthermore, the beamforming algorithm is a linearly constrained minimum variance algorithm or a minimum variance distortionless response algorithm; the numerical algorithm is a gradient projection method or an algorithm executed by a convex optimization solver. By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: 1) Configurable strategies and intuitive, flexible management: This invention employs an explicit decision-making framework based on an optimization model. Administrators can directly guide the allocation of network resources by configuring fundamental system parameters such as business priority weights and Quality of Service (QoS) thresholds, ensuring they align with specific management objectives (e.g., prioritizing production safety-related services). These fundamental system parameters are not fixed; the system can iteratively optimize and adaptively fine-tune them based on the correlation analysis between historical operational data and business experience, thereby achieving optimal long-term operational performance.
[0019] 2) Achieving cross-layer optimization and precise, efficient resource allocation: This invention breaks through the limitations of traditional layered network design by directly mapping the service experience quality requirements of the application layer to the constraints and optimization objectives of beamforming in the physical layer, thus constructing a direct path from service requirements to wireless resource scheduling. This method avoids the efficiency losses caused by layered optimization in traditional solutions and can significantly improve system capacity and communication assurance capabilities for high-value services in complex wireless environments such as construction sites.
[0020] 3) Intelligent closed-loop operation and maintenance capabilities, reducing manual intervention: The system has a built-in complete monitoring-decision-execution feedback closed loop. This closed loop continuously monitors the actual performance indicators of each logical service group, including but not limited to average throughput and average latency. The monitoring frequency can be dynamically configured according to service sensitivity. The system has a preset threshold-based performance anomaly judgment mechanism. For example, when the average throughput of a logical service group is consistently not lower than the set minimum guaranteed bandwidth and the average latency is consistently not higher than the maximum tolerable latency, it is judged as meeting performance standards; when the average throughput of a logical service group is consistently lower than the set minimum guaranteed bandwidth or the average latency is consistently higher than the maximum tolerable latency, it is judged as failing performance standards. If the performance of any logical service group fails to meet standards or the 5G antenna detects a new strong interference source, the system automatically triggers a re-sensing and optimization process, realizing autonomous response and rapid compensation to network status anomalies and changes in the external environment, thereby greatly reducing the workload of manual monitoring and adjustment. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 This is an execution flowchart of a 5G antenna adaptive beam adjustment method for complex construction sites provided by an embodiment of the present invention. Detailed Implementation
[0023] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0024] Please see Figure 1 The present invention provides a 5G antenna adaptive beam adjustment method for complex construction sites, comprising the following steps: Step 1: Install the array antenna at a high point on the construction site, connect it to the bearer network, import the regional digital map and divide the key protection areas, and set the basic system parameters. In this embodiment, step 1 specifically includes: Step 11: Install the array antenna at a high point in the coverage area, wherein the high point includes the top of the tower crane cab or a temporary communication pole; Step 12: Connect the array antenna to an industrial power supply via a power cord; Step 13: Connect the array antenna to the bearer network at the construction site via optical fiber or network cable to connect to the network management system, including the local edge computing server or central network management platform; Step 14: Import the regional digital map through the backend of the network management system. The regional digital map includes building outlines and fixed location information of large equipment. The regional digital map adopts a common GIS map format or a lightweight BIM model format, with a map accuracy of less than 1 meter, which facilitates a rough correlation with the wireless signal coverage area. Step 15: The administrator divides key protection areas on the regional digital map according to business importance. The key protection areas include at least one of video surveillance area, tower crane operation area and material entrance / exit area. This key protection area division can be dynamically adjusted and updated according to the actual business distribution after the system is running. Step 16: Associate the critical protection area with the corresponding service type (such as high-definition video backhaul) and initial priority; Step 17: Set the system basic parameters, including the optimization cycle (set according to construction requirements), the system's rated maximum transmit power (set according to the equipment's rated power and regulatory limits, such as 40dBm), the maximum number of concurrent beams (limited by the number of antenna array elements and processing capacity), the interference threshold, the minimum guaranteed bandwidth, and the maximum tolerable delay; after completing the above initialization configuration, proceed to Step 2.
[0025] Step 2: Perform spatial environment perception and service requirement perception in parallel, obtain the direction of arrival of the wireless signal corresponding to the wireless communication terminal and its service quality requirement information, and generate a joint status report; In this embodiment, step 2 specifically includes: Step 21: Start the 5G antenna (integrating the space environment perception module and the service demand perception module) according to the set optimization cycle, and execute the space environment perception and service demand perception in parallel. Step 22: In the aforementioned spatial environment perception, the array antenna receives wireless signals from the wireless communication terminal and uses a direction-finding algorithm (such as the MUSIC algorithm, which is an abbreviation for Multiple Signal Classification, a classic and commonly used high-resolution spatial spectrum estimation algorithm in array signal processing, with its core used for direction of arrival (DOA) estimation) to analyze the phase difference of each wireless signal in the array antenna, calculate the spatial spectrum of the wireless signal, and identify the direction and power value corresponding to the spectral peak with power higher than the environmental noise (set according to construction requirements), as the direction and strength of the wireless signal ("strong signal"). Step 23: In the business demand perception, the data packets of the current business are obtained in real time through the network optical mirroring or the network management system interface, the header and payload characteristics of the data packets (protocol port, data mode, etc.) are parsed, the business type (video stream, control signaling, etc.) is identified, and the service quality requirement information of the current business is mapped, including the minimum guaranteed bandwidth, the maximum tolerable latency and the priority. Step 24: Bind the origin of the wireless signal corresponding to each wireless communication terminal to its quality of service requirement information to generate a joint status report for subsequent decision-making.
[0026] Step 3: Based on the joint state report, cluster wireless communication terminals that are spatially adjacent and have similar service quality requirements into the same logical service group, and define a service quality utility function for each logical service group; construct an optimization model based on the service quality utility function with the goal of maximizing the total weighted utility of the system. In this embodiment, step 3 specifically includes: Step 31: Receive the joint status report, which includes the origin of the wireless signal and its quality of service requirements for each wireless communication terminal. Step 32: Based on the direction of the wireless signal and the quality of service requirement information, classify wireless communication terminals that are spatially adjacent and have similar demand types and levels into the same logical service group. Step 33: Define a Quality of Service (QoS) utility function for each logical service group. This QoS utility function is constructed based on the bandwidth and latency requirements of the logical service group and is used to quantify the degree to which their service experience is satisfied. The expression of the QoS utility function is:
[0027] in, k Indexes representing logical business groups Represents the service quality utility function. The average throughput actually achieved by this logical business group This represents the average latency actually achieved by this logical business group. This is the minimum guaranteed bandwidth for this logical service group. This represents the maximum tolerable latency for this logical business group. and The weighting coefficients are determined based on the business type; the value of this service quality utility function increases monotonically as the actual rate increases and the latency decreases.
[0028] Step 34: Construct an optimization model based on the service quality utility function. The optimization model aims to maximize the total weighted utility of the system and includes multiple constraints. The objective function of the optimization model is:
[0029] in, For logical business group k Priority weights, K This represents the total number of logical business groups. Several constraints include: total transmit power constraint, inter-beam interference constraint, and quality of service constraint. (1) The total transmit power constraint is:
[0030] in, To be assigned to the logical business group k Corresponding beam transmit power, This is the system's rated maximum transmit power; (2) The inter-beam interference constraint is: for any two different logical service groups, the radiation gain of their corresponding beams in the main service direction of the other party must be lower than the preset interference threshold. (3) The service quality constraint is as follows: For high-priority logical business groups, the following must be met. and .
[0031] Step 35: Set the transmit power, beam pointing angle, and beam width of the beam corresponding to each logical service group as the optimization variables to be solved.
[0032] Step 4: Solve the optimization model to obtain the optimal beam parameters corresponding to each logical service group; In this embodiment, step 4 specifically includes: Step 41: Use the direction of arrival of the wireless signal corresponding to each wireless communication terminal as the initial search direction or spatial constraint for determining the optimal beam pointing angle of each logical service group, so as to ensure that the generated beam can accurately cover the target terminal group. Step 42: Solve the optimization model using a numerical algorithm. The solution period is synchronized with the set optimization period. The optimization model is a constrained nonlinear optimization problem. The numerical algorithm is a gradient projection method or an algorithm executed by a convex optimization solver.
[0033] Step 43: Execute the solution process according to the time interval synchronized with the optimization cycle; Step 44: After the solution is completed, extract the optimal solution corresponding to each logical service group and parse out the optimal beam parameters, including the transmit power of the optimal beam, the pointing angle of the optimal beam, and the width of the optimal beam. Step 45: Generate a structured beam control command set based on the optimal beam parameters obtained from the analysis.
[0034] Step 5: Calculate the complex weighting coefficients of each element of the array antenna based on the optimal beam parameters, and schedule the transmission of multiple beams in a time-division multiplexing manner. In this embodiment, step 5 specifically includes: Step 51: The beamforming module receives a structured beam control instruction set, which includes the pointing angle of the beam corresponding to at least one logical service group. Step 52: For each beam control command, a beamforming algorithm is used to calculate the complex weighting coefficients required for each element of the array antenna, with the beam pointing angle in the beam control command as the desired direction; the beamforming algorithm is either the Linear Constrained Minimum Variance (LCMV) algorithm or the Minimum Variance Distortionless Response (MVDR) algorithm. Step 53: The scheduler adopts a time-division multiplexing strategy to divide consecutive radio frames into multiple time slots; the number of time slots is less than or equal to the preset maximum number of concurrent beams; this invention supports the generation and transmission of multiple independent beams under the time-division multiplexing framework, and ensures that co-channel interference between beams is effectively suppressed by optimizing the inter-beam interference constraints included in the model and the resource scheduling in the time / frequency domain.
[0035] Step 54: Arrange the calculated complex weighting coefficient groups corresponding to different beams into each time slot in sequence; Step 55: The RF front end loads different complex weighting coefficient groups according to the scale timing, and uses a beamforming processor based on field programmable gate array (FPGA) to drive the array antenna to generate multiple directional beams in a time-division multiplexing manner, so as to achieve low-latency configuration of the amplitude and phase weights of the antenna elements.
[0036] Step 6: Monitor the actual performance indicators of each logical service group in real time and determine whether they meet the standards. If the performance meets the standards, maintain the current beam configuration; if the performance does not meet the standards or environmental changes are detected, return to step 2.
[0037] In this embodiment, step 6 specifically includes: Step 61: Continuously obtain the actual performance metrics of each logical business group, including average throughput. and average delay ; Step 62: Compare the actual performance indicators with the preset service quality requirements, which include minimum guaranteed bandwidth. and maximum tolerance latency ; Step 63: Determine whether the current performance meets the preset compliance criteria. like and If the current performance is deemed satisfactory, the current beam configuration will be maintained; among which, and This is an adjustable coefficient that can be dynamically configured based on the service quality requirements of different business types. like < or > If the current performance is deemed substandard, and if the performance of any logical service group is substandard or a new strong interference source is detected by the 5G antenna, the process will automatically return to step 2 to start a new round of optimization, forming an adaptive closed loop.
[0038] This invention creates a business- and scenario-driven decision-making architecture that integrates perception, decision-making, execution, and feedback. By directly translating high-level business requirements into physical layer beam parameters and establishing a closed-loop optimization centered on business experience, this invention achieves precise and adaptive allocation of network resources in complex and dynamic environments, thereby prioritizing critical services and improving overall network efficiency and reliability.
[0039] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A method for adaptive beam adjustment of 5G antennas for complex construction sites, characterized in that, Including steps such as: Step 1: Install the array antenna at a high point on the construction site, connect it to the bearer network, import the regional digital map and divide the key protection areas, and set the basic system parameters. Step 2: Perform spatial environment perception and service requirement perception in parallel, obtain the direction of arrival of the wireless signal corresponding to the wireless communication terminal and its service quality requirement information, and generate a joint status report; Step 3: Based on the joint state report, cluster wireless communication terminals that are spatially adjacent and have similar service quality requirements into the same logical service group, and define a service quality utility function for each logical service group; construct an optimization model based on the service quality utility function with the goal of maximizing the total weighted utility of the system. Step 4: Solve the optimization model to obtain the optimal beam parameters corresponding to each logical service group; Step 5: Calculate the complex weighting coefficients of each element of the array antenna based on the optimal beam parameters, and schedule the transmission of multiple beams in a time-division multiplexing manner. Step 6: Monitor the actual performance indicators of each logical service group in real time and determine whether they meet the standards. If the performance meets the standards, maintain the current beam configuration; if the performance does not meet the standards or environmental changes are detected, return to step 2.
2. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 1, characterized in that, Step 1 specifically includes: Step 11: Install the array antenna at a high point in the coverage area, wherein the high point includes the top of the tower crane cab or a temporary communication pole; Step 12: Connect the array antenna to an industrial power supply via a power cord; Step 13: Connect the array antenna to the bearer network at the construction site via optical fiber or network cable to connect to the network management system, including the local edge computing server or central network management platform; Step 14: Import the regional digital map through the backend of the network management system. The regional digital map includes building outlines and fixed location information of large equipment. Step 15: Divide key protection areas on the regional digital map according to business importance, wherein the key protection areas include at least one of video surveillance area, tower crane operation area and material entrance / exit area; Step 16: Associate the key protection areas with the corresponding business types and initial priorities; Step 17: Set the basic system parameters, including optimization period, maximum rated transmit power, maximum number of concurrent beams, interference threshold, minimum guaranteed bandwidth, and maximum tolerable latency.
3. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 1, characterized in that, Step 2 specifically includes: Step 21: Start the 5G antenna according to the set optimization cycle and execute spatial environment perception and service demand perception in parallel. Step 22: In the spatial environment perception, the array antenna receives the wireless signal from the wireless communication terminal, uses a direction-finding algorithm to calculate the spatial spectrum of the wireless signal, and identifies the direction and power value corresponding to the spectral peak with power higher than the environmental noise, as the direction of arrival and intensity of the wireless signal. Step 23: In the business demand perception, the data packets corresponding to the current business are obtained in real time through the network optical mirroring or the network management system interface, the header and payload characteristics of the data packets are parsed, the business type is identified, and the service quality requirement information of the current business is mapped, including the minimum guaranteed bandwidth, the maximum tolerable latency and the priority. Step 24: Bind the origin of the wireless signal corresponding to each wireless communication terminal to its quality of service requirement information, and generate a joint status report.
4. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 1, characterized in that, Step 3 specifically includes: Step 31: Receive the joint status report, which includes the origin of the wireless signal and its quality of service requirements for each wireless communication terminal. Step 32: Based on the direction of the wireless signal and the quality of service requirement information, classify wireless communication terminals that are spatially adjacent and have similar demand types and levels into the same logical service group. Step 33: Define a service quality utility function for each logical business group to quantify the degree of satisfaction of its business experience; Step 34: Construct an optimization model based on the service quality utility function. The optimization model aims to maximize the total weighted utility of the system and includes multiple constraints. Step 35: Set the transmit power, beam pointing angle, and beam width of the beam corresponding to each logical service group as the optimization variables to be solved.
5. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 4, characterized in that, The expression for the service quality utility function is: in, k Indexes representing logical business groups Represents the service quality utility function. The average throughput actually achieved by this logical business group This represents the average latency actually achieved by this logical business group. This is the minimum guaranteed bandwidth for this logical service group. This represents the maximum tolerable latency for this logical business group. and These are weighting coefficients determined based on the business type.
6. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 4, characterized in that, The objective function of the optimization model is: in, For logical business group k Priority weights, K This represents the total number of logical business groups. Several constraints include: total transmit power constraint, inter-beam interference constraint, and quality of service constraint. (1) The total transmit power constraint is: in, To be assigned to the logical business group k Corresponding beam transmit power, This is the system's rated maximum transmit power; (2) The inter-beam interference constraint is: for any two different logical service groups, the radiation gain of their corresponding beams in the main service direction of the other party must be lower than the preset interference threshold. (3) The service quality constraint is as follows: For high-priority logical business groups, the following must be met. and .
7. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 1, characterized in that, Step 4 specifically includes: Step 41: Use the direction of arrival of the wireless signal corresponding to each wireless communication terminal as the initial search direction or spatial constraint for determining the optimal beam pointing angle of each logical service group. Step 42: Solve the optimization model using a numerical algorithm. The optimization model is a constrained nonlinear optimization problem. Step 43: Execute the solution process according to the time interval synchronized with the optimization cycle; Step 44: After the solution is completed, extract the optimal solution corresponding to each logical service group and parse out the optimal beam parameters, including the transmit power of the optimal beam, the pointing angle of the optimal beam, and the width of the optimal beam. Step 45: Generate a structured beam control command set based on the optimal beam parameters obtained from the analysis.
8. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 7, characterized in that, Step 5 specifically includes: Step 51: The beamforming module receives a structured beam control instruction set, which includes the pointing angle of the beam corresponding to at least one logical service group. Step 52: For each beam control command, use a beamforming algorithm to calculate the complex weighting coefficients required for each element of the array antenna, taking the beam pointing angle in the beam control command as the desired direction. Step 53: The scheduler uses a time-division multiplexing strategy to divide consecutive radio frames into multiple time slots; the number of time slots is less than or equal to the preset maximum number of concurrent beams. Step 54: Arrange the calculated complex weighting coefficient groups corresponding to different beams into each time slot in sequence; Step 55: The RF front end loads different complex weighted coefficient groups according to the scale timing, and uses a beamforming processor based on field programmable gate array to drive the array antenna to generate multiple directional beams in a time-division multiplexing manner.
9. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 1, characterized in that, Step 6 specifically includes: Step 61: Continuously obtain the actual performance metrics of each logical business group, including average throughput. and average delay ; Step 62: Compare the actual performance indicators with the preset service quality requirements, which include minimum guaranteed bandwidth. and maximum tolerance latency ; Step 63: Determine whether the current performance meets the preset compliance criteria. like and If the current performance is deemed satisfactory, the current beam configuration will be maintained; among which, and This is an adjustable coefficient that can be dynamically configured based on the service quality requirements of different business types. like < or > If the current performance is deemed unsatisfactory, and if the performance of any logical service group is unsatisfactory or a new strong interference source is detected by the 5G antenna, the process will automatically return to step 2 to start a new round of optimization, forming an adaptive closed loop.
10. The 5G antenna adaptive beam adjustment method for complex construction sites as described in claim 8, characterized in that, The beamforming algorithm is a linearly constrained minimum variance algorithm or a minimum variance distortionless response algorithm; the numerical algorithm is a gradient projection method or an algorithm executed by a convex optimization solver.