WO2022193717A1 - 权值优化方法、装置、通信设备及计算机可读存储介质 - Google Patents

权值优化方法、装置、通信设备及计算机可读存储介质 Download PDF

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WO2022193717A1
WO2022193717A1 PCT/CN2021/132854 CN2021132854W WO2022193717A1 WO 2022193717 A1 WO2022193717 A1 WO 2022193717A1 CN 2021132854 W CN2021132854 W CN 2021132854W WO 2022193717 A1 WO2022193717 A1 WO 2022193717A1
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sub
weight
quality data
beams
cell
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PCT/CN2021/132854
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English (en)
French (fr)
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侯越涛
林伟
芮华
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]

Definitions

  • the embodiments of the present application relate to, but are not limited to, the field of communications, and in particular, relate to a weight optimization method, an apparatus, a communication device, and a computer-readable storage medium.
  • the configuration scheme of Synchronization Signal and PBCH Block (SSB) in the New Radio (NR) system includes the three-dimensional configuration of time domain, frequency domain and beam, resulting in Massive Multi Input Multi Output, Massive MIMO) weight combinations become more complex, and the scale of sub-beam adjustment weight combinations can reach tens of thousands.
  • Traditional methods have been unable to cope with the difficulties faced by 5G wireless network optimization.
  • the main purpose of the embodiments of the present application is to provide a weight optimization method, apparatus, communication device and computer-readable storage medium, which can solve complex actual environmental signal quality requirements, and have high computational efficiency and low cost.
  • an embodiment of the present application provides a weight optimization method, including:
  • drive test quality data obtained by collecting user data, where the drive test quality data corresponds to second beam parameters of several second sub-beams;
  • For each of the first sub-beams obtain signal quality data according to the first beam parameters, the second beam parameters and the drive test quality data;
  • a first weight parameter is determined according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, and the first weight parameter is all the first sub-beams Weight parameters corresponding to the beam and the cells covered by all the second sub-beams.
  • an embodiment of the present application further provides a weight optimization apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being When executed by the processor, the weight optimization method of the foregoing first aspect is implemented.
  • an embodiment of the present application further provides a communication device, including the weight optimization apparatus of the foregoing second aspect.
  • embodiments of the present application further provide a computer-readable storage medium, where an information processing program is stored on the computer-readable storage medium, and when the information processing program is executed by a processor, the rights of the foregoing first aspect are realized. Value optimization method.
  • FIG. 1 is a flowchart of a weight optimization method provided by an embodiment of the present application
  • FIG. 2 is a flowchart of determining signal quality data in a weight optimization method provided by an embodiment of the present application
  • FIG. 3 is a flowchart of determining a first weight parameter in a weight optimization method provided by an embodiment of the present application
  • FIG. 5 is a flowchart of determining a second weight parameter of a cell in a weight optimization method provided by an embodiment of the present application
  • FIG. 6 is a flowchart of determining a second weight parameter of a cell in a weight optimization method provided by an embodiment of the present application
  • FIG. 8 is a schematic diagram of a weight optimization apparatus provided by an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a communication device provided by an embodiment of the present application.
  • the present application provides a weight optimization method, apparatus, communication device, and computer-readable storage medium, wherein the weight optimization method includes: acquiring first sub-beams to be predicted from a preconfigured weight database Beam parameters and second beam parameters of several second sub-beams; obtain drive test quality data obtained by collecting user data, and the drive test quality data corresponds to the second beam parameters of several second sub-beams; for each A sub-beam, the signal quality data is obtained according to the parameters of the first beam, the parameters of the second beam and the drive test quality data; the first weight is determined according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams parameter, the first weight parameter is the weight parameter corresponding to the cells covered by all the first sub-beams and all the second sub-beams.
  • the signal quality data of the first sub-beam to be predicted is obtained based on the drive test quality data of the second sub-beam, and then the first weight parameter of the cell is determined according to the signal quality data and the drive test quality data, and the first weight parameter is used for configuration
  • the proposed scheme can solve the complex real-world signal quality requirements, and has the advantages of high computational efficiency and low cost.
  • FIG. 1 is a flowchart of a weight optimization method provided by an embodiment of the present application.
  • the weight optimization method includes, but is not limited to, steps S110 and S140.
  • Step S110 obtaining the first beam parameters of several first sub-beams and the second beam parameters of several second sub-beams to be predicted in a preconfigured weight database;
  • Step S120 acquiring drive test quality data obtained by collecting user data, where the drive test quality data corresponds to the second beam parameters of several second sub-beams;
  • Step S130 for each first sub-beam, obtain signal quality data according to the first beam parameter, the second beam parameter and the drive test quality data;
  • Step S140 Determine a first weight parameter according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams, and the first weight parameter is covered by all the first sub-beams and all the second sub-beams The weight parameter corresponding to the cell.
  • the first beam parameters of several first sub-beams to be predicted and the second beam parameters of several second sub-beams to be predicted may be acquired in a preconfigured weight database, and obtained by collecting user data.
  • the drive test quality data corresponds to the second beam parameters of several second sub-beams, and then the signal quality of the first sub-beam can be obtained according to the first beam parameters, the second beam parameters and the drive test quality data data, traverse each first sub-beam to obtain the signal quality data of all the first sub-beams, and then determine the first weight of the cell according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams parameter.
  • the signal quality data of the first sub-beam to be predicted is obtained based on the drive test quality data of the second sub-beam, and then the first weight parameters of all cells are determined according to the signal quality data and the drive test quality data, and the first weight is used for this.
  • the scheme of parameter configuration can solve the complex actual environment signal quality requirements, and has the advantages of high computational efficiency and low cost.
  • the preconfigured weight database may be a preset database in the scenario of first antenna configuration, or may be a database to be optimized applied in the antenna use process.
  • the first beam parameter may include first gain data, or may include first beam combination information and first gain data, which are not specifically limited in this embodiment.
  • the drive test quality data may include the first reference signal receiving power (Reference Signal Receiving Power, RSRP) value of the target cell corresponding to the first beam, may include the first RSRP value, the target cell covered by the first beam
  • RSRP Reference Signal Receiving Power
  • the second RSRP value of the neighboring cell and the interference signal value to the first beam are not specifically limited in this embodiment.
  • the drive test quality data may be an RSRP value calculated by using the drive test quality data, the first beam parameter and the second beam parameter, which is not uniquely limited in this embodiment.
  • the method for collecting user data may be collected through professional collection equipment, and may be obtained through user measurement data of the base station mobile phone, which is not specifically limited in this embodiment.
  • the weight parameter of a cell may be a weight parameter corresponding to a cell covered by all the first sub-beams and all the second sub-beams, or may be covered by all the first sub-beams and all the second sub-beams
  • the weight parameters corresponding to all the cells in the may also be the weight parameters corresponding to multiple cells covered by all the first sub-beams and all the second sub-beams, which are not specifically limited in this embodiment.
  • step S130 includes but is not limited to step S210, step S220, step S230 and step S240:
  • Step S210 obtaining a distance parameter according to the first beam combination information corresponding to the first sub-beam and all the second beam combination information, where the distance parameter includes a distance value of all the second beams relative to the first beam;
  • the distance parameter can be calculated by the Euclidean distance formula. Assuming that the horizontal angle of the sub-beam is ⁇ , the vertical angle is ⁇ , the first sub-beam is k, and the second sub-beam is i, then the first sub-beam is The Euclidean distance between the beam and the second sub-beam is calculated as The closer the distance between the first sub-beam and the second sub-beam, the greater the similarity between the two sub-beams and the approximately the same coverage of the two sub-beams. Use the drive test quality data of the second sub-beam to estimate the first sub-beam The signal quality data is more accurate and reliable.
  • the calculation method of the distance parameter may be the Euclidean distance calculation method, the Manhattan distance calculation method, or the Chebyshev distance calculation method.
  • the calculation method of the distance parameter is not specifically limited in this embodiment.
  • the formula for the distance value representing the similarity between the second beam and the first beam is also within the scope of this embodiment.
  • the first beam combination information may include the horizontal angle of the sub-beam and the vertical angle of the sub-beam, and may also include the horizontal angle of the sub-beam, the vertical angle of the sub-beam, the horizontal width of the sub-beam, and the vertical width of the sub-beam, which are not implemented in this embodiment. Specific restrictions.
  • Step S220 determining several second sub-beams as reference sub-beams according to the distance parameter
  • the number of reference sub-beams may be one, two, or three, which are not specifically limited in this embodiment.
  • Step S230 acquiring second gain data and drive test quality data corresponding to the reference sub-beam
  • the corresponding second gain data and drive test quality data can be obtained for each reference sub-beam, and the second gain data and drive test quality data are used for subsequent calculation of the signal quality data of the first sub-beam.
  • Step S240 Obtain signal quality data according to the first gain data, the second gain data and the drive test quality data.
  • the signal quality data of the first sub-beam can be obtained according to the first gain data of the first sub-beam, the second gain data of the second sub-beam, and the drive test quality data, when the signal quality data is used to determine the first sub-beam.
  • the configuration scheme using the first weight parameter can solve the complex actual environmental signal quality requirements, and has the advantages of high computational efficiency and low cost.
  • the distance parameter can be obtained according to the first beam combination information corresponding to the first sub-beam and all the second beam combination information, and the distance parameter can include the distance values of all the second beams relative to the first beam, and then according to all the first beams.
  • the distance between the second beam and the first beam determines several second sub-beams as reference sub-beams, and obtains the second gain data and drive test quality data corresponding to several reference sub-beams. and drive test quality data to obtain signal quality data.
  • the signal quality data is obtained based on the drive test quality data of the second sub-beam.
  • the distance parameter can be obtained according to the first beam combination information corresponding to the first sub-beam and all the second beam combination information, and the distance parameter can include the distance values of all the second beams relative to the first beam, and then according to all the first beams.
  • the distance between the second beam and the first beam determines several second sub-beams as reference sub-beams, and the distance parameters of several reference sub-beams are smaller than the distance parameters of other second sub-beams.
  • the signal quality data may be obtained according to the first gain data, the second gain data, and the drive test quality data.
  • the configuration scheme using the first weight parameter can solve complex actual environmental signal quality requirements, and has the advantages of high computational efficiency and low cost.
  • the first sub-beam to be predicted is k
  • the two reference beams with a smaller distance parameter from the first sub-beam are i and j respectively.
  • the drive test quality data obtained by the beam by collecting user data are RSRP i and RSRP j respectively
  • the second gain data corresponding to the two reference beams are AntGain[i] and AntGain[j] respectively
  • the distance parameters of the two reference beams are respectively ⁇ k,i , ⁇ k,j
  • the position of the user relative to the cell is (h, v)
  • the first gain data of the first sub-beam is AntGain[k]
  • calculate the signal quality data RSRP k of the first sub-beam is:
  • RSRP k,i [h][v] RSRP i [h][v]+AntGain[k][h][v]-AntGain[i][h][v]
  • RSRP k,j [h][v] RSRP j [h][v]+AntGain[k][h][v]-AntGain[j][h][v]
  • All the first sub-beams can be traversed according to the above formula, and the signal quality data of all the first sub-beams can be obtained. a weight parameter step. Since the signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used to determine the first weight parameter, the configuration scheme using the first weight parameter can solve the complex actual environment signal quality requirements , and has the advantages of high computational efficiency and low cost.
  • step S140 includes, but is not limited to, steps S310 and S320 after step S140 .
  • Step S310 acquiring cell information of several cells, where each cell information includes the quantity value of the second sub-beam covering the cell, and the signal quality data and the first weight parameter corresponding to the first sub-beam;
  • cell information of the several cells can be obtained, wherein the cell information of each cell can include the number value of the second sub-beam covering the cell, and the coverage of the cell.
  • the number of cells may be one, two, or three, which are not specifically limited in this embodiment.
  • Step S320 Determine the second weight parameter of the cell according to the quantity value, several pieces of signal quality data and several first weight parameters.
  • the second weight parameter of the cell is determined according to the quantity value of the second sub-beams covering the cell, as well as the signal quality data and the first weight parameter corresponding to several first sub-beams covering the cell.
  • the quality data is obtained based on the drive test quality data of the second sub-beam.
  • the scheme of configuring the second weight parameter of the cell by using the first weight parameter can solve the complex problem.
  • the actual environmental signal quality is required, and it has the advantages of high computational efficiency and low cost.
  • step S320 includes but is not limited to step S410 , step S420 and step S430 .
  • Step S410 sort all cells according to the quantity value to obtain a cell sequence
  • all cells that need to be configured with the second weight parameter can be sorted according to the magnitude of the number of the second sub-beams covering the cells, and a cell sequence can be obtained.
  • sorting may be in descending order or ascending order, which is not specifically limited in this embodiment.
  • Step S420 determining the target cell according to the cell sequence
  • the target cell can be determined according to the cell sequence, and the target cell can be preferentially configured.
  • the second weight parameter is the more samples of the second sub-beam covering the cell.
  • Step S430 Determine the second weight parameter according to several pieces of signal quality data and several pieces of the first weight parameter corresponding to the target cell.
  • the second weight parameters that conform to the signal quality of the target cell are obtained.
  • all cells that need to be configured with the second weight parameter can be sorted according to the number of samples of the second sub-beam covering the cell, and a sequence of cells can be obtained, because the number of samples of the second sub-beam covering the cell is The more the number of samples, the higher the requirements for the signal coverage quality of the cell corresponding to the number of samples. Therefore, the target cell can be determined according to the cell sequence.
  • the target cell can preferentially configure the second weight parameter.
  • the signal quality data and several first weight parameters are used as the judgment basis, and the second weight parameters that conform to the signal quality of the target cell are obtained.
  • the solution of using the first weight parameter to configure the second weight parameter of the cell can Solve complex real-world signal quality requirements and have the advantages of high computational efficiency and low cost.
  • step S430 includes but is not limited to step S510.
  • Step S510 determining the first weight parameter corresponding to the largest signal quality data in the target cell as the second weight parameter.
  • the target cell may judge the signal quality data corresponding to several first weight parameters, and then determine the first weight parameter corresponding to the largest signal quality data in the target cell as the second weight parameter, Since the signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used to determine the first weight parameter, the solution of using the first weight parameter to configure the second weight parameter of the cell can Solve complex real-world signal quality requirements and have the advantages of high computational efficiency and low cost.
  • step S430 when the signal quality data includes the first reference signal received power RSRP value of the target cell corresponding to the first beam, the second RSRP value of the neighboring cell of the target cell covered by the first beam, and the For the interference signal value of the first beam, step S430 includes, but is not limited to, steps S610 and S620.
  • Step S610 obtain the SINR value according to the first RSRP value, the second RSRP value and the interference signal value
  • Step S610 determining the first weight parameter corresponding to the largest SINR value in the target cell as the second weight parameter.
  • the SINR value can be obtained by calculating the first RSRP value, the second RSRP value, and the interference signal value in the signal quality data, and then the first weight parameter corresponding to the largest SINR value is used as the target cell's first weight parameter.
  • the second weight parameter Since the SINR value is calculated based on the signal quality data, and the signal quality data is obtained based on the drive test quality data of the second sub-beam, when the signal quality data is used to determine the first weight parameter, the first weight is used.
  • the solution in which the parameter configures the second weight parameter of the cell can solve the complex actual environment signal quality requirements, and has the advantages of high computational efficiency and low cost.
  • FIG. 7 is a flowchart of a weight optimization method provided by another embodiment of the present application.
  • the weight optimization method includes, but is not limited to, steps S701 and S711 .
  • Step S701 performing preset weight parameter configuration on the cell to be optimized
  • the preset weight parameters include one or more groups of weight parameters, which are not specifically limited in this implementation.
  • Step S702 collecting user data of the cell to be optimized according to a preset weight parameter
  • the collected user data represents the beam coverage characteristics of different horizontal direction angles, different electronic down-tilt angles, and different beam widths of each cell, and directly reflects the actual environment of the cell; user data can include drive test quality data, Physical Cell ID (Physical Cell ID, PCI) information, SSB index information, Secondary Synchronization Signal (SSS) SNR, SSS power, SSS noise, Physical Broadcast Channel (Physical Broadcast Channel, PBCH) SNR , PBCH power, PBCH noise, user longitude, latitude, and altitude, which are not specifically limited in this embodiment.
  • the drive test quality data may include the first RSRP value, the second RSRP value of the neighboring cell of the target cell covered by the first beam, and the interference signal value to the first beam, which are not specifically limited in this embodiment. .
  • Step S703 preprocessing user data to obtain drive test quality data
  • the preprocessing of user data mainly includes user data cleaning and user data smoothing to obtain drive test quality data, which is used to calculate the signal quality data of the first beam to be predicted.
  • user data cleaning is mainly to standardize user data format, remove abnormal user data, remove duplicate user data, and retain required user data.
  • User data smoothing mainly processes user data through smooth aggregation, data normalization, etc., and reduces user data on the basis of maintaining the characteristics of the original user data.
  • the data smoothing processing method may include spatial dimension rasterization, may include time dimension rasterization, may include data sampling, or may include median filtering, which is not specifically limited in this implementation.
  • Step S704 determine the beam weight gain database that needs to be constructed, the beam weight gain database includes beam combination and beam parameters corresponding to the beam combination, and the beam combination includes several first sub-beams to be predicted and second sub-beams that have collected user data. beam;
  • selecting the composition of the beam weight gain database to be constructed according to the scene is mainly to determine the beam combination in the beam weight database and the beam parameters corresponding to the beam combination.
  • the beam weight database can be composed of many sub-beam gain tables, and each sub-beam gain table is mainly composed of beam combinations, where the beam combination can be based on the sub-beam horizontal angle, sub-beam vertical angle, sub-beam horizontal width and sub-beam vertical width.
  • the beam combination includes several first sub-beams to be predicted and second sub-beams for which user data has been collected.
  • the beam parameters may include first beam parameters of the first sub-beam and second beam parameters of the second sub-beam.
  • the first beam parameters may include first beam combination information and first gain data
  • the second beam parameters may include second beam combination information and second gain data.
  • Step S705 obtaining the relative position information of the user
  • the relative position of the user can be calculated by taking the cell as a reference point.
  • the relative position of the user can be calculated by combining the latitude and longitude position information of the user and the industrial parameter information of the cell.
  • the relative position information of the user is mainly used to determine the first first gain data for the beam and second gain data for determining the second sub-beam.
  • Step S706 Obtain a distance parameter according to the first beam parameter and the second beam parameter, and determine, according to the distance parameter, several second sub-beams closest to the first sub-beam to be predicted as reference sub-beams.
  • the distance parameter can be calculated according to the first beam parameters (sub-beam horizontal angle, sub-beam vertical angle) and the second beam parameters (sub-beam horizontal angle, sub-beam vertical angle), and the distance parameter is determined according to the distance parameter.
  • the first beam parameters sub-beam horizontal angle, sub-beam vertical angle
  • the second beam parameters sub-beam horizontal angle, sub-beam vertical angle
  • the number of reference sub-beams may be one or more, which is not limited in this embodiment.
  • the Euclidean distance calculation formula of the first sub-beam as k and the second sub-beam as i is: The closer the distance value of the calculation result, the greater the similarity between the first sub-beam and the second sub-beam, and the more accurate and reliable it is to use the drive test quality data of the second sub-beam to predict the signal quality data of the first sub-beam.
  • Step S707 Acquire second gain data and drive test quality data corresponding to the reference sub-beam.
  • the second gain data and drive test quality data corresponding to the reference sub-beams can be acquired according to the determined several reference sub-beams.
  • Step S708 Obtain signal quality data according to the first gain data, the second gain data and the drive test quality data.
  • the second drive test quality data corresponding to the two reference sub-beams are RSRP i and RSRP j respectively
  • the second gain data corresponding to the two reference sub-beams are AntGain[i] and AntGain[j] respectively
  • the two reference sub-beams are respectively AntGain[i] and AntGain[j].
  • the corresponding distance parameters are ⁇ k,i and ⁇ k,j respectively, the relative position information of the user is (h, v), the signal quality data of the first sub-beam is RSRP k , and the signal quality data of the first sub-beam can be determined according to Calculated by the following formula:
  • RSRP k,i [h][v] RSRP i [h][v]+AntGain[k][h][v]-AntGain[i][h][v]
  • RSRP k,j [h][v] RSRP j [h][v]+AntGain[k][h][v]-AntGain[j][h][v]
  • Step S709 using steps S707 and S708 to traverse all the first sub-beams to obtain all signal quality data, according to all signal quality data and all drive test quality data, the first weight parameters of all cells can be determined, and the beam weight gain database can be updated .
  • the weight gain database reflects the coverage characteristics of the base station in the actual environment.
  • the signal measurement data after the dynamic adjustment of the first beam can be predicted, which provides a data basis for broadcast weight parameter recommendation.
  • the dynamic adjustment of the beam includes adjustment of the horizontal angle of the sub-beam, the vertical angle of the sub-beam, the horizontal width of the sub-beam, and the vertical width of the sub-beam.
  • Step S710 Perform an optimal weight search for the cell according to the weight gain database, and determine the second weight parameter of the cell.
  • a plurality of candidate weight parameters can be determined according to the updated weight gain database, and then an optimal weight search is performed for the cell according to the candidate weight parameters, and the second weight parameter of the cell is determined, and the second weight parameter of the cell is determined.
  • the cell is configured using the second weight parameter.
  • this step can be applied to different beam configuration scenarios by adopting different cost functions, different candidate weight parameters, and different weight search methods according to different requirements.
  • the global cost function or the local cost function can be used to confirm the second weight parameter of the cell, so as to realize the optimization of the whole area or the local area of the wireless coverage of the cell, which is not specified in this embodiment. limited.
  • alternative weight parameters can be configured according to actual conditions, and the alternative weight parameters are applicable to the beam configuration scenarios of single beam, multi-beam, and single-multi-beam coexistence, which are not specifically limited in this embodiment.
  • the optimal weight search may use a deep learning method, a reinforcement learning method, an ant colony algorithm, or a Monte Carlo algorithm, which is not specifically limited in this embodiment.
  • the SINR value of the serving cell of the user can be used for determination. Assuming that the second beam of the serving cell is i, the calculation method of the SINR value is as follows:
  • RSRP main SSBi is the received power of beam i of the cell to be optimized
  • RSRP others SSBi is the received power of the adjacent cell beam i of the cell to be optimized
  • noise SSBi is the white noise power, here the power value calculation should use linear value calculation .
  • the mathematical characteristics of the SINR value of the serving cell are counted, a cost function is constructed, the optimization of the second weight parameter with purpose is realized, and the second weight parameter of the cell is output for configuration and delivery.
  • the cost function may also determine the second weight parameter according to the overall highest value of the RSRP value of the cell, or determine the second weight parameter according to the minimum interference value of the cell, or evaluate the results of the comprehensive results of multiple cost functions.
  • the second weight parameter is determined, which is not specifically limited in this embodiment.
  • the method for determining the second weight parameter for the lowest interference value is not limited to the method for determining the optimal SINR value in the embodiment, and this embodiment does not make a unique limitation.
  • Step S711 Monitor the drive test quality data of the cell where the second weight parameter has been configured. If the drive test quality data is greater than the signal quality threshold, the second weight parameter remains unchanged. If the drive test quality data is less than the signal quality threshold, Then step S703 is executed.
  • the optimal second weight parameter is recommended and the configuration is delivered to the cell, and the drive test data under the second weight parameter is collected. If the drive test quality data is greater than the signal quality threshold, keep the second weight parameter unchanged; if the drive test quality data is less than the signal quality threshold, collect the drive test quality data of the second weight parameter, and go back to the execution step S703.
  • the newly collected drive test quality data and historical data jointly build a new weight gain database, search for the optimal second weight parameter, and iterate until it meets the requirements, which can realize adaptive weight optimization.
  • the signal quality threshold may be set according to actual needs, which is not specifically limited in this embodiment.
  • the first embodiment specifically describes the above embodiment based on the steps and data of actual cell optimization. The details are as follows:
  • the first embodiment the number of beams in the pre-configured weight database is configured to be 8, and the SSB frequency position is 2.52495 GHz.
  • Two sets of preset weight parameters are configured for the cell to be optimized.
  • the two sets of preset weight parameters include the horizontal angle of the sub-beam, the vertical angle of the sub-beam, the horizontal width of the sub-beam, and the vertical width of the sub-beam to reflect the horizontal coverage and vertical coverage azimuth.
  • Table 1 below shows the single-layer beam and the four-layer beam as an example to configure preset weight parameters for a cell
  • Table 2 shows user data collected based on two sets of preset weight parameters.
  • the steps of data preprocessing can be as follows:
  • the grid size in the longitude direction x dimension direction x height direction is about 8.5m x 11.1m x 3m;
  • the cells to be optimized are divided into grids, and the collected user data (including drive test quality data) of the same cells and the same sub-beams in the grids are averaged.
  • the cell-based weight gain of different sub-beam horizontal angles, different sub-beam vertical angles, different sub-beam horizontal widths, and different sub-beam vertical widths can be constructed according to the pre-processed user data.
  • database the specific steps can be as follows:
  • the preset weight parameter configuration in this embodiment is an 8-beam configuration, which needs to ensure the user experience of road surfaces and high-rise buildings. Therefore, the relevant dimensions of the beam weight gain database to be constructed in this embodiment are shown in Table 3, with a total of 22572 sub-beams. Different sub-beams can be combined into respective first weight parameter configurations, and a beam weight gain database can be obtained according to the first weight parameters. See Table 3
  • the relative position of the user is mainly used to query the gain data (including the first gain data and the second gain data) in the theoretical antenna gain table, and to calculate the weight gain table of the first sub-beam to be predicted.
  • a distance parameter is obtained according to the first beam parameter and the second beam parameter, and several second sub-beams closest to the first sub-beam to be predicted are determined as reference sub-beams according to the distance parameter. details as follows:
  • the Euclidean distance is used to measure the beam similarity, and M may be set to be 2.
  • a feature vector [ ⁇ , ⁇ ] is constructed based on the horizontal angle of the sub-beam and the vertical angle of the sub-beam, and the two second sub-beams with the closest Euclidean distance to the first sub-beam to be predicted are calculated.
  • the two second sub-beams most similar to the first sub-beam k to be predicted are i and j, respectively, and their Euclidean distances are as follows:
  • second gain data and drive test quality data corresponding to the reference sub-beam are acquired, and signal quality data is obtained according to the first gain data, the second gain data, and the drive test quality data. details as follows:
  • the first sub-beam is k
  • the first gain data is AntGain[k]
  • the two reference sub-beams closest to the first sub-beam are i and j, respectively.
  • the second drive test quality data corresponding to the beams are RSRP i and RSRP j respectively
  • the second gain data corresponding to the two reference sub-beams are AntGain[i] and AntGain[j] respectively
  • the distance parameters corresponding to the two reference sub-beams are respectively are ⁇ k,i , ⁇ k,j
  • the relative position information of the user is (h, v)
  • the signal quality data of the first sub-beam is RSRP k
  • the signal quality data of the first sub-beam can be calculated according to the following formula:
  • RSRP k,i [h][v] RSRP i [h][v]+AntGain[k][h][v]-AntGain[i][h][v]
  • RSRP k,j [h][v] RSRP j [h][v]+AntGain[k][h][v]-AntGain[j][h][v]
  • the signal quality data may be calculated according to the first gain data, the second gain data, the distance parameter and the drive test quality data in Table 5.
  • All the signal quality data are obtained by traversing all the first sub-beams. According to all the signal quality data and all drive test quality data, the first weight parameters of all cells can be determined, and the beam weight gain database can be updated.
  • the collected user data (including drive test quality data) and the first weight parameter of the second sub-beam are stored, and in the first iteration, it is not necessary to update the relevant data and database.
  • an optimal weight search is performed on the cell to determine the second weight parameter of the cell. details as follows:
  • the downlink coverage of the sub-network area For the purpose of optimizing the downlink coverage of the sub-network area, it is calculated by searching for the optimal weight in the order of the cells.
  • the SINR value of the serving cell under the first weight parameter estimated by all users in the area is used as the cost function, and the optimization goal is to maximize the SINR value of the cell.
  • serving cell SINR calculation is as follows. Using the relevant data in Table 6, the SINR value of the beam 3 covered in the serving cell 201 can be calculated.
  • the SINR value is calculated using the cost function. After the cell traverses all the candidate weight parameters, the maximum value of the SINR value and the corresponding first weight parameter are calculated.
  • the first weight parameter can be used as the second weight parameter of the cell, and the second weight parameter of the cell is taken
  • the weight parameter replaces the user data of this cell as the input data of the next cell to be optimized, and starts to optimize the next cell. By traversing all the cells to be optimized in this way, the second weight parameter of each cell can be obtained. After obtaining the second weight parameters of all cells, the network management can optimize the cells according to the second weight parameters.
  • the signal quality data in the weight optimization method is obtained based on the drive test quality data of the second sub-beam.
  • the second weight parameter of the cell is determined by using the first weight parameter.
  • the second embodiment specifically describes the above embodiment based on the steps and data of actual cell optimization. The details are as follows:
  • Second embodiment perform a set of preset weight parameter configuration on the cell to be optimized, and collect user data under the preset weight parameter configuration. As shown in Table 7:
  • Quad beam sub-beam horizontal angle [-28;28;-28;28;-28;28;-28;28;28;-28;28;28;28;28;28]
  • Sub-beam vertical angle [-9;-9;-3;-3;3;3;9;9] sub-beam horizontal width [65;65;65;65;65;65;65;65;65] sub-beam vertical width [6;6;6;6;6;6]
  • the cell-based weight gain of different sub-beam horizontal angles, different sub-beam vertical angles, different sub-beam horizontal widths, and different sub-beam vertical widths can be constructed according to the pre-processed user data.
  • database the specific steps can be as follows:
  • Beam “1” provides stable and high-quality horizontal basic coverage, that is, it mainly provides road coverage; the number of beam "X”, the horizontal angle of sub-beams, the vertical angle of sub-beams, the horizontal width of sub-beams, and the vertical width of sub-beams are flexible and variable.
  • This beam structure is designed to be decoupled in the horizontal and vertical directions, which can achieve the best unification of stability and flexibility.
  • the relevant dimensions of the beam weight gain database to be constructed in this embodiment are shown in Table 8, with a total of 420 sub-beams.
  • the relative position of the user is mainly used to query the gain data (including the first gain data and the second gain data) in the theoretical antenna gain table, and to calculate the weight gain table of the first sub-beam to be predicted.
  • the distance parameter is obtained according to the first beam parameter and the second beam parameter, and several second sub-beams closest to the to-be-predicted first sub-beam are determined as reference sub-beams according to the distance parameter.
  • Acquire second gain data and drive test quality data corresponding to the reference sub-beam and obtain signal quality data according to the first gain data, the second gain data, and the drive test quality data.
  • the collected user data (including drive test quality data) and the first weight parameter of the second sub-beam are stored, and in the first iteration, it is not necessary to update the relevant data and database.
  • an optimal weight search is performed on the cell to determine the second weight parameter of the cell. details as follows:
  • the candidate SSB index value of beam "1" is [0, 1, 2, 3, 4].
  • the number of “X” beams can be configured, that is, X ⁇ [0,1,2,3], and the alternative SSB index value is [5,6,7].
  • the cell automatically The configuration of the sub-beam horizontal angle, sub-beam vertical angle, sub-beam horizontal width, and sub-beam vertical width of the selected beam "X" is adapted.
  • the cost function of this embodiment can use the SINR value of the serving cell under the first weight parameter calculated by all users in the subnet area, and determine the maximum SINR value corresponding to the first weight parameter as the second weight parameter of the cell .
  • a beam order-based weight optimization search algorithm may be used.
  • the ant colony algorithm is used to search for the optimal configuration of beam "1" to reduce inter-cell interference and ensure the horizontal coverage of the road surface.
  • the second weight parameter is optimized for the cell, and the drive test quality data of the cell that has passed the optimization of the second weight parameter is monitored. If the drive test quality data is greater than the signal quality threshold, the second weight parameter is not If the drive test quality data is less than the signal quality threshold, it is necessary to collect the drive test quality data of the second weight parameter, and return to the user data preprocessing step to optimize the cell weight.
  • the signal quality data in the weight optimization method is obtained based on the drive test quality data of the second sub-beam.
  • the second weight parameter of the cell is determined by using the first weight parameter.
  • the weight optimization apparatus 800 includes a memory 820 , a processor 810 , and a computer stored in the memory 820 and running on the processor 810 program.
  • the processor 810 and the memory 820 may be connected by a bus or otherwise.
  • the memory 820 can be used to store non-transitory software programs and non-transitory computer-executable programs. Additionally, memory 820 may include high-speed random access memory 820 and may also include non-transitory memory 820, such as at least one piece of disk memory 820, flash memory device, or other piece of non-transitory solid state memory 820. In some embodiments, the memory 820 may optionally include memory 820 located remotely from the processor 810, and these remote memories 820 may be connected to the processor 810 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the non-transitory software programs and instructions required to implement the information processing method of the above-mentioned embodiment are stored in the memory, and when executed by the processor, the weight optimization method in the above-mentioned embodiment is executed, for example, the above-described method in FIG. 1 is executed.
  • method steps S110 to S140 in FIG. 2 method steps S210 to S240 in FIG. 2 , method steps S310 to S320 in FIG. 3 , method steps S410 to S430 in FIG. 4 , method steps S510 in FIG. 5 , method steps in FIG. 6 S610 to S620, the method steps S701 to S711 in FIG. 7 .
  • an embodiment of the present application further provides a communication device.
  • the communication device 900 includes the foregoing weight optimization apparatus 800 .
  • the weight optimization apparatus 800 may obtain the first beam parameters of several first sub-beams and the second beam parameters of several second sub-beams to be predicted in a pre-configured weight database, and obtain the parameters obtained by collecting user data.
  • the drive test quality data corresponds to the second beam parameters of several second sub-beams, and then the signal of the first sub-beam can be obtained according to the first beam parameters, the second beam parameters and the drive test quality data Quality data, traverse each first sub-beam to obtain the signal quality data of all the first sub-beams, and then determine the first weight of the cell according to the signal quality data of all the first sub-beams and the drive test quality data of all the second sub-beams value parameter.
  • the signal quality data of the first sub-beam to be predicted is obtained based on the drive test quality data of the second sub-beam, and then the first weight parameter of the cell is determined according to the signal quality data and the drive test quality data, which uses the first weight parameter
  • the configuration scheme can solve the complex real-world signal quality requirements, and has the advantages of high computational efficiency and low cost.
  • an embodiment of the present application also provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by a processor or controller, for example, by the above-mentioned Executed by a processor in the weight optimization apparatus in the embodiment, the processor may execute the weight optimization method in the above-mentioned embodiment, for example, execute the method steps S110 to S140 in FIG. 1 and the method in FIG. 2 described above. Steps S210 to S240, method steps S310 to S320 in FIG. 3, method steps S410 to S430 in FIG. 4, method step S510 in FIG. 5, method steps S610 to S620 in FIG. S711.
  • Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, or may Any other medium used to store desired information and which can be accessed by a computer.
  • communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and can include any information delivery media, as is well known to those of ordinary skill in the art .

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Abstract

一种权值优化方法、装置、通信设备及计算机可读存储介质，其中权值优化方法包括：在预配置的权值数据库中获取待预测的若干个第一子波束的第一波束参数、若干个第二子波束的第二波束参数，且获取通过采集用户数据得到第二子波束的路测质量数据；对于每个第一子波束，根据第一波束参数、第二波束参数和路测质量数据得到信号质量数据；根据所有第一子波束的信号质量数据以及所有第二子波束的路测质量数据确定第一权值参数，第一权值参数为所有第一子波束和所有第二子波束所覆盖的小区对应的权值参数。

Description

权值优化方法、装置、通信设备及计算机可读存储介质
相关申请的交叉引用
本申请基于申请号为202110289746.9、申请日为2021年03月16日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
本申请实施例涉及但不限于通信领域，尤其涉及一种权值优化方法、装置、通信设备及计算机可读存储介质。
背景技术
在目前5G网络优化建设中，无线系统参数的配置是影响系统性能和系统指标的重要因素。新空口(New Radio,NR)系统中同步和广播信道(Synchronization Signal and PBCH Block，SSB)的配置方案包括了时域、频域与波束三个维度的配置，导致大规模天线(Massive Multi Input Multi Output，Massive MIMO)权值组合变得更加复杂，其子波束调整权值组合规模可达上万种。传统方法已难以应对5G无线网络优化面临的困难。
运营商正处于5G建网初期，均采用默认波束权值配置进行路测拉网，通常基于通用的数据分析软件或工具对网络覆盖进行监测，当发现网络覆盖问题时(如弱覆盖、过覆盖等)，通常是依据优化工作人员的经验，对小区制定针对性的波束配置调整方案，由于波束配置调整的方案依据工作人员的经验进行制定，需要通过反复调整与测试才能解决问题，而且容易忽略相邻小区间的相互关系，经常出现解决所发现的问题的同时产生新的问题，所以现有制定波束配置调整方案的效率低，成本高。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本申请实施例的主要目的在于提出一种权值优化方法、装置、通信设备及计算机可读存储介质，能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低。
第一方面，本申请实施例提供了一种权值优化方法，包括：
在预配置的权值数据库中获取待预测的若干个第一子波束的第一波束参数和若干个第二子波束的第二波束参数；
获取通过采集用户数据得到的路测质量数据，所述路测质量数据与若干个第二子波束的第二波束参数相对应；
对于每个所述第一子波束，根据所述第一波束参数、所述第二波束参数和所述路测质量数据得到信号质量数据；
根据所有所述第一子波束的所述信号质量数据以及所有所述第二子波束的所述路测质量数据确定第一权值参数，所述第一权值参数为所有所述第一子波束和所有所述第二子波束所覆盖的小区对应的权值参数。
第二方面，本申请实施例还提供了一种权值优化装置，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述计算机程序被所述处理器执行时实 现前述第一方面的权值优化方法。
第三方面，本申请实施例还提供了一种通信设备，包括前述第二方面的权值优化装置。
第四方面，本申请实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质上存储有信息处理程序，所述信息处理程序被处理器执行时实现前述第一方面的权值优化方法。
附图说明
图1是本申请一个实施例提供的权值优化方法的流程图；
图2是本申请一个实施例提供的权值优化方法中的确定信号质量数据的流程图；
图3是本申请一个实施例提供的权值优化方法中的确定第一权值参数的流程图；
图4是本申请一个实施例提供的权值优化方法中的优化小区权值参数的流程图；
图5是本申请一个实施例提供的权值优化方法中的确定小区第二权值参数的流程图；
图6是本申请一个实施例提供的权值优化方法中的确定小区第二权值参数的流程图；
图7是本申请另一个实施例提供的权值优化方法的流程图；
图8是本申请一个实施例提供的权值优化装置的示意图；以及
图9是本申请一个实施例提供的通信设备的示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。
需要说明的是，虽然在装置示意图中进行了功能模块划分，在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于装置中的模块划分，或流程图中的顺序执行所示出或描述的步骤。说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。
本申请提供了一种权值优化方法、装置、通信设备及计算机可读存储介质，其中权值优化方法包括：在预配置的权值数据库中获取待预测的若干个第一子波束的第一波束参数和若干个第二子波束的第二波束参数；获取通过采集用户数据得到的路测质量数据，路测质量数据与若干个第二子波束的第二波束参数相对应；对于每个第一子波束，根据第一波束参数、第二波束参数和路测质量数据得到信号质量数据；根据所有第一子波束的信号质量数据以及所有第二子波束的路测质量数据确定第一权值参数，第一权值参数为所有第一子波束和所有第二子波束所覆盖的小区对应的权值参数。基于第二子波束的路测质量数据得到待预测的第一子波束的信号质量数据，再根据信号质量数据以及路测质量数据确定小区的第一权值参数，利用第一权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
下面结合附图，对本申请实施例作进一步阐述。
如图1所示，图1是本申请一个实施例提供的权值优化方法的流程图。在图1的示例中，在一实施例中，该权值优化方法包括但不限于有步骤S110和步骤S140。
步骤S110，在预配置的权值数据库中获取待预测的若干个第一子波束的第一波束参数和若干个第二子波束的第二波束参数；
步骤S120，获取通过采集用户数据得到的路测质量数据，路测质量数据与若干个第二子 波束的第二波束参数相对应；
步骤S130，对于每个第一子波束，根据第一波束参数、第二波束参数和路测质量数据得到信号质量数据；
步骤S140，根据所有第一子波束的信号质量数据以及所有第二子波束的路测质量数据确定第一权值参数，第一权值参数为所有第一子波束和所有第二子波束所覆盖的小区对应的权值参数。
在一实施例中，可以在预配置的权值数据库中获取待预测的若干个第一子波束的第一波束参数和若干个第二子波束的第二波束参数，并获取通过采集用户数据得到的路测质量数据，路测质量数据与若干个第二子波束的第二波束参数相对应，然后根据第一波束参数、第二波束参数和路测质量数据可以得到第一子波束的信号质量数据，遍历每一个第一子波束，得到所有第一子波束的信号质量数据，然后根据所有第一子波束的信号质量数据以及所有第二子波束的路测质量数据确定小区的第一权值参数。即基于第二子波束的路测质量数据得到待预测的第一子波束的信号质量数据，再根据信号质量数据以及路测质量数据确定所有小区的第一权值参数，此利用第一权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
需要说明的是，预配置的权值数据库可以是在首次天线配置场景下的预设数据库，也可以是应用于天线使用过程中的待优化的数据库。
需要说明的是，第一波束参数可以包括第一增益数据，也可以包括第一波束组合信息和第一增益数据，本实施例对其不作具体限定。
需要说明的是，路测质量数据可以包括第一波束对应的目标小区的第一参考信号接收功率(Reference Signal Receiving Power，RSRP)值，可以包括第一RSRP值、第一波束所覆盖的目标小区的邻区的第二RSRP值以及对第一波束的干扰信号值，本实施例对其不作具体限定。
需要说明的是，路测质量数据可以是通过路测质量数据、第一波束参数和第二波束参数计算得到的RSRP值，本实施例对其不作唯一限定。
需要说明的是，采集用户数据的方法可以是通过专业采集设备采集，可以通过基站手机的用户测量数据进行获取，本实施例对其不作具体限定。
需要说明的是，小区的权值参数可以是所有第一子波束和所有第二子波束所覆盖的一个小区对应的权值参数，也可以是所有第一子波束和所有第二子波束所覆盖的所有小区对应的权值参数，还可以是所有第一子波束和所有第二子波束所覆盖的多个小区对应的权值参数，本实施例对其不作具体限定。
参照图2，在一实施例中，当第二子波束的数量为两个或以上，第一波束参数包括第一波束组合信息和第一增益数据，第二波束参数包括第二波束组合信息和第二增益数据，步骤S130包括但不限于有步骤S210、步骤S220、步骤S230以及步骤S240：
步骤S210，根据第一子波束对应的第一波束组合信息和所有第二波束组合信息得到距离参数，距离参数包括所有第二波束相对第一波束的距离值；
可以理解的是，距离参数可以通过欧式距离公式计算得出，假设子波束的水平方向角度为α，垂直方向角度为β，第一子波束为k，第二子波束为i，那么第一子波束与第二子波束 的欧式距离计算公式为
Figure PCTCN2021132854-appb-000001
第一子波束与第二子波束之间的距离越近，则代表两子波束相似度越大，两子波束覆盖范围近似一致，利用第二子波束的路测质量数据预估第一子波束的信号质量数据则更加准确可靠。
需要说明的是，距离参数的计算方法可以是欧式距离计算方法，可以是曼哈顿距离计算方法，可以是切比雪夫距离计算方法，本实施例对距离参数的计算方法不作具体限定，其他能够计算用于表示第二波束和第一波束之间相似度的距离值的公式也在本实施例的范围中。
需要说明的是，第一波束组合信息可以包括子波束水平角度和子波束垂直角度，也可以包括子波束水平角度、子波束垂直角度、子波束水平宽度以及子波束垂直宽度，本实施例对其不作具体限定。
步骤S220，根据距离参数确定若干个第二子波束作为参考子波束；
需要说明的是，参考子波束的数量可以是一个，可以是两个，也可以是三个，本实施例对其不作具体限定。
步骤S230，获取参考子波束对应的第二增益数据和路测质量数据；
可以理解的是，可以对每一个参考子波束获取其对应的第二增益数据和路测质量数据，第二增益数据和路测质量数据用于后续计算第一子波束的信号质量数据。
步骤S240，根据第一增益数据、第二增益数据和路测质量数据得到信号质量数据。
可以理解的是，可以根据第一子波束的第一增益数据，以及第二子波束的第二增益数据和路测质量数据得到第一子波束的信号质量数据，当信号质量数据用于确定第一权值参数时，利用第一权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
在一实施例中，可以根据第一子波束对应的第一波束组合信息和所有第二波束组合信息得到距离参数，距离参数可以包括所有第二波束相对第一波束的距离值，然后根据所有第二波束相对第一波束的距离值确定若干个第二子波束作为参考子波束，获取若干个参考子波束对应的第二增益数据和路测质量数据，可以根据第一增益数据、第二增益数据和路测质量数据得到信号质量数据。该信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
在一实施例中，可以根据第一子波束对应的第一波束组合信息和所有第二波束组合信息得到距离参数，距离参数可以包括所有第二波束相对第一波束的距离值，然后根据所有第二波束相对第一波束的距离值确定若干个第二子波束作为参考子波束，其中若干个参考子波束的距离参数均比其他第二子波束的距离参数小，获取若干个参考子波束对应的第二增益数据和路测质量数据，可以根据第一增益数据、第二增益数据和路测质量数据得到信号质量数据。通过欧式距离公式计算出所有第二波束相对第一波束的距离值，然后确认距离参数较小的若干个第二子波束为参考子波束，参考子波束的距离参数越小，那么参考子波束与第一波束的相似度越高，基于参考子波束的第二增益数据和路测质量数据得到确定的信号质量数据越精准，而且由于该信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
在一实施例中，当参考子波束的数量为2个，待预测的第一子波束为k，距离第一子波 束的距离参数较小的两个参考波束分别为i、j，两个参考波束通过采集用户数据得到的路测质量数据分别为RSRP i、RSRP j，两个参考波束对应的第二增益数据分别为AntGain[i]、AntGain[j]，两个参考波束的距离参数分别为ρ k,i、ρ k,j,该用户相对小区的位置为(h,v)，第一子波束的第一增益数据为AntGain[k]，那么计算第一子波束的信号质量数据RSRP k的计算方法为：
RSRP k,i[h][v]＝RSRP i[h][v]+AntGain[k][h][v]-AntGain[i][h][v]
RSRP k,j[h][v]＝RSRP j[h][v]+AntGain[k][h][v]-AntGain[j][h][v]
Figure PCTCN2021132854-appb-000002
可以根据上述公式遍历所有第一子波束，得到所有第一子波束的信号质量数据，所有第一子波束的信号质量数据和所有第二子波束的路测质量数据可以用于后续确定小区的第一权值参数的步骤中。由于该信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
参照图3，在一实施例中，步骤S140之后包括但不限于有步骤S310以及步骤S320。
步骤S310，获取若干个小区的小区信息，每个小区信息包括覆盖小区的第二子波束的数量值，以及第一子波束对应的信号质量数据和第一权值参数；
可以理解的是，当需要对若干个小区的网络覆盖进行优化，可以获取该若干个小区的小区信息，其中，每个小区的小区信息可以包括覆盖小区的第二子波束的数量值，以及覆盖小区的第一子波束对应的信号质量数据和第一权值参数。
需要说明的是，小区的数量可以是一个，可以是两个，可以是三个，本实施例对其不作具体限定。
步骤S320，根据数量值、若干个信号质量数据和若干个第一权值参数确定小区的第二权值参数。
可以理解的是，根据覆盖小区的第二子波束的数量值，以及覆盖小区的若干个第一子波束对应的信号质量数据和第一权值参数确定小区的第二权值参数，由于该信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数对小区的第二权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
参照图4，在一实施例中，步骤S320包括但不限于有步骤S410、步骤S420以及步骤S430。
步骤S410，根据数量值对所有小区进行排序，得到小区序列；
可以理解的是，可以根据覆盖小区的第二子波束的数量值的大小对需要配置第二权值参数的所有小区进行排序，能够得到小区序列。
需要说明的是，排序可以降序排序，也可以是升序排序，本实施例对其不作具体限定。
步骤S420，根据小区序列确定目标小区；
可以理解的是，覆盖小区的第二子波束的样本数量越多，那么该样本数量对应的小区对于信号覆盖质量的要求越高，因此可以根据小区序列的情况确定目标小区，目标小区可以优先配置第二权值参数。
步骤S430，根据目标小区中对应的若干个信号质量数据和若干个第一权值参数确定第二权值参数。
可以理解的是，根据目标小区中对应的若干个信号质量数据和若干个第一权值参数作为判定依据，得出符合目标小区的信号质量情况的第二权值参数。
在一实施例中，可以根据覆盖小区的第二子波束的样本数量的大小对需要配置第二权值参数的所有小区进行排序，能够得到小区序列，由于覆盖小区的第二子波束的样本数量越多，那么该样本数量对应的小区对于信号覆盖质量的要求越高，因此可以根据小区序列的情况确定目标小区，目标小区可以优先配置第二权值参数，然后可以根据目标小区中对应的若干个信号质量数据和若干个第一权值参数作为判定依据，得出符合目标小区的信号质量情况的第二权值参数。由于该信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数对小区的第二权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
参照图5，在一实施例中，步骤S430包括但不限于有步骤S510。
步骤S510，将目标小区中最大的信号质量数据对应的第一权值参数确定为第二权值参数。
在一实施例中，目标小区可以对若干个第一权值参数对应的信号质量数据进行判断，然后将目标小区中最大的信号质量数据对应的第一权值参数确定为第二权值参数，由于该信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数对小区的第二权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
参照图6，在一实施例中，当信号质量数据包括第一波束对应的目标小区的第一参考信号接收功率RSRP值、第一波束所覆盖的目标小区的邻区的第二RSRP值以及对第一波束的干扰信号值，步骤S430包括但不限于有步骤S610以及步骤S620。
步骤S610，根据第一RSRP值、第二RSRP值以及干扰信号值得到SINR值；
步骤S610，将目标小区中最大的SINR值对应的第一权值参数确定为第二权值参数。
在一实施例中，可以根据信号质量数据中的第一RSRP值、第二RSRP值以及干扰信号值进行计算得到SINR值，然后将最大的SINR值所对应的第一权值参数作为目标小区的第二权值参数。由于该SINR值是根据信号质量数据计算得出的，而信号质量数据是基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数对小区的第二权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
参照图7，图7是本申请另一个实施例提供的权值优化方法的流程图。在图7的示例中，在一实施例中，该权值优化方法包括但不限于有步骤S701和步骤S711。
步骤S701，对待优化小区进行预设权值参数配置；
可以理解的是，预设权值参数包括一组或者多组权值参数，本实施对其不作具体限定。
步骤S702，根据预设权值参数采集对待优化的小区的用户数据；
可以理解的是，所采集的用户数据表征着各小区不同水平方向角、不同电子下倾角、不同波束波宽的波束覆盖特性，直接反映着小区的实际环境；用户数据可以包括路测质量数据、物理小区ID(Physical Cell ID，PCI)信息、SSB索引信息、辅同步信息号(Secondary Synchronization Signal，SSS)信噪比、SSS功率、SSS噪声、物理广播信道(Physical  Broadcast Channel，PBCH)信噪比、PBCH功率、PBCH噪声、用户经纬度及高度，本实施例对其不作具体限定。
需要说明的是，路测质量数据可以包括第一RSRP值、第一波束所覆盖的目标小区的邻区的第二RSRP值以及对第一波束的干扰信号值，本实施例对其不作具体限定。
步骤S703，对用户数据进行预处理，得到路测质量数据；
可以理解是的，对用户数据进行预处理主要包括用户数据清理和用户数据平滑处理，得到路测质量数据，路测质量数据用于对待预测的第一波束的信号质量数据进行计算。
其中用户数据清理主要是为用户数据格式标准化、清除异常用户数据、清除重复用户数据，保留所需的用户数据。用户数据平滑处理主要通过平滑聚集、数据规范化等方式对用户数据进行处理，保持原用户数据的特性的基础上，对用户数据进行归约处理。
需要说明的是，数据平滑处理方式可以包括空间维度栅格化处理，可以包括时间维度栅格化处理，可以包括数据抽样，也可以包括中值滤波，本实施对其不作具体限定。
步骤S704，确定需要构建的波束权值增益数据库，波束权值增益数据库包括波束组合及波束组合对应的波束参数，波束组合包括待预测的若干个第一子波束和已采集用户数据的第二子波束；
可以理解的是，根据场景选取需构建的波束权值增益数据库的组成，主要是确定波束权值数据库中波束组合及波束组合对应的波束参数。波束权值数据库可以是由很多子波束增益表构成，每个子波束增益表主要依据波束组合进行构成，其中波束组合可以根据子波束水平角度、子波束垂直角度、子波束水平宽度以及子波束垂直宽度生成，波束组合包括待预测的若干个第一子波束和已采集用户数据的第二子波束。
需要说明的是，波束参数可以包括第一子波束的第一波束参数和第二子波束的第二波束参数。第一波束参数可以包括第一波束组合信息和第一增益数据，第二波束参数可以包括第二波束组合信息和第二增益数据。
步骤S705，获取用户的相对位置信息；
可以理解的是，可以以小区作为参考点，计算用户的相对位置，用户的相对位置可以通过用户的经纬度位置信息，结合小区的工参信息计算得到，用户相对位置信息主要用于确定第一子波束的第一增益数据和用于确定第二子波束的第二增益数据。
步骤S706，根据第一波束参数、第二波束参数得到距离参数，并根据距离参数确定与待预测的第一子波束最近的若干个第二子波束为参考子波束。
可以理解的是，可以根据第一波束参数(子波束水平角度、子波束垂直角度)和第二波束参数(子波束水平角度、子波束垂直角度)，计算得到距离参数，并根据距离参数确定与待预测的第一子波束最近的若干个第二子波束为参考子波束。
需要说明的是，参考子波束的数量可以是1个或者多个，本实施例对其不作限定。
在一实施例中，以欧式距离计算方式为例，假设波束水平方向角为α，垂直方向角为β，第一子波束为k与第二子波束为i的欧式距离计算公式为
Figure PCTCN2021132854-appb-000003
计算结果的距离值越近，则代表第一子波束和第二子波束的相似度越大，用第二子波束路测质量数据预测第一子波束的信号质量数据则更加准确可靠。
步骤S707，获取参考子波束对应的第二增益数据和路测质量数据。
可以理解的是，可以根据确定的若干个参考子波束，获取参考子波束对应的第二增益数据和路测质量数据。
步骤S708，根据第一增益数据、第二增益数据和路测质量数据得到信号质量数据。
可以理解的是，假设参考子波束的数量为2个，第一子波束为k，第一增益数据为AntGain[k]，距离第一子波束距离最近的两个参考子波束分别为i、j，两个参考子波束对应的第二路测质量数据分别为RSRP i、RSRP j，两个参考子波束对应的第二增益数据分别为AntGain[i]、AntGain[j]，两个参考子波束对应的距离参数分别为ρ k,i、ρ k,j，用户的相对位置信息为(h，v)，第一子波束的信号质量数据为RSRP k，第一子波束的信号质量数据可以根据以下公式计算得到：
RSRP k,i[h][v]＝RSRP i[h][v]+AntGain[k][h][v]-AntGain[i][h][v]
RSRP k,j[h][v]＝RSRP j[h][v]+AntGain[k][h][v]-AntGain[j][h][v]
Figure PCTCN2021132854-appb-000004
步骤S709，利用步骤S707和S708遍历所有第一子波束得到所有信号质量数据，根据所有信号质量数据以及所有路测质量数据，可以确定所有小区的第一权值参数，并更新波束权值增益数据库。
可以理解的是，权值增益数据库反映基站在实际环境中的覆盖特性，利用波束权值增益数据库，可以预测第一波束动态调整后的信号测量数据，为广播权值参数推荐提供数据基础。其中波束动态调整包括子波束水平角度、子波束垂直角度、子波束水平宽度以及子波束垂直宽度的调整。
步骤S710，根据权值增益数据库对小区进行最优权值搜索，确定小区的第二权值参数。
可以理解的是，可以根据更新后的权值增益数据库中确定多个备选权值参数，然后根据备选权值参数对小区进行最优权值搜索，确定小区的第二权值参数，并利用第二权值参数对小区进行配置。
需要说明的是，本步骤可以根据不同的需求，可以采用不同的代价函数、不同的备选权值参数、不同的权值搜索方法，应用于不同的波束配置场景。
需要说明的是，可以使用全局代价函数，也可以使用局部代价函数对小区的第二权值参数进行确认，实现对小区的无线覆盖的全部区域优化或局部区域优化，本实施例对其不作具体限定。
需要说明的是，可以根据实际情况配置备选权值参数，备选权值参数适用于单波束、多波束、单多波束共存的波束配置场景，本实施例对其不作具体限定。
需要说明的是，最优权值搜索可以使用深度学习方式，可以使用强化学习方式，可以使用蚁群算法，也可以使用蒙特卡洛算法，本实施例对其不作具体限定。
对于小区的第二权值参数的确定，可以以用户的服务小区的SINR值进行判定，假设服务小区的第二波束为i，那么SINR值的计算方法如下：
Figure PCTCN2021132854-appb-000005
其中，RSRP main,SSBi为待优化小区波束i的接收功率，RSRP others,SSBi为待优化小区的邻区波 束i的接收功率，noise SSBi为白噪声功率，此处功率值计算应采用线性值计算。
统计服务小区的SINR值的数学特性，构建代价函数，实现具有目的性的第二权值参数的优化，输出小区的第二权值参数进行配置并下发。
代价函数也可以根据小区的RSRP值整体最高值对第二权值参数进行确定，也可以根据小区的干扰最低值对第二权值参数进行确定，也可以进行多个代价函数的综合结果评价结果对第二权值参数进行确定，本实施例对其不作具体限定。
需要说明的是，对于干扰最低值对第二权值参数进行确定的方式中，不限于实施例中的最优SINR值的判定方式，本实施例不作唯一限定。
步骤S711，对已配置第二权值参数的小区的路测质量数据进行监控，若路测质量数据大于信号质量阈值，那么第二权值参数不变，若路测质量数据小于信号质量阈值，则执行步骤S703。
可以理解的是，基于N组权值权值增益数据库，并利用权值搜索，推荐最优的第二权值参数并向小区下发配置，采集第二权值参数下路测数据。若路测质量数据大于信号质量阈值，则保持第二权值参数不变；若路测质量数据小于信号质量阈值，则采集此次第二权值参数的路测质量数据，并回退执行步骤S703。新采集的路测质量数据与历史数据共同搭建新的权值增益数据库，搜索最优的第二权值参数，迭代至满足需求，能够实现自适应权值优化。
需要说明的是，信号质量阈值可以根据实际需要进行设定，本实施例对其不作具体限定。
基于图7权值优化方法的第一实施例中，第一实施例是以实际小区优化的步骤和数据对以上实施例进行具体说明。具体内容如下：
第一实施例：在预配置的权值数据库中的波束个数配置为8个，SSB频率位置为2.52495GHz。对待优化的小区进行两组预设权值参数配置，两组预设权值参数包括子波束水平角度、子波束垂直角度、子波束水平宽度以及子波束垂直宽度等反映水平覆盖范围和垂直覆盖方位的数据。以下表1为单层波束和四层波束作为例子对小区进行预设权值参数配置，表2为基于两组预设权值参数下采集的用户数据。
预配置 单层波束 四层波束
子波束水平角度 [-49；-32；-16；-8；8；16；32；49] [-28；28；-28；28；-28；28；-28；28]
子波束垂直角度 [3；3；3；3；3；3；3；3] [-9；-9；-3；-3；3；3；9；9]
子波束水平宽度 [16；10；10；10；10；10；10；16] [65；65；65；65；65；65；65；65]
子波束垂直宽度 [6；6；6；6；6；6；6；6] [6；6；6；6；6；6；6；6]
表1
Time PCI SSBIdx SSS Sinr SSS RSRP SSS Pn PBCH Sinr PBCH RSRP PBCH Pn UE Lot UE Lat UE Alt
00:00.4 201 3 17.75 -99.25 -117 9 -95 -104 121.61 31.204 1.5
00:00.4 201 6 4.25 -112.75 -117 2.75 -106.5 -109.25 121.61 31.204 1.5
00:00.4 307 0 14.38 -102.75 -117.13 2.5 -98.63 -101.13 121.61 31.204 1.5
... ... ... ... ... ... ... ... ... ... ... ...
46:13.4 383 0 14.38 -102.75 -117.13 2.5 -98.63 -101.13 121.60 31.207 1.5
表2
将采集的用户数据进行归约处理，以空间为度栅格化作为例子，数据预处理的步骤可以如下：
设置经纬度栅格，以经度116.46度，维度39.92度，经纬度步进0.0001度、高度步进3m，那么经度方向x维度方向x高度方向的栅格大小约为8.5m x 11.1m x 3m；
将待优化的小区划分栅格，并将栅格内相同小区且相同子波束的采集的用户数据(包括路测质量数据)进行均值处理。
用户数据预处理完毕以后，可以根据预处理后的用户数据构建基于小区的不同的子波束水平角度、不同的子波束垂直角度、不同的子波束水平宽度以及不同的子波束垂直宽度的权值增益数据库，具体步骤可以如下：
确定需要构建的波束权值增益数据库，本实施例的预设权值参数配置为8波束配置，需要保障路面与高楼用户使用体验。所以本实施例需构建的波束权值增益数据库的相关维度见表3，共计22572个子波束。不同的子波束可以组合成各个第一权值参数配置，根据第一权值参数可得到波束权值增益数据库。见表3
Figure PCTCN2021132854-appb-000006
表3
然后，获取用户的相对位置信息，即利用用户数据及小区的[经纬、维度、高度]信息、小区的法线方向及机械下倾角，以小区为参考点，计算用户的相对位置。用户的相对位置的主要用于查询理论天线增益表中的增益数据(包括第一增益数据和第二增益数据)，并计算待预测的第一子波束的权值增益表。
再根据第一波束参数、第二波束参数得到距离参数，并根据距离参数确定与待预测的第一子波束最近的若干个第二子波束为参考子波束。具体如下：
如计算待预测的第k个第一子波束距离最近的若干个第二子波束，为计算待预测的小区的第一权值参数提供基础用户数据。
本实施例以欧氏距离度量波束相似度，可以将M取值为2。以子波束水平方向角度、子波束垂直角度构建特征矢量[α,β]，计算与待预测的第一子波束欧氏距离最近的两个第二子波束。
假设第一子波束为k，及两个第二子波束分别为i、j，第一子波束的第一波束参数和与第一子波束距离较近的两个第二子波束的第二波束参数如表4所示：
波束来源 子波束标识 子波束水平角度 子波束垂直角度 子波束水平宽度 子波束垂直宽度
待预测波束 k 1 3 10 16
临近路测波束 i -8 3 10 16
临近路测波束 j 8 3 10 16
表4
与待预测的第一子波束k最相似的两个第二子波束分别为i，j，其欧氏距离如下：
Figure PCTCN2021132854-appb-000007
Figure PCTCN2021132854-appb-000008
然后，获取参考子波束对应的第二增益数据和路测质量数据，根据第一增益数据、第二增益数据和路测质量数据得到信号质量数据。具体如下：
假设参考子波束的数量为2个，第一子波束为k，第一增益数据为AntGain[k]，距离第一子波束距离最近的两个参考子波束分别为i、j，两个参考子波束对应的第二路测质量数据分别为RSRP i、RSRP j，两个参考子波束对应的第二增益数据分别为AntGain[i]、AntGain[j]，两个参考子波束对应的距离参数分别为ρ k,i、ρ k,j，用户的相对位置信息为(h，v)，第一子波束的信号质量数据为RSRP k，第一子波束的信号质量数据可以根据以下公式计算得到：
RSRP k,i[h][v]＝RSRP i[h][v]+AntGain[k][h][v]-AntGain[i][h][v]
RSRP k,j[h][v]＝RSRP j[h][v]+AntGain[k][h][v]-AntGain[j][h][v]
Figure PCTCN2021132854-appb-000009
可以根据表5的第一增益数据、第二增益数据、距离参数和路测质量数据对信号质量数据进行计算。
Figure PCTCN2021132854-appb-000010
表5
那么第一子波束的信号质量数据RSRP k
Figure PCTCN2021132854-appb-000011
遍历所有第一子波束得到所有信号质量数据，根据所有信号质量数据以及所有路测质量数据，可以确定所有小区的第一权值参数，并更新波束权值增益数据库。
保存第二子波束的采集的用户数据(包括路测质量数据)与第一权值参数，第一次迭代时，可以无需更新相关数据与数据库。
再根据权值增益数据库对小区进行最优权值搜索，确定小区的第二权值参数。具体如下：
以优化子网区域下行覆盖为目的，采用按小区顺序搜索最优权值的方式计算。对于子网区域内，以区域内所有用户估计的第一权值参数下服务小区的SINR值作为代价函数，优化目标是使小区的SINR值为最大值。
服务小区SINR计算示例如下。利用表6的相关数据，可以计算服务小区201中覆盖的波束3的SINR值。
Time PCI SSBIdx RSRP(dBm) SSS Pn(dBm)
00:10.4 201 3 -89.25 -117
00:10.4 202 3 -112.75 -117
00:10.4 307 3 -102.75 -117
表6
Figure PCTCN2021132854-appb-000012
以采集的用户数据为输入路测数据，统计路测数据在待优化小区出现的次数，对小区进行降序排序，排序第一的小区可以优先选择备选权值参数，替代此小区用户数据，再利用代价函数计算得出SINR值。小区遍历所有备选权值参数后，计算得出SINR值的最大值及其对应的第一权值参数，可以将第一权值参数作为此小区的第二权值参数，取该小区第二权值参替代此小区的用户数据，作为下一个待优化小区的输入数据，并开始优化下一小区。以此方法遍历所有待优化的小区，即可得到各小区的第二权值参数。得到所有小区的第二权值参数后，网管可以根据第二权值参数对小区进行优化。
对已通过第二权值参数的优化后的小区的路测质量数据进行监控，若路测质量数据大于信号质量阈值，那么第二权值参数不变，若路测质量数据小于信号质量阈值，则需要采集此次第二权值参数的路测质量数据，并返回用户数据预处理的步骤开始，对小区的权值进行优化。
该权值优化方法中的信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数对小区的第二权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
基于图7权值优化方法的第二实施例中，第二实施例是以实际小区优化的步骤和数据对以上实施例进行具体说明。具体内容如下：
第二实施例：对待优化小区进行一组预设权值参数配置，采集此预设权值参数配置下的用户数据。如表7所示：
预配置 四层波束
子波束水平角度 [-28；28；-28；28；-28；28；-28；28]
子波束垂直角度 [-9；-9；-3；-3；3；3；9；9]
子波束水平宽度 [65；65；65；65；65；65；65；65]
子波束垂直宽度 [6；6；6；6；6；6；6；6]
表7
数据预处理的步骤与第一实施例中相同，此处不作赘述。
用户数据预处理完毕以后，可以根据预处理后的用户数据构建基于小区的不同的子波束水平角度、不同的子波束垂直角度、不同的子波束水平宽度以及不同的子波束垂直宽度的权值增益数据库，具体步骤可以如下：
确定需要构建的波束权值增益数据库，本实施例以保障下行覆盖以及节能减耗为目的，采用“1+X”的SSB覆盖方案。波束“1”提供稳定、优质的水平基础覆盖，即主要提供路面覆盖；波束“X”的个数、子波束水平角度、子波束垂直角度、子波束水平宽度以及子波束垂直宽度灵活可变，按需提供场景化垂直覆盖，即主要提供高楼用户覆盖。此波束结构在水平方向与垂直方向上设计解耦，能够实现稳定性与灵活性的最佳统一。
本实施例需构建的波束权值增益数据库的相关维度见表8，共计420个子波束.
Figure PCTCN2021132854-appb-000013
表8
然后，获取用户的相对位置信息，即利用用户数据及小区的[经纬、维度、高度]信息、小区的法线方向及机械下倾角，以小区为参考点，计算用户的相对位置。用户的相对位置的主要用于查询理论天线增益表中的增益数据(包括第一增益数据和第二增益数据)，并计算待预测的第一子波束的权值增益表。
根据第一波束参数、第二波束参数得到距离参数，并根据距离参数确定与待预测的第一子波束最近的若干个第二子波束为参考子波束。
获取参考子波束对应的第二增益数据和路测质量数据，根据第一增益数据、第二增益数据和路测质量数据得到信号质量数据。
遍历所有第一子波束得到所有信号质量数据，根据所有信号质量数据以及所有路测质量数据，确定小区的第一权值参数，并更新波束权值增益数据库。
保存第二子波束的采集的用户数据(包括路测质量数据)与第一权值参数，第一次迭代时，可以无需更新相关数据与数据库。
上述计算与确定第一权值参数的步骤与第一实施例相同，此处不作赘述。
再根据权值增益数据库对小区进行最优权值搜索，确定小区的第二权值参数。具体如下：
此实施例中波束“1”备选SSB索引值为[0,1,2,3,4]，通过SSB时域错开发送，能够降低邻区间SSB的相互干扰，可以有效保障路面水平优质的基础覆盖；还可通过业务信道自动打孔处理，降低相邻小区SSB与业务信道之间的干扰。
此实施例中波束“X”个数可配置，即X∈[0,1,2,3]，其备选SSB索引值为[5,6,7]，通过高楼用户的测量信息，小区自适应选择波束“X”的子波束水平角度、子波束垂直角度、子波束水平宽度以及子波束垂直宽度的配置。
此实施例的代价函数，可使用子网区域内所有用户的计算的第一权值参数下服务小区的SINR值，将最大SINR值对应第一权值参数，确定为小区的第二权值参数。
此实施例的权值搜索方法，可采用基于波束顺序的权值优化搜索算法。首先利用蚁群算法搜索波束“1”的最优配置，降低小区间干扰，保证路面水平覆盖。其次利用蚁群算法算法依次搜索波束“X＝1、2、3”的最优配置，保障中高层楼宇用户感知。
然后将第二权值参数对小区进行优化，对已通过第二权值参数的优化后的小区的路测质量数据进行监控，若路测质量数据大于信号质量阈值，那么第二权值参数不变，若路测质量数据小于信号质量阈值，则需要采集此次第二权值参数的路测质量数据，并返回用户数据预处理的步骤开始，对小区的权值进行优化。
该权值优化方法中的信号质量数据基于第二子波束的路测质量数据得到，当信号质量数据用于确定第一权值参数时，利用第一权值参数对小区的第二权值参数进行配置的方案能够 解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
另外，本申请的一个实施例还提供了一种权值优化装置，参照图8，权值优化装置800包括存储器820、处理器810及存储在存储器820上并可在处理器810上运行的计算机程序。
处理器810和存储器820可以通过总线或者其他方式连接。
存储器820作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序以及非暂态性计算机可执行程序。此外，存储器820可以包括高速随机存取存储器820，还可以包括非暂态存储器820，例如至少一个磁盘存储器820件、闪存器件、或其他非暂态固态存储器820件。在一些实施方式中，存储器820可选包括相对于处理器810远程设置的存储器820，这些远程存储器820可以通过网络连接至该处理器810。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
实现上述实施例的信息处理方法所需的非暂态软件程序以及指令存储在存储器中，当被处理器执行时，执行上述实施例中的权值优化方法，例如，执行以上描述的图1中的方法步骤S110至S140、图2中的方法步骤S210至S240、图3中的方法步骤S310至S320、图4中的方法步骤S410至S430、图5中的方法步骤S510图6中的方法步骤S610至S620，图7中的方法步骤S701至S711。
此外，本申请的一个实施例还提供了一种通信设备，参照图9，通信设备900包括上述的权值优化装置800。该权值优化装置800可以在预配置的权值数据库中获取待预测的若干个第一子波束的第一波束参数、若干个第二子波束的第二波束参数，并获取通过采集用户数据得到的路测质量数据，其中路测质量数据与若干个第二子波束的第二波束参数相对应，然后根据第一波束参数、第二波束参数和路测质量数据可以得到第一子波束的信号质量数据，遍历每一个第一子波束，得到所有第一子波束的信号质量数据，然后根据所有第一子波束的信号质量数据以及所有第二子波束的路测质量数据确定小区的第一权值参数。即基于第二子波束的路测质量数据得到待预测的第一子波束的信号质量数据，再根据信号质量数据以及路测质量数据确定小区的第一权值参数，此利用第一权值参数进行配置的方案能够解决复杂的实际环境信号质量要求，并且具有计算效率高和成本低的优点。
此外，本申请的一个实施例还提供了一种计算机可读存储介质，该计算机可读存储介质存储有计算机可执行指令，该计算机可执行指令被一个处理器或控制器执行，例如，被上述实施例中权值优化装置中的一个处理器执行，可使得处理器执行上述实施例中的权值优化方法，例如，执行以上描述的图1中的方法步骤S110至S140、图2中的方法步骤S210至S240、图3中的方法步骤S310至S320、图4中的方法步骤S410至S430、图5中的方法步骤S510图6中的方法步骤S610至S620，图7中的方法步骤S701至S711。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统可以被实施为软件、固件、硬件及其适当的组合。某些物理组件或所有物理组件可以被实施为由处理器，如中央处理器、数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、 数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。
以上是对本申请的较佳实施进行了具体说明，但本申请并不局限于上述实施方式，熟悉本领域的技术人员在不违背本申请精神的前提下还可作出种种的等同变形或替换，这些等同的变形或替换均包含在本申请权利要求所限定的范围内。

Claims (11)

  1. 一种权值优化方法，包括：
    在预配置的权值数据库中获取待预测的若干个第一子波束的第一波束参数和若干个第二子波束的第二波束参数；
    获取通过采集用户数据得到的路测质量数据，所述路测质量数据与若干个第二子波束的第二波束参数相对应；
    对于每个所述第一子波束，根据所述第一波束参数、所述第二波束参数和所述路测质量数据得到信号质量数据；以及
    根据所有所述第一子波束的所述信号质量数据以及所有所述第二子波束的所述路测质量数据确定第一权值参数，所述第一权值参数为所有所述第一子波束和所有所述第二子波束所覆盖的小区对应的权值参数。
  2. 根据权利要求1所述的权值优化方法，其中，当所述第二子波束的数量为两个或以上，所述第一波束参数包括第一波束组合信息和第一增益数据，所述第二波束参数包括第二波束组合信息和第二增益数据，所述根据所述第一波束参数、所述第二波束参数和所述路测质量数据得到信号质量数据，包括：
    根据所述第一子波束对应的第一波束组合信息和所有所述第二波束组合信息得到距离参数，所述距离参数包括所有所述第二波束相对所述第一波束的距离值；
    根据所述距离参数确定若干个所述第二子波束作为参考子波束；
    获取所述参考子波束对应的所述第二增益数据和所述路测质量数据；以及
    根据所述第一增益数据、所述第二增益数据和所述路测质量数据得到信号质量数据。
  3. 根据权利要求2所述的权值优化方法，其中，所述根据所述第一子波束对应的第一波束组合信息和所有所述第二波束组合信息得到距离参数，包括：
    利用所述第一子波束对应的第一波束组合信息和所有所述第二波束组合信息通过欧式距离公式计算得到距离参数。
  4. 根据权利要求2或3所述的权值优化方法，其中，若干个所述参考子波束的距离参数均比其他第二子波束的距离参数小。
  5. 根据权利要求1所述的权值优化方法，还包括：
    获取若干个小区的小区信息，每个所述小区信息包括覆盖所述小区的所述第二子波束的数量值，以及所述第一子波束对应的所述信号质量数据和所述第一权值参数；以及
    根据所述数量值、若干个所述信号质量数据和若干个所述第一权值参数确定所述小区的第二权值参数。
  6. 根据权利要求5所述的权值优化方法，其中，所述根据所述数量值、若干个所述信号质量数据和若干个所述第一权值参数确定所述小区的第二权值参数，包括：
    根据所述数量值对所有所述小区进行排序，得到小区序列；
    根据所述小区序列确定目标小区；以及
    根据所述目标小区中对应的若干个所述信号质量数据和若干个所述第一权值参数确定第二权值参数。
  7. 根据权利要求6所述的权值优化方法，其中，所述根据所述目标小区中对应的若干个所述信号质量数据和若干个所述第一权值参数确定第二权值参数，包括：
    将所述目标小区中最大的所述信号质量数据对应的所述第一权值参数确定为第二权值参数。
  8. 根据权利要求6所述的权值优化方法，其中，所述信号质量数据包括所述第一波束对应的所述目标小区的第一参考信号接收功率RSRP值、所述第一波束所覆盖的所述目标小区的邻区的第二RSRP值以及对所述第一波束的干扰信号值，所述根据所述目标小区中对应的若干个所述信号质量数据和若干个所述第一权值参数确定第二权值参数，包括：
    根据所述第一RSRP值、所述第二RSRP值以及所述干扰信号值得到信号与干扰加噪声比SINR值；以及
    将所述目标小区中最大的所述SINR值对应的所述第一权值参数确定为第二权值参数。
  9. 一种权值优化装置，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现如权利要求1至8中任意一项所述的权值优化方法。
  10. 一种通信设备，包括权利要求9所述的权值优化装置。
  11. 一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令用于执行权利要求1至8中任意一项所述的权值优化方法。
PCT/CN2021/132854 2021-03-16 2021-11-24 权值优化方法、装置、通信设备及计算机可读存储介质 WO2022193717A1 (zh)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10171150B1 (en) * 2017-06-26 2019-01-01 Sprint Communications Company L.P. Dynamic optimization of beamforming weights
CN110730466A (zh) * 2018-07-16 2020-01-24 中兴通讯股份有限公司 确定广播波束权值的方法及装置、网元及存储介质
CN111385039A (zh) * 2018-12-30 2020-07-07 上海华为技术有限公司 一种小区广播波束参数的调整方法及装置
CN111465026A (zh) * 2020-03-26 2020-07-28 北京拓明科技有限公司 天线权重参数调整方法
CN112350756A (zh) * 2019-08-08 2021-02-09 中国移动通信集团广东有限公司 一种天线的权值参数的优化方法、装置及电子设备

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10171150B1 (en) * 2017-06-26 2019-01-01 Sprint Communications Company L.P. Dynamic optimization of beamforming weights
CN110730466A (zh) * 2018-07-16 2020-01-24 中兴通讯股份有限公司 确定广播波束权值的方法及装置、网元及存储介质
CN111385039A (zh) * 2018-12-30 2020-07-07 上海华为技术有限公司 一种小区广播波束参数的调整方法及装置
CN112350756A (zh) * 2019-08-08 2021-02-09 中国移动通信集团广东有限公司 一种天线的权值参数的优化方法、装置及电子设备
CN111465026A (zh) * 2020-03-26 2020-07-28 北京拓明科技有限公司 天线权重参数调整方法

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