Weak coverage cell optimization method and apparatus
By automatically determining the weak coverage aggregation area and SSB weight matrix at the base station, efficient, low-cost, and timely optimization of weak coverage cells is achieved, solving the problems of low efficiency, high cost, and limited flexibility of manual operation in existing technologies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA MOBILE GRP GUANGDONG CO LTD
- Filing Date
- 2021-09-28
- Publication Date
- 2026-06-26
AI Technical Summary
Existing solutions for optimizing weak coverage cells are inefficient, costly, and unable to adapt to network changes in a timely manner. Their reliance on manual operation limits their flexibility.
By identifying weak coverage aggregation areas in the target cell, an initial weak coverage guidance matrix is formed. Combined with the initial SSB optimization vector matrix and weight matrix, weak coverage cells are automatically optimized, and SSB beam adjustment is performed using the base station.
It enables automatic optimization of weak coverage cells, improving efficiency, reducing costs, and ensuring the timeliness and flexibility of optimization.
Smart Images
Figure CN115884210B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of communication technology, specifically to a method and apparatus for optimizing weak coverage cells. Background Technology
[0002] Existing weak coverage cell optimization schemes mainly involve manually identifying specific weak coverage cells and areas in the network based on data analysis, then proposing adjustment schemes and implementing them manually.
[0003] Existing weak coverage cell optimization schemes have the following drawbacks:
[0004] First, the process of weak coverage optimization involves manually collecting data, manually analyzing the data to provide optimization solutions, and manually testing and verifying the results. The entire process requires manual participation and takes a relatively long time to complete, resulting in low optimization efficiency.
[0005] Secondly, the entire optimization process requires manual labor, and large-scale optimization requires sufficient manpower, resulting in high labor costs.
[0006] Third, each optimization requires manual analysis of historical data, which cannot be adapted to changes in network user distribution and business in a timely manner, thus limiting the flexibility of optimization. Summary of the Invention
[0007] This application provides a method and apparatus for optimizing weak coverage cells, which overcomes the shortcomings of existing weak coverage cell optimization methods such as low efficiency, high cost, and untimely optimization.
[0008] In a first aspect, embodiments of this application provide a method for optimizing weak coverage cells, including:
[0009] Identify multiple weak coverage aggregation areas in the target cell, and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas;
[0010] Based on the initial weak coverage guidance matrix of each target cell, determine the initial synchronization signal and PBCH block SSB optimization vector matrix of the target cell set;
[0011] Based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set, multiple candidate SSB weight matrices are determined.
[0012] The target SSB weight matrix is determined based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set.
[0013] The weak coverage aggregation region is optimized based on the target SSB weight matrix.
[0014] In one embodiment, determining multiple weak coverage aggregation areas in the target cell and forming an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas includes:
[0015] From the sampling points of the target cell, determine the target sampling points where the reference signal received power (RSRP) is less than the RSRP threshold value;
[0016] The target sampling points are aggregated according to the aggregation conditions to obtain the multiple weak coverage aggregation areas;
[0017] The weak coverage aggregation regions are sorted from largest to smallest according to their weak coverage impact to determine the initial weak coverage guidance matrix.
[0018] In one embodiment, determining the initial SSB optimization vector matrix of the target cell set based on the initial weak coverage guidance matrix of each target cell includes:
[0019] Based on the sorting of each weak coverage aggregation area in the initial weak coverage guidance matrix, SSB beams are matched for at least a portion of the weak coverage aggregation areas in the initial weak coverage guidance matrix to determine the SSB optimization vector for each target cell.
[0020] The initial SSB optimization vector matrix of the target cell set is determined based on the SSB optimization vector of each target cell.
[0021] In one embodiment, determining multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set includes:
[0022] Based on the initial SSB optimization vector matrix and the initial SSB weight matrix, determine multiple optional SSB weight matrices for the target cell set;
[0023] Based on the optional target values corresponding to each optional SSB weight matrix, the plurality of candidate SSB weight matrices are determined from the plurality of optional SSB weight matrices;
[0024] The optional target value is determined based on the optional SSB weight matrix and the preset target value function.
[0025] In one embodiment, determining the target SSB weight matrix based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set includes:
[0026] The candidate SSB weight matrices are sorted from largest to smallest according to the candidate target values corresponding to each candidate SSB weight matrix;
[0027] Based on the available SSB weight matrix, the candidate SSB weight matrix is iteratively optimized sequentially to determine multiple undetermined SSB weight matrices.
[0028] The target SSB weight matrix is determined from the plurality of undetermined SSB weight matrices;
[0029] The candidate target value is determined based on the candidate SSB weight matrix and a preset target value function.
[0030] In one embodiment, the step of iteratively optimizing each candidate SSB weight matrix based on the available SSB weight matrix to determine multiple undetermined SSB weight matrices includes:
[0031] For each candidate SSB weight matrix, the following steps are executed iteratively until the number of executions is greater than or equal to the threshold:
[0032] Determine the reference SSB weight matrix corresponding to the current candidate SSB weight matrix from the available SSB weight matrices;
[0033] If the reference target value corresponding to the reference SSB weight matrix is greater than the candidate target value corresponding to the current candidate SSB weight matrix, the current candidate SSB weight matrix is updated to the reference SSB weight matrix.
[0034] If the number of executions is greater than or equal to the number of executions threshold, the current candidate SSB weight matrix is determined as the undetermined SSB weight matrix;
[0035] The reference target value is determined based on the reference SSB weight matrix and a preset target value function.
[0036] In one embodiment, determining the target SSB weight matrix from the plurality of undetermined SSB weight matrices includes:
[0037] Determine the target value corresponding to each undetermined SSB weight matrix, and take the undetermined SSB weight matrix corresponding to the largest undetermined target value as the target SSB weight matrix.
[0038] Secondly, embodiments of this application provide a weak coverage cell optimization device, comprising:
[0039] The guidance matrix determination module is used to determine multiple weak coverage aggregation areas in the target cell and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas;
[0040] The vector matrix determination module is used to determine the initial SSB optimization vector matrix of the target cell set based on the initial weak coverage guidance matrix of each target cell.
[0041] The candidate matrix determination module is used to determine multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set.
[0042] The matrix determination module is used to determine the target SSB weight matrix based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set.
[0043] The optimization module is used to optimize the weak coverage cells in the target cell set according to the target SSB weight matrix.
[0044] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the weak coverage cell optimization method described in the first aspect.
[0045] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the weak coverage cell optimization method described in the first aspect.
[0046] The weak coverage cell optimization method and apparatus provided in this application determine the weak coverage aggregation area in the target cell, and then determine the target SSB weight matrix by combining the available SSB weight matrix of the target cell set. Based on this target SSB weight matrix, the weak coverage aggregation area is optimized, thus achieving automatic optimization of weak coverage cells. Therefore, compared with the prior art where weak coverage cell optimization relies on manual operation, this method significantly improves optimization efficiency, reduces optimization costs, and ensures timely optimization. Attached Figure Description
[0047] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0048] Figure 1 This is a flowchart illustrating the weak coverage cell optimization method provided in the embodiments of this application;
[0049] Figure 2 This is a schematic diagram of the structure of the weak coverage cell optimization device provided in the embodiments of this application;
[0050] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0051] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0052] Figure 1 This is a flowchart illustrating the weak coverage cell optimization method provided in an embodiment of this application. (Refer to...) Figure 1 This application provides a method for optimizing weak coverage cells, which may include:
[0053] Step 110: Identify multiple weak coverage aggregation areas in the target cell, and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas;
[0054] Step 120: Determine the initial SSB (Synchronization Signal and PBCH Block) optimization vector matrix of the target cell set based on the initial weak coverage guidance matrix of each target cell.
[0055] Step 130: Determine multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set;
[0056] Step 140: Determine the target SSB weight matrix based on multiple candidate SSB weight matrices and the available SSB weight matrix of the target cell set;
[0057] Step 150: Optimize the weak coverage aggregation area based on the target SSB weight matrix.
[0058] It should be noted that the entity executing the uplink interference processing method provided by this invention can be a network-side device, such as a base station.
[0059] The technical solution of this application will be described in detail below, taking the weak coverage cell optimization method provided in the embodiments of this application as an example.
[0060] The target cell set can be a set of weakly covered 5G cells that require SSB weight optimization. It can be determined manually based on coverage parameters, or automatically by the base station based on certain indicators. Target cells within the target cell set should be geographically adjacent to each other.
[0061] For each target cell, the base station can record the current SSB weight setting of the antenna and establish a correlation data table of "cell ID-SSB weight-angle of arrival-RSRP (Reference Signal Receiving Power) value" based on the MR (Measurement Report) measurement data returned by the field terminal. The cell ID is used to uniquely identify the target cell occupied by the sampling point, the angle of arrival is the azimuth angle of the sampling point relative to the target cell, which is used to uniquely identify the position of the sampling point, and the RSRP value characterizes the received signal strength of the sampling point.
[0062] It should be noted that the SSB weight is a numerical matrix. Assuming that each target cell contains K SSB beams, each beam has 5 weight parameters, which are:
[0063] Horizontal azimuth angle: denoted by HA, and its value range is [HAmin, HAmax] depending on the antenna performance;
[0064] Horizontal beamwidth: denoted by HW, and its value range is [HWmin, HWmax] depending on the antenna performance;
[0065] Vertical direction angle: denoted by VA, and its value range is [VAmin, VAmax] depending on the antenna performance;
[0066] Vertical beamwidth: denoted by VW, and its value range is [VWmin, VWmax] depending on the antenna performance;
[0067] Beaming is enabled: indicated by E, E=1 for enabled, E=0 for disabled.
[0068] The SSB weights of each target cell can be represented by a matrix as follows:
[0069]
[0070] By combining the SSB weight matrices of all target cells in the target cell set C in order, we can construct the SSB weight matrix of cell set C:
[0071]
[0072] Where M is the number of target cells contained in the target cell set C.
[0073] Optionally, the base station may first determine each weak coverage aggregation area in each target cell, and then combine each weak coverage aggregation area into an initial weak coverage guidance matrix for the target cell.
[0074] This initial weak coverage guidance matrix represents information about the target cell that requires weak coverage optimization, such as the location and related parameters where weak coverage optimization is needed.
[0075] The base station can match SSB beams for weak coverage aggregation areas based on the initial weak coverage guidance matrix of each target cell, and then determine the initial SSB optimization vector matrix of the target cell set.
[0076] The base station can determine multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set;
[0077] Among them, for multiple candidate SSB weight matrices, it may include the target SSB weight matrix that needs to be determined in the end, or it may only be used for subsequent determination of the target SSB weight matrix.
[0078] The base station can determine the target SSB weight matrix based on multiple candidate SSB weight matrices and the available SSB weight matrices of the target cell set.
[0079] After determining the target SSB weight matrix, the base station can adjust the SSB beam according to the determined target SSB weight matrix, thereby optimizing at least part of the weak coverage aggregation area of the target cell concentration.
[0080] The weak coverage cell optimization method provided in this application identifies weak coverage aggregation areas within a target cell and then determines a target SSB weight matrix by combining it with the available SSB weight matrix of the target cell set. The weak coverage aggregation areas are then optimized based on this target SSB weight matrix, enabling automatic optimization of weak coverage cells. Therefore, compared to existing technologies that rely on manual optimization, this method significantly improves optimization efficiency, reduces optimization costs, and ensures timely optimization.
[0081] In one embodiment, determining multiple weak coverage aggregation areas in the target cell and forming an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas may include:
[0082] From the sampling points of the target cell, determine the target sampling points where RSRP is less than the RSRP threshold value;
[0083] The target sampling points are aggregated according to the aggregation conditions to obtain multiple weak coverage aggregation areas;
[0084] The weak coverage clusters are sorted from largest to smallest based on their degree of weak coverage influence in order to determine the initial weak coverage guidance matrix.
[0085] For example, for all sampling points in each target cell, the base station can filter out samples with RSRP values less than the RSRP threshold value R.L1 The target sampling points are identified, and these target sampling points are aggregated. The conditions for aggregation are as follows:
[0086]
[0087] Wherein, ΔDOA h ΔDOA v D represents the difference between the horizontal and vertical angles of arrival between the target sampling points, respectively. L Adjust the aggregation threshold as set. The aggregation results in the initial weak coverage guide matrix FV. Ci Each list represents one weak coverage aggregation region:
[0088]
[0089] Where the subscript Ci represents the i-th cell in cell set C; the number of weak coverage aggregation areas is Ri; DH Ci,j DV Ci,j WD represents the horizontal angle of arrival (mean of the horizontal angle of arrival of all sampling points in the j-th weak coverage aggregation area in the i-th cell) and the vertical angle of arrival (mean of the vertical angle of arrival of all sampling points in the aggregation area); Ci,j The degree of influence of the corresponding weak coverage aggregation area is related to the number of sampling points in the aggregation area and the degree of weak coverage of the sampling points, which can be expressed by the formula:
[0090]
[0091] Where, N Ci,j R represents the number of sampling points in the j-th weak coverage aggregation area of the i-th cell. Ci,j The RSRP values of the sampling points in the aggregation region are used to characterize the region; both a and b are exponents greater than 1.
[0092] Initial weak cover guidance matrix FV Ci Weak coverage aggregation area in WD Ci,j Sort by size in columns, i.e., WD Ci,1 ≥WD Ci,2 ≥…≥WD Ci,Ri .
[0093] The weak coverage cell optimization method provided in this application determines the weak coverage aggregation area by aggregating sampling points whose RSRP is less than the RSRP threshold value. This enables accurate positioning of the weak coverage aggregation area and improves the optimization efficiency of weak coverage cells.
[0094] In one embodiment, determining the initial SSB optimization vector matrix for the target cell set based on the initial weak coverage guidance matrix of each target cell may include:
[0095] Based on the sorting of each weak coverage aggregation area in the initial weak coverage guidance matrix, SSB beams are matched for at least some of the weak coverage aggregation areas in the initial weak coverage guidance matrix to determine the SSB optimization vector for each target cell.
[0096] The initial SSB optimization vector matrix of the target cell set is determined based on the SSB optimization vector of each target cell.
[0097] For example, each target cell Ci is based on its corresponding initial weak coverage guidance matrix FV Ci Generate the initial SSB optimization vector matrix ΔSSB for the target cell. Ci,0 This can be implemented in the following steps:
[0098] Step (1): Set the initial count value j = 1. The selectable SSB beams are all the SSB beams in cell Ci.
[0099] Step (2): Select the initial weak cover guide matrix FV Ci The j-th weak coverage aggregation region will The SSB beam with the smallest value is denoted as SSB. Ci,j ;
[0100] Step (3), for SSB Ci,j The corresponding SSB beam is set to have an optimized vector of ΔHA. Ci,j =DH Ci,j -HA Ci,j ΔVA Ci,j =DV Ci,j -VA Ci,j ;
[0101] Step (4) Place SSB Ci,j The corresponding SSB beam is removed from the selectable SSB beams, and j = j + 1;
[0102] Repeat steps (2) to (4) until the selectable SSB beam is 0, or j>Ri.
[0103] By arranging the beam optimization vectors of all target cells, the initial SSB optimization vector matrix ΔSSBC0 of the target cell set can be generated:
[0104]
[0105] The weak coverage cell optimization method provided in this application determines the SSB optimization vector of each target cell by matching SSB beams to at least a portion of the weak coverage aggregation areas in the initial weak coverage guidance matrix, thereby determining the initial SSB optimization vector matrix of the target cell set. This enables rapid positioning of the SSB beam parameters that need to be adjusted, thus improving the optimization efficiency of weak coverage cells.
[0106] In one embodiment, determining multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set may include:
[0107] Based on the initial SSB optimization vector matrix and the initial SSB weight matrix, determine multiple optional SSB weight matrices for the target cell set;
[0108] Based on the optional target values corresponding to each optional SSB weight matrix, multiple candidate SSB weight matrices are determined from multiple optional SSB weight matrices;
[0109] The optional target value is determined based on the optional SSB weight matrix and the preset target value function. The preset target value function is defined as follows:
[0110] Objective value function
[0111] Where N is the total number of valid sampling points in all cells of cell set C; R L1 R is the RSRP threshold value; i The RSRP value (R) of the i-th sampling point L1 and R i (Both are negative numbers); a and b are both exponents greater than 1.
[0112] As can be seen from the definition of the objective function, a larger number of sampling points indicates a larger number of users, and the RSRP of the sampling points is greater than R. L1 The higher the proportion, the higher the overall level of the sampling points, and the larger the target value Tf.
[0113] For example, the available solution space E can be set, initially including all possible SSB weight settings, which is the available SSB weight matrix;
[0114] Set up a candidate solution space G to store the candidate SSB weight matrix. Its space capacity is |G| (G is a positive integer greater than or equal to 2). Initially, it contains only 1 element, namely the initial weight matrix SSBC0, and its corresponding objective function is Tf0.
[0115] Set up an optimal solution space S to store the local optimal weight matrix, which is initially an empty set;
[0116] Set up a disabled solution space B to store the SSB weight matrices that are eliminated during the iterative optimization process. Initially, it is an empty set.
[0117] Set the threshold for disabling target value functions Tf ban When the target value Tf corresponds to the SSB weight matrix SSBC of the target cell set C <Tf banIf the corresponding weight matrix is removed from the available solution space E, it will be included in the forbidden solution space B.
[0118] make
[0119] Where i = 1, 2, ..., |G|-1. Based on the directional gain characteristics of the antenna beam and combined with the RSRP values of the initial MR sampling points, the weight matrix SSBC of each MR sampling point in the current selectable SSB is updated. i The RSRP value under the weight setting is then used to calculate the value of each SSBC. i The target value function Tf corresponding to the weight setting i And include it in the candidate solution space G.
[0120] The weak coverage cell optimization method provided in this application improves the optimization efficiency of weak coverage cells by determining multiple candidate SSB weight matrices from multiple optional SSB weight matrices based on the optional target values corresponding to each optional SSB weight matrix.
[0121] In one embodiment, determining the target SSB weight matrix based on multiple candidate SSB weight matrices and the available SSB weight matrices of the target cell set may include:
[0122] The candidate SSB weight matrices are sorted from largest to smallest according to the candidate target values corresponding to each candidate SSB weight matrix;
[0123] Based on the available SSB weight matrix, the candidate SSB weight matrix is iteratively optimized in turn to determine multiple undetermined SSB weight matrices.
[0124] Determine the target SSB weight matrix from multiple undetermined SSB weight matrices;
[0125] The candidate target value is determined based on the candidate SSB weight matrix and the preset target value function.
[0126] Optionally, in one embodiment, the candidate SSB weight matrices are iteratively optimized sequentially based on the available SSB weight matrices to determine a plurality of undetermined SSB weight matrices, including:
[0127] For each candidate SSB weight matrix, the following steps are executed iteratively until the number of executions is greater than or equal to the threshold:
[0128] Determine the reference SSB weight matrix corresponding to the current candidate SSB weight matrix from the available SSB weight matrices;
[0129] If the reference target value corresponding to the reference SSB weight matrix is greater than the candidate target value corresponding to the current candidate SSB weight matrix, the current candidate SSB weight matrix is updated to the reference SSB weight matrix.
[0130] If the number of executions is greater than or equal to the threshold, the current candidate SSB weight matrix is determined as the undetermined SSB weight matrix;
[0131] The reference target value is determined based on the reference SSB weight matrix and the preset target value function.
[0132] For example, the above embodiments can be implemented in the following way:
[0133] Set the maximum number of optimization iterations Dmax, the maximum optimization iteration time Tmax, and the maximum number of stalls Rmax; set the initial value of the number of optimization iterations D to 1, the initial value of the optimization iteration time T to 0, and the initial value of the number of stalls R to 0.
[0134] Set the lower limit value for spatial matrix changes |ΔSSB min |、Upper limit of spatial matrix variation|ΔSSB max | and the coefficient matrix WSSB is used to adjust the influence range of each weight element. Repeat the following iterative steps until D≥Dmax, or T≥Tmax.
[0135] Step (1): Sort all candidate SSB weight matrices in the candidate solution space G in descending order of candidate objective value, and denote the last candidate SSB weight matrix after sorting as SSBC. Z The corresponding candidate target value is Tf Z Let SSBC be the weight matrix of the first candidate SSB after sorting. A The corresponding candidate target value is Tf A .
[0136] Step (2): Set the upper limit P for the number of comparisons. max The initial value of the number of comparisons P is set to 0 for SSBC. A Further optimization will be performed, and the steps are as follows:
[0137] Step (2.1), P = P + 1;
[0138] Step (2.2): From the available solution space E, in SSBC A Randomly select one qualified reference SSB weight matrix SSBC from the surrounding area (within a preset range). P The condition is: |ΔSSB min |≤|WSSB·(SSBC P -SSBC A )|≤|ΔSSBmax |;
[0139] Step (2.3): Calculate SSBC P The corresponding objective function Tf P If Tf P <Tf A Then R = R + 1, if Tf P <Tf ban Then SSBC P Transfer from the available solution space E to the disabled solution space B; if Tf P >Tf A Then replace the first element in the current candidate solution space G with SSBC. P R is set to 0;
[0140] Step (2.4): Compare P with P max Size, if P≥P max If so, the loop will exit.
[0141] Step (3): Sort all elements in the candidate solution space G in descending order according to their corresponding objective value Tf, and denote the first element in the sort as SSBC'. A If SSBC' A =SSBC A And R≥R max Then the current SSBC A The elements are moved from the candidate solution space G into the optimal solution space S. The order of the remaining elements in the candidate solution space G is shifted forward in turn. The vacancies in G are randomly drawn from the currently available solution space E.
[0142] Step (4): D = D + 1, T = T + time;
[0143] Step (5): Repeat steps (1) to (4) until D≥Dmax or T≥Tmax.
[0144] The weak coverage cell optimization method provided in this application determines multiple undetermined SSB weight matrices by iteratively optimizing each candidate SSB weight matrix sequentially using the available SSB weight matrix. This ensures that the final target SSB weight matrix is a better solution, thereby effectively improving the optimization effect of weak coverage cells.
[0145] In one embodiment, determining the target SSB weight matrix from a plurality of undetermined SSB weight matrices may include:
[0146] Determine the target value corresponding to each undetermined SSB weight matrix, and take the undetermined SSB weight matrix corresponding to the largest undetermined target value as the target SSB weight matrix.
[0147] Specifically, after iteratively optimizing each candidate SSB weight matrix, the undetermined SSB weight matrices in the optimal solution space S can be sorted from largest to smallest according to their corresponding undetermined target values, and finally the first element in the set S is taken as the target SSB weight matrix.
[0148] The weak coverage cell optimization method provided in this application, by using the undetermined SSB weight matrix corresponding to the maximum undetermined target value as the target SSB weight matrix, can ensure that the final target SSB weight matrix is the optimal solution, thereby effectively improving the optimization effect of weak coverage cells.
[0149] The weak coverage cell optimization apparatus provided in the embodiments of this application is described below. The weak coverage cell optimization apparatus described below can be referred to in correspondence with the weak coverage cell optimization method described above.
[0150] Figure 2 This is a schematic diagram of the weak coverage cell optimization device provided in an embodiment of this application. Figure 2 As shown in the embodiments of this application, the weak coverage cell optimization device may include:
[0151] The guidance matrix determination module 210 is used to determine multiple weak coverage aggregation areas in the target cell and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas.
[0152] The vector matrix determination module 220 is used to determine the initial SSB optimization vector matrix of the target cell set based on the initial weak coverage guidance matrix of each target cell.
[0153] The candidate matrix determination module 230 is used to determine multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set.
[0154] The matrix determination module 240 is used to determine the target SSB weight matrix based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set.
[0155] The optimization module 250 is used to optimize the weak coverage cells in the target cell set according to the target SSB weight matrix.
[0156] The weak coverage cell optimization device provided in this application determines the weak coverage aggregation area in the target cell and then determines the target SSB weight matrix by combining it with the available SSB weight matrix of the target cell set. Based on this target SSB weight matrix, the weak coverage aggregation area is optimized, thus achieving automatic optimization of weak coverage cells. Therefore, compared with the prior art where weak coverage cell optimization relies on manual operation, this significantly improves optimization efficiency, reduces optimization costs, and ensures timely optimization.
[0157] In one embodiment, the guidance matrix determination module 210 is specifically used for:
[0158] From the sampling points of the target cell, determine the target sampling points where the reference signal received power (RSRP) is less than the RSRP threshold value;
[0159] The target sampling points are aggregated according to the aggregation conditions to obtain the multiple weak coverage aggregation areas;
[0160] The weak coverage aggregation regions are sorted from largest to smallest according to their weak coverage impact to determine the initial weak coverage guidance matrix.
[0161] In one embodiment, the vector matrix determination module 220 is specifically used for:
[0162] Based on the sorting of each weak coverage aggregation area in the initial weak coverage guidance matrix, SSB beams are matched for at least a portion of the weak coverage aggregation areas in the initial weak coverage guidance matrix to determine the SSB optimization vector for each target cell.
[0163] The initial SSB optimization vector matrix of the target cell set is determined based on the SSB optimization vector of each target cell.
[0164] In one embodiment, the candidate matrix determination module 230 is specifically used for:
[0165] Based on the initial SSB optimization vector matrix and the initial SSB weight matrix, determine multiple optional SSB weight matrices for the target cell set;
[0166] Based on the optional target values corresponding to each optional SSB weight matrix, the plurality of candidate SSB weight matrices are determined from the plurality of optional SSB weight matrices;
[0167] The optional target value is determined based on the optional SSB weight matrix and the preset target value function.
[0168] In one embodiment, the matrix determination module 240 is specifically used for:
[0169] The candidate SSB weight matrices are sorted from largest to smallest according to the candidate target values corresponding to each candidate SSB weight matrix;
[0170] Based on the available SSB weight matrix, the candidate SSB weight matrix is iteratively optimized sequentially to determine multiple undetermined SSB weight matrices.
[0171] The target SSB weight matrix is determined from the plurality of undetermined SSB weight matrices;
[0172] The candidate target value is determined based on the candidate SSB weight matrix and a preset target value function.
[0173] In one embodiment, the matrix determination module 240 is specifically used for:
[0174] For each candidate SSB weight matrix, the following steps are executed iteratively until the number of executions is greater than or equal to the threshold:
[0175] Determine the reference SSB weight matrix corresponding to the current candidate SSB weight matrix from the available SSB weight matrices;
[0176] If the reference target value corresponding to the reference SSB weight matrix is greater than the candidate target value corresponding to the current candidate SSB weight matrix, the current candidate SSB weight matrix is updated to the reference SSB weight matrix.
[0177] If the number of executions is greater than or equal to the number of executions threshold, the current candidate SSB weight matrix is determined as the undetermined SSB weight matrix;
[0178] The reference target value is determined based on the reference SSB weight matrix and a preset target value function.
[0179] In one embodiment, the matrix determination module 240 is specifically used for:
[0180] Determine the target value corresponding to each undetermined SSB weight matrix, and take the undetermined SSB weight matrix corresponding to the largest undetermined target value as the target SSB weight matrix.
[0181] Figure 3 An example is a schematic diagram of the physical structure of an electronic device. For example... Figure 3As shown in the illustration, this application also provides an electronic device, which may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call a computer program in the memory 330 to execute the steps of a weak coverage cell optimization method, such as including:
[0182] Identify multiple weak coverage aggregation areas in the target cell, and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas;
[0183] Based on the initial weak coverage guidance matrix of each target cell, determine the initial synchronization signal and PBCH block SSB optimization vector matrix of the target cell set;
[0184] Based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set, multiple candidate SSB weight matrices are determined.
[0185] The target SSB weight matrix is determined based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set.
[0186] The weak coverage aggregation region is optimized based on the target SSB weight matrix.
[0187] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0188] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the weak coverage cell optimization method provided in the above embodiments, such as including:
[0189] Identify multiple weak coverage aggregation areas in the target cell, and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas;
[0190] Based on the initial weak coverage guidance matrix of each target cell, determine the initial synchronization signal and PBCH block SSB optimization vector matrix of the target cell set;
[0191] Based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set, multiple candidate SSB weight matrices are determined.
[0192] The target SSB weight matrix is determined based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set.
[0193] The weak coverage aggregation region is optimized based on the target SSB weight matrix.
[0194] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the methods provided in the above embodiments, such as including:
[0195] Identify multiple weak coverage aggregation areas in the target cell, and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas;
[0196] Based on the initial weak coverage guidance matrix of each target cell, determine the initial synchronization signal and PBCH block SSB optimization vector matrix of the target cell set;
[0197] Based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set, multiple candidate SSB weight matrices are determined.
[0198] The target SSB weight matrix is determined based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set.
[0199] The weak coverage aggregation region is optimized based on the target SSB weight matrix.
[0200] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).
[0201] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0202] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0203] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A method for optimizing weak coverage cells, characterized in that, include: Identify multiple weak coverage aggregation areas in the target cell, and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas; Based on the initial weak coverage guidance matrix of each target cell, determine the initial synchronization signal and PBCH block SSB optimization vector matrix of the target cell set; Based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set, multiple candidate SSB weight matrices are determined. The target SSB weight matrix is determined based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set. The weak coverage aggregation area is optimized based on the target SSB weight matrix; The step of determining the target SSB weight matrix based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set includes: The candidate SSB weight matrices are sorted from largest to smallest according to the candidate target values corresponding to each candidate SSB weight matrix; Based on the available SSB weight matrix, the candidate SSB weight matrix is iteratively optimized sequentially to determine multiple undetermined SSB weight matrices. The target SSB weight matrix is determined from the plurality of undetermined SSB weight matrices; The candidate target value is determined based on the candidate SSB weight matrix and a preset target value function. The step of iteratively optimizing each candidate SSB weight matrix based on the available SSB weight matrix to determine multiple undetermined SSB weight matrices includes: For each candidate SSB weight matrix, the following steps are executed iteratively until the number of executions is greater than or equal to the threshold: Determine the reference SSB weight matrix corresponding to the current candidate SSB weight matrix from the available SSB weight matrices; If the reference target value corresponding to the reference SSB weight matrix is greater than the candidate target value corresponding to the current candidate SSB weight matrix, the current candidate SSB weight matrix is updated to the reference SSB weight matrix. If the number of executions is greater than or equal to the number of executions threshold, the current candidate SSB weight matrix is determined as the undetermined SSB weight matrix; The reference target value is determined based on the reference SSB weight matrix and a preset target value function.
2. The weak coverage cell optimization method according to claim 1, characterized in that, The process of determining multiple weak coverage aggregation areas in the target cell and forming an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas includes: From the sampling points of the target cell, determine the target sampling points where the reference signal received power (RSRP) is less than the RSRP threshold value; The target sampling points are aggregated according to the aggregation conditions to obtain the multiple weak coverage aggregation areas; The weak coverage aggregation regions are sorted from largest to smallest according to their weak coverage impact to determine the initial weak coverage guidance matrix.
3. The weak coverage cell optimization method according to claim 2, characterized in that, The step of determining the initial SSB optimization vector matrix of the target cell set based on the initial weak coverage guidance matrix of each target cell includes: Based on the sorting of each weak coverage aggregation area in the initial weak coverage guidance matrix, SSB beams are matched for at least a portion of the weak coverage aggregation areas in the initial weak coverage guidance matrix to determine the SSB optimization vector for each target cell. The initial SSB optimization vector matrix of the target cell set is determined based on the SSB optimization vector of each target cell.
4. The weak coverage cell optimization method according to claim 1, characterized in that, The step of determining multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set includes: Based on the initial SSB optimization vector matrix and the initial SSB weight matrix, determine multiple optional SSB weight matrices for the target cell set; Based on the optional target values corresponding to each optional SSB weight matrix, the plurality of candidate SSB weight matrices are determined from the plurality of optional SSB weight matrices; The optional target value is determined based on the optional SSB weight matrix and the preset target value function.
5. The weak coverage cell optimization method according to claim 1, characterized in that, Determining the target SSB weight matrix from the plurality of undetermined SSB weight matrices includes: Determine the target value corresponding to each undetermined SSB weight matrix, and take the undetermined SSB weight matrix corresponding to the largest undetermined target value as the target SSB weight matrix.
6. A device for optimizing weak coverage cells, characterized in that, include: The guidance matrix determination module is used to determine multiple weak coverage aggregation areas in the target cell and form an initial weak coverage guidance matrix based on the multiple weak coverage aggregation areas; The vector matrix determination module is used to determine the initial SSB optimization vector matrix of the target cell set based on the initial weak coverage guidance matrix of each target cell. The candidate matrix determination module is used to determine multiple candidate SSB weight matrices based on the initial SSB optimization vector matrix and the initial SSB weight matrix of the target cell set. The matrix determination module is used to determine the target SSB weight matrix based on the plurality of candidate SSB weight matrices and the available SSB weight matrices of the target cell set. The optimization module is used to optimize the weak coverage cells in the target cell set according to the target SSB weight matrix. The matrix determination module is specifically used for: The candidate SSB weight matrices are sorted from largest to smallest according to the candidate target values corresponding to each candidate SSB weight matrix; Based on the available SSB weight matrix, the candidate SSB weight matrix is iteratively optimized sequentially to determine multiple undetermined SSB weight matrices. The target SSB weight matrix is determined from the plurality of undetermined SSB weight matrices; The candidate target value is determined based on the candidate SSB weight matrix and a preset target value function. The step of iteratively optimizing each candidate SSB weight matrix based on the available SSB weight matrix to determine multiple undetermined SSB weight matrices includes: For each candidate SSB weight matrix, the following steps are executed iteratively until the number of executions is greater than or equal to the threshold: Determine the reference SSB weight matrix corresponding to the current candidate SSB weight matrix from the available SSB weight matrices; If the reference target value corresponding to the reference SSB weight matrix is greater than the candidate target value corresponding to the current candidate SSB weight matrix, the current candidate SSB weight matrix is updated to the reference SSB weight matrix. If the number of executions is greater than or equal to the number of executions threshold, the current candidate SSB weight matrix is determined as the undetermined SSB weight matrix; The reference target value is determined based on the reference SSB weight matrix and a preset target value function.
7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the weak coverage cell optimization method according to any one of claims 1 to 5.
8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the weak coverage cell optimization method according to any one of claims 1 to 5.