WO2023083088A1 - 5g权值自适应优化方法、装置、计算设备及计算机存储介质 - Google Patents

5g权值自适应优化方法、装置、计算设备及计算机存储介质 Download PDF

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WO2023083088A1
WO2023083088A1 PCT/CN2022/129509 CN2022129509W WO2023083088A1 WO 2023083088 A1 WO2023083088 A1 WO 2023083088A1 CN 2022129509 W CN2022129509 W CN 2022129509W WO 2023083088 A1 WO2023083088 A1 WO 2023083088A1
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grid
coverage
path loss
weight combination
cluster
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PCT/CN2022/129509
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French (fr)
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朱文涛
高峰
李文智
王西点
吴远
张晨曦
刘斐
靳侃侃
徐金鹏
高明皓
王时檬
赵永红
邱禹
郝佳佳
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中国移动通信集团设计院有限公司
中国移动通信集团有限公司
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Publication of WO2023083088A1 publication Critical patent/WO2023083088A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • the present invention relates to the field of communication technology, in particular to a 5G weight self-adaptive optimization method, device, computing equipment and computer storage medium.
  • the depth coverage optimization of wireless signals in high-rise vertical scenes has always been a difficult problem in network optimization.
  • the existing 5th Generation Mobile Communication Technology (5G) weight optimization schemes for vertical scenes mainly include: , 3D)
  • the high-precision map is directly layered according to the floor height, and the simulation optimization is directly carried out in combination with the 5G beam scene of the equipment manufacturer.
  • simulate 5G cell distribution through 4G Minimization Drive Test (MDT) data, and the 4G building cell information can be obtained from the MDT data of 4G cells.
  • MDT Minimization Drive Test
  • MDT data contains latitude and longitude information, which can be combined with 3D high-precision electronic maps to geographically present the coverage of 4G wireless signals in buildings, automatically identify 4G building cell information, and sort out 4G/5G cell co-site coverage Then, according to the distance between the 5G cell and the building it covers, the height of the building, and the basic antenna feed information of the 5G cell, the 5G vertical wave width suitable for building coverage is calculated through an algorithm.
  • embodiments of the present invention are proposed to provide a 5G weight adaptive optimization method, device, computing device, and computer storage medium that overcome the above problems or at least partially solve the above problems.
  • a 5G weight adaptive optimization method including:
  • a 5G weight adaptive optimization device including:
  • the grid mapping part is configured to establish a three-dimensional grid, and map the MR sampling points of the 5G measurement report to each grid in the three-dimensional grid;
  • the grid identification part is configured to identify whether the corresponding grid is a business grid or a deep weak coverage grid based on the sampling point data in each grid;
  • the service path loss determining part is configured to determine a service distribution center grid cluster based on the service grid, and determine a first path loss between the center grid and the cell grid of the service distribution center grid cluster;
  • the service grid evaluation part is configured to evaluate the coverage of the center grid of the service distribution center grid cluster based on the first path loss, determine a weight combination that improves the coverage, and obtain the initial weight value combination;
  • the depth weak coverage grid evaluation part is configured to evaluate the coverage of the depth weak coverage grid based on the second path loss between the depth weak coverage grid and the cell grid in the initial weight combination, and determine The weight combination with improved coverage is obtained to obtain the final weight combination.
  • a computing device including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete the mutual communication via the communication bus. communication between
  • the memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operation of the above-mentioned 5G weight adaptive optimization method.
  • a computer storage medium is provided, and at least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to perform the above-mentioned 5G weight adaptive optimization method. operate.
  • the business grid and the depth weak coverage grid can be automatically identified, and the coverage effect of the weight combination on the business grid and the depth weak coverage grid can be evaluated at the same time, so as to select the best The optimal weight combination improves the accuracy of 5G wireless signal coverage evaluation.
  • FIG. 1 shows a flowchart of a 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 2 shows a schematic diagram of a vertical plane in the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 3 shows a schematic diagram of a horizontal plane in the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 4 shows a schematic diagram of antenna gains in the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 5 shows another flow chart of the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention.
  • FIG. 6 shows a schematic structural diagram of a 5G weight adaptive optimization device provided by Embodiment 2 of the present invention
  • Fig. 7 shows a schematic structural diagram of a computing device provided by an embodiment of the present invention.
  • FIG. 1 shows a flowchart of a 5G weight adaptive optimization method provided by Embodiment 1 of the present invention. As shown in Figure 1, the method includes the following steps:
  • Step S110 establishing a three-dimensional grid, and mapping 5G measurement report (Measurement Report, MR) sampling points to each grid in the three-dimensional grid.
  • mapping 5G measurement report Measurement Report, MR
  • WGS84 World Geodetic System 1984
  • WGS84 can be used to establish a three-dimensional grid of 10 meters * 10 meters * 10 meters, and three parameters of grid center point longitude X, latitude Y and height H are used to represent each grid specific location.
  • the WGS84 is a coordinate system established for use by the Global Positioning System (GPS).
  • GPS Global Positioning System
  • the association between the 5G MR sampling points and the three-dimensional grid needs to be established, that is, the 5G MR sampling points are mapped to each grid in the three-dimensional grid.
  • the latitude, longitude and altitude can be obtained by locating the 5G MR sampling points in combination with the base station industrial parameter data.
  • the 5G MR sampling point data carries VDOA (vertical direction angle relative to the antenna, similar to the downtilt angle from the sampling point to the antenna), HDOA (horizontal direction angle relative to the antenna, similar to the azimuth from the sampling point to the normal direction of the antenna) Angle), TA (distance from the antenna), combined with the antenna's latitude and longitude, height, and azimuth angle, after calculation, the latitude and longitude (see Figure 3) and height (see Figure 2) of each 5G MR sampling point can be obtained. After that, each 5G MR sampling point is mapped according to its longitude, latitude, height and three-dimensional grid. Among them, Table 1 is an example of 5G MR sampling points:
  • the synchronization signal ssrsrp (synchronization signal reference signal received power) is the average power of the synchronization signal on each carrier.
  • the calculation method of the height of the sampling point is shown in Figure 2. Assuming VDOA is ⁇ , the antenna height is H, and the distance from the sampling point to the antenna is TA, then the height of the sampling point h2 is calculated by formula (1) and formula (2).
  • the three-dimensional (x, y, z) coordinates of each 5G MR sampling point can be obtained by the following latitude-longitude coordinate conversion formula (6) and formula (7):
  • Step S120 based on the sampling point data in each grid, identify whether the corresponding grid is a service grid or a deep weak coverage grid.
  • the depth weak coverage grid is defined as: the number of sampling points in the grid is greater than a preset threshold (for example, 80) and the average Reference Signal Received Power (Reference Signal Receiving Power, RSRP) is less than the first preset threshold (for example, -105dBm ); the deep weak coverage grid can also be defined as RSRP less than a second preset threshold (for example, -110dBm) and the proportion of sampling points is greater than a preset percentage (for example, 20%).
  • a preset threshold for example, 80
  • RSRP Reference Signal Receiving Power
  • the total number of sampling points in each grid, the average RSRP, and the proportion of sampling points with deep weak coverage are calculated through 5G MR sampling points.
  • the following judgment is made during the calculation, if the number of sampling points in the grid i is greater than 80, directly use the following formula (8) to find its average RSRP:
  • RSRP j is the RSRP value of the jth 5G MR sampling point in the grid i
  • J is the total number of 5G MR sampling points in the grid i.
  • the number of sampling points in grid i is less than or equal to 80, sort the sampling points in grid i in the order of RSRP from small to large, select all sampling points less than -110dBm, and calculate the sampling points less than -110dBm
  • the proportion of all sampling points in grid i if the proportion is greater than 20%, it means that the grid is a depth weak coverage grid, and only these 20% sampling points are calculated by formula (8), and it is obtained And put it into the data set D weak ; on the contrary, if the RSRP value is less than -110dBm sampling points account for less than 20% of all sampling points in grid i, then calculate through formula (8), and get And put it into the data set D business .
  • Step S130 Determine the service distribution center grid cluster based on the service grid, and determine the first path loss between the center grid of the service distribution center grid cluster and the cell grid.
  • the 5G MR sampling points have been associated with the three-dimensional grid, and according to the quality and quantity of the 5G MR sampling points in each three-dimensional grid, the service grid and the grid with insufficient depth coverage (that is, the depth weak coverage grid), and averaged the RSRP values of the MR sampling points in each grid, and took the average as the RSRP value of the grid.
  • the business distribution center grid cluster is determined based on the business grid.
  • the value of the business grid is sorted according to the number of sampling points, and the number of sampling points is used as the criterion for judging the value.
  • the value judgment of each business grid is expressed as Among them, h is the community, i is the business grid, starting from the business grid with the highest value, it is added in sequence according to the order to form a three-dimensional graph, and the business value of all the business grids in the three-dimensional graph is calculated until the business value in the three-dimensional graph accounts for When the proportion of the total business value of the service area exceeds 80%, the convergence ends, and a high-value business distribution three-dimensional figure is formed, that is, the business distribution central grid cluster.
  • the center of this three-dimensional figure is the central grid of the service distribution central grid cluster.
  • the path loss of the scene is calculated based on the actual RSRP value of the grid in the vertical scene. After that, assuming that the default path loss remains unchanged, starting from the location of the cell, determine the center grid of the central grid cluster of service distribution. Calculate the path loss between the cell grid and the center grid, and use it as the default path loss for beam optimization in the service distribution center.
  • the determination of the service distribution center grid cluster based on the service grid may be implemented in the following manner. According to the value of each business grid, add the corresponding business grid into the business grid set in descending order, until the sum of the values of all business grids in the business grid set is greater than the first preset threshold, the current The business grid collection of is used as the business distribution center grid cluster.
  • Step1 For the set D business composed of business grids, calculate the value ⁇ of each grid in the set D business , and sort all the grids according to the value ⁇ from large to small, and the serial number is n business ;
  • Step2 Add the grid with the smallest serial number in the set D business to the set C business , and delete it from the set D business at the same time;
  • Step3 Calculate the sum ⁇ of the ⁇ values of all objects in the set C business , and judge whether ⁇ is greater than 0.8.
  • Step4 Repeat steps Step2 to Step3, stop when ⁇ >0.8, and output the business area set C business , which is the business distribution center grid cluster.
  • Step5. Extract the center point of the service area, that is, the center grid of the service distribution center grid cluster.
  • the center point of the service area is shown in formula (9).
  • Step6 Record the RSRP value of the center point of the business area (from formula (8)) according to Calculate the first path loss.
  • Step 6 for example, according to the Friesian transmission formula in the antenna theory, as shown in formula (10).
  • P r is the received power
  • P t is the transmitted power
  • G t is the gain of the transmitter
  • G r is the gain of the receiver
  • ⁇ is the wavelength of the transmitted electromagnetic wave
  • R is the distance between the transmitter and the receiver.
  • the path loss that is, the path loss L
  • the path loss L can be simplified as shown in formula (12).
  • RSRP is the measured RSRP of the grid
  • P is the transmission power
  • G is the antenna gain (which can be read from the three-dimensional gain pattern of the antenna).
  • Step S140 based on the first path loss, evaluate the coverage of the central grid of the service distribution central grid cluster, determine a weight combination that improves the coverage, and obtain an initial weight combination.
  • the combination of weights includes at least one weight of electron orientation angle, electron downtilt angle, horizontal wave width and vertical wave width.
  • multiple weight combinations can be set based on the first path loss, and the multiple weight combinations are respectively used to evaluate the coverage of the central grid of the service distribution central grid cluster, and to filter out the service distribution central grid
  • the weight combination with improved coverage of the central grid of the grid cluster is obtained to obtain the initial weight combination.
  • the initial weight combination includes multiple sets of weight combinations.
  • Step S150 based on the second path loss between the deep weak coverage grid and the cell grid in the initial weight combination, evaluate the coverage of the deep weak coverage grid, determine the weight combination that improves the coverage, and obtain The final weight combination.
  • the second path loss between the depth weak coverage grid and the cell grid is determined first, and the coverage of the depth weak coverage grid is determined based on the weight combination in the initial weight combination and the corresponding second path loss.
  • the final weight combination includes at least one set of weight combinations.
  • this embodiment is applicable to the situation where there are few depth weak coverage grids and their distribution is relatively scattered. Although the coverage evaluation for all depth weak coverage grids will consume certain computing resources and time resources, the initial weight There is a more refined evaluation of the weak coverage boosting effect of value combinations.
  • the final weight combination If it is not empty, you can directly output a set of weight combinations as the final output.
  • the final weight combination of the output is generally used
  • the first set of weight combinations in As the final output, the central grid of the service distribution central grid cluster has coverage improvement (RSRP new -RSRP>3dbm) and the deep weak coverage grid has the best improvement (corresponding to the largest ) weight combination.
  • one solution uses 3D high-precision maps combined with device manufacturers’ 5G beam scenarios to directly perform simulation optimization, only relying on simulation and building information, without real user information, it is impossible to establish The feedback mechanism, therefore, can only be used as an initialization scheme for the wireless network, i.e. only one adjustment is made, reducing the accuracy of the assessment of the 5G wireless signal coverage situation.
  • the 5G vertical wave width suitable for building coverage is calculated according to the building height and the horizontal position information of the 4G MDT data. There is no 5G user information, and only the user horizontal position information in the 4G MDT data is used to locate the 5G network. Covering cells is equivalent to using two-dimensional data to optimize the coverage of three-dimensional space. Therefore, vertical dimension information in vertical scenarios cannot be identified, which reduces the accuracy of 5G wireless signal coverage evaluation.
  • the present invention embodies a method for self-adaptive optimization of 5G weight parameters based on vertical scenarios, using the vertical dimension information in the cell-level 5G MR data (that is, each MR data can correspond to a corresponding cell) to establish a three-dimensional grid, through Carry out cluster analysis on the problem grid, locate the user distribution in the vertical scene and locate the coverage problem in the vertical scene, so as to output an adaptive 5G weight scheme.
  • the weight combination that can simultaneously improve the coverage of the business area and the weak coverage area is selected, and the optimization of the 5G vertical coverage scene is realized, thereby improving
  • the 5G resident ratio, user perception and other indicators have been improved, and the accuracy of the 5G weight optimization scheme has been improved.
  • the embodiment of the present invention can automatically identify the service grid and the depth weak coverage grid while generating the 5G MR three-dimensional grid, and use the current antenna transmission power and the three-dimensional gain pattern of the antenna to simultaneously evaluate the combination of weights in the service grid.
  • the coverage effect on the grid and depth weak coverage grid is used to select the optimal weight combination, and the calculation results can cover all default weight combinations, which improves the accuracy of 5G wireless signal coverage evaluation.
  • step S140 may include:
  • Step S1401. Calculate the combination of theoretical weights according to the central point of the service grid.
  • the theoretical weight combination may be a theoretical weight quadruple composed of electron direction angle, electron downtilt angle, horizontal wave width and vertical wave width.
  • Electronic downtilt calculation first calculate the coordinates (x h , y h , z h ) and business center point of cell h The height difference between is shown in formula (15).
  • tilt is the mechanical downtilt angle of cell h.
  • Calculation of horizontal wave width in the business distribution center grid cluster set C business , find out all the business centers Points in the same plane, that is, to find all points in the plane
  • the set of business points in for collections For each point in , use the formula (14) to calculate the angle between each point and the cell location coordinates (x h , y h , z h ) and the y-axis (that is, the direction of true north), and find the maximum The angle angle max and the minimum angle angle min .
  • the total orientation angle angle total between the cell and the center of the service grid calculated according to formula (14) can be selected from the values of 2*(angle max -angle total ) and 2*(angle total -angle min )
  • the maximum is the ideal horizontal wave width hbw ideal of the output.
  • Step S1402 according to the default 5G weight table of each equipment manufacturer, map the theoretical weight combination to the actual supportable weight combination of each equipment manufacturer.
  • a default weight combination close to the ideal weight quadruple is selected from the default weight table, and the default weight table is traversed, and the step size of the electronic downtilt (default The minimum is 1°) and the step size of the electron orientation angle (default The minimum is 1°), find out all the weight combinations that can satisfy the coverage of the ideal weight quadruple (eazimuth ideal , etilt ideal , hbw ideal , vbw ideal ) (that is, the weight combination can contain the ideal weight quadruple Coverage), which can ensure that the obtained new weight combination has a good coverage of the existing business distribution area.
  • the ideal weight quadruple eazimuth ideal , etilt ideal , hbw ideal , vbw ideal
  • the second way is to traverse the default weight table as a whole.
  • the step size of the electronic downtilt (default The minimum is 1°) and the step size of the electron orientation angle (default The minimum is 1°)
  • find out all weight combinations that cover more than 95% of the business grid set C business in this way, as many optional weight combinations as possible can be obtained under the premise of ensuring the coverage of existing services to a certain extent.
  • the second method When the weight combination calculated by the first method is empty, the second method is automatically triggered; if the weight set calculated by the second method is still empty, the step size Reduce the ratio of the number of grids covered by the business, and perform the calculation again until it is reduced to 75%, and stop without further reduction.
  • Step S1403 Evaluate the coverage of the center grid of the service distribution center grid cluster based on the antenna gain, antenna transmit power, and first path loss corresponding to the supported weight combination.
  • the above step S1403 may be implemented in the following manner. Based on the antenna gain, antenna transmit power and first path loss corresponding to the supportable weight combination, determine the reference signal received power RSRP of the center grid of each service distribution center grid cluster; based on the center grid of each service distribution center grid cluster The RSRP of the grid determines the coverage improvement of the central grid of each business distribution central grid cluster; based on the coverage improvement of the central grid of each business distribution central grid cluster, the weight combination that improves the coverage is determined.
  • the RSRP new at this time is RSRP new -RSRP>3dbm compared with the value before adjustment, that is, when the coverage improvement is greater than the preset threshold, it means that the weight combination covers the center grid of the current business distribution center grid cluster If it can be improved effectively, the obtained weight combination W i is used as the initial weight combination. After the traversal is completed, the initial weight combination ⁇ W ⁇ is output.
  • step S150 may include:
  • Step S1501 when the number of depth weak coverage grids is greater than the second preset threshold and the distribution is concentrated, perform clustering processing on the depth weak coverage grids to obtain depth weak coverage grid clusters.
  • the depth can be analyzed by a three-dimensional density-based clustering method (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) with noise Weakly covered grids are clustered to improve the efficiency of subsequent calculations.
  • a three-dimensional density-based clustering method Density-Based Spatial Clustering of Applications with Noise, DBSCAN
  • the implementation algorithm is as follows:
  • Input a data set D weak containing n weak depth and weak coverage grids, the longitude, latitude and height information of each grid center point has been converted into three-dimensional coordinate system coordinates (xi , y , zi ) ;
  • Output a set of deep weakly covered raster clusters C weak based on density clustering
  • Step. Mark all depth weak coverage grids as unvisited
  • Step2 When there is a grid marked as unvisited; execute 1-4;
  • N be the set in the ⁇ neighborhood of p, for each point p′ in N;
  • Step3 Output collection ⁇ C weak ⁇ .
  • the above-mentioned algorithm for locating the center grid of the business distribution center grid cluster can also be used to locate the center grid of each depth weak coverage sufficient grid cluster Combined with formula (8) calculated for subsequent basis Prepare for the second path loss between the center grid and the cell grid of the deep weak coverage grid cluster.
  • Step S1502. Determine the second path loss between the center grid and the cell grid of the deep weak coverage grid cluster.
  • the calculation method of the second path loss in this step is the same as the calculation method of the first path loss.
  • Step S1503 Evaluate the coverage of the center grid of the deep weak coverage grid cluster based on the antenna gain, antenna transmission power and second path loss corresponding to the initial weight combination.
  • step S1503 may be implemented in the following manner. Based on the antenna gain, antenna transmission power and second path loss corresponding to the initial weight combination, determine the RSRP of the center grid of each service distribution center grid cluster; determine the coverage based on the RSRP of the center grid of the M depth weak coverage grid clusters Lifting degree: Evaluate the coverage of the center grid of the deep weak coverage grid cluster based on the coverage lifting degree.
  • k represents the kth set of initial weights in the initial weight combination ⁇ W ⁇ (there are K sets of weights in total), Indicates the mean value of RSRP before the cluster center grid optimization, That is, the evaluation of the overall improvement of the set of weights to the weak coverage grid set ⁇ C weak ⁇ .
  • the initial weight combination ⁇ W ⁇ can also be called a candidate weight set, and all K sets of weights in the candidate weight set ⁇ W ⁇ are evaluated by formula (17) and formula (18). discard all For the weight combination whose value is less than 0, for the remaining weight combination in the initial weight combination ⁇ W ⁇ , according to Sort the values from large to small to get the final weight combination
  • the final weight combination If it is not empty, you can directly output a set of weight combinations as the final output.
  • the final weight combination of the output is generally used
  • the first set of weight combinations in As the final output, the central grid of the service distribution central grid cluster has coverage improvement (RSRP new -RSRP>3dbm) and the deep weak coverage grid has the best improvement (corresponding to the largest ) weight combination.
  • This embodiment is suitable for evaluating the coverage of the central grid of each depth weak coverage grid cluster, that is, it is suitable for the situation where there are many depth weak coverage grids and their distribution is relatively concentrated, and only for each depth weak coverage grid cluster Evaluate the coverage of the central grid, which can effectively reduce computing consumption and improve computing efficiency.
  • FIG. 5 shows another flowchart of the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention.
  • the 5G weight adaptive optimization method includes S51-S65.
  • the business area and the deep weak coverage area of the three-dimensional coverage area of the real vertical scene are located.
  • S54 and S62 are respectively performed based on the volumetric grid mapping of the MR data.
  • the RSRP data in the MR data is combined with the propagation model to evaluate the path loss of the link from the cell to the grid, so as to prepare for the evaluation of the subsequent weight scheme.
  • the propagation model is used to calculate the link loss in the service area and the deep weak coverage area, and evaluate the coverage effect of each set of candidate weight schemes, so as to find the optimal solution among them, and improve the accuracy of the coverage effect and effectiveness.
  • the embodiment of the present invention adopts the three-dimensional DBSCAN density clustering algorithm to cluster the weakly covered grids, which reduces the amount of data to be processed by subsequent algorithms, and is suitable for scenes with many deep weakly covered grids, while ensuring the calculation accuracy of the algorithm , which improves the efficiency of the algorithm and realizes the analysis of big data.
  • FIG. 6 shows a schematic structural diagram of a 5G weight adaptive optimization device provided by Embodiment 2 of the present invention.
  • the device includes: a grid mapping part 31, a grid identification part 32, a service path loss determination part 33, a service grid evaluation part 34 and a deep weak coverage grid evaluation part 35; wherein,
  • the grid mapping part 31 is configured to establish a three-dimensional grid, and map the MR sampling points of the 5G measurement report into each grid in the three-dimensional grid;
  • the grid identification part 32 is configured to identify whether the corresponding grid is a service grid or a deep weak coverage grid based on the sampling point data in each grid;
  • the service path loss determining part 33 is configured to determine a service distribution center grid cluster based on the service grid, and determine a first path loss between the center grid and the cell grid of the service distribution center grid cluster;
  • the service grid evaluation part 34 is configured to evaluate the coverage of the center grid of the service distribution center grid cluster based on the first path loss, determine a weight combination that improves the coverage, and obtain the initial weight value combination;
  • the depth weak coverage grid evaluation part 35 is configured to evaluate the coverage of the depth weak coverage grid based on the second path loss between the depth weak coverage grid and the cell grid in the initial weight combination, and determine The weight combination with improved coverage is obtained to obtain the final weight combination.
  • the service path loss determining part 33 is further configured to: according to the value of each service grid, add the corresponding service grid into the service grid set in descending order, until the service grid If the sum of the values of all business grids in the grid set is greater than the first preset threshold, the current business grid set is used as the business distribution center grid cluster.
  • the service grid evaluation part 34 is further configured to: calculate a combination of theoretical weights according to the center point of the service grid; combine the theoretical weights according to the default 5G weight table of each equipment manufacturer Mapping to the actual supportable weight combination of each equipment manufacturer; based on the antenna gain, antenna transmission power and first path loss corresponding to the supportable weight combination, the central grid of the service distribution central grid cluster coverage is evaluated.
  • the depth weak coverage grid evaluation part 35 is further configured to: when the number of depth weak coverage grids is greater than a second preset threshold and the distribution is relatively concentrated, perform clustering on the depth weak coverage grids processing to obtain the depth weak coverage grid cluster; determine the second path loss between the central grid of the depth weak coverage grid cluster and the cell grid; combine the corresponding antenna gain, antenna transmission power and the second path loss based on the initial weight value combination The second path loss evaluates the coverage of the central grid of the deep weak coverage grid cluster.
  • the service grid evaluation part 34 is further configured to: determine the value of each service distribution center grid cluster based on the antenna gain, antenna transmit power, and first path loss corresponding to the supportable weight combination
  • the reference signal received power RSRP of the central grid based on the RSRP of the central grids of the central grid clusters of each service distribution, determine the coverage improvement range of the central grids of the central grid clusters of each service distribution;
  • the coverage improvement of the center grid determines the combination of weights that improves the coverage.
  • the deep weak coverage grid evaluation part 35 is further configured to: determine the value of each service distribution center grid cluster based on the antenna gain, antenna transmission power, and second path loss corresponding to the initial weight combination The RSRP of the central grid; determine the coverage improvement based on the RSRP of the central grids of the M deep weak coverage grid clusters; evaluate the coverage of the central grids of the deep weak coverage grid clusters based on the coverage improvement .
  • the combination of weights includes at least one weight of electron direction angle, electron downtilt angle, horizontal wave width and vertical wave width.
  • the 5G weight adaptive optimization device described in the embodiment of the present invention is used to implement the 5G weight adaptive optimization method described in the above embodiment, and its working principle is similar to the technical effect, so it will not be repeated here.
  • An embodiment of the present invention provides a non-volatile computer storage medium, the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the 5G weight adaptive optimization method in any of the above method embodiments .
  • FIG. 7 shows a schematic structural diagram of a computing device provided by an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
  • the computing device may include: a processor (processor), a communication interface (Communications Interface), a memory (memory), and a communication bus.
  • a processor processor
  • a communication interface Communication Interface
  • a memory memory
  • a communication bus a communication bus
  • the processor, the communication interface, and the memory complete the mutual communication through the communication bus.
  • the communication interface is used to communicate with network elements of other devices such as clients or other servers.
  • the processor is configured to execute the program, specifically, it can execute the relevant steps in the above-mentioned embodiments of the 5G weight adaptive optimization method for computing equipment and the cell azimuth prediction method.
  • the program may include program code, and the program code includes computer operation instructions.
  • the processor may be a central processing unit CPU, or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.
  • the one or more processors included in the computing device may be of the same type, such as one or more CPUs, or may be different types of processors, such as one or more CPUs and one or more ASICs.
  • the memory may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
  • the program may specifically be used to enable the processor to execute the 5G weight adaptive optimization method in any of the above method embodiments.
  • each step in the program refer to the description of the corresponding steps and units in the above-mentioned embodiment of the 5G weight adaptive optimization method, and details are not repeated here.
  • Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described devices and parts can refer to the corresponding process description in the foregoing method embodiments, and details are not repeated here.
  • parts of the devices in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment.
  • Parts or units or components in the embodiments may be combined into one part or unit or component, and further they may be divided into a plurality of sub-parts or sub-units or sub-assemblies.
  • All features disclosed in this specification including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined.
  • Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
  • the various component embodiments of the present invention may be implemented in hardware, or in software running on one or more processors, or in a combination thereof.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components according to the embodiments of the present invention.
  • Embodiments of the present invention can also be implemented as a device or apparatus program (eg, computer program and computer program product) for performing a part or all of the methods described herein.
  • Such a program implementing an embodiment of the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals.
  • Such a signal may be downloaded from an Internet site, or provided on a carrier signal, or provided in any other form.
  • the invention discloses a 5G weight adaptive optimization method, device, computing equipment and computer storage medium.
  • the 5G weight adaptive optimization method includes: mapping the 5G measurement report MR sampling points to each grid in the three-dimensional grid grid; determine the first path loss between the center grid of the service distribution center grid cluster and the cell grid; evaluate the coverage of the center grid of the service distribution center grid cluster based on the first path loss, and determine the The weight combination with improved coverage is obtained to obtain the initial weight combination; based on the second path loss between the depth weak coverage grid and the cell grid in the initial weight combination, the coverage of the depth weak coverage grid is evaluated, Determine the combination of weights that improves the coverage to obtain the final combination of weights.
  • the embodiment of the present invention can automatically identify the service grid and the depth weak coverage grid, and can simultaneously evaluate the coverage effect of the weight combination on the service grid and the depth weak coverage grid, so as to select the optimal weight combination, Improved the accuracy of 5G wireless signal coverage evaluation.

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Abstract

本发明公开了一种5G权值自适应优化方法、装置、计算设备及计算机存储介质，方法包括：将5G测量报告MR采样点映射到三维立体栅格中的各栅格内；确定业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损；基于第一路损对业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合；基于初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。本发明可以自动识别出业务栅格和深度弱覆盖栅格，同时评估权值组合在业务栅格和深度弱覆盖栅格上的覆盖效果，从而选出最优的权值组合，提高了评估的准确性。

Description

5G权值自适应优化方法、装置、计算设备及计算机存储介质
相关申请的交叉引用
本申请基于申请号为202111335448.5、申请日为2021年11月11日、申请名称为“5G权值自适应优化方法、装置、设备及存储介质”的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本发明涉及通信技术领域，具体涉及一种5G权值自适应优化方法、装置、计算设备及计算机存储介质。
背景技术
高楼垂直场景无线信号的深度覆盖优化一直是网络优化中的难题，现有针对垂直场景的第五代移动通信技术(5th Generation Mobile Communication Technology，5G)权值优化方案主要有：根据三维(three dimensional，3D)高精度地图直接按楼层高度分层，结合设备厂家5G波束场景直接进行仿真优化。或，通过4G最小化路测(Minimization Drive Test，MDT)数据模拟5G小区分布，4G楼宇小区信息可从4G小区的MDT数据中获取。示例性的，MDT数据包含有经纬度信息，可结合3D高精度电子地图，将楼宇内的4G无线信号覆盖情况进行地理化的呈现，自动识别4G楼宇小区信息，通过梳理4G/5G小区共站覆盖情况，定位出5G楼宇覆盖小区；之后，根据5G小区与其所覆盖楼宇的距离、楼宇站高以及5G小区的基本天线天馈信息，通过算法计算出适配于楼宇覆盖的5G垂直波宽。
但是，上述技术方案在进行垂直场景的5G权值优化时，计算只涉及到了楼宇高度和4G MDT数据的水平位置信息，无法精准识别垂直维度信息，降低了5G无线信号覆盖情况评估的准确性。
发明内容
鉴于上述问题，提出了本发明实施例以便提供一种克服上述问题或者至少部分地解决上述问题的5G权值自适应优化方法、装置、计算设备及计算机存储介质。
根据本发明实施例的一个方面，提供了一种5G权值自适应优化方法，包括：
建立三维立体栅格，并将5G测量报告MR采样点映射到所述三维立体栅格中的各栅格内；
基于各栅格内的采样点数据识别对应栅格是业务栅格还是深度弱覆盖栅格；
基于业务栅格确定业务分布中心栅格簇，并确定所述业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损；
基于所述第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合；
基于所述初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对所述深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。
根据本发明实施例的另一方面，提供了一种5G权值自适应优化装置，包括：
栅格映射部分，被配置为建立三维立体栅格，并将5G测量报告MR采样点映射到所述三维立体栅格中的各栅格内；
栅格识别部分，被配置为基于各栅格内的采样点数据识别对应栅格是业务栅格还是深度弱覆盖栅格；
业务路损确定部分，被配置为基于业务栅格确定业务分布中心栅格簇，并确定所述业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损；
业务栅格评估部分，被配置为基于所述第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合；
深度弱覆盖栅格评估部分，被配置为基于所述初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对所述深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。
根据本发明实施例的另一方面，提供了一种计算设备，包括：处理器、存储器、通信接口和通信总线，所述处理器、所述存储器和所述通信接口通过所述通信总线完成相互间的通信；
所述存储器用于存放至少一可执行指令，所述可执行指令使所述处理器执行上述5G权值自适应优化方法的操作。
根据本发明实施例的另一方面，提供了一种计算机存储介质，所述存储介质中存储有至少一可执行指令，所述可执行指令使处理器执行如上述5G权值自适应优化方法的操作。
根据本发明上述实施例提供的方案，可以自动识别出业务栅格和深度弱覆盖栅格，并可以同时评估权值组合在业务栅格和深度弱覆盖栅格上的覆盖效果，从而选出最优的权值组合，提高了5G无线信号覆盖情况评估的准确性。
上述说明仅是本发明实施例技术方案的概述，为了能够更清楚了解本 发明实施例的技术手段，而可依照说明书的内容予以实施，并且为了让本发明实施例的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明实施例的具体实施方式。
附图说明
通过阅读下文优选实施方式的详细描述，各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。附图仅用于示出优选实施方式的目的，而并不认为是对本发明实施例的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：
图1示出了本发明实施例一提供的5G权值自适应优化方法的流程图；
图2示出了本发明实施例一提供的5G权值自适应优化方法中的垂直面示意图；
图3示出了本发明实施例一提供的5G权值自适应优化方法中的水平面示意图；
图4示出了本发明实施例一提供的5G权值自适应优化方法中的天线增益示意图；
图5示出了本发明实施例一提供的5G权值自适应优化方法的另一种流程图；
图6示出了本发明实施例二提供的5G权值自适应优化装置的结构示意图；
图7示出了本发明实施例提供的计算设备的结构示意图。
具体实施方式
下面将参照附图更详细地描述本发明的示例性实施例。虽然附图中显示了本发明的示例性实施例，然而应当理解，可以以各种形式实现本发明而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本发明，并且能够将本发明的范围完整的传达给本领域的技术人员。
实施例一
图1示出了本发明实施例一提供的5G权值自适应优化方法的流程图。如图1所示，该方法包括以下步骤：
步骤S110、建立三维立体栅格，并将5G测量报告(Measurement Report，MR)采样点映射到三维立体栅格中的各栅格内。
示例性的，可以采用WGS84(World Geodetic System 1984)建立10米*10米*10米的三维立体栅格，并用栅格中心点经度X、纬度Y和高度H三个参数来表示每个栅格的具体位置。该WGS84是为全球定位系统(Global Positioning System，GPS)使用而建立的坐标系统。
在建立三维立体栅格之后，需要建立5G MR采样点和三维立体栅格之间的关联，即将5G MR采样点映射到三维立体栅格中的各栅格内。示例性的，可以结合基站工参数据对5G MR采样点进行定位得出经纬度和高度。示例性的，5G MR采样点数据携带VDOA(相对天线的垂直方向角，类似于采样点到天线的下倾角)、HDOA(相对天线的水平方向角，类似于采样点到天线法线方向的方位角)、TA(距离天线的距离)，结合天线的经纬度、高度、方位角，经过计算可得出每个5G MR采样点的经纬度(参见图3)和高度(参见图2)。之后，将每个5G MR采样点按照其经度、纬度、高度与三维立体栅格进行映射。其中，表一为5G MR采样点示例：
表一
MR标识 小区标识 ssrsrp 距离 水平到达角 垂直到达角
559364495198130176_xx_100043 xx -93 604 -56 -12
559364495198130176_xx_100132 xx -85 616 -58 0
559364495198130176_xx_96140 xx -80 240 -36 -20
559364495198130176_xx_96188 xx -88 384 44 -14
其中，同步信号ssrsrp(synchronization signal reference signal received power)是同步信号在每个载波的平均功率。
其中，采样点高度计算方法参见图2所示，设VDOA为α，天线高度H，采样点到天线距离TA，则采样点高度h2通过公式(1)和公式(2)计算得到。
h1＝TA·tanα     (1)
h2＝H-h1     (2)
其中，采样点经纬度计算方法参见图3所示，天线总方位角γ，HDOAθ，天线经纬度(long0，lat0)，采样点经纬度(long1，lat1)通过公式(3)、公式(4)和公式(5)计算得到。
β＝γ+θ        (3)
Figure PCTCN2022129509-appb-000001
Figure PCTCN2022129509-appb-000002
其中，C1，C2是与地球半径相关的常量，一般可以取C1＝111320，C2＝110540。可以通过下述经纬度坐标转换公式(6)和公式(7)即得到每个5G MR采样点的三维(x，y，z)坐标：
x＝lom*20037508.34/180     (6)
Figure PCTCN2022129509-appb-000003
步骤S120、基于各栅格内的采样点数据识别对应栅格是业务栅格还是深度弱覆盖栅格。
其中，深度弱覆盖栅格定义为：栅格中采样点数量大于预设阈值(例 如，80)且平均参考信号接收功率(Reference Signal Receiving Power，RSRP)小于第一预设阈值(例如，-105dBm)；深度弱覆盖栅格也可以定义为RSRP小于第二预设阈值(例如，-110dBm)且采样点占比大于预设百分比(例如，20％)。
示例性的，通过5G MR采样点计算每个栅格内的采样点总数、平均RSRP、深度弱覆盖(RSRP小于-105dBm)采样点占比。示例性的，计算时进行如下判断，如果栅格i中采样点数量大于80，直接采用下述公式(8)求其平均RSRP：
Figure PCTCN2022129509-appb-000004
其中，RSRP j为栅格i中的第j个5G MR采样点的RSRP值，J为该栅格i中5G MR采样点的总数。若
Figure PCTCN2022129509-appb-000005
则把该值记为
Figure PCTCN2022129509-appb-000006
并放入深度弱覆盖栅格数据集D weak中，用于后续处理；反之，则把该值记为
Figure PCTCN2022129509-appb-000007
并放入业务栅格数据集D business中，用于后续处理。
此外，若栅格i中采样点数量小于或等于80，则对栅格i中采样点按照RSRP从小到大的顺序进行排序，选出全部小于-110dBm采样点，并计算小于-110dBm采样点在栅格i全部采样点中的占比，若占比大于20％，说明该栅格为深度弱覆盖栅格，则只对这20％采样点通过公式(8)进行计算，得出
Figure PCTCN2022129509-appb-000008
并放入数据集D weak中；反之，若RSRP值小于-110dBm采样点在栅格i全部采样点中的占比不足20％，则通过公式(8)进行计算，得出
Figure PCTCN2022129509-appb-000009
并放入数据集D business中。
步骤S130、基于业务栅格确定业务分布中心栅格簇，并确定业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损。
通过上述步骤已经把5G MR采样点与立体栅格进行了关联，并根据每个立体栅格内5G MR采样点的质量和数量，识别出了业务栅格和深度覆盖不足栅格(即深度弱覆盖栅格)，并对每个栅格中MR采样点的RSRP值进行了取平均计算，将平均值作为该栅格的RSRP值。对栅格进行识别确定了各栅格的类型之后，基于业务栅格确定业务分布中心栅格簇。
示例性的，将业务栅格按照采样点数量进行价值排序，以采样点数量作为判断价值标准，每个业务栅格价值判断表示为
Figure PCTCN2022129509-appb-000010
其中，h为小区，i为业务栅格，从价值最高的业务栅格开始，按照排序依次加入形成立体图形，并计算立体图形内所有业务栅格的业务价值，直到立体图形内的业务价值占服务小区总业务价值的比例超过80％，收敛结束，形成高价值业务分布立体图形即业务分布中心栅格簇，此立体图形的中心即为业务分布中心栅格簇的中心栅格。然后，通过垂直场景下栅格实际的RSRP值，计算出该场景的路损，之后，假设默认路损保持不变，以小区所在位置为起点，确定业务分布中心栅格簇的中心栅格，计算小区栅格和该中心栅格之间的路损，作为业务分布中心波束寻优的默认路损。
在一些实施例中，上述基于业务栅格确定业务分布中心栅格簇可以通过以下方式实现。根据各业务栅格的价值按照从大到小的顺序将对应业务栅格加入业务栅格集合中，直至业务栅格集合中的所有业务栅格的价值之和大于第一预设阈值，将当前的业务栅格集合作为业务分布中心栅格簇。
一种可实现算法如下：
Step1、对业务栅格构成的集合D business，计算集合D business中每个栅格的价值α，并将所有栅格按照价值α从大到小进行排序，序号为n business；
Step2、将集合D business中序号最小的栅格加入集合C business，同时从集合D business中删除；
Step3、计算集合C business中所有对象的α值之和∑α，判断∑α是否大于0.8。
Step4、重复步骤Step2至Step3，当∑α>0.8时停止，输出业务区域集合C business即业务分布中心栅格簇。
Step5、提取业务区域中心点即业务分布中心栅格簇的中心栅格，业务区域中心点如公式(9)所示。
Figure PCTCN2022129509-appb-000011
Step6、记业务区域中心点的RSRP值为
Figure PCTCN2022129509-appb-000012
(由公式(8)得出)根据
Figure PCTCN2022129509-appb-000013
计算得到第一路损。
关于Step6，示例性的，根据天线理论当中的弗里斯传输公式，如公式(10)所示。
Figure PCTCN2022129509-appb-000014
其中，P r为接收功率，P t为发射功率，G t为发射端增益，G r为接收端增益，λ为传输电磁波的波长，R为发射端和接收端之间的距离。对于两边取对数(用于转换成dB形式)，如公式(11)所示。
Figure PCTCN2022129509-appb-000015
因此，对于一条特定链路，路径损耗即路损L可简化为如公式(12)所示。
L＝P+G-RSRP    (12)
其中，RSRP即测量到栅格的RSRP，P为发射功率，G为天线增益(可由天线三维增益方向图读出)。由此，通过中心栅格的平均RSRP值
Figure PCTCN2022129509-appb-000016
和天线三维增益方向图，即可得到当前中心栅格的路损L c。同理，可以根据当前的权值匹配对应的天线增益G和深度弱覆盖栅格的平均RSRP(如公式(8)所示)，计算出深度弱覆盖栅格集合D business中的n weak个弱覆盖栅格的路损
Figure PCTCN2022129509-appb-000017
其中，i∈{1,...,K}；如果此时因为深度弱覆盖栅格数量过多(例如，大于100)而进行了聚类计算，则对于集合{C weak}，计算出每个深度弱覆盖栅格簇中心的路损
Figure PCTCN2022129509-appb-000018
其中i∈{1,...,C}，C为集合{C weak}中聚类的簇数。
步骤S140、基于第一路损对业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合。
在一些实施例中，权值组合包括电子方向角、电子下倾角、水平波宽和垂直波宽中的至少一项权值。
示例性的，可以基于第一路损设定多个权值组合，分别采用该多个权值组合对业务分布中心栅格簇的中心栅格的覆盖情况进行评估，筛选出对业务分布中心栅格簇的中心栅格的覆盖情况有所改善的权值组合，得到初始权值组合。初始权值组合中包括多套的权重组合。
步骤S150、基于初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。
示例性的，首先确定深度弱覆盖栅格和小区栅格之间的第二路损，基于初始权值组合中的权值组合和对应的第二路损对深度弱覆盖栅格的覆盖情况进行评估，筛选出对深度弱覆盖栅格的覆盖情况有所改善的权值组合，得到最优的权值组合(即最终的权值组合)，最终的权值组合中包括至少一套权重组合。
示例性的，本实施例适用于深度弱覆盖栅格较少且分布较为分散的情况，虽然对全部的深度弱覆盖栅格进行覆盖评估会消耗一定的计算资源和时间资源，但是会对初始权值组合的弱覆盖提升效果有一个较为精细的评估。
示例性的，以集合的形式对初始权值组合和最终的权值组合为例，对全部的深度覆盖不足栅格进行覆盖评估进行说明，对于初始权值组合{W}中每套权值组合，对全部的n weak个深度弱覆盖栅格，结合上述公式(8)得出平均RSRP，以及上述公式(12)得出每个深度弱覆盖栅格的路损
Figure PCTCN2022129509-appb-000019
通过公式(17)计算出该栅格在当前初始权值组合下的RSRP取值
Figure PCTCN2022129509-appb-000020
再通过上述公式(8)计算出该套初始权值组合对全部的n weak个深度弱覆盖栅格的覆盖提升值
Figure PCTCN2022129509-appb-000021
舍去所有
Figure PCTCN2022129509-appb-000022
取值小于0的权值组合，对于{W}中剩下的权值组合，按照
Figure PCTCN2022129509-appb-000023
取值从大到小进行排序，得到最终的权值组合
Figure PCTCN2022129509-appb-000024
若最终的权值组合
Figure PCTCN2022129509-appb-000025
不为空，则可以直接输出其中的一套权值组合作为最终的输出，这里一般以输出的最终的权值组合
Figure PCTCN2022129509-appb-000026
中的第一套权值组合
Figure PCTCN2022129509-appb-000027
作为最终的输出，即对业务分布中心栅格簇的中心栅格有覆盖提升(RSRP new-RSRP＞3dbm)且深度弱覆盖栅格改善最好(对应于最大的
Figure PCTCN2022129509-appb-000028
)的权值组合。
相关技术中在进行垂直场景的5G权值优化时，一种方案中，根据3d高精度地图结合设备厂家5G波束场景直接进行仿真优化，只依靠仿真和楼宇信息，没有真实的用户信息，无法建立反馈机制，因此，只能作为无线网络的一种初始化方案，即只进行一次调整，降低了5G无线信号覆盖情况 评估的准确性。另一种方案中，根据楼宇高度和4G MDT数据的水平位置信息，计算出适配于楼宇覆盖的5G垂直波宽，没有5G用户信息，仅仅依靠4G MDT数据中的用户水平位置信息来定位5G覆盖小区，相当于使用二维数据优化三维空间的覆盖问题，因此无法识别垂直场景下垂直维度信息，降低了5G无线信号覆盖情况评估的准确性。
本发明实施例了一种基于垂直场景的5G权值参数自适应优化方法，采用小区级5G MR数据(即每个MR数据可以对应到相应的小区)中的垂直维度信息建立立体栅格，通过对问题栅格进行聚类分析，定位出垂直场景下的用户分布并定位出垂直场景覆盖问题，从而输出自适应的5G权值方案。通过计算出的业务中心栅格的路损和深度弱覆盖栅格的路损，筛选出能够同时提升业务区域和弱覆盖区域覆盖情况的权值组合，实现了5G垂直覆盖场景的优化，从而提高了5G驻留比、用户感知等指标，提高了5G权值优化方案的准确性。
本发明实施例可以在生成5G MR立体栅格的同时，自动识别出业务栅格和深度弱覆盖栅格，并利用当前天线发射功率和天线三维增益方向图，可以同时评估权值组合在业务栅格和深度弱覆盖栅格上的覆盖效果，从而选出最优的权值组合，其计算结果可以覆盖全部默认权值组合，提高了5G无线信号覆盖情况评估的准确性。
在一些实施例中，步骤S140可以包括：
步骤S1401、根据业务栅格的中心点计算出理论权值组合。
其中，理论权值组合可以为由电子方向角、电子下倾角、水平波宽和垂直波宽组成的理论权值四元组。示例性的，可根据业务栅格的中心点
Figure PCTCN2022129509-appb-000029
计算出理论权值四元组，如下：
电子方向角计算：计算小区h的坐标(x h，y h，z h)和业务中心点
Figure PCTCN2022129509-appb-000030
在z＝0对应的x、y平面上投影点所构成线段的与y轴(即正北方向)的夹角，即计算点(x h，y h)和点
Figure PCTCN2022129509-appb-000031
构成线段的极坐标角度angle total。首先计算两个点之间的水平距离，如公式(13)所示。
Figure PCTCN2022129509-appb-000032
则可以得出线段与北向的夹角，如公式(14)所示。
Figure PCTCN2022129509-appb-000033
计算出的angle total即为此时总的方向角，再根据已有的机械下倾角azimuth，即得到理论电子方向角eazimuth ideal＝angle total-azimuth。
电子下倾角计算：首先计算小区h的坐标(x h，y h，z h)和业务中心点
Figure PCTCN2022129509-appb-000034
之间的高度差如公式(15)所示。
Figure PCTCN2022129509-appb-000035
再根据公式(13)计算出的两个点之间的水平距离，得出理论电子下倾角如公式(16)所示。
Figure PCTCN2022129509-appb-000036
其中，tilt为小区h的机械下倾角。
水平波宽计算：在业务分布中心栅格簇集合C business中，找出所有与业务中心
Figure PCTCN2022129509-appb-000037
处于同一平面的点，即找出所有处于平面
Figure PCTCN2022129509-appb-000038
中的业务点集合
Figure PCTCN2022129509-appb-000039
对于集合
Figure PCTCN2022129509-appb-000040
中的每个点，通过公式(14)计算出每个点和小区位置坐标(x h，y h，z h)的连接线与y轴(即正北方向)的夹角，从中找出最大角度angle max和最小角度angle min。可以根据公式(14)计算出的小区与业务栅格中心之间的总方向角angle total，从2*(anggle max-angle total)和2*(angle total-angle min)的取值中选出最大，即为输出的理想水平波宽hbw ideal。
垂直波宽计算：找出业务分布中心栅格簇集合C business中业务中心在垂直方向对应的最低栅格
Figure PCTCN2022129509-appb-000041
和最高栅格
Figure PCTCN2022129509-appb-000042
再根据公式(15)和公式(16)计算出最低、最高栅格相对于小区h的坐标(x h，y h，z h)位置的下倾角etilt max、etilt min，同理，选取2*(etilt max-etilt ideal)和2*(etilt ideal-etilt min)中最大的作为输出的理想水平波宽vbw ideal。
步骤S1402、根据各设备厂家默认的5G权值表将理论权值组合映射为各设备厂家实际可支持的权值组合。
示例性的，可结合各个设备厂家默认的5G权值表，如下表二所示，为5G默认权值表：
表二
Figure PCTCN2022129509-appb-000043
Figure PCTCN2022129509-appb-000044
从中筛选中所有能够包含输出的理想权值四元组(eazimuth ideal、etilt ideal、hbw ideal、vbw ideal)的所有权值组合作为输出，得到各设备厂家实际可支持的权值组合。示例性的，这里可以通过两种方法实现：
第一种方式，从默认权值表中选出与理想权值四元组相近的默认权值组合为起始，对默认权值表进行遍历，电子下倾角的步长(默认
Figure PCTCN2022129509-appb-000045
最小为1°)和电子方向角的步长(默认
Figure PCTCN2022129509-appb-000046
最小为1°)，找出所有可以满足理想权值四元组(eazimuth ideal、etilt ideal、hbw ideal、vbw ideal)覆盖的权值组合(即该权值组合可以包含理想权值四元组的覆盖范围)，这样可以保证得出的新的权值组合对现有的业务分布区域具有良好的覆盖。
第二种方式，对默认权值表整体进行遍历，在遍历时，电子下倾角的步长(默认
Figure PCTCN2022129509-appb-000047
最小为1°)和电子方向角的步长(默认
Figure PCTCN2022129509-appb-000048
最小为1°)，找出所有覆盖业务栅格数占业务栅格集合C business比例95％以上的权值组合。这样能够一定程度上保证现有业务覆盖的前提下，得到尽量多的可选权值组合。
在第一种方式计算出的权值组合为空时，自动触发第二种方式；若第二种方式计算得到的权值集合仍为空，则以步长
Figure PCTCN2022129509-appb-000049
减小覆盖业务栅格数比例，再次进行计算，直到减小到75％时停止，不再进一步减小。
步骤S1403、基于可支持的权值组合对应的天线增益、天线发射功率及第一路损对业务分布中心栅格簇的中心栅格的覆盖情况进行评估。
在一些实施例中，上述步骤S1403可以通过以下方式实现。基于可支持的权值组合对应的天线增益、天线发射功率及第一路损确定各业务分布中心栅格簇的中心栅格的参考信号接收功率RSRP；基于各业务分布中心栅格簇的中心栅格的RSRP确定各业务分布中心栅格簇的中心栅格的覆盖提升幅度；基于各业务分布中心栅格簇的中心栅格的覆盖提升幅度确定对覆盖情况有所改善的权值组合。
示例性的，对于遍历到的可支持的权值组合中每个权值组合W i，再根据该权值组合对应的默认权值pattern的三维天线增益方向图，如图4所示，读出当前权值组合的配置的天线增益G new，再通过根据公式(12)计算出业务分布中心栅格簇的中心栅格的路损L c，结合当前的天线发射功率P new，通过如下公式(17)计算出当前业务分布中心栅格簇的中心栅格的RSRP值：
RSRP new＝P new+G new-L c     (17)
若此时的RSRP new相比于调整前的值有RSRP new-RSRP＞3dbm，即覆盖提升幅度大于预设阈值时，说明该权值组合对当前业务分布中心栅格簇的中心栅格的覆盖能有效提升，则把得到权值组合W i作为初始权值组合， 遍历完成后，输出初始权值组合{W}。
在一些实施例中，步骤S150可以包括：
步骤S1501、当深度弱覆盖栅格的数量大于第二预设阈值且分布较为集中时，对深度弱覆盖栅格进行聚类处理，得到深度弱覆盖栅格簇。
示例性的，当深度弱覆盖栅格的数量大于一定数值时(例如，大于100)，可以通过三维具有噪声的基于密度的聚类方法(Density-Based Spatial Clustering of Applications with Noise，DBSCAN)对深度弱覆盖栅格进行聚类，从而提升后续计算的效率。实现算法如下：
输入：包含n weak个深度弱覆盖栅格的数据集D weak，每个栅格中心点的经度、维度和高度信息已经转化为三维坐标系坐标(x i，y i，z i)；
邻域半径：ε＝30米；
密度阈值：MinPts＝5；
输出：基于密度聚类的深度弱覆盖栅格簇集合C weak；
Step1、将所有深度弱覆盖栅格标记为unvisited；
Step2、当有标记为unvisited的栅格时；执行1-4；
1、随机选取一个unvisited对象p；
2、标记p为visited；
3、若p的ε邻域内至少有MinPts个对象，则执行3.1-3.3；
3.1、创建一个新的簇C，并把p放入C中；
3.2、设N是p的ε邻域内的集合，对N中的每个点p′；
如果点p′是unvisited，标记p′是visited；若p′的ε邻域内至少有MinPts个对象，则把这些点添加到集合N中；若p′还不是任何簇的成员，则把p′添加到C；
3.3、保存C；
4、否则标记p为噪声；
Step3、输出集合{C weak}。
在得到集合{C weak}后，同样可以利用上述定位业务分布中心栅格簇的中心栅格的算法定位出每个深度弱覆盖足栅格簇的中心栅格
Figure PCTCN2022129509-appb-000050
结合公式(8)计算出的
Figure PCTCN2022129509-appb-000051
为后续根据
Figure PCTCN2022129509-appb-000052
得到深度弱覆盖栅格簇的中心栅格和小区栅格之间的第二路损做准备。
步骤S1502、确定深度弱覆盖栅格簇的中心栅格和小区栅格之间的第二路损。
本步骤第二路损的计算方法与第一路损的计算方法相同，相关内容可参见上述计算第一路损的相关描述，这里不再赘述。
步骤S1503、基于初始权值组合对应的天线增益、天线发射功率及第二路损对深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估。
在一些实施例中，步骤S1503可以通过以下方式实现。基于初始权值 组合对应的天线增益、天线发射功率及第二路损确定各业务分布中心栅格簇的中心栅格的RSRP；基于M个深度弱覆盖栅格簇的中心栅格的RSRP确定覆盖提升度；基于覆盖提升度对深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估。
示例性的，对于初始权值组合{W}中每个权值组合，结合深度弱覆盖栅格簇集合{C weak}中每个深度弱覆盖栅格簇的中心栅格
Figure PCTCN2022129509-appb-000053
计算出的路损
Figure PCTCN2022129509-appb-000054
利用公式(17)计算出当前每个深度弱覆盖簇的中心栅格的RSRP值
Figure PCTCN2022129509-appb-000055
最后，对全部的M个深度弱覆盖栅格簇的中心栅格的权值调整前后的RSRP变化进行统计，如公式(18)所示。
Figure PCTCN2022129509-appb-000056
其中，k表示初始权值组合{W}中的第k套初始权值(总计有K套权值)，
Figure PCTCN2022129509-appb-000057
表示簇中心栅格优化前的RSRP均值，
Figure PCTCN2022129509-appb-000058
即为该套权值对弱覆盖栅格集合{C weak}整体改善情况的评估。初始权值组合{W}也可以称为候选权值集合，对候选权值集合{W}中全部K套权值均通过公式(17)和公式(18)进行深度弱覆盖栅格提升评估，舍去所有
Figure PCTCN2022129509-appb-000059
取值小于0的权值组合，对于初始权值组合{W}中剩下的权值组合，按照
Figure PCTCN2022129509-appb-000060
取值从大到小进行排序，得到最终的权值组合
Figure PCTCN2022129509-appb-000061
若最终的权值组合
Figure PCTCN2022129509-appb-000062
不为空，则可以直接输出其中的一套权值组合作为最终的输出，这里一般以输出的最终的权值组合
Figure PCTCN2022129509-appb-000063
中的第一套权值组合
Figure PCTCN2022129509-appb-000064
作为最终的输出，即对业务分布中心栅格簇的中心栅格有覆盖提升(RSRP new-RSRP＞3dbm)且深度弱覆盖栅格改善最好(对应于最大的
Figure PCTCN2022129509-appb-000065
)的权值组合。
本实施例适用于对每个深度弱覆盖栅格簇的中心栅格进行覆盖评估，即适用于深度弱覆盖栅格较多且分布较为集中的情况，通过只对每个深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估，可以有效的降低计算消耗、提升计算效率。
下面，将说明本申请实施例在一个实际的应用场景中的示例性应用。
如图5所示，图5示出了本发明实施例一提供的5G权值自适应优化方法的另一种流程图。该5G权值自适应优化方法包括S51-S65。
S51、垂直场景主覆盖小区筛选。
S52、5G MR数据预处理。
本发明实施例基于小区真实的5G MR数据，利用垂直到达角和TA数据，定位出真实的垂直场景的立体覆盖区域的业务区域和深度弱覆盖区域。
S53、MR数据立体栅格映射。
在得到MR数据立体栅格映射之后，分别基于MR数据立体栅格映射执行S54和S62。
S54、业务分布热点栅格识别。
S55、基于传播模型的链路路损评估。
本发明实施例在波束参数自适应寻优中，利用MR数据中的RSRP数据结合传播模型，评估出小区到栅格的链路路损，为后续权值方案的评估做准备。示例性的，利用传播模型对业务区域和深度弱覆盖区域链路路损进行计算，评估出每套候选权值方案的覆盖效果，从而找出其中的最优解，提高了覆盖效果的准确性和有效性。
S56、业务分布中心自适应波束方案计算。
S57、判断候选集合是否为空。
若是，则执行S58，若否，则执行S64。
S58、判断业务栅格数比例是否<75％。
若是，则程序结束，若否，则依次执行S59和S55。
S59、降低业务栅格覆盖数量。
S60、深度覆盖不足栅格识别。
S61、判断弱覆盖栅格数是否<100。
若否，则依次执行S62、S63和S64，若是，则依次执行S63和S64。
S62、弱覆盖栅格聚类。
本发明实施例采用三维DBSCAN密度聚类算法对弱覆盖栅格进行聚类，降低了后续算法所需处理的数据量，适应于深度弱覆盖栅格较多的场景，在保证算法计算精度的同时，提高了算法效率，实现了对大数据的分析。
S63、基于传播模型的链路路损评估。
S64、深度覆盖不足栅格自适应波束寻优。
S65、判断候选集合是否为空。
若是，则执行S58，若否，则程序结束。
实施例二
图6示出了本发明实施例二提供的5G权值自适应优化装置的结构示意图。如图6所示，该装置包括：栅格映射部分31、栅格识别部分32、业务路损确定部分33、业务栅格评估部分34和深度弱覆盖栅格评估部分35；其中，
栅格映射部分31被配置为建立三维立体栅格，并将5G测量报告MR采样点映射到所述三维立体栅格中的各栅格内；
栅格识别部分32被配置为基于各栅格内的采样点数据识别对应栅格是业务栅格还是深度弱覆盖栅格；
业务路损确定部分33被配置为基于业务栅格确定业务分布中心栅格簇，并确定所述业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损；
业务栅格评估部分34被配置为基于所述第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合；
深度弱覆盖栅格评估部分35被配置为基于所述初始权值组合中深度弱 覆盖栅格和小区栅格之间的第二路损对所述深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。
在一些实施例中，所述业务路损确定部分33还被配置为：根据各业务栅格的价值按照从大到小的顺序将对应业务栅格加入业务栅格集合中，直至所述业务栅格集合中的所有业务栅格的价值之和大于第一预设阈值，将当前的业务栅格集合作为业务分布中心栅格簇。
在一些实施例中，所述业务栅格评估部分34还被配置为：根据业务栅格的中心点计算出理论权值组合；根据各设备厂家默认的5G权值表将所述理论权值组合映射为所述各设备厂家实际可支持的权值组合；基于所述可支持的权值组合对应的天线增益、天线发射功率及第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估。
在一些实施例中，所述深度弱覆盖栅格评估部分35还被配置为：当深度弱覆盖栅格的数量大于第二预设阈值且分布较为集中时，对深度弱覆盖栅格进行聚类处理，得到深度弱覆盖栅格簇；确定深度弱覆盖栅格簇的中心栅格和小区栅格之间的第二路损；基于所述初始权值组合对应的天线增益、天线发射功率及第二路损对所述深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估。
在一些实施例中，所述业务栅格评估部分34还被配置为：基于所述可支持的权值组合对应的天线增益、天线发射功率及第一路损确定各业务分布中心栅格簇的中心栅格的参考信号接收功率RSRP；基于各业务分布中心栅格簇的中心栅格的RSRP确定各业务分布中心栅格簇的中心栅格的覆盖提升幅度；基于各业务分布中心栅格簇的中心栅格的覆盖提升幅度确定对覆盖情况有所改善的权值组合。
在一些实施例中，所述深度弱覆盖栅格评估部分35还被配置为：基于所述初始权值组合对应的天线增益、天线发射功率及第二路损确定各业务分布中心栅格簇的中心栅格的RSRP；基于M个深度弱覆盖栅格簇的中心栅格的RSRP确定覆盖提升度；基于所述覆盖提升度对所述深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估。
在一些实施例中，所述权值组合包括电子方向角、电子下倾角、水平波宽和垂直波宽中的至少一项权值。
本发明实施例所述的5G权值自适应优化装置用于执行上述实施例所述的5G权值自适应优化方法，其工作原理与技术效果类似，这里不再赘述。
实施例三
本发明实施例提供了一种非易失性计算机存储介质，所述计算机存储介质存储有至少一可执行指令，该计算机可执行指令可执行上述任意方法实施例中的5G权值自适应优化方法。
实施例四
图7示出了本发明实施例提供的计算设备的结构示意图，本发明具体实施例并不对计算设备的具体实现做限定。
如图7所示，该计算设备可以包括：处理器(processor)、通信接口(Communications Interface)、存储器(memory)、以及通信总线。
其中：处理器、通信接口、以及存储器通过通信总线完成相互间的通信。通信接口，用于与其它设备比如客户端或其它服务器等的网元通信。处理器，用于执行程序，具体可以执行上述用于计算设备的5G权值自适应优化方法及小区方位角预测方法实施例中的相关步骤。
示例性的，程序可以包括程序代码，该程序代码包括计算机操作指令。
处理器可能是中央处理器CPU，或者是特定集成电路ASIC(Application Specific Integrated Circuit)，或者是被配置成实施本发明实施例的一个或多个集成电路。计算设备包括的一个或多个处理器，可以是同一类型的处理器，如一个或多个CPU；也可以是不同类型的处理器，如一个或多个CPU以及一个或多个ASIC。
存储器，用于存放程序。存储器可能包含高速RAM存储器，也可能还包括非易失性存储器(non-volatile memory)，例如至少一个磁盘存储器。
程序具体可以用于使得处理器执行上述任意方法实施例中的5G权值自适应优化方法。程序中各步骤的具体实现可以参见上述5G权值自适应优化方法实施例中的相应步骤和单元中的描述，在此不赘述。所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的设备和部分的具体工作过程，可以参考前述方法实施例中的对应过程描述，在此不再赘述。
在此提供的算法或显示不与任何特定计算机、虚拟系统或者其它设备固有相关。各种通用系统也可以与基于在此的示教一起使用。根据上面的描述，构造这类系统所要求的结构是显而易见的。此外，本发明实施例也不针对任何特定编程语言。应当明白，可以利用各种编程语言实现在此描述的本发明实施例的内容，并且上面对特定语言所做的描述是为了披露本发明实施例的最佳实施方式。
在此处所提供的说明书中，说明了大量相关细节。然而，能够理解，本发明的实施例可以在没有这些相关细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本发明实施例并帮助理解各个发明方面中的一个或多个，在上面对本发明的示例性实施例的描述中，本发明实施例的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该公开的方法解释成反映如下意图：即所要求保护的本发明实施例要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如下面的权利要求书所反映的那样，发明方面在于少于前面公开的单 个实施例的所有特征。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本发明的单独实施例。
本领域那些技术人员可以理解，可以对实施例中的设备中的部分进行自适应性地改变并且把它们设置在与该实施例不同的一个或多个设备中。可以把实施例中的部分或单元或组件组合成一个部分或单元或组件，以及此外可以把它们分成多个子部分或子单元或子组件。除了这样的特征和/或过程或者单元中的至少一些是相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如，在下面的权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本发明的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件部分实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本发明实施例的一些或者全部部件的一些或者全部功能。本发明实施例还可以实现为用于执行这里所描述的方法的一部分或者全部的设备或者装置程序(例如，计算机程序和计算机程序产品)。这样的实现本发明实施例的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
应该注意的是上述实施例对本发明实施例进行说明而不是对本发明进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。单词“包含”不排除存在未列在权利要求中的元件或步骤。位于元件之前的单词“一”或“一个”不排除存在多个这样的元件。本发明实施例可以借助于包括有若干不同元件的硬件以及借助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。上述实施例中的步骤，除有特殊说明外，不应理解为对执行顺序的限定。
工业实用性
本发明公开了一种5G权值自适应优化方法、装置、计算设备及计算机存储介质，该5G权值自适应优化方法包括：将5G测量报告MR采样点映射到三维立体栅格中的各栅格内；确定业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损；基于第一路损对业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合；基于初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。本发明实施例可以自动识别出业务栅格和深度弱覆盖栅格，并可以同时评估权值组合在业务栅格和深度弱覆盖栅格上的覆盖效果，从而选出最优的权值组合，提高了5G无线信号覆盖情况评估的准确性。

Claims (10)

  1. 一种5G权值自适应优化方法，包括：
    建立三维立体栅格，并将5G测量报告MR采样点映射到所述三维立体栅格中的各栅格内；
    基于各栅格内的采样点数据识别对应栅格是业务栅格还是深度弱覆盖栅格；
    基于业务栅格确定业务分布中心栅格簇，并确定所述业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损；
    基于所述第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合；
    基于所述初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对所述深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。
  2. 根据权利要求1所述的方法，其中，所述基于业务栅格确定业务分布中心栅格簇，包括：
    根据各业务栅格的价值按照从大到小的顺序将对应业务栅格加入业务栅格集合中，直至所述业务栅格集合中的所有业务栅格的价值之和大于第一预设阈值，将当前的业务栅格集合作为业务分布中心栅格簇。
  3. 根据权利要求1所述的方法，其中，所述基于所述第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估，包括：
    根据业务栅格的中心点计算出理论权值组合；
    根据各设备厂家默认的5G权值表将所述理论权值组合映射为所述各设备厂家实际可支持的权值组合；
    基于所述可支持的权值组合对应的天线增益、天线发射功率及第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估。
  4. 根据权利要求1所述的方法，其中，所述基于所述初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对所述深度弱覆盖栅格的覆盖情况进行评估，包括：
    当深度弱覆盖栅格的数量大于第二预设阈值且分布较为集中时，对深度弱覆盖栅格进行聚类处理，得到深度弱覆盖栅格簇；
    确定深度弱覆盖栅格簇的中心栅格和小区栅格之间的第二路损；
    基于所述初始权值组合对应的天线增益、天线发射功率及第二路损对所述深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估。
  5. 根据权利要求3所述的方法，其中，所述基于所述可支持的权值组合对应的天线增益、天线发射功率及第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估，包括：
    基于所述可支持的权值组合对应的天线增益、天线发射功率及第一路损确定各业务分布中心栅格簇的中心栅格的参考信号接收功率RSRP；
    基于各业务分布中心栅格簇的中心栅格的RSRP确定各业务分布中心栅格簇的中心栅格的覆盖提升幅度；
    基于各业务分布中心栅格簇的中心栅格的覆盖提升幅度确定对覆盖情况有所改善的权值组合。
  6. 根据权利要求4所述的方法，其中，所述基于所述初始权值组合对应的天线增益、天线发射功率及第二路损对所述深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估，包括：
    基于所述初始权值组合对应的天线增益、天线发射功率及第二路损确定各业务分布中心栅格簇的中心栅格的RSRP；
    基于M个深度弱覆盖栅格簇的中心栅格的RSRP确定覆盖提升度；
    基于所述覆盖提升度对所述深度弱覆盖栅格簇的中心栅格的覆盖情况进行评估。
  7. 根据权利要求1-6任一项所述的方法，其中，所述权值组合包括电子方向角、电子下倾角、水平波宽和垂直波宽中的至少一项权值。
  8. 一种5G权值自适应优化装置，包括：
    栅格映射部分，被配置为建立三维立体栅格，并将5G测量报告MR采样点映射到所述三维立体栅格中的各栅格内；
    栅格识别部分，被配置为基于各栅格内的采样点数据识别对应栅格是业务栅格还是深度弱覆盖栅格；
    业务路损确定部分，被配置为基于业务栅格确定业务分布中心栅格簇，并确定所述业务分布中心栅格簇的中心栅格和小区栅格之间的第一路损；
    业务栅格评估部分，被配置为基于所述第一路损对所述业务分布中心栅格簇的中心栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到初始权值组合；
    深度弱覆盖栅格评估部分，被配置为基于所述初始权值组合中深度弱覆盖栅格和小区栅格之间的第二路损对所述深度弱覆盖栅格的覆盖情况进行评估，确定对覆盖情况有所改善的权值组合，得到最终的权值组合。
  9. 一种计算设备，包括：处理器、存储器、通信接口和通信总线，所述处理器、所述存储器和所述通信接口通过所述通信总线完成相互间的通信；
    所述存储器用于存放至少一可执行指令，所述可执行指令使所述处理器执行如权利要求1-7中任一项所述的5G权值自适应优化方法的操作。
  10. 一种计算机存储介质，所述存储介质中存储有至少一可执行指令，所述可执行指令使处理器执行如权利要求1-7中任一项所述的5G权 值自适应优化方法的操作。
PCT/CN2022/129509 2021-11-11 2022-11-03 5g权值自适应优化方法、装置、计算设备及计算机存储介质 WO2023083088A1 (zh)

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