WO2024078145A1 - 波束权值优化方法及装置 - Google Patents

波束权值优化方法及装置 Download PDF

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Publication number
WO2024078145A1
WO2024078145A1 PCT/CN2023/114299 CN2023114299W WO2024078145A1 WO 2024078145 A1 WO2024078145 A1 WO 2024078145A1 CN 2023114299 W CN2023114299 W CN 2023114299W WO 2024078145 A1 WO2024078145 A1 WO 2024078145A1
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vertical
weight
optimization
user
weights
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PCT/CN2023/114299
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English (en)
French (fr)
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顾健
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中兴通讯股份有限公司
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Publication of WO2024078145A1 publication Critical patent/WO2024078145A1/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

Definitions

  • the embodiments of the present disclosure relate to the field of communications, and in particular, to a beam weight optimization method and device.
  • the second technology also has such a problem.
  • the original horizontal 8 beams covered the road.
  • 3 beams were taken out to cover the building, but the horizontal road coverage was reduced from 8 beams to 5 beams, and the coverage was reduced.
  • the existing technology cannot improve the vertical building coverage while minimizing the impact on the optimization results of the horizontal road coverage, and avoids the use of manual testing methods to optimize the vertical building coverage.
  • the embodiments of the present disclosure provide a beam weight optimization method and device to at least solve the problem in the related art that it is impossible to improve the vertical building coverage while taking into account the optimization results of the horizontal road coverage.
  • a beam weight optimization method comprising: configuring initial beam weights, wherein the initial beam weights include horizontal beam weights, the number of vertical beams, and the vertical beam weights; keeping the horizontal beam weights unchanged, and optimizing the number of vertical beams and the vertical beam weights.
  • a beam weight optimization device including: an initial weight configuration system, configured to configure initial beam weights, wherein the initial beam weights include horizontal beam weights, the number of vertical beams and the vertical beam weights; an optimization weight adjustment system, configured to keep the horizontal beam weights unchanged and optimize the number of vertical beams and the vertical beam weights.
  • a computer-readable storage medium in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when running.
  • an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.
  • FIG1 is a hardware structure block diagram of a computer terminal of a beam weight optimization method according to an embodiment of the present disclosure
  • FIG2 is a flow chart of a beam weight optimization method according to an embodiment of the present disclosure
  • FIG3 is a flow chart of a vertical beam optimization method according to an embodiment of the present disclosure.
  • FIG4 is a flow chart of a vertical beam optimization method according to an embodiment of the present disclosure.
  • FIG5 is a flow chart of a vertical beam optimization method according to an embodiment of the present disclosure.
  • FIG6 is a structural block diagram of a beam weight optimization device according to an embodiment of the present disclosure.
  • FIG7 is a structural block diagram of an optimization weight adjustment system according to an embodiment of the present disclosure.
  • FIG8 is a structural block diagram of an optimization weight adjustment system according to an embodiment of the present disclosure.
  • FIG9 is a schematic diagram of a network architecture of a beam weight optimization method according to an embodiment of the present disclosure.
  • FIG. 10 is a flowchart of a beam weight optimization method according to a scenario embodiment of the present disclosure.
  • FIG1 is a hardware structure block diagram of a computer terminal of a beam weight optimization method in an embodiment of the present disclosure.
  • the computer terminal may include one or more (only one is shown in FIG1 ) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions.
  • FIG1 is only for illustration, and it does not limit the structure of the above-mentioned computer terminal.
  • the computer terminal may also include more or fewer components than those shown in FIG1 , or have a configuration different from that shown in FIG1 .
  • 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 beam weight optimization method in the embodiment of the present disclosure.
  • the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method.
  • the memory 104 may include a high-speed random access memory, and may also include a 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 remotely arranged relative to the processor 102, and these remote memories may be connected to the computer 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 transmission device 106 is used to receive or send data via a network.
  • the specific example of the above network may include a wireless network provided by a communication provider of a computer terminal.
  • the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet.
  • the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.
  • RF Radio Frequency
  • FIG. 2 is a flow chart of the beam weight optimization method according to an embodiment of the present disclosure. As shown in FIG. 2 , the process includes the following steps:
  • Step S202 configuring initial beam weights, wherein the initial beam weights include horizontal beam weights, vertical beam quantity, and vertical beam weights;
  • Step S204 keeping the horizontal beam weight unchanged, and optimizing the number of vertical beams and the vertical beam weight.
  • the initial beam weights are configured, wherein the initial beam weights include the horizontal beam weights, the number of vertical beams and the vertical beam weights; the horizontal beam weights are kept unchanged, and the number of vertical beams and the vertical beam weights are optimized, thereby solving the problem in related technologies that it is impossible to optimize the horizontal coverage of roads while improving the vertical building coverage.
  • This achieves the effect of adaptively outputting the optimal vertical beam weights while keeping the horizontal beam weights unchanged, thereby improving 5G coverage and traffic.
  • the execution subject of the above steps may be a base station, a terminal, etc., but is not limited thereto.
  • FIG3 is a flow chart of a vertical beam optimization method according to an embodiment of the present disclosure. As shown in FIG3 , the process includes the following steps:
  • Step S302 obtaining the user's signal arrival angle (Direction Of Arrival, DOA) and measurement report;
  • Step S304 obtaining the network topology and user distribution map of the user cell according to the DOA and the measurement report;
  • Step S306 Optimize the number of vertical beams and the vertical beam weights according to the network topology and the user distribution diagram, and obtain the optimized number of vertical beams and the vertical beam weights of the user cell.
  • obtaining the signal arrival angle DOA and measurement report of the user in step S302 is obtaining the signal arrival angle DOA and measurement report of the current user cell and the neighboring cells.
  • the optimized number of vertical beams and vertical beam weights of a user cell are obtained, including: using an ant colony search algorithm to determine the optimized number of vertical beams and vertical beam weights of the user cell with the coverage and access capability (Synchronization Signal Reference, SS-RSRP) of the optimal broadcast channel as the goal.
  • SS-RSRP Synchronization Signal Reference
  • FIG4 is a flow chart of the vertical beam optimization method according to an embodiment of the present disclosure. As shown in FIG4, the process includes the following steps:
  • Step S402 configuring the neighboring cell relationship and Xn link of the user cell
  • Step S404 obtaining the user's signal arrival angle DOA and measurement report
  • Step S406 obtaining the network topology and user distribution map of the user cell according to the DOA and the measurement report;
  • Step S408 Optimize the number of vertical beams and the vertical beam weights according to the network topology and the user distribution diagram, and obtain the optimized number of vertical beams and the vertical beam weights of the user cell.
  • the process further includes: evaluating the optimized number of vertical beams and vertical beam weights, and determining the beam weight optimization process of the user cell according to the evaluation result.
  • FIG5 is a flow chart of a vertical beam optimization method according to an embodiment of the present disclosure. As shown in FIG5, the process includes the following steps:
  • Step S502 configuring the neighboring cell relationship and Xn link of the user cell
  • Step S504 obtaining the user's signal arrival angle DOA and measurement report
  • Step S506 obtaining the network topology and user distribution map of the user cell according to the DOA and the measurement report;
  • Step S508 optimizing the number of vertical beams and the vertical beam weights according to the network topology and the user distribution diagram, and obtaining the optimized number of vertical beams and the vertical beam weights of the user cell;
  • Step S510 Evaluate the optimized number of vertical beams and vertical beam weights, and determine the beam weight optimization process of the user cell according to the evaluation result.
  • the optimized number of vertical beams and vertical beam weights are evaluated, and the beam weight optimization process of the user cell is determined according to the evaluation result, including: obtaining the key business of the user cell before and after optimization; Key Performance Indicator (KPI), if the optimized KPI meets the KPI preset threshold compared with the KPI before optimization, the user cell maintains the optimized number of vertical beams and the vertical beam weights; if the KPI preset threshold is not met, the optimized number of vertical beams and the vertical beam weights are returned, where the KPI includes at least one of the following: wireless connection rate, user context drop rate, switching success rate within the communication system, 5G coverage and/or 5G diversion ratio.
  • KPI Key Performance Indicator
  • the horizontal beam weight and/or the vertical beam weight includes at least one of the following: azimuth angle, downtilt angle, horizontal beam width, and vertical beam width.
  • the technical solution of the embodiment of the present disclosure is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the method described in the embodiment of the present disclosure.
  • a storage medium such as ROM/RAM, a disk, or an optical disk
  • a beam weight optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated.
  • the term "system” can implement a combination of software and/or hardware of a predetermined function.
  • the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.
  • Figure 6 is a structural block diagram of a beam weight optimization device according to an embodiment of the present disclosure.
  • the optimization device 60 includes: an initial weight configuration system 610, used to configure the initial beam weight, wherein the initial beam weight includes the horizontal beam weight, the number of vertical beams and the vertical beam weight; an optimization weight adjustment system 620, used to keep the horizontal beam weight unchanged and optimize the number of vertical beams and the vertical beam weight.
  • the initial weight configuration system is also used to configure the neighboring cell relationship and Xn link of the user cell.
  • Figure 7 is a structural block diagram of an optimization weight adjustment system according to an embodiment of the present disclosure.
  • the optimization weight adjustment system 620 includes: a data acquisition subsystem 710, used to obtain the user's signal arrival angle DOA and measurement report; a data analysis subsystem 720, used to obtain the network topology and user distribution map of the user cell based on the DOA and the above; a weight determination subsystem 730, used to optimize the number of vertical beams and vertical beam weights according to the network topology and user distribution map, and obtain the optimized number of vertical beams and vertical beam weights of the user cell.
  • the weight determination subsystem 730 uses an ant colony search algorithm to determine the optimized number of vertical beams and vertical beam weights of the user cell with the coverage and access capability SS-RSRP of the optimal broadcast channel as the goal.
  • Figure 8 is a structural block diagram of an optimization weight adjustment system according to an embodiment of the present disclosure.
  • the optimization weight adjustment system 620 also includes: a weight evaluation subsystem 810, which is used to evaluate the optimized number of vertical beams and vertical beam weights, and determine the beam weight optimization process of the user cell based on the evaluation results.
  • the weight evaluation subsystem 810 evaluates the optimized number of vertical beams and vertical beam weights, and determines the beam weight optimization process of the user cell based on the evaluation results, including: obtaining the key performance indicators KPI of the user cell before and after optimization, if the optimized KPI meets the KPI preset threshold compared with the KPI before optimization, the user cell maintains the optimized number of vertical beams and the vertical beam weights; if the KPI preset threshold is not met, the optimized number of vertical beams and vertical beam weights are returned, wherein the KPI includes at least one of the following: wireless connection rate, user context drop rate, switching success rate within the communication system, 5G coverage and/or 5G diversion ratio.
  • the above subsystems can be implemented by software or hardware.
  • the following method is used for implementation, but not limited thereto: the above subsystems are all located in the same processor; or, the above subsystems are located in different processors in any combination.
  • the embodiments of the present disclosure further provide a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when running.
  • the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.
  • ROM read-only memory
  • RAM random access memory
  • mobile hard disk a magnetic disk or an optical disk.
  • An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein 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/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
  • a beam weight optimization method is provided.
  • broadcast 1 beam In the process of optimizing 5G vertical building coverage, first, broadcast 1 beam.
  • Fixed horizontal road coverage, without affecting the optimization results of horizontal road coverage, start broadcasting X beams take into account the use of broadcast beam (5G broadcast) synchronization signal and PBCH block (Synchronization Signal and PBCH block, SSB) 1+X weight scheme in vertical building coverage, decouple the horizontal and vertical beams of broadcast SSB, maintain the stability of the horizontal coverage network structure, and flexibly configure X vertical beams to meet the needs of different high-rise building scenarios. Due to the decoupling of horizontal beams and vertical beams, the problem of shrinkage of horizontal road coverage after covering high-rise buildings can be well solved.
  • “1” represents the use of a horizontal wide beam to cover roads/ground/low-rise buildings, and keep the horizontal coverage network structure stable.
  • the boosting power enhancement technology is used to keep the coverage strength consistent with the existing network's horizontal 8 beams, and the SSB interference between neighboring areas is further reduced by SSB beam time domain stagger.
  • "X” represents the flexible configuration of X vertical beams based on scenario requirements to meet the requirements of shallow indoor coverage in medium and high-rise buildings and effectively attract 5G users.
  • the user distribution and network topology are calculated.
  • the ant colony search algorithm is used to After multiple iterations, the optimal vertical beam weight is adaptively output while keeping the horizontal beam weight unchanged, thereby improving 5G coverage and traffic.
  • FIG9 is a schematic diagram of a network architecture of a beam weight optimization method according to an embodiment of the disclosed scenario.
  • the network architecture provided by the embodiment of the disclosed scenario is improved based on the architecture of an existing wireless communication system.
  • the existing architecture of the existing wireless communication system includes a terminal, a base station, a network management room, and a core network.
  • the terminal establishes a wireless connection and maintains communication with the base station.
  • the base station is connected to the network management room and then connected to the core network.
  • the 5G cell broadcast weight initial configuration system is used in the embodiment of the present disclosure.
  • the 5G cell broadcast initial configuration is initially configured according to SSB1+X, including 1 beam weight, X number of beams, and X beam weight.
  • the data collection and analysis system is used to collect DOA measurements, neighboring cell measurements and MR measurements of terminals, analyze user distribution and neighboring cell relationships, and automatically generate network topology and user distribution maps.
  • the 5G broadcast weight adjustment system is used to optimize and adjust the 5G cell broadcast weights based on the network topology and user distribution map, using an ant colony algorithm and taking the optimal SS-RSRP as the optimization target. Through multiple iterations, the vertical X beam is optimized while the horizontal 1 beam remains unchanged.
  • the scenario embodiment of the present disclosure provides a method for adaptively optimizing the vertical beam weight by fixing the 5G broadcast horizontal beam based on the actual distribution of users.
  • FIG10 is a flow chart of the beam weight optimization method according to the scenario embodiment of the present disclosure. As shown in FIG10 , the method includes the following steps:
  • Step 1 Configure the initial SSB 1+X weight
  • SMART Hippo tool uses the SMART Hippo tool to output the SSB 1+X initial weights, including 1 beam weight, X beam number, and X beam weight, based on the base station engineering parameters and building distribution and height.
  • Step 2 Configure neighbor relationship and Xn link
  • SSB weight optimization is based on neighboring cells and Xn links. Neighboring cells and Xn links need to be configured before weight optimization. It is recommended to deploy SON neighboring cells and Xn self-configuration functions to complete neighboring cells and Xn links.
  • Step 3 Collect the user's signal arrival angle DOA measurement, neighboring cell measurement and MR measurement;
  • the base station collects the measurement reports MR reported by 5G commercial terminals.
  • the service cell and the neighboring cell jointly measure the detection reference signal SRS signal of the 5G commercial user, and calculates the signal arrival angle DOA based on the SRS signal, thereby establishing the angle and channel strength relationship between the service cell and multiple neighboring cells based on measurements of different cells of the same UE.
  • Step 4 Fix the horizontal 1 beam weight and optimize the vertical X beam weight
  • the broadcast weight of beam 1 is fixed, such as azimuth, downtilt and beam width.
  • the corresponding 1+X candidate weight set is searched in the weight library, and then adaptive optimization is performed to output the optimal SSB weight.
  • the specific method is:
  • the user distribution and neighboring cell relationship are calculated to generate the network topology and user distribution map.
  • the ant colony search algorithm is used with the optimal SS-RSRP as the goal.
  • a set of weights is randomly selected from the broadcast weight library to start the search.
  • the pheromone is updated after each search.
  • the optimal solution of SS-RSRP within the specified optimization range is obtained.
  • Step 5 Output the optimal SSB weight and send it down
  • Step 6 Weight evaluation.
  • Step 1 Initial SSB 1+X weight configuration:
  • the initial SSB 1+X weight configuration is given
  • SSB 1+X there are a total of 3468 macro base stations in the optimized area, and the cells configured with the initial weights of SSB 1+X are as follows: 2232 SSB 1+0 cells, 207 SSB 1+1 cells, 187 SSB1+2 cells, and 842 SSB 1+3 cells.
  • Step 2 Configure neighbor relations and Xn links:
  • Step 3 Collect user DOA measurements, neighboring cell measurements, and MR measurements:
  • Step 4 Optimize vertical X-beam weights:
  • the vertical X-beam adjustment is completed adaptively. After optimization, the vertical X-beam weights of a total of 1,120 cells are changed.
  • Step 5 The optimized SSB weights are issued and evaluated.
  • the key KPI indicators before and after deployment are evaluated. If the deterioration of the wireless connection rate, UE context drop rate, and system handover success rate is within 0.1%, and the 5G coverage or 5G diversion ratio indicators are improved, the evaluation is passed and the process ends. If the above requirements are not met, the evaluation fails, the SSB weights are rolled back, and the process ends.
  • the 5G split ratio increased by 0.86%, meeting the evaluation requirements for improved 5G split ratio.
  • the wireless connection rate deteriorated by 0.03%
  • the UE context drop rate deteriorated by 0.04%
  • the intra-system handover success rate deteriorated by 0.06%, all within 0.1%, meeting the evaluation requirements.
  • Step 1 Initial SSB 1+X weight configuration
  • the cells configured with the initial weights of SSB 1+X are as follows: 325 SSB 1+0 cells, 12 SSB 1+1 cells, 49 SSB1+2 cells, and 144 SSB 1+3 cells.
  • Step 2 Configure neighbor relations and Xn links
  • Step 3 Collect user DOA measurements, neighboring cell measurements, and MR measurements
  • DOA measurements, neighboring cell measurements, and MR measurements of commercial users are collected over a one-day period.
  • Step 4 Optimize vertical X-beam weights
  • the vertical X-beam adjustment is completed adaptively. After optimization, the vertical X-beam weights of a total of 153 cells are changed.
  • Step 5 The optimized SSB weights are issued and evaluated.
  • the key KPI indicators before and after deployment are evaluated. If the deterioration of the wireless connection rate, UE context drop rate, and system handover success rate is within 0.1%, and the 5G coverage or 5G diversion ratio indicators are improved, the evaluation is passed and the process ends. If the above requirements are not met, the evaluation fails, the SSB weights are rolled back, and the process ends.
  • the 5G split ratio increased by 0.62%, meeting the evaluation requirements for improved 5G split ratio.
  • the wireless connection rate deteriorated by 0.03%
  • the UE context drop rate deteriorated by 0.04%
  • the handover success rate within the system deteriorated by X.XX%, all within 0.1%, meeting the evaluation requirements.
  • the beam weight optimization method provided by the embodiment of the present disclosure proposes a fixed 5G NR broadcast horizontal beam, optimizes the vertical beam separately, decouples the horizontal and vertical coverage optimization, optimizes the vertical building coverage without affecting the horizontal road coverage, and improves the coverage and traffic of 5G NR.
  • the coverage performance of the network is comprehensively considered to complete the adaptive broadcast weight adjustment, and the optimal broadcast weight is intelligently estimated, so as to achieve the optimal coverage and improve the 5G network traffic.
  • the equipment manufacturer if the embodiment of the present disclosure is applied in the existing network, it involves the automatic modification of the 5G broadcast beam weights. It is necessary to communicate with the operator in advance and introduce the functional principles of the relevant characteristics. At this time, it can be judged whether to adopt the technical solution of the embodiment of the present disclosure.
  • the difficulty of identification will increase, and the operator will not take the initiative to report these things to the equipment manufacturer.
  • the final application of the embodiment of the present disclosure must adjust the 5G broadcast beam weights, it can be indirectly judged by the frequency and quantity of parameter operation logs.

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  • Computer Networks & Wireless Communication (AREA)
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  • Feedback Control In General (AREA)
  • Conveying And Assembling Of Building Elements In Situ (AREA)

Abstract

本公开实施例提供了一种波束权值优化方法及装置，通过配置初始波束权值，其中，初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；保持水平波束权值不变，对垂直波束数量和垂直波束权值进行优化。

Description

波束权值优化方法及装置
相关申请的交叉引用
本申请基于2022年10月12日提交的发明名称为“波束权值优化方法及装置”的中国专利申请CN202211256375.5，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申请。
技术领域
本公开实施例涉及通信领域，具体而言，涉及一种波束权值优化方法及装置。
背景技术
5G网络建设初期，为了快速建网，统一采用广播水平多波束来实现新空口(New Radio，NR)覆盖，所有广播赋形波束基本用于覆盖道路和地面，无法兼顾中高层楼宇的覆盖。为兼顾楼宇覆盖，业界通常采用的技术有如下两种：(1)将多个广播波束的垂直波宽拉宽，从而提升垂直覆盖；(2)从多个水平覆盖的波束中拿出几个波束做垂直楼宇覆盖。这两种技术存在一个问题，为了兼顾垂直楼宇覆盖，原先的水平覆盖发生了较大的变化。譬如多个广播波束垂直波宽拉宽的方案，从6度拉宽到12度，水平覆盖会减少3db。第二个技术也存在这样的问题，譬如原先水平8波束覆盖道路，为兼顾楼宇覆盖，拿出3个波束朝楼宇覆盖，但水平道路覆盖从8波束减少为5波束，覆盖缩水。
综上，现有技术无法在提升垂直楼宇覆盖的同时，尽量不影响道路水平覆盖的优化成果，同时避免采用人工测试的方法对垂直楼宇覆盖进行优化。
发明内容
本公开实施例提供了一种波束权值优化方法及装置，以至少解决相关技术中无法在提升垂直楼宇覆盖的同时，兼顾道路水平覆盖优化成果的问题。
根据本公开的一个实施例，提供了一种波束权值优化方法，包括：配置初始波束权值，其中，所述初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；保持所述水平波束权值不变，对所述垂直波束数量和所述垂直波束权值进行优化。
根据本公开的另一个实施例，提供了一种波束权值优化装置，包括：初始权值配置系统，设置为配置初始波束权值，其中，所述初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；优化权值调整系统，设置为保持所述水平波束权值不变，对所述垂直波束数量和所述垂直波束权值进行优化。
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，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，所述存储器中存储有计算机程序，所述处理器被设置为运行所述计算机程序以执行上述任一项方法实施例中的步骤。
附图说明
图1是本公开实施例的一种波束权值优化方法的计算机终端的硬件结构框图；
图2是根据本公开实施例的波束权值优化方法的流程图；
图3是根据本公开实施例的垂直波束优化方法的流程图；
图4是根据本公开实施例的垂直波束优化方法的流程图；
图5是根据本公开实施例的垂直波束优化方法的流程图；
图6是根据本公开实施例的波束权值优化装置的结构框图；
图7是根据本公开实施例的优化权值调整系统的结构框图；
图8是根据本公开实施例的优化权值调整系统的结构框图；
图9是根据本公开场景实施例的波束权值优化方法的网络架构示意图；
图10是根据本公开的场景实施例的波束权值优化方法的流程图。
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实施例。
需要说明的是，本公开实施例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波束权值优化方法的计算机终端的硬件结构框图。如图1所示，计算机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，上述计算机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，其并不对上述计算机终端的结构造成限定。例如，计算机终端还可包括比图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波束权值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计算机终端。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。
传输装置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计算机终端的通信供应商提供的无线网络。在一个实例中，传输装置106包括一个网络适配器(Network Interface Controller，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，传输装置106可以为射频(Radio Frequency，RF)模块，其用于通过无线方式与互联网进行通讯。
在本实施例中提供了一种运行于上述计算机终端的波束权值优化方法，图2是根据本公开实施例的波束权值优化方法的流程图，如图2所示，该流程包括如下步骤：
步骤S202，配置初始波束权值，其中，初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；
步骤S204，保持水平波束权值不变，对垂直波束数量和垂直波束权值进行优化。
通过上述步骤，配置初始波束权值，其中，初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；保持水平波束权值不变，对垂直波束数量和垂直波束权值进行优化，解决了相关技术中无法在提升垂直楼宇覆盖的同时，兼顾道路水平覆盖优化成果的问题，达到在保持水平波束权值不变的前提下，自适应输出最优垂直波束权值，提升5G覆盖和流量的效果。
其中，上述步骤的执行主体可以为基站、终端等，但不限于此。
在一个示例性实施例中，图3是根据本公开实施例的垂直波束优化方法的流程图，如图3所示，该流程包括以下步骤：
步骤S302，获取用户的信号到达角(Direction Of Arrival，DOA)和测量报告；
步骤S304，根据DOA和测量报告，获取用户小区的网络拓扑和用户分布图；
步骤S306，根据网络拓扑和用户分布图，对垂直波束数量和垂直波束权值进行优化，获取用户小区的优化的垂直波束数量和垂直波束权值。
在一具体实施例中，步骤S302中获取用户的信号到达角DOA和测量报告是获取当前用户小区和邻区的信号到达角DOA和测量报告。
在一个示例性实施例中，获取用户小区的优化的垂直波束数量和垂直波束权值，包括：采用蚁群搜索算法，以最优广播信道的覆盖与接入能力(Synchronization Signal Reference，SS-RSRP)为目标，确定用户小区的优化的垂直波束数量和垂直波束权值。
在一个示例性实施例中，在获取用户的信号到达角DOA和测量报告之前，还包括：配置用户小区的邻区关系和Xn链路。图4是根据本公开实施例的垂直波束优化方法的流程图，如图4所示，该流程包括以下步骤：
步骤S402，配置用户小区的邻区关系和Xn链路；
步骤S404，获取用户的信号到达角DOA和测量报告；
步骤S406，根据DOA和测量报告，获取用户小区的网络拓扑和用户分布图；
步骤S408，根据网络拓扑和用户分布图，对垂直波束数量和垂直波束权值进行优化，获取用户小区的优化的垂直波束数量和垂直波束权值。
在一个示例性实施例中，在获取用户小区的优化的垂直波束数量和垂直波束权值之后，还包括：对优化的垂直波束数量和垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程。图5是根据本公开实施例的垂直波束优化方法的流程图，如图5所示，该流程包括以下步骤：
步骤S502，配置用户小区的邻区关系和Xn链路；
步骤S504，获取用户的信号到达角DOA和测量报告；
步骤S506，根据DOA和测量报告，获取用户小区的网络拓扑和用户分布图；
步骤S508，根据网络拓扑和用户分布图，对垂直波束数量和垂直波束权值进行优化，获取用户小区的优化的垂直波束数量和垂直波束权值；
步骤S510，对优化的垂直波束数量和垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程。
在一个示例性实施例中，对优化的所述垂直波束数量和垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程，包括：获取优化前和优化后的用户小区的关键业 绩指标(Key Performance Indicator，KPI)，若优化后的KPI与优化前的KPI相比满足KPI预设阈值，用户小区则保持优化后的垂直波束数量和所垂直波束权值；若不满足KPI预设阈值，则退回优化后的垂直波束数量和垂直波束权值，其中，KPI至少包括以下之一：无线接通率、用户上下文掉线率、通信系统内切换成功率、5G覆盖率和/或5G分流比。
在一个示例性实施例中，水平波束权值和/或垂直波束权值至少包括以下之一：方位角、下倾角、水平波宽、垂直波宽。
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，本公开实施例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)执行本公开实施例所述的方法。
在本实施例中还提供了一种波束权值优化装置，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。如以下所使用的，术语“系统”可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。
图6是根据本公开实施例的波束权值优化装置的结构框图，如图6所示，该优化装置60包括：初始权值配置系统610，用于配置初始波束权值，其中，初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；优化权值调整系统620，用于保持水平波束权值不变，对垂直波束数量和垂直波束权值进行优化。
在一个示例性实施例中，初始权值配置系统还用于配置用户小区的邻区关系和Xn链路。
在一个示例性实施例中，图7是根据本公开实施例的优化权值调整系统的结构框图，如图7所示，优化权值调整系统620包括：数据采集子系统710，用于获取用户的信号到达角DOA和测量报告；数据分析子系统720，用于根据DOA和所述，获取用户小区的网络拓扑和用户分布图；权值确定子系统730，用于根据网络拓扑和用户分布图，对垂直波束数量和垂直波束权值进行优化，获取用户小区的优化的垂直波束数量和垂直波束权值。
在一个示例性实施例中，权值确定子系统730采用蚁群搜索算法，以最优广播信道的覆盖与接入能力SS-RSRP为目标，确定用户小区的优化的垂直波束数量和垂直波束权值。
在一个示例性实施例中，图8是根据本公开实施例的优化权值调整系统的结构框图，如图8所示，优化权值调整系统620除了包括图7中的各个子系统之外，还包括：权值评估子系统810，用于对优化的垂直波束数量和垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程。
在一个示例性实施例中，权值评估子系统810对优化的所述垂直波束数量和垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程，包括：获取优化前和优化后的用户小区的关键业绩指标KPI，若优化后的KPI与优化前的KPI相比满足KPI预设阈值，用户小区则保持优化后的垂直波束数量和所垂直波束权值；若不满足KPI预设阈值，则退回优化后的垂直波束数量和垂直波束权值，其中，KPI至少包括以下之一：无线接通率、用户上下文掉线率、通信系统内切换成功率、5G覆盖率和/或5G分流比。
需要说明的是，上述各个子系统是可以通过软件或硬件来实现的，对于后者，可以通过 以下方式实现，但不限于此：上述子系统均位于同一处理器中；或者，上述各个子系统以任意组合的形式分别位于不同的处理器中。
本公开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，ROM)、随机存取存储器(Random Access Memory，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，上述的本公开实施例的各系统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系统或步骤制作成单个集成电路模块来实现。这样，本公开实施例不限制于任何特定的硬件和软件结合。
为了使得本领域的技术人员更好地理解本公开实施例的技术方案，下面结合具体的场景实施例对本公开实施例的技术方案进行阐述。
场景实施例一
在对水平道路进行覆盖优化时，一般采用连接路测设备，开车拉网测试的方法。但垂直楼宇的覆盖优化，无法对城市里的每一栋楼宇都进行遍历测试，一些高档酒店和写字楼的房间也无法让测试人员进入。因此需要一种新的垂直楼宇覆盖方案和优化方法，在提升楼宇覆盖的同时，尽量不影响道路水平覆盖的优化成果，同时避免采用人工测试的方法对垂直楼宇覆盖进行优化。
在本公开的场景实施例中，提供了一种波束权值优化方法，在5G垂直楼宇覆盖优化过程中，首先通过广播1波束。固定水平道路覆盖，在不影响水平道路覆盖优化成果的基础上，开启广播X波束，兼顾垂直楼宇覆盖中采用广播波束(5G广播)同步信号和PBCH块(Synchronization Signal and PBCH block，SSB)1+X权值方案，将广播SSB水平和垂直波束解耦，保持水平覆盖网络结构稳定的同时，可以灵活配置X个垂直波束满足不同高楼场景需求。由于水平波束和垂直波束解耦，能够很好的解决覆盖高层楼宇后，水平道路覆盖出现缩水的问题。其中，“1”代表采用一个水平宽波束覆盖道路/地面/低层楼宇，并保持水平覆盖网络结构稳定，通过boosting功率增强技术保持与现网水平8波束覆盖强度一致，通过SSB波束时域错开进一步降低邻区间SSB干扰。“X”代表根据场景需求，灵活配置X个垂直波束，满足中、高层楼宇室内浅层覆盖要求，有效吸收5G用户。
通过采集5G商用用户的信号到达角DOA、邻区测量信息和测量报告(Measurement Report，MR)测量信息，计算用户分布和网络拓扑。根据用户分布和网络拓扑，采用蚁群搜索算法， 经过多次迭代后，在保持水平波束权值不变的前提下，自适应输出最优垂直波束权值，提升5G覆盖和流量。
图9是根据本公开场景实施例的波束权值优化方法的网络架构示意图，如图9所示，本公开场景实施例提供的网络架构是基于现有的无线通讯系统的架构进行了改进。现有的无线通讯系统已有架构包括终端、基站、网管机房、核心网，终端建立无线连接，和基站保持通讯，基站和网管机房连接，然后再连接到核心网。
在现有的无线通讯系统的架构基础上，新增：
5G小区广播权值初始配置系统，用于在本公开实施例中，5G小区广播初始配置按照SSB1+X进行初始配置，包括1波束权值、X波束个数、X波束权值。
数据采集和分析系统，用于采集终端的DOA测量、邻区测量和MR测量，分析用户分布和邻区关系，自动生成网络拓扑和用户分布图。
5G广播权值调整系统，用于根据网络拓扑和用户分布图，采用蚁群算法，以SS-RSRP最优为优化目标，通过多次迭代，优化调整5G小区广播权值，在水平1波束保持不变的前提下，对垂直X波束进行优化。
本公开的场景实施例提供一种基于用户实际分布，固定5G广播水平波束，对垂直波束权值进行自适应优化的方法，图10是根据本公开的场景实施例的波束权值优化方法的流程图，如图10所示，包括以下步骤：
步骤一：配置初始SSB 1+X权值；
使用SMART Hippo工具，根据基站工参和建筑物分布及高度，输出SSB 1+X初始权值，包括1波束权值、X波束个数、X波束权值。
步骤二：配置邻区关系和Xn链路；
SSB权值优化基于邻区和Xn链路，在进行权值优化前需配置邻区和Xn链路，建议部署SON邻区和Xn自配置功能，补齐邻区和Xn链路。
步骤三：采集用户的信号到达角DOA测量、邻区测量和MR测量；
基站采集5G商用终端上报的测量报告MR，同时服务小区和邻区协同测量5G商用用户的探测参考信号SRS信号，根据SRS信号计算信号达到角DOA，从而基于相同UE不同小区测量建立服务小区和多个邻区的角度、信道强度关系。
步骤四：固定水平1波束权值，优化垂直X波束权值；
固定1波束的广播权值，如方位角、下倾角和波宽不变，在权值库中寻找相应的1+X候选权值集合，然后进行自适应优化，输出最优SSB权值。具体方法为：
首先，确定优化范围以及对应的小区；
然后在所划定的优化范围内，根据步骤三采集得到的5G商用用户DOA、邻区测量信息和MR，计算用户分布和邻区关系，生成网络拓扑和用户分布图。
最后采用蚁群搜索算法，以最优SS-RSRP为目标，从广播权值库中随机选择一组权值开始搜索，每完成一次搜索更新信息素，最终通过多次迭代，获取所划定优化范围内的SS-RSRP最优解。
步骤五：输出最优SSB权值并下发；
由于优化“X”波束权值，部分小区的X波束个数会增加，而SSB波束个数增加会导致小区复位，所以我们对X波束优化后的权值采用手动方式下发，一般选择晚上12点以后，下发 权值。
步骤六：权值评估。
(1)如果评估通过，则权值生效，SSB权值优化结束；
(2)如果评估不通过，则权值回退，SSB权值优化结束。
场景实施例二
在本场景实施例中结合具体的数值计算，阐述本公开实施例的技术方案。
步骤1：初始SSB 1+X权值配置：
根据3D电子地图、基站信息表及图候选5G广播天线权值库，给出初始SSB 1+X权值配置
以A城市为例，优化区域总计宏站小区3468个，SSB 1+X初始权值配置的小区如下：其中SSB 1+0小区2232个，SSB 1+1小区207个，SSB1+2小区187个，SSB 1+3小区842个。
步骤2：配置邻区关系和Xn链路：
开启SON功能，补齐优化区域的邻区关系和Xn链路；
步骤3：采集用户的DOA测量、邻区测量和MR测量：
采集商用用户的DOA测量、邻区测量和MR测量，采集时间1天；
步骤4：优化垂直X波束权值：
固定1波束权值，启动垂直X波束自适应优化任务。根据步骤3采集得到的数据，运用蚁群算法，以SS-RSRP最优为目标，估算最优的X波束广播权值。
以A城市为例，基于商用用户的分布信息和MR测量，自适应的完成垂直X波束的调整。优化后，总计1120个小区的垂直X波束权值发生改变。
以A城市几个典型小区优化前后的SSB权值举例，看一下垂直X波束权值调整的具体方案。
A城市小区1，优化前后SSB权值如下：
A城市小区2，优化前后SSB权值如下：
A城市小区3，优化前后SSB权值如下：
步骤5：优化后SSB权值下发并评估。
优化后的SSB权值下发后，对部署前后的关键KPI指标进行评估。如果无线接通率、UE上下文掉线率和系统内切换成功率的恶化幅度均在0.1％以内，同时5G覆盖率或5G分流比指标有改善，则评估通过，流程结束。如果不满足上述要求，则评估不通过，SSB权值回退，流程结束。
以A城市为例，2021年12月8日开启垂直X波束的优化，2021年12月12日优化完毕。评估情况如下：
垂直X波束优化后，5G分流比提升0.86％，满足5G分流比有提升的评估要求。
垂直X波束优化后，无线接通率恶化0.03％、UE上下文掉线率恶化0.04％、系统内切换成功率的恶化0.06％，幅度均在0.1％以内，满足评估要求。
垂直X波束优化后，综合覆盖率提升0.48％，满足评估要求。
场景实施例三
步骤1：初始SSB 1+X权值配置
根据3D电子地图、基站信息表及图X所示的候选5G广播天线权值库，给出初始SSB 1+X权值配置。
以B城市优化区域为例，总计宏站小区540个，SSB 1+X初始权值配置的小区如下：其中SSB 1+0小区325个，SSB 1+1小区12个，SSB1+2小区49个，SSB 1+3小区144个。
步骤2：配置邻区关系和Xn链路；
开启SON功能，补齐优化区域的邻区关系和Xn链路。
步骤3：采集用户的DOA测量、邻区测量和MR测量；
采集商用用户的DOA测量、邻区测量和MR测量，采集时间1天。
步骤4：优化垂直X波束权值；
固定1波束权值，启动垂直X波束自适应优化任务。根据步骤3采集得到的数据，运用蚁群算法，以SS-RSRP最优为目标，估算最优的X波束广播权值。
以B城市为例，基于商用用户的分布信息和MR测量，自适应的完成垂直X波束的调整。优化后，总计153个小区的垂直X波束权值发生改变。
以B城市几个典型小区优化前后的SSB权值举例，看一下垂直X波束权值调整的具体方案。
B城市小区1，优化前后SSB权值如下：
B城市小区2，优化前后SSB权值如下：
B城市小区3，优化前后SSB权值如下：
步骤5：优化后SSB权值下发并评估。
优化后的SSB权值下发后，对部署前后的关键KPI指标进行评估。如果无线接通率、UE上下文掉线率和系统内切换成功率的恶化幅度均在0.1％以内，同时5G覆盖率或5G分流比指标有改善，则评估通过，流程结束。如果不满足上述要求，则评估不通过，SSB权值回退，流程结束。
以B城市优化区域为例，宏站小区540个，优化前1+X小区205个，2021年11月22日开启垂直X波束的优化，2021年11月23日优化完毕。评估情况如下：
垂直X波束优化后，5G分流比提升0.62％，满足5G分流比有提升的评估要求。
垂直X波束优化后，无线接通率恶化0.03％、UE上下文掉线率恶化0.04％、系统内切换成功率的恶化X.XX％，幅度均在0.1％以内，满足评估要求。
垂直X波束优化后，综合覆盖率提升0.3％，满足评估要求。
综上，本公开实施例提供的波束权值优化方法，与现有技术相比，提出了固定5G NR广播水平波束，单独优化垂直波束，水平和垂直覆盖优化解耦，在不影响水平道路覆盖的基础上，优化垂直楼宇覆盖，提升5G NR的覆盖和流量。同时在垂直波束的优化方法上，根据UE的位置分布信息、MR测量，综合考虑网络的覆盖性能完成自适应的广播权值调整，智能估算最优的广播权值，从而实现最优覆盖，提升5G网络流量。
在实际实施过程中，通常是设备厂家或者无线网络运营商采用本公开实施例提供的技术方案，对于设备厂家，如果采用本公开实施例在现网中应用，涉及到5G广播波束权值的自动修改，必然要向运营商提前沟通，介绍相关特性的功能原理等，此时可以判断是否采用本公开实施例的技术方案。对于无线网络运营商，其如果采用本公开实施例在现网中应用，识别难度会加大，运营商不会主动向设备厂家报告这些事情。但是由于本公开实施例的最终应用，必然是要调整5G广播波束权值，所以可以通过参数操作日志的频繁程度、数量等来间接判断。
以上所述仅为本公开实施例的优选实施例而已，并不用于限制本公开实施例，对于本领域的技术人员来说，本公开实施例可以有各种更改和变化。凡在本公开实施例的原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开实施例的保护范围之内。

Claims (13)

  1. 一种波束权值优化方法，包括：
    配置初始波束权值，其中，所述初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；
    保持所述水平波束权值不变，对所述垂直波束数量和所述垂直波束权值进行优化。
  2. 根据权利要求1所述的方法，其中，对所述垂直波束数量和所述垂直波束权值进行优化，包括：
    获取用户的信号到达角DOA和测量报告；
    根据所述DOA和所述测量报告，获取用户小区的网络拓扑和用户分布图；
    根据所述网络拓扑和用户分布图，对所述垂直波束数量和所述垂直波束权值进行优化，获取所述用户小区的优化的所述垂直波束数量和所述垂直波束权值。
  3. 根据权利要求2所述的方法，其中，所述获取所述用户小区的优化的所述垂直波束数量和所述垂直波束权值，包括：
    采用蚁群搜索算法，以最优广播信道的覆盖与接入能力SS-RSRP为目标，确定所述用户小区的优化的所述垂直波束数量和所述垂直波束权值。
  4. 根据权利要求2所述的方法，其中，在所述获取用户的信号到达角DOA和测量报告之前，所述方法还包括：
    配置所述用户小区的邻区关系和Xn链路。
  5. 根据权利要求2所述的方法，其中，在获取所述用户小区的优化的所述垂直波束数量和所述垂直波束权值之后，所述方法还包括：
    对所述优化的所述垂直波束数量和所述垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程。
  6. 根据权利要求5所述的方法，其中，对所述优化的所述垂直波束数量和所述垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程，包括：
    获取优化前和优化后的用户小区的关键业绩指标KPI，若优化后的所述KPI与优化前的所述KPI相比满足KPI预设阈值，用户小区则保持优化后的所述垂直波束数量和所述垂直波束权值；若不满足KPI预设阈值，则退回优化后的所述垂直波束数量和所述垂直波束权值，其中，所述KPI至少包括以下之一：无线接通率、用户上下文掉线率、通信系统内切换成功率、5G覆盖率和/或5G分流比。
  7. 根据权利要求1所述的方法，其中，所述水平波束权值和/或所述垂直波束权值至少包括以下之一：方位角、下倾角、水平波宽、垂直波宽。
  8. 一种波束权值优化装置，包括：
    初始权值配置系统，设置为配置初始波束权值，其中，所述初始波束权值包括水平波束权值、垂直波束数量和垂直波束权值；
    优化权值调整系统，设置为保持所述水平波束权值不变，对所述垂直波束数量和所述垂直波束权值进行优化。
  9. 根据权利要求8所述的装置，其中，所述优化权值调整系统包括：
    数据采集子系统，设置为获取用户的信号到达角DOA和测量报告；
    数据分析子系统，设置为根据所述DOA和所述，获取用户小区的网络拓扑和用户分布图；
    权值确定子系统，设置为根据所述网络拓扑和用户分布图，对所述垂直波束数量和所述垂直波束权值进行优化，获取所述用户小区的优化的所述垂直波束数量和所述垂直波束权值。
  10. 根据权利要求8所述的装置，其中，所述初始权值配置系统还设置为配置用户小区的邻区关系和Xn链路。
  11. 根据权利要求8所述的装置，其中，所述优化权值调整系统包括：
    权值评估子系统，设置为对所述优化的所述垂直波束数量和所述垂直波束权值进行评估，根据评估结果确定用户小区的波束权值优化进程。
  12. 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运行时执行所述权利要求1至7任一项中所述的方法。
  13. 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，所述处理器运行所述计算机程序时实现所述权利要求1至7任一项中所述的方法。
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