WO2023030263A1 - 小区优化方法、装置、存储介质和电子装置 - Google Patents

小区优化方法、装置、存储介质和电子装置 Download PDF

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WO2023030263A1
WO2023030263A1 PCT/CN2022/115552 CN2022115552W WO2023030263A1 WO 2023030263 A1 WO2023030263 A1 WO 2023030263A1 CN 2022115552 W CN2022115552 W CN 2022115552W WO 2023030263 A1 WO2023030263 A1 WO 2023030263A1
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cell
target cell
target
antenna
neighbor
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PCT/CN2022/115552
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English (en)
French (fr)
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夏白
杜春梅
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中兴通讯股份有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/00837—Determination of triggering parameters for hand-off

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  • Embodiments of the present disclosure relate to the communication field, and in particular, relate to a cell optimization method, device, storage medium, and electronic device.
  • network adjustment is generally carried out in different regions, and the adjustment of wireless parameters of some cells will affect the neighboring cells, which will cause the optimization of the cells in the target area to affect the indicators of the surrounding cells.
  • Embodiments of the present disclosure provide a cell optimization method, device, storage medium, and electronic device, so as to at least solve the problem in the related art that the adjustment of wireless parameters of a cell affects adjacent cells.
  • a cell optimization method including: determining the target cell to be optimized from the target area; determining the neighbor cells of the target cell, wherein the distance between the neighbor cell and the target cell is less than or equal to the first threshold and the handover threshold of the neighbor cell is greater than or equal to the second threshold, the handover threshold is the maximum number of times the neighbor cell is allowed to hand over; according to the antenna data of the target cell and the neighbor cell Based on the measurement results, an optimization strategy is determined; and the aforementioned antenna data of the aforementioned target cell is optimized using the aforementioned optimization strategy.
  • a cell optimization device including: a first determination unit configured to determine a target cell to be optimized from a target area; a second determination unit configured to determine the target cell neighbor cell, wherein the distance between the neighbor cell and the target cell is less than or equal to the first threshold and the handover threshold of the neighbor cell is greater than or equal to the second threshold, and the handover threshold is the threshold at which the neighbor cell is allowed to handover The maximum number of times; the third determination unit is configured to determine an optimization strategy based on the measurement results of the antenna data of the above-mentioned target cell and the above-mentioned neighbor cell; the optimization unit is configured to use the above-mentioned optimization strategy to optimize the above-mentioned antenna data of the above-mentioned target cell.
  • a computer-readable storage medium in which a computer program is stored in the above-mentioned computer-readable storage medium, wherein the above-mentioned computer program is configured to execute any one of the above-mentioned method embodiments when running in the steps.
  • an electronic device including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the above method embodiments in the steps.
  • the neighbor cells of the target cell are determined, and then the target cell is optimized according to the measurement results of the antenna data of the target cell and the neighbor cells, therefore, it can be solved
  • the problem that affects the neighboring cells can achieve the effect of improving the accuracy of cell optimization.
  • FIG. 1 is a schematic diagram of a terminal according to a cell optimization method according to an embodiment of the present disclosure
  • FIG. 2 is a flowchart of a cell optimization method according to an embodiment of the present disclosure
  • FIG. 3 is a flowchart of another cell optimization method according to an embodiment of the present disclosure.
  • Fig. 4 is a flowchart of another cell optimization method according to an embodiment of the present disclosure.
  • FIG. 5 is a schematic diagram of cell division according to a cell optimization method according to an embodiment of the present disclosure
  • FIG. 6 is a schematic diagram of cell division according to a cell optimization method according to an embodiment of the present disclosure.
  • FIG. 7 is a schematic diagram of cell division according to a cell optimization method according to an embodiment of the present disclosure.
  • Fig. 8 is a structural block diagram of a cell optimization device according to an embodiment of the present disclosure.
  • FIG. 1 is a block diagram of a hardware structure of a mobile terminal according to a cell optimization method according to an embodiment of the present disclosure.
  • the mobile terminal may include one or more (only one is shown in Figure 1) processors 102 (processors 102 may include but not limited to processing devices such as microprocessor MCU or programmable logic device FPGA, etc.) and a memory 104 for storing data, wherein the above-mentioned mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions.
  • processors 102 may include but not limited to processing devices such as microprocessor MCU or programmable logic device FPGA, etc.
  • a memory 104 for storing data
  • the above-mentioned mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions.
  • the structure shown in FIG. 1 is only for illustration, and it does not limit the structure of the above mobile terminal.
  • the mobile terminal may also include more or fewer components than those shown in FIG. 1
  • the memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the cell optimization method in the embodiment of the present disclosure, and the processor 102 executes various functions by running the computer programs stored in the memory 104 A functional application and data processing, that is, to realize the above-mentioned method.
  • the memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory.
  • the memory 104 may further include a memory that is remotely located relative to the processor 102, and these remote memories may be connected to the mobile terminal through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • Transmission device 106 is used to receive or transmit data via a network.
  • the specific example of the above network may include a wireless network provided by the communication provider of the mobile terminal.
  • the transmission device 106 includes a network interface controller (NIC for short), which can be connected to other network devices through a base station so as to communicate with the Internet.
  • the transmission device 106 may be a radio frequency (Radio Frequency, RF for short) module, which is used to communicate with the Internet in a wireless manner.
  • RF Radio Frequency
  • Fig. 2 is a flow chart of a cell optimization method according to an embodiment of the present disclosure. As shown in Fig. 2, the process includes the following steps:
  • Step S202 determining the target cell to be optimized from the target area
  • Step S204 determine the neighbor cells of the target cell, wherein the distance between the neighbor cell and the target cell is less than or equal to the first threshold and the handover threshold of the neighbor cell is greater than or equal to the second threshold, and the handover threshold is allowed for neighbor cells the maximum number of switches;
  • Step S206 determining an optimization strategy according to the measurement results of the antenna data of the target cell and the neighbor cell;
  • Step S208 using an optimization strategy to optimize antenna data of the target cell.
  • the user may define a target area, and the target area may include multiple cells.
  • the method and scope of defining the target area are not limited in this embodiment. For example, a small area may be centered, a radius may be set, and the range of the circle may be determined as the target area. Alternatively, set any other shape or irregular shape as the target area.
  • the target cell may be determined first.
  • the target cell may be a cell that needs to be optimized, and other cells are cells that do not need to be optimized.
  • the selection of the target cell can be determined according to the parameters of the current cell. For example, qualified parameters may be preset, cells in the target area may be detected, and cells corresponding to unqualified parameters may be determined as target cells for optimization.
  • the number of target cells may be one or more.
  • neighbor cells of the target cell must be determined.
  • a neighbor cell of a target cell is a cell within a certain range of the target cell, such as within a first threshold range, and a handover threshold greater than or equal to a second threshold. That is to say, within the range of the first threshold around a target cell, the cells whose handover threshold is greater than or equal to the second threshold are regarded as neighbor cells of the target cell.
  • Each target cell may have one or more neighbor cells.
  • the measurement results of the antenna data of the target cell and neighboring cells are obtained, the optimization strategy of each target cell is determined, and the antenna data of the target cell is optimized using the optimization strategy to obtain the optimized The final target cell, so as to achieve the effect of improving the accuracy of cell optimization.
  • the determining the optimization strategy according to the measurement results of the antenna data of the target cell and the neighbor cell includes:
  • the adjustment strategy corresponding to the largest target value is determined as the optimization strategy.
  • the target cell and the neighbor cells of each target cell are determined, for each target cell and the neighbor cells of the target cell (hereinafter referred to as a cell group), it is necessary to determine the Antenna data of target cells and neighbor cells in the group, and multiple adjustment strategies are estimated. For example, if the antenna data of the target cell is adjusted, the target value is estimated, and if the antenna data of the target cell is adjusted to another value, the target value is estimated. After obtaining the estimated multiple target values, the adjustment strategy corresponding to the largest target value among the multiple target values is the optimization strategy for the target cell.
  • the target cell can be optimized, and the target cell and neighbor cells can also be optimized.
  • the estimating multiple adjustment strategies according to the antenna data of the target cell and the neighbor cell includes:
  • Changing at least one of the following parameters in the antenna data of the target cell directional angle of the antenna, downtilt angle of the antenna, horizontal wave width of the antenna, vertical wave width of the antenna, and transmit power of the antenna;
  • the antenna data of the neighbor cell and the changed antenna data of the target cell are determined as one of the adjustment strategies.
  • At least one of the parameters in the above antenna data may be simulated and adjusted, and after the antenna data is simulated and adjusted, measurement data under the antenna data is estimated and a target value is calculated. After multiple simulation adjustments, multiple antenna data and the target value under each antenna data are obtained, the antenna data corresponding to the largest target value is determined as the optimal antenna data, and the antenna data of the target cell is adjusted to the optimal antenna data. antenna data.
  • the number of adjustments to the antenna data of the target cell is one, that is, the optimization process. In the process of calculating the target value, the antenna data is simulated and adjusted, and the target value is estimated.
  • the calculating the target value of each adjustment strategy according to the measurement results includes:
  • the calculating the target value according to the reference signal received power of the target cell and the reference signal received power of the neighbor cell includes:
  • the sum of the reference signal received power of the neighboring cells plus the sum result of the first parameter is determined by the ratio of the reference signal received power of the target cell to the sum result is the target value.
  • determining the neighbor cells of the target cell includes:
  • the handover threshold may be the number of times the cell itself is allowed to be handed over. It is a kind of attribute information of the community. It can be set manually or adjusted by the system following the system.
  • a target cell for a target cell, firstly, other cells within a certain range around the target cell are determined, and then a cell whose handover threshold value is greater than or equal to the second threshold is selected from other cells as a neighbor cell of the target cell.
  • the cell to be optimized in the target area is used as the target cell, and the set of all neighbor cells of the target cell is queried , remove the neighbors in the current target area to get the set of neighbors outside the target area.
  • it includes first selecting a combination of neighboring cells whose distance between the cell and neighboring cells is less than or equal to the physical distance threshold based on the physical distance threshold between the cell and neighboring cells set by the user.
  • MR border neighbor cell Report
  • the data of the target cell and the border neighbors of the target cell are considered at the same time, so as to achieve the goal of comprehensively optimizing the target cell and the neighbors of the target cell.
  • the set physical distance threshold and the longitude and latitude position information of its neighbors are used to filter the border neighbors, remove the neighbors outside the distance threshold, and then use the set handover times threshold and the existing neighbors.
  • the number of handovers in the target area is removed, and the adjacent areas with the number of handovers lower than the handover threshold are removed to obtain the closest border adjacent area to the target cell as the data collection layer of the target cell; when the target cell is optimized, the target cell’s own MR data and The MR data of the adjacent cells in the data collection layer are used as the optimization data of the target cell, and the optimization result is obtained and delivered to the cell to take effect.
  • the impact of target cell optimization on border neighboring cells is balanced according to the MR measurement data of the UE terminal, which helps the operation and maintenance personnel to ensure the stability and communication quality of the network in the border neighboring cells of the target area when optimizing the problem area , improve network performance and improve user experience.
  • Fig. 3 and Fig. 4 are a kind of flowchart of present embodiment, comprise the following content:
  • the user selects the target optimization area, and the target optimization area can include multiple cells;
  • border neighbors According to the list of border neighbors output in step 4, query the handover times threshold KPI handover information, and the neighbors whose handover times are greater than or equal to the "handover times threshold" are output as the border neighbors of the target area, hereinafter referred to as border neighbors.
  • the neighboring cells in this formula refer to the neighboring cells of the target cell within the target area and the border neighboring cells outside the target area.
  • White noise power -130dBm, considering UE receiver noise figure, white noise power can be fine-tuned.
  • the rsrp information of the border neighbor cell is added here mainly to consider the impact on the border neighbor cell when the antenna weight of the target cell changes, which will affect the value of the neighbor cell rsrp.
  • Select the strategy with the largest SINR as the optimal strategy to optimize the target cell For example, adjust the parameters of the target cell and neighbor cells: at least one of the direction angle, downtilt angle, horizontal wave width, vertical wave width, and transmit power, and calculate the target value SINR, and select the parameter scheme with the largest target value SINR as the target Parameters of the cell and neighbor cells.
  • Fig. 5 shows a situation in which all neighbor cells of the target cell are within the target area in this embodiment.
  • the user setting information is as follows:
  • the default value of the physical distance threshold is 500, and the default switching times threshold is 200.
  • the neighbor cell of P2 is N2 (the physical distance N21 from the target cell, the number of handovers is 10), and P1 (the physical distance N22 from the target cell, the number of handovers is 5000).
  • the target cell P1 has no adjacent cells and no data collection layer
  • the target cell P1 has no data collection layer, and the target cell P2 has no data collection layer.
  • the target area enters the data acquisition stage, and the MR data of P1 and P2 are collected respectively.
  • FIG. 6 shows the situation in which the adjacent cells of the target cell are partly within the target area and partly outside the target area, and the data collection layer of the target cell is divided.
  • the system sets the physical distance threshold as: 500 according to the density of network deployment, and sets the default switching times threshold as: 200 according to the traffic volume in the current area.
  • the target cell (P1, P2, P3), the neighboring cell of P1 is W1 (the physical distance N11 from the target cell, the number of handovers is 3500), P2 (the physical distance N21 from the target cell, the number of handovers is 3000), W2 (distance The physical distance N22 of the target cell, the number of handovers is 60), W3 (the physical distance N23 from the target cell, the number of handovers is 90).
  • Task data collection layer division :
  • W1 the physical distance N11 from the target cell, and the number of handovers is 3500
  • W2 the physical distance N22 from the target cell , the number of handovers is 60
  • W3 the physical distance N23 from the target cell, the number of handovers is 90
  • P2 and P3 have no adjacent cells, so there is no data collection layer.
  • the target area enters the data collection stage, the MR data of the target cell P1 collects the MR data of P1 and W1 respectively; the target cells P2 and P3 collect their own MR data.
  • Fig. 7 shows the situation where all the neighboring cells of the target cell are outside the target area, and there is a data collection layer in the target cell.
  • the default value of the physical distance threshold is 500, and the default switching times threshold is 200.
  • the target cell P1, its neighbors are W1 (the physical distance N11 from the target cell, the number of handovers is 3500), W2 (the physical distance N21 from the target cell, the number of handovers is 3000), W3 (the physical distance N22 from the target cell, The number of handovers is 200), W4 (the physical distance N23 from the target cell, the number of handovers is 90).
  • Task data collection layer division :
  • W1 the physical distance N11 from the target cell, and the number of handovers is 3500
  • W2 the physical distance N21 from the target cell , the number of handovers is 3000
  • W3 the physical distance N22 from the target cell, the number of handovers is 200
  • W4 the physical distance N23 from the target cell, the number of handovers is 90
  • the target area enters the data collection stage, the MR data of the target cell P1 collects the MR data of P1, W1, and W3 respectively; the target cells P2 and P3 collect their own MR data.
  • the method according to the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present disclosure can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to make a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) execute the methods of various embodiments of the present disclosure.
  • a cell optimization device is also provided, which is used to implement the above embodiments and preferred implementation modes, and what has already been described will not be repeated.
  • Fig. 8 is a structural block diagram of a cell optimization device according to an embodiment of the present disclosure. As shown in Fig. 8, the device includes:
  • the first determining unit 802 is configured to determine the target cell to be optimized from the target area
  • the second determination unit 804 is configured to determine the neighbor cell of the target cell, wherein the distance between the neighbor cell and the target cell is smaller than the first threshold and the handover threshold of the neighbor cell is greater than the second threshold, and the handover threshold is The maximum number of switches allowed;
  • the third determination unit 806 is configured to determine the optimization strategy according to the measurement results of the antenna data of the target cell and the neighbor cell;
  • the optimization unit 808 is configured to use an optimization strategy to optimize antenna data of the target cell.
  • Embodiments of the present disclosure also provide a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps in any one of the above method embodiments when running.
  • the above-mentioned computer-readable storage medium may include but not limited to: U disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM) , mobile hard disk, magnetic disk or optical disk and other media that can store computer programs.
  • ROM read-only memory
  • RAM random access memory
  • mobile hard disk magnetic disk or optical disk and other media that can store computer programs.
  • Embodiments of the present disclosure also provide an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.
  • the electronic device may further include a transmission device and an input and output device, wherein the transmission device is connected to the processor, and the input and output device is connected to the processor.
  • each module or each step of the above-mentioned disclosure can be realized by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed in a network composed of multiple computing devices In fact, they can be implemented in program code executable by a computing device, and thus, they can be stored in a storage device to be executed by a computing device, and in some cases, can be executed in an order different from that shown here. Or described steps, or they are fabricated into individual integrated circuit modules, or multiple modules or steps among them are fabricated into a single integrated circuit module for implementation. As such, the present disclosure is not limited to any specific combination of hardware and software.

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Abstract

本公开实施例提供了一种小区优化方法、装置、存储介质和电子设备，该方法包括：从目标区域中确定出待优化的目标小区；确定出目标小区的邻居小区，其中，邻居小区与目标小区的距离小于或等于第一阈值且邻居小区的切换门限值大于或等于第二阈值，切换门限值为邻居小区被允许切换的最大次数；根据目标小区和邻居小区的天线数据的测量结果，确定优化策略；使用优化策略优化目标小区的天线数据。通过本公开，解决了小区优化对邻区造成干扰的问题，进而达到了提高小区优化准确度的效果。

Description

小区优化方法、装置、存储介质和电子装置
相关申请的交叉引用
本公开基于2021年09月06日提交的发明名称为“小区优化方法、装置、存储介质和电子装置”的中国专利申请CN202111035773.X，并且要求该专利申请的优先权，通过引用将其所公开的内容全部并入本公开。
技术领域
本公开实施例涉及通信领域，具体而言，涉及一种小区优化方法、装置、存储介质和电子装置。
背景技术
现有技术中，网络调整一般是分区域进行，一些小区无线参数的调整会影响周边邻区，这就会造成目标区域内小区的优化会影响周边邻区的指标。
发明内容
本公开实施例提供了一种小区优化方法、装置、存储介质和电子装置，以至少解决相关技术中对小区的无线参数进行调整时，影响邻区的问题。
根据本公开的一个实施例，提供了一种小区优化方法，包括：从目标区域中确定出待优化的目标小区；确定出上述目标小区的邻居小区，其中，上述邻居小区与上述目标小区的距离小于或等于第一阈值且上述邻居小区的切换门限值大于或等于第二阈值，上述切换门限值为上述邻居小区被允许切换的最大次数；根据上述目标小区和上述邻居小区的天线数据的测量结果，确定优化策略；使用上述优化策略优化上述目标小区的上述天线数据。
根据本公开的另一个实施例，提供了一种小区优化装置，包括：第一确定单元，设置为从目标区域中确定出待优化的目标小区；第二确定单元，设置为确定出上述目标小区的邻居小区，其中，上述邻居小区与上述目标小区的距离小于或等于第一阈值且上述邻居小区的切换门限值大于或等于第二阈值，上述切换门限值为上述邻居小区被允许切换的最大次数；第三确定单元，设置为根据上述目标小区和上述邻居小区的天线数据的测量结果，确定优化策略；优化单元，设置为使用上述优化策略优化上述目标小区的上述天线数据。
根据本公开的又一个实施例，还提供了一种计算机可读存储介质，上述计算机可读存储介质中存储有计算机程序，其中，上述计算机程序被设置为运行时执行上述任一项方法实施例中的步骤。
根据本公开的又一个实施例，还提供了一种电子装置，包括存储器和处理器，上述存储器中存储有计算机程序，上述处理器被设置为运行上述计算机程序以执行上述任一项方法实施例中的步骤。
通过本公开，由于在对目标区域中的目标小区进行优化时，是确定出目标小区的邻居小区，然后根据目标小区和邻居小区的天线数据的测量结果来对目标小区进行优化，因此，可以解决优化小区时，对邻区造成影响的问题，达到提高小区优化准确度的效果效果。
附图说明
图1是根据本公开实施例的小区优化方法的终端的示意图；
图2是根据本公开实施例的小区优化方法的流程图；
图3是根据本公开实施例的另一种小区优化方法的流程图；
图4是根据本公开实施例的又一种小区优化方法的流程图；
图5是根据本公开实施例的小区优化方法的小区划分示意图；
图6是根据本公开实施例的小区优化方法的小区划分示意图；
图7是根据本公开实施例的小区优化方法的小区划分示意图；
图8是根据本公开实施例的小区优化装置的结构框图。
具体实施方式
下文中将参考附图并结合实施例来详细说明本公开的实施例。
需要说明的是，本公开的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。
本申请实施例中所提供的方法实施例可以在移动终端、计算机终端或者类似的运算装置中执行。以运行在移动终端上为例，图1是本公开实施例的一种小区优化方法的移动终端的硬件结构框图。如图1所示，移动终端可以包括一个或多个(图1中仅示出一个)处理器102(处理器102可以包括但不限于微处理器MCU或可编程逻辑器件FPGA等的处理装置)和用于存储数据的存储器104，其中，上述移动终端还可以包括用于通信功能的传输设备106以及输入输出设备108。本领域普通技术人员可以理解，图1所示的结构仅为示意，其并不对上述移动终端的结构造成限定。例如，移动终端还可包括比图1中所示更多或者更少的组件，或者具有与图1所示不同的配置。
存储器104可用于存储计算机程序，例如，应用软件的软件程序以及模块，如本公开实施例中的小区优化方法对应的计算机程序，处理器102通过运行存储在存储器104内的计算机程序，从而执行各种功能应用以及数据处理，即实现上述的方法。存储器104可包括高速随机存储器，还可包括非易失性存储器，如一个或者多个磁性存储装置、闪存、或者其他非易失性固态存储器。在一些实例中，存储器104可进一步包括相对于处理器102远程设置的存储器，这些远程存储器可以通过网络连接至移动终端。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。
传输设备106用于经由一个网络接收或者发送数据。上述的网络具体实例可包括移动终端的通信供应商提供的无线网络。在一个实例中，传输设备106包括一个网络适配器(Network Interface Controller，简称为NIC)，其可通过基站与其他网络设备相连从而可与互联网进行通讯。在一个实例中，传输设备106可以为射频(Radio Frequency，简称为RF)模块，其用于通过无线方式与互联网进行通讯。
图2是根据本公开实施例的一种小区优化方法的流程图，如图2所示，该流程包括如下步骤：
步骤S202，从目标区域中确定出待优化的目标小区；
步骤S204，确定出目标小区的邻居小区，其中，邻居小区与目标小区的距离小于或等于第一阈值且邻居小区的切换门限值大于或等于第二阈值，切换门限值为邻居小区被允许切换的最大次数；
步骤S206，根据目标小区和邻居小区的天线数据的测量结果，确定优化策略；
步骤S208，使用优化策略优化目标小区的天线数据。
可选地，本实施例中，可以由用户划定目标区域，目标区域中可以包括多个小区。目标区域的划定方法和划定范围本实施例并不限定。例如，可以以一个小区为中心，设定一个半径，将圆的范围确定为目标区域。或者，设定任意其他图形或不规则图形作为目标区域。
可选地，本实施例总，对于目标区域内的小区，可以先确定出目标小区。目标小区可以为需要优化的小区，其他小区为无需优化的小区。而目标小区的选择，可以根据当前小区的参数确定。例如，可以预先设定合格参数，检测目标区域内的小区，将不合格的参数对应的小区确定为目标小区进行优化。目标小区的数量可以为一个或者多个。
可选地，本实施例中，对于每一个目标小区，都要确定该目标小区的邻居小区。一个目标小区的邻居小区，为该目标小区一定范围内，如第一阈值范围内，且切换门限值大于或等于第二阈值的小区。也就是说，一个目标小区的周围第一阈值范围内，切换门限值大于或等于第二阈值的小区作为该目标小区的邻居小区。每一个目标小区可以有一个或多个邻居小区。
可选地，本实施例中，对于每一个目标小区，获取目标小区和邻居小区的天线数据的测量结果，确定每一个目标小区的优化策略，并使用优化策略优化目标小区的天线数据，得到优化后的目标小区，从而实现提高小区优化准确度的效果。
作为一种可选的实施方式，所述根据所述目标小区和所述邻居小区的天线数据的测量结果，确定优化策略包括：
根据所述目标小区和所述邻居小区的所述天线数据，预估多种调整策略，其中，每一种所述调整策略中所述目标小区的所述天线数据不同；
根据所述测量结果，计算每一种所述调整策略的目标值；
将最大的所述目标值对应的所述调整策略确定为所述优化策略。
可选地，本实施例中，在确定好目标小区和每一个目标小区的邻居小区之后，对于每一个目标小区和该目标小区的邻居小区(以下称为一个小区组)，要确定每一个小区组中的目标小区和邻居小区的天线数据，并预估出多个调整策略。例如，假如调整目标小区的天线数据，预估目标值，再假如调整目标小区的天线数据为另外的值，预估目标值。得到预估的多个目标值后，多个目标值中最大的目标值所对应的调整策略即为对目标小区的优化策略。
本实施例中，在确定出优化策略后，可以对目标小区进行优化，也可以对目标小区和邻居小区进行优化。
作为一种可选的实施方式，所述根据所述目标小区和所述邻居小区的所述天线数据，预估多种调整策略包括：
改变所述目标小区的所述天线数据中的以下至少一种参数：天线的方向角、天线的下倾角、天线的水平波宽、天线的垂直波宽和天线的发射功率；
将所述邻居小区的天线数据和改变后的所述目标小区的天线数据确定为一种所述调整策略。
可选地，本实施例中，可以模拟调整上述天线数据中的参数中的至少一个，模拟调整天线数据后，预估该天线数据下的测量数据，并计算出目标值。经过多次模拟调整之后，得到多个天线数据和每一个天线数据下的目标值，将最大的目标值对应的天线数据确定为最优的天线数据，将目标小区的天线数据调整为该最优的天线数据。本实施例中，对目标小区的天线数据的调整次数为一次，即优化过程。计算目标值的过程中，均为模拟调整天线数据，并预估目标值。
作为一种可选的实施方式，所述根据所述测量结果，计算每一种所述调整策略的目标值包括：
根据所述测量结果，预估每一种所述调整策略下所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率；
根据所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率计算所述目标值。
作为一种可选的实施方式，所述根据所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率计算所述目标值包括：
对于每种所述调整策略，确定所述邻居小区的参考信号接收功率之和加上第一参数的求和结果，其中，所述第一参数为预设的数值；
将所述目标小区的所述参考信号接收功率与所述求和结果的比值确定为所述目标值。
可选地，本实施例中，如果目标小区有一个，则邻居小区的参考信号接收功率之和加上第一参数的求和结果，将目标小区的参考信号接收功率与求和结果的比值确定为目标值。
如果目标小区有多个，则求取每一个目标小区的上述参考信号接收功率与求和结果的比值，得到多个比值结果，将多个比值结果的和确定为上述目标值。
作为一种可选的实施方式，确定出目标小区的邻居小区包括：
确定与目标小区的距离小于或等于第一阈值的第一小区；
获取每一个第一小区的切换门限值；
将切换门限值大于或等于第二阈值的第一小区确定为邻居小区。
本实施例中，切换门限值可以为小区自身被允许切换的次数。是小区的一种属性信息。可以人为设置或者跟随系统由系统调整。
本实施例中，对于一个目标小区，首先，确定目标小区周围一定范围内的其他小区，然后从其他小区中选择切换门限值大于或等于第二阈值的小区作为该目标小区的邻居小区。
本实施例中，针对目标区域内的小区优化对其边界邻区的性能的影响，提出在目标区域优化过程中，以目标区域内的待优化的小区为目标小区，查询目标小区所有邻区集合，去除掉当前目标区域内的邻区得到目标区域外的邻区集合。具体包括首先通过用户设定的小区与邻区间的物理距离门限，选择小区与邻区距离小于等于物理距离门限的邻区组合。在此基础上，再通过用户设定的切换次数门限(切换门限值)过滤，划定最终的目标小区的边界邻区(邻居小区)，收集目标小区和划定的边界邻区上报的测量报告(Measurement Report，简称 为MR)数据。在目标小区优化计算过程中，同时考虑目标小区和目标小区边界邻区的数据，以达到目标小区和目标小区邻区综合达到最优的目的。具体可以为首先根据目标小区经纬度，已设定物理距离门限和其邻区经纬度位置信息进行边界邻区的过滤，去除距离门限外的邻区，再利用已设定切换次数门限和已有的邻区的切换次数，去除切换次数低于切换门限的邻区，获得与目标小区最为密切的边界邻区，作为该目标小区的数据采集层；在目标小区优化时，分别采集目标小区自身MR数据和数据采集层邻区的MR数据作为目标小区的优化数据，得到优化结果，下发小区生效。本实施例中，根据UE终端的MR测量数据来平衡目标小区优化对边界邻区影响，有助于运维人员在对问题区域优化时，保障目标区域边界邻区的网络的稳定性和通信质量，提高网络性能，改善用户体验。
图3与图4是本实施例的一种流程图，包括以下内容：
1、用户选择目标优化区域，目标优化区域内可以包括多个小区；
2、设置距离门限、切换次数门限；
3、查询目标优化区域中目标小区的邻区信息，获得目标区域外的邻区；
4、计算目标小区与目标区域外邻区的距离，距离小于等于“距离门限”的邻区，存入边界邻区列表；
5、根据第4步输出的边界邻区列表，查询切换次数门限KPI切换信息，切换次数大于等于“切换次数门限”的邻区，输出为该目标区域的边界邻区，以下简称边界邻区。
6、启动目标小区和边界邻区的数据采集；
7、收集MR数据，并进行计算，选取最优的参数作为最终优化结果。
算法如下：
预估参考信号结构功率rsrp在其他天线权值下的结果；
计算每个权值下的目标值SINR；
Figure PCTCN2022115552-appb-000001
这个公式中的邻区指目标小区在目标区域内的邻区以及目标区域外的边界邻区。
白噪声功率：-130dBm，考虑UE接收机噪声系数，白噪声功率可以微调。
此处增加边界邻区的rsrp信息，主要是考虑目标小区的天线权值变化时对边界邻区的影响，会影响到邻区rsrp的值。
选取SINR最大的策略，作为最优策略，对目标小区进行优化。例如，调整目标小区和邻居小区的参数：方向角、下倾角、水平波宽、垂直波宽、发射功率中的至少之一，并计算目标值SINR，选择目标值SINR最大的参数方案，作为目标小区和邻居小区的参数。
图5是本实施例的一种目标小区的邻居小区均在目标区域内的情况。
用户设置信息如下：
物理距离门限为默认值：500，切换次数门限默认为：200。
2个目标小区(P1，P2)，P2的邻区是N2(距离目标小区的物理距离N21，切换次数为10)，P1(距离目标小区的物理距离N22，切换次数为5000)。
1.目标小区的数据采集层划分：
1：过滤到已在目标区域中的邻区，得目标小区P2的邻区只有N2；
2：目标小区P1无邻区，无数据采集层；
3：以P2的距离目标小区的物理距离为中心，结合物理距离门限500画方框，获取四角的距离目标小区的物理距离信息(PS1，PS2，PS3，PS4)，取落在这个范围内的目标小区的邻区(N21在这个区域内)N2，邻区N2的切换次数小于用户设定的切换门限200，所以排除N2。
综上，目标小区P1无数据采集层，目标小区P2无数据采集层。
2.目标区域进入数据采集阶段，分别采集P1，P2的MR数据。
3.数据采集完成后，进入优化算法，得到P1，P2的优化权值。
4.权值下发P1，P2生效。
图6为本实施例的目标小区的邻区部分在目标区域内，部分在目标区域外，划分得到目标小区的数据采集层的情况。
系统根据网络部署密集程度设定物理距离门限为：500，根据当前地区的话务量设定切换次数门限默认为：200。
目标小区(P1，P2，P3)，P1的邻区是W1(距离目标小区的物理距离N11，切换次数为3500)，P2(距离目标小区的物理距离N21，切换次数为3000)，W2(距离目标小区的物理距离N22，切换次数为60)，W3(距离目标小区的物理距离N23，切换次数为90)。
1.任务数据采集层划分：
1：过滤到已在目标区域中的邻区，得目标小区P1的目标区域外的邻区为W1(距离目标小区的物理距离N11，切换次数为3500)，W2(距离目标小区的物理距离N22，切换次数为60)，W3(距离目标小区的物理距离N23，切换次数为90)。
2：以P1的距离目标小区的物理距离为中心，结合物理距离门限500画方框，获取四角的距离目标小区的物理距离信息(S1，S2，S3，S4)；取落在这个范围内的目标小区的邻区(N11，N22，N23在这个范围内)：W1、W2、W3，取切换次数大约等于200，其中W2(切换次数为60)，W3(切换次数为90)，所以W2，W3排除，得到目标小区P1的数据采集层邻区为：W1。
3：P2，P3无邻区，所以无数据采集层。
2：目标区域进入数据采集阶段，目标小区P1的MR数据分别采集P1和W1的MR数据；目标小区P2，P3采集自身MR数据。
3：数据采集完成后，进入优化算法，得到P1，P2，P3的优化权值。
4：权值下发P1，P2，P3生效。
图7为目标小区的邻区全部在目标区域外，目标小区存在数据采集层的情况。
物理距离门限为默认值：500，切换次数门限默认为：200。
目标小区P1，其邻区是W1(距离目标小区的物理距离N11，切换次数为3500)，W2(距离目标小区的物理距离N21，切换次数为3000)，W3(距离目标小区的物理距离N22，切换次数为200)，W4(距离目标小区的物理距离N23，切换次数为90)。
1.任务数据采集层划分：
1：过滤到已在目标区域中的邻区，得目标小区P1的目标区域外的邻区为W1(距离目标小区的物理距离N11，切换次数为3500)，W2(距离目标小区的物理距离N21，切换次数为3000)，W3(距离目标小区的物理距离N22，切换次数为200)，W4(距离目标小区的物理距离N23，切换次数为90)。
2：以P1的距离目标小区的物理距离为中心，结合物理距离门限500画方框，获取四角的 距离目标小区的物理距离信息(S1，S2，S3，S4)；取落在这个范围内的目标小区的邻区(N11，N22，N23在这个范围内，N21不在这个范围内)W1、W3、W4，取切换次数需大约等于200，其中W4(切换次数为90)，所以W4排除，得到目标小区P1的数据采集层邻区为：W1，W3。
2.目标区域进入数据采集阶段，目标小区P1的MR数据分别采集P1和W1、W3的MR数据；目标小区P2，P3采集自身MR数据。
3.数据采集完成后，进入优化算法，得到P13的优化权值。
4.权值下发P1生效。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到根据上述实施例的方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本公开的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，或者网络设备等)执行本公开各个实施例的方法。
在本实施例中还提供了一种小区优化装置，该装置用于实现上述实施例及优选实施方式，已经进行过说明的不再赘述。图8是根据本公开实施例的小区优化装置的结构框图，如图8所示，该装置包括：
第一确定单元802，设置为从目标区域中确定出待优化的目标小区；
第二确定单元804，设置为确定出目标小区的邻居小区，其中，邻居小区与目标小区的距离小于第一阈值且邻居小区的切换门限值大于第二阈值，切换门限值为邻居小区被允许切换的最大次数；
第三确定单元806，设置为根据目标小区和邻居小区的天线数据的测量结果，确定优化策略；
优化单元808，设置为使用优化策略优化目标小区的天线数据。
本实施例的其他示例请参见上述示例，在此不再赘述。
本公开的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中存储有计算机程序，其中，该计算机程序被设置为运行时执行上述任一项方法实施例中的步骤。
在一个示例性实施例中，上述计算机可读存储介质可以包括但不限于：U盘、只读存储器(Read-Only Memory，简称为ROM)、随机存取存储器(Random Access Memory，简称为RAM)、移动硬盘、磁碟或者光盘等各种可以存储计算机程序的介质。
本公开的实施例还提供了一种电子装置，包括存储器和处理器，该存储器中存储有计算机程序，该处理器被设置为运行计算机程序以执行上述任一项方法实施例中的步骤。
在一个示例性实施例中，上述电子装置还可以包括传输设备以及输入输出设备，其中，该传输设备和上述处理器连接，该输入输出设备和上述处理器连接。
本实施例中的具体示例可以参考上述实施例及示例性实施方式中所描述的示例，本实施例在此不再赘述。
显然，本领域的技术人员应该明白，上述的本公开的各模块或各步骤可以用通用的计算装置来实现，它们可以集中在单个的计算装置上，或者分布在多个计算装置所组成的网络上，它们可以用计算装置可执行的程序代码来实现，从而，可以将它们存储在存储装置中由计算装置来执行，并且在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤，或者将它们分别制作成各个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。这样，本公开不限制于任何特定的硬件和软件结合。
以上所述仅为本公开的优选实施例而已，并不用于限制本公开，对于本领域的技术人员来说，本公开可以有各种更改和变化。凡在本公开的原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开的保护范围之内。

Claims (14)

  1. 一种小区优化方法，包括：
    从目标区域中确定出待优化的目标小区；
    确定出所述目标小区的邻居小区，其中，所述邻居小区与所述目标小区的距离小于或等于第一阈值且所述邻居小区的切换门限值大于或等于第二阈值，所述切换门限值为所述邻居小区被允许切换的最大次数；
    根据所述目标小区和所述邻居小区的天线数据的测量结果，确定优化策略；
    使用所述优化策略优化所述目标小区的所述天线数据。
  2. 根据权利要求1所述的方法，其中，所述根据所述目标小区和所述邻居小区的天线数据的测量结果，确定优化策略包括：
    根据所述目标小区和所述邻居小区的所述天线数据，预估多种调整策略，其中，每一种所述调整策略中所述目标小区的所述天线数据不同；
    根据所述测量结果，计算每一种所述调整策略的目标值；
    将最大的所述目标值对应的所述调整策略确定为所述优化策略。
  3. 根据权利要求2所述的方法，其中，所述根据所述目标小区和所述邻居小区的所述天线数据，预估多种调整策略包括：
    改变所述目标小区的所述天线数据中的以下至少一种参数：天线的方向角、天线的下倾角、天线的水平波宽、天线的垂直波宽和天线的发射功率；
    将所述邻居小区的天线数据和改变后的所述目标小区的天线数据确定为一种所述调整策略。
  4. 根据权利要求2所述的方法，其中，所述根据所述测量结果，计算每一种所述调整策略的目标值包括：
    根据所述测量结果，预估每一种所述调整策略下所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率；
    根据所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率计算所述目标值。
  5. 根据权利要求4所述的方法，其中，所述根据所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率计算所述目标值包括：
    对于每种所述调整策略，确定所述邻居小区的参考信号接收功率之和加上第一参数的求和结果，其中，所述第一参数为预设的数值；
    将所述目标小区的所述参考信号接收功率与所述求和结果的比值确定为所述目标值。
  6. 根据权利要求1至5任意一项所述的方法，其中，所述确定出所述目标小区的邻居小 区包括：
    确定与所述目标小区的距离小于或等于第一阈值的第一小区；
    获取每一个所述第一小区的所述切换门限值；
    将所述切换门限值大于或等于所述第二阈值的第一小区确定为所述邻居小区。
  7. 一种小区优化装置，包括：
    第一确定单元，设置为从目标区域中确定出待优化的目标小区；
    第二确定单元，设置为确定出所述目标小区的邻居小区，其中，所述邻居小区与所述目标小区的距离小于第一阈值且所述邻居小区的切换门限值大于第二阈值，所述切换门限值为所述邻居小区被允许切换的最大次数；
    第三确定单元，设置为根据所述目标小区和所述邻居小区的天线数据的测量结果，确定优化策略；
    优化单元，设置为使用所述优化策略优化所述目标小区的所述天线数据。
  8. 根据权利要求7所述的装置，其中，所述第三确定单元包括：
    预估模块，设置为根据所述目标小区和所述邻居小区的所述天线数据，预估多种调整策略，其中，每一种所述调整策略中所述目标小区的所述天线数据不同；
    计算模块，设置为根据所述测量结果，计算每一种所述调整策略的目标值；
    确定模块，设置为将最大的所述目标值对应的所述调整策略确定为所述优化策略。
  9. 根据权利要求8所述的装置，其中，所述预估模块包括：
    调整子模块，设置为改变所述目标小区的所述天线数据中的以下至少一种参数：天线的方向角、天线的下倾角、天线的水平波宽、天线的垂直波宽和天线的发射功率；
    确定子模块，设置为将所述邻居小区的天线数据和改变后的所述目标小区的天线数据确定为一种所述调整策略。
  10. 根据权利要求8所述的装置，其中，所述计算模块包括：
    预估子模块，设置为根据所述测量结果，预估每一种所述调整策略下所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率；
    计算子模块，设置为根据所述目标小区的参考信号接收功率和所述邻居小区的参考信号接收功率计算所述目标值。
  11. 根据权利要求10所述的装置，其中，所述计算子模块还设置为：
    对于每种所述调整策略，确定所述邻居小区的参考信号接收功率之和加上第一参数的求和结果，其中，所述第一参数为预设的数值；
    将所述目标小区的所述参考信号接收功率与所述求和结果的比值确定为所述目标值。
  12. 根据权利要求7至11任意一项所述的装置，其中，所述第二确定单元包括：
    第一确定模块，设置为确定与所述目标小区的距离小于第一阈值的第一小区；
    获取模块，设置为获取每一个所述第一小区的所述切换门限值；
    第二确定模块，设置为将所述切换门限值大于所述第二阈值的第一小区确定为所述邻居小区。
  13. 一种计算机可读存储介质，所述计算机可读存储介质中存储有计算机程序，其中，所述计算机程序被处理器执行时实现所述权利要求1至6任一项中所述的方法的步骤。
  14. 一种电子装置，包括存储器、处理器以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，所述处理器执行所述计算机程序时实现所述权利要求1至6任一项中所述的方法的步骤。
PCT/CN2022/115552 2021-09-06 2022-08-29 小区优化方法、装置、存储介质和电子装置 WO2023030263A1 (zh)

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