Optimization Method for Overlapping Coverage in Co-frequency Networks Based on 5G MR-DoA User Distribution Awareness

Abstract

This invention relates to the field of wireless communication technology, addressing the technical problems of large interference, poor user experience, and poor network coverage quality caused by overlapping coverage in existing 5G networks operating on the same frequency. It proposes a method for optimizing overlapping coverage in 5G networks operating on the same frequency based on 5G MR-DoA user distribution awareness. The method includes: collecting measured MR data from the 5G network in the cell; identifying the concentrated distribution area and density center of users based on DoA data information in the MR data; adjusting relevant parameters of the antenna transmission direction according to the identified concentrated distribution area and density center, so that the adjusted antenna transmission direction matches the density center of the concentrated user distribution; constructing a 5G antenna weight optimization model based on the adjusted antenna transmission direction; using a particle swarm optimization algorithm to intelligently iterate and optimize the 5G antenna weight optimization model to obtain the optimal antenna weights; and distributing the optimal antenna weights to the base station to reconstruct the antenna lobe coverage pattern, achieving precise control and interference suppression of overlapping coverage in 5G networks operating on the same frequency.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness. Background Technology

[0002] 5G networks often employ a co-frequency networking approach, a wireless communication networking method that improves frequency utilization efficiency. It plays a crucial role in signal carrying and user access during the improvement of mobile communication network coverage and capacity. It includes base stations, Massive MIMO antennas, beamforming modules, signal processing units, and MR data acquisition units, and consists of two links: downlink signal coverage and uplink signal acquisition. Specifically, the base station antenna transmits 5G downlink signals to the coverage area to provide service access for user terminals; simultaneously, the user terminal sends uplink SRS signals to the base station. The base station, through its signal processing unit, calculates MR data and angle of arrival (DoA) information to achieve user location and signal quality awareness. Based on network parameter configuration, bidirectional communication between the base station and the user terminal is then completed.

[0003] Currently, 5G systems typically use massive MIMO antennas and beamforming technology to improve network coverage. When co-located with LTE, the coverage areas of multiple cells often overlap or are very close to each other, differing only in antenna azimuth, downtilt angle, and beam direction. Therefore, the target serving cell will inevitably receive co-channel overlapping signals transmitted by neighboring cells, resulting in severe co-channel interference to the desired useful signal. If the overlapping coverage interference is not optimized, the superposition of interference signals and useful signals will continuously reduce the network SINR, leading to decreased user speed and increased latency. The interference intensity will accumulate in the network, seriously affecting the user experience.

[0004] Therefore, it is essential to develop an optimization scheme that can accurately match the antenna coverage direction with the user concentration area and reduce unnecessary overlapping coverage between cells to solve the problem of overlapping coverage interference in 5G networks, thereby improving network coverage quality and meeting the user's perceived speed and network operation efficiency requirements. Summary of the Invention

[0005] The purpose of this invention is to solve the technical problems of large interference, poor user experience and network coverage quality caused by overlapping coverage in existing 5G networks with the same frequency, and to propose an optimization method for overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness.

[0006] The design concept of this invention is as follows: using DoA information in 5G MR data to complete the identification of user distribution and density center, and initially matching antenna coverage with user area; then, with coverage rate as a constraint and reducing overlapping coverage as the goal, the antenna weights are intelligently optimized through particle swarm optimization algorithm, and finally the optimal coverage optimization scheme is output to achieve precise control of 5G overlapping coverage.

[0007] To achieve the above objectives, the technical solution proposed by this invention is as follows: The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness is unique in that it includes the following steps: S1. Collect the measured MR data of the 5G network in the cell, and identify the concentrated distribution area and density center of users based on the DoA data information in the MR data; S2. Based on the identified user concentration distribution area and density center, adjust the relevant parameters of the antenna transmission direction so that the adjusted antenna transmission direction matches the density center of the user concentration distribution. S3. Based on the adjusted antenna transmission direction, construct a 5G antenna weight optimization model; S4. The particle swarm optimization algorithm is used to intelligently iterate and optimize the 5G antenna weight optimization model to obtain the optimal antenna weight. S5. The optimal antenna weights are sent to the base station to reconstruct the antenna beam coverage pattern, thereby achieving precise control and interference suppression of overlapping coverage under 5G co-frequency networking.

[0008] Further, step S1 includes: S1.1 Clean the collected MR data and filter out the horizontal angle of arrival (HDoA) data. Interval, Vertical Angle of Arrival (VDoA) The interval, and the time lead time (TA) value belongs to Sampling points; S1.2. The selected sampling points are rasterized according to the set vertical and horizontal angle of arrival dimensions to form an angle raster. S1.3 By calculating the number and proportion of sampling points in each angle grid, angle grids with a sampling point proportion greater than 5% are selected as hot spot grids; S1.4 Identify the coverage boundary of the set vertical angle of arrival dimension and construct the three-dimensional coverage boundary of the entire cell; S1.5. Based on the hotspot sampling points within the hotspot grid described in step S1.3, identify the density center of the user concentration distribution area.

[0009] Furthermore, the vertical arrival angle dimension set in step S1.2 is divided into four levels, namely... , , and ; The horizontal arrival angle dimension is set as follows: for interval according to Divide the mixture evenly at intervals.

[0010] Furthermore, in step S1.3, the total number of sampling points is set to... The total number of angle grids is Each angle grid The number of sampling points is , ∈ (1, 2, ..., The number and proportion of sampling points within each angle grid are calculated using the following formula: in, Angle grid The proportion of internal sampling points to the total number of sampling points; Hotspot grid set The area where users are concentrated is calculated using the following formula: .

[0011] Furthermore, the construction of the three-dimensional coverage boundary of the entire cell in step S1.4 includes: The maximum horizontal angle of arrival of the sampling points in the vertical angle of arrival dimension is set as follows: The minimum horizontal reach angle is Then the coverage boundary of the hierarchy Represented as: in: For the vertical reach angular dimension of the hierarchy, ; If there are no hotspot grids in the hierarchy, then the coverage boundary is... If empty, the set L of the three-dimensional coverage boundary of the entire community is represented as: .

[0012] Furthermore, the identification of density centers in the concentrated distribution area of ​​users in step S1.5 includes: S1.5.1, Set the set of hotspot sampling points within the hotspot grid as follows: , Hotspot sampling points The vertical angle of arrival is Then the vertical center point of all hotspot sampling points within the hotspot grid satisfy: in: Vertical angle of arrival Greater than The number of hotspot sampling points; Vertical angle of arrival Less than The number of hotspot sampling points; S1.5.2 Setting Hotspot Sampling Points The horizontal angle of arrival is Then the horizontal center point of all hotspot sampling points within the hotspot grid satisfy: in: Angle of arrival at horizontal level Greater than The number of hotspot sampling points; Angle of arrival at horizontal level Less than The number of hotspot sampling points; S1.5.3, Based on the vertical dimension center point and horizontal dimension center point The density center of the identified user concentration area is obtained as .

[0013] Further, step S2 includes: S2.1 Adjust the azimuth, downtilt, and beamwidth of the antenna according to the identified user concentration area and its density center; S2.2 Adjust the antenna's transmission direction to The adjustment value of the antenna azimuth angle is then... The adjustment value for the understeer angle is ; S2.3, Set the maximum vertical angle of arrival of the sampling points in the aforementioned level as... The minimum vertical reach angle is Based on the three-dimensional coverage boundary of the entire community The beamwidth configuration for each level in the vertical angle of arrival dimension is calculated using the following formula. : = , ; in: The electron azimuth angle of the wavelobe. The electron downtilt angle of the wavelobe. The beamwidth is the value of the beam. ; Then the set of beamwidth configurations for the entire cell .

[0014] Furthermore, the construction of the 5G antenna weight optimization model in step S3 includes: S3.1, Let the set of cells to be adjusted be I, I = Among them, the first The antenna configuration set for each cell is as follows: Antenna configuration set The parameters include: azimuth angle, downtilt angle, transmit power, and beamwidth; No. The set of lobes for each cell is Lobe set The parameters include: vertical direction of the beam, horizontal direction of the beam, vertical beamwidth, and horizontal beamwidth; The set of network configurations to be adjusted within the cell area is as follows ; The set of geographic grids within the community area to be adjusted is ; Among them, the geographic grid is a grid of equal size that divides the optimization area according to geographical location; S3.2. Using the particle swarm optimization algorithm, a 5G antenna weight optimization model is constructed using the following formula: in: The weights are determined based on the user distribution density within the geographic raster. To optimize the weight values ​​for target coverage; To optimize the weight values ​​for target overlap coverage; To cover the judgment function, This is the overlap / coverage determination function, where RSRP is the reference signal received power. This represents the total number of geographic grid cells. is the preset RSRP compliance rate threshold, and st is the constraint condition, which is that the coverage rate is greater than the threshold value.

[0015] Furthermore, the step S4, which involves using a particle swarm optimization algorithm to perform intelligent iterative optimization of the 5G antenna weight optimization model, includes: S4.1 Set the total number of particles to Q, the maximum number of iterations to T, and randomly initialize the position of each particle q. and speed , where q = (1, 2, ..., Q); S4.2. Based on the network configuration set B within the cell area to be adjusted, calculate the network configuration of each cell. RSRP value for each geographic raster; S4.3 Calculate the fitness of the t-th iteration from the calculated RSRP value, i.e., optimize the objective function. , where t = (1, 2, ..., T); S4.4 Compare the current fitness of each particle q with the historical best solution; If the current fitness is greater than the historical best solution, then the current fitness is taken as the updated historical best solution. ; If the current fitness value is not greater than the historical best solution, then the historical best solution is used as the updated historical best solution. ; S4.5 Compare the updated historical best solutions for all particles q. The maximum value obtained from the comparison is taken as the globally optimal antenna weight. ; S4.6, Based on the globally optimal antenna weights Update the position of each particle q and speed Then the position of particle q at time t+1 and speed Satisfy the following formula: in: Inertial weight; , Here is the acceleration constant; It is a random number; S4.7. Iterate continuously until the maximum number of iterations is reached, and output the optimal antenna weights. Together with network configuration set B, it can complete the intelligent iterative optimization of the 5G antenna weight optimization model.

[0016] Meanwhile, the present invention also proposes a computer storage medium storing a computer program, the special feature of which is that when the program is executed by a processor, it implements the steps of the above-described method for optimizing overlapping coverage of co-frequency networking based on 5G MR-DoA user distribution awareness.

[0017] The technical solution provided by this invention may include the following beneficial effects: This invention presents a method for optimizing overlapping coverage in co-channel networks based on 5G MR-DoA user distribution awareness. By utilizing the DoA direction-of-arrival (DOA) information in MR data, it can accurately analyze the spatial location of users, thereby identifying concentrated user distribution areas and density centers. This effectively improves the targeting and rationality of antenna coverage optimization and reduces unnecessary overlapping coverage between cells. By adjusting antenna transmission direction parameters to match the adjusted antenna transmission direction with the density center of concentrated user distribution, it significantly improves the optimization efficiency of the particle swarm optimization algorithm for the 5G antenna weight optimization model, ensuring the accuracy and reliability of the solution results. By reconstructing the antenna lobe coverage pattern, it can effectively suppress co-channel interference between cells, improve network signal stability and the overall user communication experience. The optimization method proposed in this invention not only improves the intelligence level of 5G network coverage optimization but also significantly reduces the time and labor costs of network optimization.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 The flowchart illustrates an embodiment of the co-frequency network overlapping coverage optimization method based on 5G MR-DoA user distribution awareness according to the present invention; Figure 2 This diagram illustrates the DoA data information acquisition model in an embodiment of the present invention. Figure 3 This diagram illustrates the rasterization process in an embodiment of the present invention. (a) is a schematic diagram of the horizontal arrival angle dimension rasterization process; (b) is a schematic diagram of the rasterization process for the vertical angle of arrival dimension; Figure 4 This diagram illustrates the angle grid presentation in an embodiment of the present invention. Figure 5 This diagram illustrates the presentation of a hotspot grid in an embodiment of the present invention. Figure 6 This diagram illustrates the identification of user-concentrated distribution areas in an embodiment of the present invention. Figure 7 This diagram illustrates the identification of density centers in a user-concentrated distribution area according to an embodiment of the present invention. Figure 8 This diagram illustrates antenna adjustment in an embodiment of the present invention. Figure 9 The flowchart of the particle swarm algorithm in an embodiment of the present invention is shown. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0022] This embodiment proposes a method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness, such as... Figure 1 As shown, it includes the following steps: S1. Collect the measured MR data of the 5G network in the cell, and identify the concentrated distribution area and density center of users based on the DoA data information in the MR data; This embodiment utilizes the Direction of Arrival (DoA) information from measured MR data of 5G networks to accurately analyze the spatial location of users, thereby identifying the concentrated distribution areas and density centers of users. Based on the actual spatial distribution of users, the optimization can effectively improve the targeting and rationality of antenna coverage optimization, and avoid resource waste caused by the optimization direction deviating from user needs.

[0023] S2. Based on the identified user concentration distribution area and density center, adjust the relevant parameters of the antenna transmission direction so that the adjusted antenna transmission direction matches the density center of the user concentration distribution. This embodiment adjusts the antenna transmission direction parameters to match the density center of the concentrated user distribution, which can reduce the optimization range of subsequent antenna weights in advance, reduce the computational complexity of the particle swarm optimization algorithm, reduce the number of optimization iterations, significantly improve optimization efficiency, and avoid the process redundancy problem caused by unguided optimization.

[0024] S3. Based on the adjusted antenna transmission direction, construct a 5G antenna weight optimization model; This embodiment uses the particle swarm optimization algorithm to intelligently iteratively optimize the 5G antenna weight optimization model. Compared with the traditional local optimization algorithm, it has global parallel search capability, which can effectively avoid getting trapped in local optima. It can adapt to the complex optimization scenario of multiple cells and multiple antenna parameters in 5G co-frequency networking and accurately solve for the optimal antenna weight combination.

[0025] S4. The particle swarm optimization algorithm is used to intelligently iterate and optimize the 5G antenna weight optimization model to obtain the optimal antenna weight. S5. The optimal antenna weights are sent to the base station to reconstruct the antenna beam coverage pattern, thereby achieving precise control and interference suppression of overlapping coverage under 5G co-frequency networking.

[0026] This embodiment distributes the optimal antenna weights to the base station, enabling the reconfiguration of the antenna beam coverage pattern. Under the premise of strictly ensuring network coverage, it precisely controls the overlapping coverage area under 5G co-frequency networking, effectively suppressing inter-cell co-frequency interference, improving network SINR and signal stability, and thus improving user communication speed and call quality. At the same time, it realizes fully automated optimization from user distribution perception, antenna direction adaptation, intelligent optimization to beam reconstruction, greatly reducing manual intervention, improving the intelligence level and implementation efficiency of 5G network coverage optimization, and reducing the manpower cost of network optimization.

[0027] In this embodiment, step S1 involves collecting measured MR data from the 5G network in the cell and identifying the concentrated distribution area and density center of users based on the DoA data information in the MR data. This step includes: S1.1, such as Figure 2 As shown, the collected MR data is cleaned, and the horizontal angle of arrival (HDoA) is selected. Interval, Vertical Angle of Arrival (VDoA) The interval, and the time lead time (TA) value belongs to Sampling points; S1.2, such as Figure 3 As shown in (a) and (b), the selected sampling points are rasterized according to the set vertical and horizontal angles of arrival dimensions, as follows: Figure 4 As shown, an angular grid is formed; The vertical angle of arrival dimension is set into four levels, namely: , , and ; The horizontal arrival angle dimension is set as follows: for interval according to Divide the intervals evenly; S1.3, such as Figure 5 As shown, by calculating the number and proportion of sampling points in each angle grid, angle grids with a sampling point proportion greater than 5% are selected as hotspot grids; The total number of sampling points is set to The total number of angle grids is Each angle grid The number of sampling points is , ∈ (1, 2, ..., The number and proportion of sampling points within each angle grid are calculated using the following formula: in, Angle grid The proportion of internal sampling points to the total number of sampling points; like Figure 6 As shown, hotspot grid set The area where users are concentrated is calculated using the following formula: .

[0028] S1.4 Identify the coverage boundary of the set vertical angle of arrival dimension, and construct the three-dimensional coverage boundary of the entire cell, including: Set the maximum horizontal angle of arrival of the sampling points in the vertical angle of arrival dimension level as: The minimum horizontal reach angle is Then the coverage boundary of the hierarchy Represented as: in: For the vertical reach angular dimension of the hierarchy, ; If there are no hotspot grids in the hierarchy, then the coverage boundary is... If empty, the set L of the three-dimensional coverage boundary of the entire community is represented as: ; S1.5. Based on the hotspot sampling points within the hotspot grid in step S1.3, identify the density center of the user concentration area, including: S1.5.1, Set the set of hotspot sampling points within the hotspot grid as follows: , Hotspot sampling points The vertical angle of arrival is Then the vertical center point of all hotspot sampling points within the hotspot grid satisfy: in: Vertical angle of arrival Greater than The number of hotspot sampling points; Vertical angle of arrival Less than The number of hotspot sampling points; S1.5.2 Setting Hotspot Sampling Points The horizontal angle of arrival is Then the horizontal center point of all hotspot sampling points within the hotspot grid satisfy: in: Angle of arrival at horizontal level Greater than The number of hotspot sampling points; Angle of arrival at horizontal level Less than The number of hotspot sampling points; S1.5.3, such as Figure 7 As stated, based on the vertical dimension center point and horizontal dimension center point The density center of the identified user concentration area is obtained as .

[0029] S2. Based on the identified concentrated user distribution area and density center, adjust the relevant parameters of the antenna transmission direction to match the adjusted antenna transmission direction with the density center of the concentrated user distribution. The steps include: S2.1, such as Figure 8 As shown, the azimuth, downtilt, and beamwidth of the antenna are adjusted according to the identified user concentration areas and their density centers. S2.2 Adjust the antenna's transmission direction to The adjustment value for the antenna azimuth angle is then... The adjustment value for the understeer angle is ; S2.3, Set the maximum vertical angle of arrival of the sampling points in the hierarchy as The minimum vertical reach angle is Based on the three-dimensional coverage boundary of the entire community The beamwidth configuration for each level in the vertical angle of arrival dimension is calculated using the following formula. : = , ; in: The electron azimuth angle of the wavelobe. The electron downtilt angle of the wavelobe. The beamwidth is the value of the beam. ; Then the wave of the entire community Width configuration set .

[0030] S3, such as Figure 9 As shown, based on the adjusted antenna transmission direction, a 5G antenna weight optimization model is constructed. The implementation steps include: S3.1, Let the set of cells to be adjusted be I, I = Among them, the first The antenna configuration set for each cell is as follows: Antenna configuration set The parameters include: azimuth angle, downtilt angle, transmit power, and beamwidth; No. The set of lobes for each cell is Lobe set The parameters include: vertical direction of the beam, horizontal direction of the beam, vertical beamwidth, and horizontal beamwidth; The set of network configurations to be adjusted within the cell area is as follows ; The set of geographic grids within the community area to be adjusted is ; Among them, the geographic grid is a grid of equal size that divides the optimization area according to geographical location; S3.2. Using the particle swarm optimization algorithm, a 5G antenna weight optimization model is constructed using the following formula: in: The weights are determined based on the user distribution density within the geographic raster. To optimize the weight values ​​for target coverage; To optimize the weight values ​​for target overlap coverage; To cover the judgment function, This is the overlap / coverage determination function, where RSRP is the reference signal received power. This represents the total number of geographic grid cells. The preset RSRP compliance rate threshold is 'st', where 'st' is a constraint condition: the coverage rate must be greater than the threshold value. Where: when the maximum RSRP value within a geographic raster is greater than At that time, =1; When the maximum RSRP value within a geographic raster is not greater than At that time, =0; When a geographic raster meets the conditions for overlapping coverage, then =0; When a geographic raster does not meet the conditions for overlapping coverage, then =1; S4. The particle swarm optimization algorithm is used to intelligently iteratively optimize the 5G antenna weight optimization model to obtain the optimal antenna weight. The implementation steps include: S4.1 Set the total number of particles to Q, the maximum number of iterations to T, and randomly initialize the position of each particle q. and speed , where q = (1, 2, ..., Q); S4.2. Based on the network configuration set B within the cell area to be adjusted, calculate the network configuration of each cell. RSRP value for each geographic raster; S4.3 Calculate the fitness of the t-th iteration from the calculated RSRP value, i.e., optimize the objective function. , where t = (1, 2, ..., T); S4.4 Compare the current fitness of each particle q with the historical best solution; Wherein: Each particle is initially randomly generated with an initial position, and the current fitness of that initial position is the particle's first historical best solution; As the particle moves and changes position, a fitness value will be generated at each new position. The historical best solution is the best solution in the search process of a single particle itself. If the current fitness is greater than the historical best solution, then the current fitness is taken as the updated historical best solution. ; If the current fitness value is not greater than the historical best solution, then the historical best solution is used as the updated historical best solution. ; S4.5 Compare the updated historical best solutions for all particles q. The maximum value obtained from the comparison is taken as the globally optimal antenna weight. ; S4.6, Based on the globally optimal antenna weights Update the position of each particle q and speed Then the position of particle q at time t+1 and speed Satisfy the following formula: in: Inertial weight; , Here is the acceleration constant; It is a random number; S4.7. Iterate continuously until the maximum number of iterations is reached, and output the optimal antenna weights. Together with network configuration set B, it can complete the intelligent iterative optimization of the 5G antenna weight optimization model.

[0031] S5. Optimal antenna weights The data is sent to the base station to reconstruct the antenna beam coverage pattern, enabling precise control and interference suppression of overlapping coverage under 5G co-frequency networking.

Claims

1. A method for optimizing same-frequency networking and overlapping coverage based on 5G MR-DoA user distribution perception, characterized in that, Includes the following steps: S1. Collect the measured MR data of the 5G network in the cell, and identify the concentrated distribution area and density center of users based on the DoA data information in the MR data; S2. Based on the identified user concentration distribution area and density center, adjust the relevant parameters of the antenna transmission direction so that the adjusted antenna transmission direction matches the density center of the user concentration distribution. S3. Based on the adjusted antenna transmission direction, construct a 5G antenna weight optimization model; S4. The particle swarm optimization algorithm is used to intelligently iterate and optimize the 5G antenna weight optimization model to obtain the optimal antenna weight. S5. The optimal antenna weights are sent to the base station to reconstruct the antenna beam coverage pattern, thereby achieving precise control and interference suppression of overlapping coverage under 5G co-frequency networking.

2. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 1, characterized in that, Step S1 includes: S1.1 Clean the collected MR data and filter out the horizontal angle of arrival (HDoA) data. Interval, Vertical Angle of Arrival (VDoA) The interval, and the time lead TA value belongs to Sampling points; S1.

2. The selected sampling points are rasterized according to the set vertical and horizontal angle of arrival dimensions to form an angle raster. S1.3 By calculating the number and proportion of sampling points in each angle grid, angle grids with a sampling point proportion greater than 5% are selected as hot spot grids; S1.4 Identify the coverage boundary of the set vertical angle of arrival dimension and construct the three-dimensional coverage boundary of the entire cell; S1.

5. Based on the hotspot sampling points within the hotspot grid described in step S1.3, identify the density center of the user concentration distribution area.

3. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 2, characterized in that: The vertical arrival angle dimension set in step S1.2 is divided into four levels, namely: , , and ; The horizontal arrival angle dimension is set as follows: for interval according to Divide the mixture evenly at intervals.

4. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 3, characterized in that: In step S1.3, the total number of sampling points is set to... The total number of angle grids is Each angle grid The number of sampling points is , ∈ (1, 2, ..., The number and proportion of sampling points within each angle grid are calculated using the following formula: in, Angle grid The proportion of internal sampling points to the total number of sampling points; Hotspot grid set The area where users are concentrated is calculated using the following formula: 。 5. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 4, characterized in that: Step S1.4, which involves constructing the three-dimensional coverage boundary of the entire cell, includes: The maximum horizontal angle of arrival of the sampling points in the vertical angle of arrival dimension level is set as follows: The minimum horizontal reach angle is Then the coverage boundary of the hierarchy Represented as: in: For the vertical reach angular dimension of the hierarchy, ; If there are no hotspot grids in the hierarchy, then the coverage boundary is... If empty, the set L of the three-dimensional coverage boundary of the entire community is represented as: 。 6. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 5, characterized in that: The identification of density centers in the concentrated distribution area of ​​users in step S1.5 includes: S1.5.1, Set the set of hotspot sampling points within the hotspot grid as follows: , Hotspot sampling points The vertical angle of arrival is Then the vertical center point of all hotspot sampling points within the hotspot grid satisfy: in: Vertical angle of arrival Greater than The number of hotspot sampling points; Vertical angle of arrival Less than The number of hotspot sampling points; S1.5.2 Setting Hotspot Sampling Points The horizontal angle of arrival is Then the horizontal center point of all hotspot sampling points within the hotspot grid satisfy: in: Angle of arrival at horizontal level Greater than The number of hotspot sampling points; Angle of arrival at horizontal level Less than The number of hotspot sampling points; S1.5.3, Based on the vertical dimension center point and horizontal dimension center point The density center of the identified user concentration area is obtained as .

7. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 6, characterized in that, Step S2 includes: S2.1 Adjust the azimuth, downtilt, and beamwidth of the antenna according to the identified user concentration area and its density center; S2.2 Adjust the antenna's transmission direction to The adjustment value of the antenna azimuth angle is then... The adjustment value for the understeer angle is ; S2.3, Set the maximum vertical angle of arrival of the sampling points in the aforementioned level as... The minimum vertical reach angle is Based on the three-dimensional coverage boundary of the entire community The beamwidth configuration for each level in the vertical angle of arrival dimension is calculated using the following formula. : = ， ； in: The electron azimuth angle of the wavelobe. The electron downtilt angle of the wavelobe. The beamwidth is the value of the beam. ; Then the set of beamwidth configurations for the entire cell .

8. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 7, characterized in that, The construction of the 5G antenna weight optimization model in step S3 includes: S3.1, Let the set of cells to be adjusted be I, I = Among them, the first The antenna configuration set for each cell is as follows: Antenna configuration set The parameters include: azimuth angle, downtilt angle, transmit power, and beamwidth; No. The set of lobes for each cell is Lobe set The parameters include: vertical direction of the beam, horizontal direction of the beam, vertical beamwidth, and horizontal beamwidth; The set of network configurations to be adjusted within the cell area is as follows ; The set of geographic grids within the community area to be adjusted is ; Among them, the geographic grid is a grid of equal size that divides the optimization area according to geographical location; S3.

2. Using the particle swarm optimization algorithm, a 5G antenna weight optimization model is constructed using the following formula: in: The weights are determined based on the user distribution density within the geographic raster. To optimize the weight values ​​for target coverage; To optimize the weight values ​​for target overlap coverage; To cover the judgment function, This is the overlap / coverage determination function, where RSRP is the reference signal received power. This represents the total number of geographic grid cells. is the preset RSRP compliance rate threshold, and st is the constraint condition, which is that the coverage rate is greater than the threshold value.

9. The method for optimizing overlapping coverage in co-frequency networks based on 5G MR-DoA user distribution awareness according to claim 8, characterized in that, Step S4, which describes using the particle swarm optimization algorithm to perform intelligent iterative optimization of the 5G antenna weight optimization model, includes: S4.1 Set the total number of particles to Q, the maximum number of iterations to T, and randomly initialize the position of each particle q. and speed , where q = (1, 2, ..., Q); S4.

2. Based on the network configuration set B within the cell area to be adjusted, calculate the network configuration of each cell. RSRP value for each geographic raster; S4.3 Calculate the fitness of the t-th iteration from the calculated RSRP value, i.e., optimize the objective function. , where t = (1, 2, ..., T); S4.4 Compare the current fitness of each particle q with the historical best solution; If the current fitness is greater than the historical best solution, then the current fitness is taken as the updated historical best solution. ; If the current fitness value is not greater than the historical best solution, then the historical best solution is used as the updated historical best solution. ; S4.5 Compare the updated historical best solutions for all particles q. The maximum value obtained from the comparison is taken as the globally optimal antenna weight. ; S4.6, Based on the globally optimal antenna weights Update the position of each particle q and speed Then the position of particle q at time t+1 and speed Satisfy the following formula: in: Inertial weights; , Here is the acceleration constant; It is a random number; S4.

7. Iterate continuously until the maximum number of iterations is reached, and output the optimal antenna weights. Together with network configuration set B, it can complete the intelligent iterative optimization of the 5G antenna weight optimization model.

10. A computer storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the co-frequency network overlapping coverage optimization method based on 5G MR-DoA user distribution awareness as described in any one of claims 1 to 9.

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