WO2024060877A1 - 波束参数计算方法、电子设备及存储介质 - Google Patents

波束参数计算方法、电子设备及存储介质 Download PDF

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Publication number
WO2024060877A1
WO2024060877A1 PCT/CN2023/112920 CN2023112920W WO2024060877A1 WO 2024060877 A1 WO2024060877 A1 WO 2024060877A1 CN 2023112920 W CN2023112920 W CN 2023112920W WO 2024060877 A1 WO2024060877 A1 WO 2024060877A1
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Prior art keywords
target
cell
beam parameter
parameter
value
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PCT/CN2023/112920
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English (en)
French (fr)
Inventor
张劲超
李建国
王郭燕
周先文
庞磊
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中兴通讯股份有限公司
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Publication of WO2024060877A1 publication Critical patent/WO2024060877A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA

Definitions

  • Embodiments of the present application relate to, but are not limited to, the field of communication technology, and in particular, to beam parameter calculation methods, electronic devices, and storage media.
  • each cell usually has thousands of preset beam parameter combinations; according to the actual scenario, the most appropriate beam parameter combination is selected from the thousands of preset beam parameter combinations as the beam parameters of the cell.
  • the most appropriate beam parameter combination is selected from the thousands of preset beam parameter combinations as the beam parameters of the cell.
  • Embodiments of the present application provide beam parameter calculation methods, electronic devices, and storage media.
  • embodiments of the present application provide a beam parameter calculation method, which includes: obtaining the work parameter data of the network in the target area; clustering the cells in the target area according to the work parameter data to obtain cell clusters ; Determine the target beam parameter value according to the numerical range corresponding to the beam parameter, and then obtain the target beam parameter.
  • the target beam parameter value is used to optimize the preset optimization target.
  • embodiments of the present application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the computer program, the above is implemented.
  • an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are used to execute the beam parameter calculation method as described above.
  • Figure 1 is a step diagram of the beam parameter calculation method provided by the embodiment of the present application.
  • FIG. 2 is a sub-step diagram of step S200 provided in an embodiment of the present application.
  • FIG. 3 is a sub-step diagram of step S210 provided by the embodiment of the present application.
  • FIG. 4 is a sub-step diagram of step S300 provided by the embodiment of the present application.
  • FIG. 5 is a sub-step diagram of step S310 provided by the embodiment of the present application.
  • FIG6 is a sub-step diagram of step S330 provided in an embodiment of the present application.
  • FIG. 7 is another sub-step diagram of step S300 provided by the embodiment of the present application.
  • Figure 8 is a sub-step diagram of step S340 provided by the embodiment of the present application.
  • FIG9 is a sub-step diagram of step S350 provided in an embodiment of the present application.
  • Figure 10 is a structural diagram of an electronic device provided by an embodiment of the present application.
  • This application provides a beam parameter calculation method, electronic equipment and storage media, by obtaining the work parameter data of the network in the target area; clustering the cells in the target area according to the work parameter data to obtain the cell cluster; and calculating the beam parameters of the cell cluster. , determine the target beam parameter value according to the numerical range corresponding to the beam parameter, and obtain the target beam parameter according to the target beam parameter value.
  • the target beam parameter value is used to optimize the preset optimization goal; a multi-cell joint beam parameter optimization method can be realized, and Searching for the target beam parameter value from the numerical range corresponding to the beam parameter makes the beam parameter search range wider, and at the same time makes the beam parameter optimization efficient and accurate, and optimizes the network coverage and efficiency of the target area through the target beam parameter.
  • FIG. 1 is a step diagram of a beam parameter calculation method.
  • Beam parameter calculation methods include but are not limited to the following steps:
  • Step S100 Obtain the work parameter data of the network in the target area
  • Step S200 Cluster the cells in the target area according to the engineering parameter data to obtain cell clusters
  • Step S300 Determine the target beam parameter value for optimizing the preset optimization target according to the numerical range corresponding to the beam parameter, and obtain the target beam parameter according to the target beam parameter value.
  • the target area is determined.
  • the target area usually includes multiple cells, each cell is equipped with multiple base station antennas, each base station antenna emits multiple single beams, each single beam includes multiple beam parameters, and the single beams are combined to form multiple beams.
  • the antenna in the target area is set according to the initial beam parameters and transmits the corresponding beam to the user equipment; then the industrial parameter data of the network in the target area is collected, and the industrial parameter data is reported by the user equipment.
  • the engineering parameter data may include reference signal receiving power (RSRP), direction of arrival (DOA) and path loss (PL), etc.
  • RSRP reference signal receiving power
  • DOA direction of arrival
  • PL path loss
  • the embodiment of the present application gives an example in which the industrial parameter data includes the reference signal received power, the direction of arrival and the path loss, but this does not limit the type of the industrial parameter data in the embodiment of the present application.
  • the work parameter data may include other types of data, such as Signal to Interference plus Noise Ratio (SINR), etc.
  • step S200 the cells in the target area are clustered according to the work parameter data to obtain cell clusters, including but not limited to the following steps:
  • Step S210 Obtain the overlapping coverage of the cells in the target area according to the work parameter data
  • Step S220 Cluster the cells in the target area according to the overlapping coverage to obtain cell clusters.
  • step S210 obtaining the overlapping coverage of the cells in the target area according to the work parameter data includes but is not limited to the following steps:
  • Step S211 determine the first user equipment and the second user equipment from the user equipment in the target area according to the industrial parameter data
  • the first user equipment is a user equipment that satisfies a first preset condition
  • the second user equipment is a user equipment that satisfies at least two of the first preset condition, the second preset condition and the third preset condition
  • Step S212 Obtain the overlapping coverage of the cells in the target area according to the number of first user equipment and the number of second user equipment;
  • the first preset condition is that the value of the first working parameter data is greater than or equal to the preset first threshold value, and the first working parameter data is the working parameter data corresponding to the user equipment in the first cell of the target area;
  • the second preset condition is that the value of the second working parameter data is greater than or equal to the preset second threshold value, and the second working parameter data is the working parameter data corresponding to the user equipment in the second cell of the target area.
  • the second cell is a cell different from the first cell;
  • the third preset condition is that the difference between the second working parameter data and the first working parameter data is greater than or equal to a preset third threshold value.
  • step S212 in some embodiments, the quotient of the number of second user equipment and the number of first user equipment is used as the overlapping coverage of the cells in the target area.
  • n 1 is the number of first user equipment
  • n 2 is the number of second user equipment.
  • the first preset condition is that the reference signal received power in cell vi reported by the user equipment is greater than or equal to the "serving cell coverage RSRP threshold", and the "serving cell coverage RSRP threshold” is the preset first threshold value.
  • the first threshold may be set to 90dB.
  • the second preset condition is that the reference signal received power reported by the user equipment in the cell vj is greater than or equal to the "neighboring cell overlapping coverage RSRP threshold", and the "neighboring cell overlapping coverage RSRP threshold” is the preset second threshold value.
  • the second threshold value can be set to 90dB.
  • the third preset condition is that the difference between the reference signal received power in cell v j reported by the user equipment and the reference signal received power in cell vi reported by the user equipment is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold", ""Neighboring cell overlapping coverage RSRP difference threshold” is the preset third threshold value.
  • the third threshold value may be set to 6dB.
  • the embodiment of the present application gives an example in which the first threshold value is 90dB, the second threshold value is 90dB, and the third threshold value is 6dB.
  • the first threshold value can be set to other larger or smaller values, such as 80dB, etc.
  • the second threshold value can be set to other larger or smaller values, such as 70dB, etc.
  • the third threshold value is another larger or smaller value, such as 5DB, etc.
  • the first user equipment is a user equipment that meets the above-mentioned first preset condition, that is, a user equipment whose reference signal received power in cell vi is greater than or equal to the "serving cell coverage RSRP threshold".
  • the second user equipment is a user equipment that satisfies at least two of the above-mentioned first preset condition, second preset condition and third preset condition.
  • the second user equipment is a user equipment that satisfies the first preset condition, the second preset condition and the third preset condition, that is, the reference signal received power in cell vi is greater than or equal to the "serving cell coverage RSRP threshold",
  • the reference signal received power in cell v j is greater than or equal to the "neighbor cell overlapping coverage RSRP threshold”
  • the reference signal received power in cell v j The difference between the rate and the reference signal received power in cell vi reported by the user equipment is greater than or equal to the "neighboring cell overlapping coverage RSRP difference threshold".
  • the embodiment of the present application gives an example of obtaining the overlapping coverage of the cells in the target area based on the reference signal received power, but this cannot be used to calculate the overlap of the cells in the target area in the embodiment of the present application.
  • the type of coverage work parameter data is limited. In other embodiments, other types of industrial parameter data may be used to calculate the overlapping coverage of cells in the target area, such as path loss, etc.
  • the cells in the target area are clustered according to the overlapping coverage to obtain cell clusters.
  • clustering can be performed using a clustering method, for example, using a hierarchical clustering method to cluster cells in the target area to obtain cell clusters.
  • the embodiment of the present application provides a method of performing cluster division by clustering, but this does not limit the method of cluster division in the embodiment of the present application.
  • cluster division can also be performed by other methods.
  • step S300 set the numerical range corresponding to the beam parameter; set the optimization target; for the beam parameter of the cell cluster, determine the target beam parameter value used to optimize the optimization target according to the numerical range corresponding to the beam parameter, and obtain the target beam parameter according to the target beam parameter value.
  • the target beam parameter can be used to optimize the network coverage and efficiency of the target area.
  • the numerical range of the azimuth angle is: [-60°, 60°], step size is 5°; the value range of the vertical angle is [-20°, 20°], with a step size of 1°; the value range of the vertical wave width is: [6°, 12°], with a step size of 6°; the horizontal wave width
  • the value range is: [10°, 60°], and the step size is 10°.
  • constraints can also be added between different beams.
  • the constraint is that the 3dB wave width between each beam does not overlap with each other.
  • beam parameters include azimuth angle, vertical angle, horizontal wave width, and vertical wave width, but this does not limit the types of beam parameters in the embodiments of the present application.
  • beam parameters may include other types of data.
  • the embodiment of the present application provides an example in which the numerical range of the azimuth angle is: [-60°, 60°], the numerical range of the vertical angle is: [-20°, 20°], the numerical range of the vertical wave width is: [6°, 12°], and the numerical range of the horizontal wave width is: [10°, 60°].
  • this does not limit the numerical range of the azimuth angle, the numerical range of the vertical angle, the numerical range of the vertical wave width, and the numerical range of the horizontal wave width in the embodiment of the present application.
  • the numerical range of the azimuth angle may be other, such as [-70°, 70°], etc.; the numerical range of the vertical angle may be other, such as [-10°, 10°], etc.; the numerical range of the vertical wave width may be other, such as [5°, 10°], etc.; the numerical range of the horizontal wave width may be other, such as [5°, 55°], etc.
  • determining a target beam parameter value according to a numerical range corresponding to the beam parameter includes but is not limited to the following steps:
  • Step S310 Determine the beam parameters to be adjusted from the first beam parameter set of the cell cluster, where the first beam parameter set includes multiple beam parameters of the cell cluster;
  • Step S320 Determine the first target parameter value according to the numerical range corresponding to the beam parameter to be adjusted, and obtain the adjusted beam parameter set according to the first target parameter value;
  • Step S330 determine a first target beam parameter set from a plurality of adjustment beam parameter sets, and set the first target beam parameter set to The values of the beam parameters in the number set are used as the target beam parameter values.
  • each cell cluster includes multiple cells.
  • Each cell is equipped with multiple antennas.
  • Each antenna emits multiple beams.
  • Each beam is equipped with multiple beams. beam parameters.
  • the first beam parameter set of the cell cluster includes multiple beam parameters of all cells in the cell cluster;
  • the second beam parameter set of the cell includes multiple beam parameters of the cell;
  • the first beam parameter set of a cell cluster is composed of the cell The second beam parameter set is composed of each cell of the cluster.
  • the beam parameters to be adjusted from the first beam parameter set of the cell cluster including but not limited to the following steps:
  • Step S311 determine the target cell from the cells in the cell cluster
  • Step S312 determine the target beam from the beams of the target cell
  • Step S313 Determine the parameters to be adjusted from the beam parameters of the target beam.
  • the target cell is determined from the cells in the cell cluster.
  • a first comparison value is configured for each cell in the cell cluster. According to the first comparison value corresponding to the cell in the cell cluster and the preset The first threshold determines the target cell.
  • the first comparison value is x1, and x1 can be a random number in the range of (0,1).
  • the first threshold is the cell layer mutation rate, and the cell layer mutation rate is set to 0.1.
  • the embodiment of the present application gives an example in which the cell layer mutation rate is 0.1, but this does not limit the numerical value of the cell layer mutation rate in the embodiment of the present application.
  • the cell layer mutation rate may be other larger or smaller values, such as 0.2.
  • the embodiment of the present application gives an example in which the first comparison value is a random number in the range of (0,1), but this does not limit the value of the first comparison value in the embodiment of the present application.
  • the first comparison value may be randomly selected from other larger or smaller ranges, for example, randomly selected from the range of (0,2).
  • the target beam is determined from the beams of the target cell.
  • a second comparison value is configured for each beam of the target cell. According to the second comparison value corresponding to the beam of the target cell and the preset The second threshold determines the target beam.
  • the second comparison value is x2, and x2 can be a random number in the range of (0,1).
  • the second threshold is the beam layer mutation rate, and the beam layer mutation rate is set to 0.1.
  • the embodiment of the present application gives an example in which the beam layer variation rate is 0.1, but this does not limit the value of the beam layer variation rate in the embodiment of the present application.
  • the beam layer variability rate may be other larger or smaller values, such as 0.3.
  • the embodiment of the present application gives an example in which the second comparison value is a random number in the range of (0,1), but this does not limit the value of the second comparison value in the embodiment of the present application.
  • the second comparison value may be randomly selected from other larger or smaller ranges, for example, randomly selected from the range of (0,3).
  • the parameters to be adjusted are determined from the beam parameters of the target beam.
  • a third comparison value is configured for each beam parameter of the target beam, and the third comparison value corresponding to the beam parameter of the target beam is and the preset third threshold to determine the parameters to be adjusted.
  • the third comparison value is x3, and x3 can be a random number in the range of (0,1).
  • the third threshold is the beam parameter Layer mutation rate, set the beam parameter layer mutation rate to 0.1.
  • the parameter to be adjusted may be one or multiple.
  • the embodiment of the present application gives an example in which the variation rate of the beam parameter layer is 0.1, but this does not limit the value of the variation rate of the beam parameter layer in the embodiment of the present application.
  • the beam layer variability rate may be other larger or smaller values, such as 0.15.
  • the embodiment of the present application provides an example in which the third comparison value is a random number in the range of (0,1), but this does not limit the value of the third comparison value in the embodiment of the present application.
  • the third comparison value can be randomly selected from other larger or smaller ranges, such as randomly selected from the range of (0,5).
  • the beam parameter to be adjusted is determined from the numerical range corresponding to the beam parameter to be adjusted to determine the first target parameter value, and then the adjusted beam parameter set is obtained from the first beam parameter set according to the first target parameter value.
  • the beam parameter to be adjusted is the horizontal wave width of beam 1 of cell 1.
  • the numerical range corresponding to the horizontal wave width is [10°, 60°]. Randomly select a value from the numerical range corresponding to the horizontal wave width to determine the first If the target parameter value is 30°, then the value of the horizontal beam width of beam 1 of cell 1 is replaced with 30°, and the values of other beam parameters of the first beam parameter set of the cell cluster remain unchanged, and then the adjusted beam parameters are obtained gather.
  • determining a first target beam parameter set from a plurality of adjusted beam parameter sets includes but is not limited to the following steps:
  • Step S331 Calculate the first objective function value for each adjusted beam parameter set according to the first objective function corresponding to the optimization objective;
  • Step S332 Determine a fourth objective function value from the plurality of first objective function values, and determine the adjusted beam parameter set corresponding to the fourth objective function value as the first target beam parameter set.
  • the set optimization goal is the signal to interference plus noise ratio
  • the first objective function value of each adjusted beam parameter set is calculated, that is, the signal to interference plus noise ratio of each adjusted beam parameter set is calculated, and the first objective function value of each adjusted beam parameter set is calculated.
  • the largest signal to interference plus noise ratio is determined as the fourth target function value
  • the adjusted beam parameter set corresponding to the largest signal to interference plus noise ratio is determined as the first target beam parameter set.
  • the value of the beam parameter in the first target beam parameter set is used as the target beam parameter value. That is, the beam parameters in the first target beam parameter set are used as the target beam parameters of the cell cluster.
  • a population of a cell cluster is initialized.
  • the population includes N individuals, each individual is the first beam parameter set of the cell cluster, and the beam parameters of each individual have an initialized beam parameter value.
  • the first comparison value is x1, x1 is a random number in the range of (0,1), and the first threshold is the cell layer mutation rate. , set the cell layer mutation rate to 0.1. When the first comparison value of a cell satisfies x1 ⁇ 0.1, the cell is determined as the target cell.
  • the second comparison value is x2.
  • x2 is a random number in the range of (0,1).
  • the second threshold is the beam layer mutation rate. Set the beam layer mutation rate. 0.1.
  • the third comparison value is x3.
  • x3 is a random number in the range of (0,1).
  • the third threshold is the mutation rate of the beam parameter layer. Set the beam parameter layer. The mutation rate is 0.1.
  • the beam parameters to be adjusted are taken from the numerical range corresponding to the beam parameters to be adjusted, and then the adjusted beam parameter set is obtained from the first beam parameter set.
  • the above process is iterated k times to obtain N*k different adjustment beam parameter sets, and a first target beam parameter set that optimizes the optimization target is determined from the multiple different adjustment beam parameter sets.
  • the beam parameters of the first target beam parameter set are the target beam parameters.
  • the target beam parameters are sent to each cell in the target area.
  • a multi-cell joint beam parameter optimization method can be implemented, and target beam parameter values can be searched from the numerical range corresponding to the preset beam parameters, making the beam parameter search range wider.
  • the above beam parameter optimization method can be efficient and Accurately obtain optimal beam parameters to optimize network coverage and efficiency in the target area.
  • determining the target beam parameter value according to the numerical range corresponding to the beam parameter includes but is not limited to the following steps:
  • Step S340 obtaining a second target beam parameter set for the cell according to a second objective function corresponding to the optimization target, a numerical range corresponding to the beam parameter, and a second beam parameter set for the cell, where the second beam parameter set for the cell includes multiple beam parameters for the cell;
  • Step S350 Obtain the third target beam parameter set of the cell cluster based on the third objective function corresponding to the optimization target and the second target beam parameter set of the cell, and use the value of the beam parameter in the third target beam parameter set as the target beam. parameter value.
  • step S340 obtain the second target beam parameter set of the cell based on the second objective function corresponding to the optimization target, the numerical range corresponding to the beam parameter, and the second beam parameter set of the cell, including but not limited to the following steps :
  • Step S341 determine the second target parameter value according to the numerical range corresponding to the beam parameter of the cell, and obtain the cell beam parameter set according to the second target parameter value;
  • Step S342 Calculate the second objective function value of each cell beam parameter set according to the second objective function
  • Step S343 Sort the cell beam parameter set according to the second target function value, and select the cell beam parameter set ranked within the preset interval to determine as the second target beam parameter set.
  • step S341 for the beam parameters of the cell, take values from the numerical range corresponding to the beam parameters of the cell to determine the second target parameter value, and then obtain the cell beam parameter set from the second beam parameter set of the cell according to the second target parameter value.
  • the first M cell beam parameter sets with the largest second objective function values are searched out through an intelligent optimization algorithm, and the M cell beam parameter sets are determined as the second target beam parameter set.
  • the intelligent optimization algorithm can use algorithms such as ant colony algorithm, genetic algorithm and particle swarm algorithm.
  • step S350 obtain the third target beam parameter set of the cell cluster based on the third objective function corresponding to the optimization target and the second target beam parameter set of the cell, including but not limited to the following steps:
  • Step S351 combine the second target beam parameter set of the cell to obtain the cluster beam parameter set of the cell cluster;
  • Step S352 calculating a third objective function value of a cluster beam parameter set of the cell cluster according to a third objective function corresponding to the optimization target;
  • Step S353 Determine a fifth objective function value from a plurality of third objective function values, and determine the cluster beam parameter set corresponding to the fifth objective function value as the third target beam parameter set.
  • step S351 the M cell beam parameter sets corresponding to the cells obtained in step S340 are used as a new beam parameter library, and the second target beam parameter sets of the cells are combined to obtain cluster beam parameter sets of multiple cell clusters.
  • cell cluster 1 includes cell 1 and cell 2. Then one of the second target beam parameter sets corresponding to cell 1 and one of the second target beam parameter sets corresponding to cell 2 are combined to obtain the cluster beam parameters of the cell cluster of cell cluster 1. gather.
  • step S352 it can be understood that the second objective function and the third objective function may be the same function or different functions.
  • the cell with the largest third objective function value is searched through an intelligent optimization algorithm.
  • the cluster beam parameter set that is, the fifth objective function value is the largest third objective function value.
  • the cell cluster beam parameter set with the largest third objective function value is determined as the third target beam parameter set.
  • the intelligent optimization algorithm can use algorithms such as ant colony algorithm, genetic algorithm and particle swarm algorithm.
  • the beam parameters of the third target beam parameter set are used as the target beam parameters.
  • the target beam parameters are delivered to each cell in the target area.
  • a multi-cell joint beam parameter optimization method can be implemented, and target beam parameter values can be searched from the numerical range corresponding to the preset beam parameters, making the beam parameter search range wider.
  • the above beam parameter optimization method can be efficient and Accurately obtain optimal beam parameters to optimize network coverage and efficiency in the target area.
  • FIG10 is a structural diagram of the electronic device.
  • the electronic device includes a memory 120, a processor 110, and a computer program stored in the memory 120 and executable on the processor 110.
  • the memory 120 and the processor 110 are connected in communication via a bus 130.
  • the processor 110 executes the computer program, the above beam parameter calculation method is implemented.
  • the memory 120 may include a program area and a data area, wherein the program area may store an operating system and application programs required for at least one function; the data area may store data required to execute the beam parameter calculation method in the above-mentioned embodiment of the present application, etc.
  • the memory 120 may include a high-speed random access memory 120, and may also include a non-volatile memory 120, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.
  • the memory 120 may include a memory 120 remotely arranged relative to the processor 110, and these remote memories 120 may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the non-transient software programs and programs required to implement the beam parameter calculation method in the above embodiment of the present application are stored in the memory 120.
  • the beam parameters in the above embodiment of the present application are executed. Calculation method.
  • Embodiments of this application include: obtaining the work parameter data of the network in the target area; clustering the cells in the target area according to the work parameter data to obtain cell clusters; determining the target based on the beam parameters of the cell cluster according to the numerical range corresponding to the beam parameters Beam parameter value, the target beam parameter value is obtained according to the target beam parameter value, and the target beam parameter value is used to optimize the preset optimization goal; it can realize the multi-cell joint beam parameter optimization method and search for the target from the numerical range corresponding to the beam parameter
  • the beam parameter value makes the beam parameter search range wider, and at the same time makes the beam parameter optimization efficient and accurate, and optimizes the network coverage and efficiency of the target area through the target beam parameters.
  • node embodiments described above are only illustrative, and the units described as separate components may or may not be physically separate, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • Embodiments of the present application also provide a computer-readable storage medium.
  • the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the above beam parameter calculation method.
  • computer storage medium includes media used for storing information (such as computer readable instructions, data structures, program modules or other data) on volatile and non-volatile, removable and non-removable media implemented in any method or technology.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, tapes, disk storage or other magnetic storage devices, or may Any other medium used to store the desired information and that 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 may include any information delivery media .

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Abstract

本申请实施例提供了波束参数计算方法、电子设备及存储介质，通过获取目标区域的网络的工参数据(S100)；根据工参数据对目标区域中的小区进行簇划分得到小区簇(S200)；对小区簇的波束参数，根据波束参数对应的数值范围确定目标波束参数值，根据目标波束参数值得到目标波束参数(S300)。

Description

波束参数计算方法、电子设备及存储介质
相关申请的交叉引用
本申请基于申请号为202211136264.0、申请日为2022年09月19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技术领域，尤其涉及波束参数计算方法、电子设备及存储介质。
背景技术
目前，为了应对不同的实际场景，每个小区通常会预先设置上千种波束参数组合；根据实际场景从预先设置的上千种波束参数组合选择最为合适的波束参数组合作为该小区的波束参数。但随着覆盖场景的越来越复杂化和多样化，上千种波束参数组合已经无法完全应对实际的各种复杂场景。
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。
本申请实施例提供了波束参数计算方法、电子设备及存储介质。
第一方面，本申请实施例提供了一种波束参数计算方法，包括：获取目标区域的网络的工参数据；根据所述工参数据对所述目标区域中的小区进行簇划分，得到小区簇；根据所述波束参数对应的数值范围确定目标波束参数值，进而得到目标波束参数，所述目标波束参数值用于优化预设的优化目标。
第二方面，本申请实施例提供了一种电子设备，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，所述处理器执行所述计算机程序时实现如上所述的波束参数计算方法。
第三方面，本申请实施例提供了一种计算机可读存储介质，其中，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如上所述的波束参数计算方法。
附图说明
附图用来提供对本申请技术方案的进一步理解，并且构成说明书的一部分，与本申请的实施例一起用于解释本申请的技术方案，并不构成对本申请技术方案的限制。
图1是本申请实施例所提供的波束参数计算方法的步骤图；
图2是本申请实施例所提供的步骤S200的子步骤图；
图3是本申请实施例所提供的步骤S210的子步骤图；
图4是本申请实施例所提供的步骤S300的一个子步骤图；
图5是本申请实施例所提供的步骤S310的子步骤图；
图6是本申请实施例所提供的步骤S330的子步骤图；
图7是本申请实施例所提供的步骤S300的另一子步骤图；
图8是本申请实施例所提供的步骤S340的子步骤图；
图9是本申请实施例所提供的步骤S350的子步骤图；
图10是本申请实施例所提供的电子设备的结构图。
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、权利要求书或上述附图中的术语“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。
本申请提供了波束参数计算方法、电子设备及存储介质，通过获取目标区域的网络的工参数据；根据工参数据对目标区域中的小区进行簇划分，得到小区簇；对小区簇的波束参数，根据波束参数对应的数值范围确定目标波束参数值，根据目标波束参数值得到目标波束参数，目标波束参数值用于优化预设的优化目标；能够实现多小区联合的波束参数寻优方式，并从波束参数对应的数值范围中搜索目标波束参数值，使得波束参数搜索范围更广泛，同时使波束参数的寻优高效和准确，通过目标波束参数实现优化目标区域的网络覆盖率和效率。
下面结合附图，对本申请实施例作进一步阐述。
本申请实施例提供了波束参数计算方法。参照图1，图1是波束参数计算方法的步骤图。波束参数计算方法包括但不限于以下步骤：
步骤S100，获取目标区域的网络的工参数据；
步骤S200，根据工参数据对目标区域中的小区进行簇划分，得到小区簇；
步骤S300，根据波束参数对应的数值范围确定用于优化预设的优化目标的目标波束参数值，根据目标波束参数值得到目标波束参数。
对于步骤S100，确定目标区域。目标区域通常包括多个小区，每个小区设有多个基站天线，每个基站天线发射多个单波束，每个单波束包括多个波束参数，单波束组合形成多波束。
目标区域的天线根据初始的波束参数设置，并向用户设备发射对应的波束；然后采集目标区域的网络的工参数据，工参数据由用户设备上报。
在一些实施例中，工参数据可以包括参考信号接收功率(Reference Signal Receiving Power,RSRP)、波达方向(Direction of Arrival,DOA)和路径损耗(Path Loss,PL)等。
可以理解的是，本申请实施例给出了工参数据包括参考信号接收功率、波达方向和路径损耗的例子，但这并不能对本申请实施例中的工参数据的类型进行限制。在其他实施例中，工参数据可以包括其他类型的数据，例如信号与干扰加噪声比(Signal to Interference plus Noise Ratio，SINR)等。
参照图2，对于步骤S200，根据工参数据对目标区域中的小区进行簇划分，得到小区簇，包括但不限于以下步骤：
步骤S210，根据工参数据得到目标区域中的小区的重叠覆盖度；
步骤S220，根据重叠覆盖度对目标区域中的小区进行簇划分，得到小区簇。
参照图3，对于步骤S210，根据工参数据得到目标区域中的小区的重叠覆盖度包括但不限于以下步骤：
步骤S211，根据工参数据从目标区域的用户设备中确定第一用户设备和第二用户设备；
其中，第一用户设备为满足第一预设条件的用户设备，第二用户设备为满足第一预设条件、第二预设条件和第三预设条件中至少两个条件的用户设备；
步骤S212，根据第一用户设备的数量和第二用户设备的数量，得到目标区域中的小区的重叠覆盖度；
其中，第一预设条件为第一工参数据的值大于或等于预设的第一门限值，第一工参数据为用户设备在目标区域的第一小区所对应的工参数据；
第二预设条件为第二工参数据的值大于或等于预设的第二门限值，第二工参数据为用户设备在目标区域的第二小区所对应的工参数据，第二小区为不同于第一小区的小区；
第三预设条件为第二工参数据与第一工参数据的差值大于或等于预设的第三门限值。
对于步骤S212，在一些实施例中，将第二用户设备的数量与第一用户设备的数量的商作为目标区域中的小区的重叠覆盖度。
例如，对于根据参考信号接收功率得到目标区域中的小区的重叠覆盖度，小区vi与小区vj的重叠覆盖度的计算公式如下：其中，n1为第一用户设备的数量，n2为第二用户设备的数量。
第一预设条件为用户设备上报的在小区vi的参考信号接收功率大于或等于“服务小区覆盖RSRP门限”，“服务小区覆盖RSRP门限”即为预设的第一门限值。例如，在某些实施例中，第一门限值可以设置为90dB。
第二预设条件为用户设备上报的在小区vj的参考信号接收功率大于或等于“邻区重叠覆盖RSRP门限”，“邻区重叠覆盖RSRP门限”即为预设的第二门限值。例如，在某些实施例中，第二门限值可以设置为90dB。
第三预设条件为用户设备上报的在小区vj的参考信号接收功率与用户设备上报的在小区vi的参考信号接收功率之差大于或等于“邻区重叠覆盖RSRP差值门限”，“邻区重叠覆盖RSRP差值门限”即为预设的第三门限值。例如，在某些实施例中，第三门限值可以设置为6dB。
可以理解的是，本申请实施例给出了第一门限值为90dB、第二门限值为90dB、第三门限值为6dB的例子，但这并不能对本申请实施例中第一门限值的数值、第二门限值的数值和第三门限值的数值进行限制。在其他实施例中，可以将第一门限值取值为其他更大或更小数值，例如80dB等；将第二门限值取值为其他更大或更小数值，例如70dB等；将第三门限值取值为其他更大或更小数值，例如5DB等。
第一用户设备为满足上述第一预设条件的用户设备，即在小区vi的参考信号接收功率大于或等于“服务小区覆盖RSRP门限”的用户设备。
第二用户设备为满足上述第一预设条件、第二预设条件和第三预设条件中至少两个条件的用户设备。例如，第二用户设备为满足第一预设条件、第二预设条件和第三预设条件的用户设备，即在小区vi的参考信号接收功率大于或等于“服务小区覆盖RSRP门限”、在小区vj的参考信号接收功率大于或等于“邻区重叠覆盖RSRP门限”、在小区vj的参考信号接收功 率与用户设备上报的在小区vi的参考信号接收功率之差大于或等于“邻区重叠覆盖RSRP差值门限”的用户设备。
其中将同时满足三个预设条件的用户设备作为第二用户设备能使得计算得到的重叠覆盖度更准确。
可以理解的是，本申请实施例给出了根据参考信号接收功率得到目标区域中的小区的重叠覆盖度的例子，但这并不能对本申请实施例中的用于计算目标区域中的小区的重叠覆盖度的工参数据的类型进行限制。在其他实施例中，可以采用其他类型的工参数据进行计算目标区域中的小区的重叠覆盖度，例如路径损耗等。
对于步骤S220，根据重叠覆盖度对目标区域中的小区进行簇划分，得到小区簇。在一些实施例中，可以通过聚类的方法进行簇划分，例如使用层次聚类的方法对目标区域的小区进行簇划分，得到小区簇。
可以理解的是，本申请实施例给出了通过聚类进行簇划分的方式，但这并不能对本申请实施例中簇划分的方式进行限制。在其他实施例中，也可以通过其他方式进行簇划分。
对于步骤S300，设置波束参数对应的数值范围；设置优化目标；对小区簇的波束参数，根据波束参数对应的数值范围确定用于优化该优化目标目标波束参数值，根据目标波束参数值得到目标波束参数，则通过该目标波束参数能够优化目标区域的网络覆盖率和效率。
对于设置波束参数对应的数值范围，可以设置单波束的方位角、垂直角、水平波宽和垂直波宽的数值范围，例如方位角的数值范围为：[-60°，60°]，步长为5°；垂直角的数值范围为[-20°，20°]，步长为1°；垂直波宽的数值范围为：[6°，12°]，步长为6°；水平波宽的数值范围为：[10°，60°]，步长为10°。
另外，对于不同的波束之间也可以增加约束条件，例如约束条件为每个波束之间的3dB波宽不互相重叠。
可以理解的是，本申请实施例给出了波束参数包括方位角、垂直角、水平波宽和垂直波宽的例子，但这并不能对本申请实施例中的波束参数的类型进行限制。在其他实施例中，波束参数可以包括其他类型的数据。
另外，本申请实施例给出了方位角的数值范围为：[-60°，60°]、垂直角的数值范围为[-20°，20°]、垂直波宽的数值范围为：[6°，12°]、水平波宽的数值范围为：[10°，60°]的例子，但这并不能对本申请实施例中的方位角的数值范围、垂直角的数值范围、垂直波宽的数值范围和水平波宽的数值范围进行限制。在其他实施例中，方位角的数值范围可以为其他，例如[-70°，70°]等；垂直角的数值范围可以为其他，例如[-10°，10°]等；垂直波宽的数值范围可以为其他，例如[5°，10°]等；水平波宽的数值范围可以为其他，例如[5°，55°]等。
参照图4，其中，一个方面，根据波束参数对应的数值范围确定目标波束参数值，包括但不限于以下步骤：
步骤S310，从小区簇的第一波束参数集合中确定待调整波束参数，其中第一波束参数集合包括小区簇的多个波束参数；
步骤S320，根据待调整波束参数对应的数值范围确定第一目标参数值，根据第一目标参数值得到调整波束参数集合；
步骤S330，从多个调整波束参数集合中确定第一目标波束参数集合，将第一目标波束参 数集合中的波束参数的数值作为目标波束参数值。
对于步骤S310，对目标区域的小区进行簇划分得到多个小区簇，每个小区簇包括多个小区，每个小区设有多个天线，每个天线发射多个波束，每个波束设有多个波束参数。则小区簇的第一波束参数集合包括该小区簇的所有小区的多个波束参数；小区的第二波束参数集合包括该小区的多个波束参数；一个小区簇的第一波束参数集合由该小区簇的各个小区的第二波束参数集合组成。
参照图5，对各个小区簇的第一波束参数集合，从小区簇的第一波束参数集合中确定待调整波束参数，包括但不限于以下步骤：
步骤S311，从小区簇的小区中确定目标小区；
步骤S312，从目标小区的波束中确定目标波束；
步骤S313，从目标波束的波束参数中确定待调整参数。
对于步骤S311，从小区簇的小区中确定目标小区，在一些实施例中为：为小区簇的每个小区配置一个第一比较值，根据小区簇的小区对应的第一比较值和预设的第一阈值，确定目标小区。
其中，第一比较值为x1，x1可以是(0,1)范围内的一个随机数。第一阈值为小区层变异率，设置小区层变异率为0.1，当一个小区的第一比较值满足x1<0.1，则将该小区确定为目标小区。
可以理解的是，本申请实施例给出了小区层变异率为0.1的例子，但这并不能对本申请实施例中的小区层变异率的数值进行限制。在其他实施例中，小区层变异率可以为其他更大或更小的数值，例如0.2。
另外，本申请实施例给出了第一比较值是(0,1)范围内的一个随机数的例子，但这并不能对本申请实施例中的第一比较值的数值进行限制。在其他实施例中，第一比较值可以从其他更大或更小的范围内随机取值，例如从(0,2)范围内随机取值。
对于步骤S312，从目标小区的波束中确定目标波束，在一些实施例中为：为目标小区的每个波束配置一个第二比较值，根据目标小区的波束对应的第二比较值和预设的第二阈值，确定目标波束。
其中，第二比较值为x2，x2可以是(0,1)范围内的一个随机数。第二阈值为波束层变异率，设置波束层变异率为0.1，当一个目标小区的波束的第二比较值满足x2<0.1，则将该目标小区的波束确定为目标波束。
可以理解的是，本申请实施例给出了波束层变异率为0.1的例子，但这并不能对本申请实施例中的波束层变异率的数值进行限制。在其他实施例中，波束层变异率可以为其他更大或更小的数值，例如0.3。
另外，本申请实施例给出了第二比较值是(0,1)范围内的一个随机数的例子，但这并不能对本申请实施例中的第二比较值的数值进行限制。在其他实施例中，第二比较值可以从其他更大或更小的范围内随机取值，例如从(0,3)范围内随机取值。
对于步骤S313，从目标波束的波束参数中确定待调整参数，在一些实施例中为：为目标波束的每个波束参数配置一个第三比较值，根据目标波束的波束参数对应的第三比较值和预设的第三阈值，确定待调整参数。
其中，第三比较值为x3，x3可以是(0,1)范围内的一个随机数。第三阈值为波束参数 层变异率，设置波束参数层变异率为0.1，当一个目标波束的波束参数的第三比较值满足x3<0.1，则将该目标波束的波束参数确定为待调整参数。
则实际上，待调整参数可以为一个，也可以为多个。
可以理解的是，本申请实施例给出了波束参数层变异率为0.1的例子，但这并不能对本申请实施例中的波束参数层变异率的数值进行限制。在其他实施例中，波束层变异率可以为其他更大或更小的数值，例如0.15。
另外，本申请实施例给出了第三比较值是(0,1)范围内的一个随机数的例子，但这并不能对本申请实施例中的第三比较值的数值进行限制。在其他实施例中，第三比较值可以从其他更大或更小的范围内随机取值，例如从(0,5)范围内随机取值。
对于步骤S320，对待调整波束参数从待调整波束参数对应的数值范围中取值确定第一目标参数值，进而根据第一目标参数值由第一波束参数集合得到调整波束参数集合。
例如，确定了待调整波束参数为小区1的波束1的水平波宽，水平波宽对应的数值范围为[10°，60°]，从水平波宽对应的数值范围中随机取值，确定第一目标参数值为30°，则使小区1的波束1的水平波宽的数值替换为30°，并且小区簇的第一波束参数集合的其他波束参数的数值保持不变，进而得到调整波束参数集合。
执行多次步骤S310和步骤S320，得到不同的多个调整波束参数集合。
参照图6，对于步骤S330，其中，从多个调整波束参数集合中确定第一目标波束参数集合，包括但不限于以下步骤：
步骤S331，根据与优化目标对应的第一目标函数，对各个调整波束参数集合计算得到第一目标函数值；
步骤S332，从多个第一目标函数值中确定第四目标函数值，将第四目标函数值对应的调整波束参数集合确定为第一目标波束参数集合。
例如，设置的优化目标为信号与干扰加噪声比，计算各个调整波束参数集合的第一目标函数值，即计算各个调整波束参数集合的信号与干扰加噪声比值，从多个调整波束参数集合的信号与干扰加噪声比值中确定最大的信号与干扰加噪声比值为第四目标函数值，将最大的信号与干扰加噪声比值所对应的调整波束参数集合确定为第一目标波束参数集合。
并且，将第一目标波束参数集合中的波束参数的数值作为目标波束参数值。即将第一目标波束参数集合中的波束参数作为该小区簇的目标波束参数。
例如，初始化一个小区簇的种群，该种群包括N个个体，每个个体为该小区簇的第一波束参数集合，每个个体的波束参数具有初始化的波束参数值。
对种群的每个个体，为小区簇的每个小区配置一个第一比较值，第一比较值为x1，x1为(0,1)范围内的一个随机数，第一阈值为小区层变异率，设置小区层变异率为0.1，当一个小区的第一比较值满足x1<0.1，则将该小区确定为目标小区。
为目标小区的每个波束配置一个第二比较值，第二比较值为x2，x2为(0,1)范围内的一个随机数，第二阈值为波束层变异率，设置波束层变异率为0.1，当一个目标小区的波束的第二比较值满足x2<0.1，则将该目标小区的波束确定为目标波束。
为目标波束的每个波束参数配置一个第三比较值，第三比较值为x3，x3为(0,1)范围内的一个随机数，第三阈值为波束参数层变异率，设置波束参数层变异率为0.1，当一个目标波束的波束参数的第三比较值满足x3<0.1，则将该目标波束的波束参数确定为待调整参数。
对待调整波束参数从待调整波束参数对应的数值范围中取值，进而由第一波束参数集合得到调整波束参数集合。
对上述过程迭代k次，则得到N*k个不同的调整波束参数集合，从多个不同的调整波束参数集合中确定使优化目标最优的第一目标波束参数集合，该第一目标波束参数集合的波束参数即为目标波束参数。给目标区域内的每个小区下发该目标波束参数。
能够实现多小区联合的波束参数寻优方式，并从预设的波束参数对应的数值范围中搜索目标波束参数值，使得波束参数搜索范围更广泛，同时通过上述的波束参数寻优方法能够高效和准确地获取最优的波束参数，使目标区域的网络覆盖率和效率达到最优。
参照图7，其中，另一个方面，根据波束参数对应的数值范围确定目标波束参数值，包括但不限于以下步骤：
步骤S340，根据与优化目标对应的第二目标函数、波束参数对应的数值范围和小区的第二波束参数集合，得到小区的第二目标波束参数集合，小区的第二波束参数集合包括小区的多个波束参数；
步骤S350，根据与优化目标对应的第三目标函数和小区的第二目标波束参数集合，得到小区簇的第三目标波束参数集合，将第三目标波束参数集合中的波束参数的数值作为目标波束参数值。
参照图8，对于步骤S340，根据与优化目标对应的第二目标函数、波束参数对应的数值范围和小区的第二波束参数集合，得到小区的第二目标波束参数集合，包括但不限于以下步骤：
步骤S341，根据小区的波束参数对应的数值范围确定第二目标参数值，根据第二目标参数值得到小区波束参数集合；
步骤S342，根据第二目标函数计算得到各个小区波束参数集合的第二目标函数值；
步骤S343，根据第二目标函数值对小区波束参数集合进行排序，选择排名在预设区间内的小区波束参数集合确定为第二目标波束参数集合。
对于步骤S341，对小区的波束参数，从小区的波束参数对应的数值范围中取值以确定第二目标参数值，进而按照第二目标参数值由小区的第二波束参数集合得到小区波束参数集合。
对于步骤S343，通过智能优化算法搜索出第二目标函数值最大的前M个小区波束参数集合，将该M个小区波束参数集合确定为第二目标波束参数集合。其中，智能优化算法可以采用蚁群算法、遗传算法和粒子群算法等算法。
参照图9，对于步骤S350，根据与优化目标对应的第三目标函数和小区的第二目标波束参数集合，得到小区簇的第三目标波束参数集合，包括但不限于以下步骤：
步骤S351，对小区的第二目标波束参数集合进行组合，得到小区簇的簇波束参数集合；
步骤S352，根据与优化目标对应的第三目标函数，计算得到小区簇的簇波束参数集合的第三目标函数值；
步骤S353，从多个第三目标函数值中确定第五目标函数值，将第五目标函数值对应的簇波束参数集合确定为第三目标波束参数集合。
对于步骤S351，将步骤S340所得到的对应小区的M个小区波束参数集合作为新的波束参数库，将小区的第二目标波束参数集合进行组合，得到多个小区簇的簇波束参数集合。例如小区簇1包括小区1和小区2，则将小区1对应的其中一个第二目标波束参数集合和小区2对应的其中一个第二目标波束参数集合组合得到小区簇1的小区簇的簇波束参数集合。
对于步骤S352，可以理解的是，第二目标函数和第三目标函数可以是同一个函数，也可以是不同的函数。
在一些实施例中，对于步骤S353，通过智能优化算法搜索出第三目标函数值最大的小区 簇波束参数集合，即第五目标函数值为最大的第三目标函数值。将该第三目标函数值最大的小区簇波束参数集合确定为第三目标波束参数集合。其中，智能优化算法可以采用蚁群算法、遗传算法和粒子群算法等算法。
然后，将第三目标波束参数集合的波束参数作为目标波束参数。给目标区域内的每个小区下发该目标波束参数。
能够实现多小区联合的波束参数寻优方式，并从预设的波束参数对应的数值范围中搜索目标波束参数值，使得波束参数搜索范围更广泛，同时通过上述的波束参数寻优方法能够高效和准确地获取最优的波束参数，使目标区域的网络覆盖率和效率达到最优。
本申请的实施例还提供了一种电子设备。参照图10，图10是电子设备的结构图。电子设备包括存储器120、处理器110及存储在存储器120上并可在处理器110上运行的计算机程序，存储器120和处理器110之间通过总线130进行通信连接；处理器110执行计算机程序时实现如上的波束参数计算方法。
存储器120可以包括程序区和数据区，其中，程序区可存储操作系统、至少一个功能所需要的应用程序；数据区可存储执行上述本申请实施例中的波束参数计算方法所需的数据等。此外，存储器120可以包括高速随机存取存储器120，还可以包括非暂态存储器120，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施方式中，存储器120可包括相对于处理器110远程设置的存储器120，这些远程存储器120可以通过网络连接至该终端。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。
实现上述本申请实施例中的波束参数计算方法所需的非暂态软件程序以及程序存储在存储器120中，当被一个或者多个处理器110执行时，执行上述本申请实施例中的波束参数计算方法。
本申请实施例包括：获取目标区域的网络的工参数据；根据工参数据对目标区域中的小区进行簇划分，得到小区簇；对小区簇的波束参数，根据波束参数对应的数值范围确定目标波束参数值，根据目标波束参数值得到目标波束参数，目标波束参数值用于优化预设的优化目标；能够实现多小区联合的波束参数寻优方式，并从波束参数对应的数值范围中搜索目标波束参数值，使得波束参数搜索范围更广泛，同时使波束参数的寻优高效和准确，通过目标波束参数实现优化目标区域的网络覆盖率和效率。
以上所描述的节点实施例仅仅是示意性的，其中作为分离部件说明的单元可以是或者也可以不是物理上分开的，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
本申请的实施例还提供了一种计算机可读存储介质。计算机可读存储介质存储有计算机可执行指令，计算机可执行指令用于执行如上的波束参数计算方法。
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。

Claims (14)

  1. 一种波束参数计算方法，包括：
    获取目标区域的网络的工参数据；
    根据所述工参数据对所述目标区域中的小区进行簇划分，得到小区簇；
    根据所述小区簇的波束参数对应的数值范围确定目标波束参数值，进而得到目标波束参数，其中，所述目标波束参数值用于优化预设的优化目标。
  2. 根据权利要求1所述的波束参数计算方法，其中，所述根据所述工参数据对所述目标区域中的小区进行簇划分，得到小区簇，包括：
    根据所述工参数据得到所述目标区域中的小区的重叠覆盖度；
    根据所述重叠覆盖度对所述目标区域中的小区进行簇划分，得到小区簇。
  3. 根据权利要求2所述的波束参数计算方法，其中，所述根据所述工参数据得到所述目标区域中的小区的重叠覆盖度，包括：
    根据所述工参数据从所述目标区域的用户设备中确定第一用户设备和第二用户设备，所述第一用户设备为满足第一预设条件的用户设备，所述第二用户设备为满足第一预设条件、第二预设条件和第三预设条件中至少两个条件的用户设备；
    根据所述第一用户设备的数量和所述第二用户设备的数量，得到所述目标区域中的小区的重叠覆盖度；
    其中，所述第一预设条件为第一工参数据的值大于或等于预设的第一门限值，所述第一工参数据为所述用户设备在所述目标区域的第一小区所对应的工参数据；
    所述第二预设条件为第二工参数据的值大于或等于预设的第二门限值，所述第二工参数据为所述用户设备在所述目标区域的第二小区所对应的工参数据，所述第二小区为不同于所述第一小区的小区；
    所述第三预设条件为所述第二工参数据与所述第一工参数据的差值大于或等于预设的第三门限值。
  4. 根据权利要求1所述的波束参数计算方法，其中，所述根据所述波束参数对应的数值范围确定目标波束参数值，包括：
    从所述小区簇的第一波束参数集合中确定待调整波束参数，其中，所述第一波束参数集合包括所述小区簇的多个波束参数；
    根据所述待调整波束参数对应的数值范围确定第一目标参数值，根据所述第一目标参数值得到调整波束参数集合；
    从多个所述调整波束参数集合中确定第一目标波束参数集合，将所述第一目标波束参数集合中的波束参数的数值作为所述目标波束参数值。
  5. 根据权利要求4所述的波束参数计算方法，其中，从所述小区簇的第一波束参数集合中确定待调整波束参数，包括：
    从所述小区簇的小区中确定目标小区；
    从所述目标小区的波束中确定目标波束；
    从所述目标波束的波束参数中确定所述待调整波束参数。
  6. 根据权利要求5所述的波束参数计算方法，其中，所述从所述小区簇的小区中确定目 标小区，包括：
    根据所述小区簇的小区对应的第一比较值和预设的第一阈值，确定所述目标小区。
  7. 根据权利要求5所述的波束参数计算方法，其中，所述从所述目标小区的波束中确定目标波束，包括：
    根据所述目标小区的波束对应的第二比较值和预设的第二阈值，确定所述目标波束。
  8. 根据权利要求5所述的波束参数计算方法，其中，所述从所述目标波束的波束参数中确定所述待调整参数，包括：
    根据所述目标波束的波束参数对应的第三比较值和预设的第三阈值，确定所述待调整参数。
  9. 根据权利要求4所述的波束参数计算方法，其中，所述从多个所述调整波束参数集合中确定第一目标波束参数集合，包括：
    根据与所述优化目标对应的第一目标函数，对各个所述调整波束参数集合计算得到第一目标函数值；
    从多个所述第一目标函数值中确定第四目标函数值，将所述第四目标函数值对应的所述调整波束参数集合确定为所述第一目标波束参数集合。
  10. 根据权利要求1所述的波束参数计算方法，其中，所述根据所述波束参数对应的数值范围确定目标波束参数值，包括：
    根据与所述优化目标对应的第二目标函数、所述波束参数对应的数值范围和所述小区的第二波束参数集合，得到所述小区的第二目标波束参数集合，所述小区的第二波束参数集合包括所述小区的多个波束参数；
    根据与所述优化目标对应的第三目标函数和所述小区的第二目标波束参数集合，得到所述小区簇的第三目标波束参数集合，将所述第三目标波束参数集合中的波束参数的数值作为所述目标波束参数值。
  11. 根据权利要求10所述的波束参数计算方法，其中，所述根据与所述优化目标对应的第二目标函数、所述波束参数对应的数值范围和所述小区簇的小区的第二波束参数集合，得到所述小区簇的小区的第二目标波束参数集合，包括：
    根据小区的波束参数对应的数值范围确定第二目标参数值，根据所述第二目标参数值得到小区波束参数集合；
    根据所述第二目标函数计算得到各个所述小区波束参数集合的第二目标函数值；
    根据所述第二目标函数值对所述小区波束参数集合进行排序，选择排名在预设区间内的所述小区波束参数集合确定为第二目标波束参数集合。
  12. 根据权利要求10所述的波束参数计算方法，其中，所述根据与所述优化目标对应的第三目标函数和所述小区的第二目标波束参数集合，得到所述小区簇的第三目标波束参数集合，包括：
    对所述小区的第二目标波束参数集合进行组合，得到所述小区簇的簇波束参数集合；
    根据与所述优化目标对应的第三目标函数，计算得到所述小区簇的簇波束参数集合的第三目标函数值；
    从多个所述第三目标函数值中确定第五目标函数值，将所述第五目标函数值对应的所述簇波束参数集合确定为所述第三目标波束参数集合。
  13. 一种电子设备，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，所述处理器运行所述计算机程序时执行如权利要求1至12中任一项所述的波束参数计算方法。
  14. 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如权利要求1至12中任一项所述的波束参数计算方法。
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