Method, device and electronic device for adjusting beam parameters

Abstract

The present application discloses a method, device and electronic device for adjusting beam parameters. The method includes: obtaining the current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle; determining the first perceived benefit of the beam based on the parameter information, wherein the first perceived benefit is used to indicate the degree of optimization of the beam; when the first perceived benefit meets the preset conditions, adjusting the parameter information, and determining the second perceived benefit based on the adjusted parameter information; when the second perceived benefit does not meet the preset conditions, stopping adjusting the parameter information, and determining the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information. The present application solves the technical problem in the prior art that beam optimization relies on expert experience, has a large workload and low efficiency.

Description

Technical Field

[0001] The present application relates to the field of mobile communication technology, and in particular to a method, device and electronic device for adjusting beam parameters. Background Art

[0002] 5G Massive MIMO multi-beam technology is a key technology for improving network coverage and system capacity in the fifth generation of mobile communication technology. Improving beamforming and controlling the direction of electromagnetic waves can concentrate broadcast signals in multiple narrow beams for transmission, thereby enhancing the coverage of 5G cells. However, for 5G beam optimization, the weights involve multiple parameters such as horizontal azimuth, vertical azimuth, number of beams, downtilt angle, etc. However, considering that in actual networks, 5G beam optimization also needs to consider parameters such as coverage and switching between surrounding 5G neighboring cells and the main service cell, resulting in more than 10,000 combinations of 5G beam optimization, making 5G beam optimization extremely difficult. In existing technologies, beam optimization relies on expert experience, which is labor-intensive and inefficient.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method, device and electronic device for adjusting beam parameters, so as to at least solve the technical problem in the prior art that beam optimization relies on expert experience, has a large workload and low efficiency.

[0005] According to one aspect of an embodiment of the present application, a method for adjusting beam parameters is provided, including: obtaining current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle; determining a first perceived benefit of the beam based on the parameter information, wherein the first perceived benefit is used to indicate a degree of optimization of the beam; when the first perceived benefit meets a preset condition, adjusting the parameter information, and determining a second perceived benefit based on the adjusted parameter information; when the second perceived benefit does not meet the preset condition, stopping adjusting the parameter information, and determining the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information.

[0006] Optionally, determining a first perceived benefit of the beam based on parameter information includes: determining a plurality of optimization factors based on the parameter information; and determining the first perceived benefit of the beam based on the plurality of optimization factors.

[0007] Optionally, adjusting the parameter information includes: determining the value ranges corresponding to the parameter information respectively to obtain multiple value sets; traversing the multiple value sets, selecting different parameter information from the multiple value sets in a preset order to obtain a parameter combination; and adjusting the parameter information according to the parameter combination.

[0008] Optionally, the network coverage factor among multiple optimization factors is determined in the following manner: under the parameter information, obtaining the first data in the base station corresponding to the first core network, wherein the first data at least includes the reference signal received power and the signal-to-noise ratio of the main service cell; calculating the first mean of the reference signal received power and the second mean of the signal-to-noise ratio of each cell in the main service cell; determining a normal distribution curve based on multiple first means and multiple second means of the main service cell; determining a first probability of the reference signal received power and a second probability of the signal-to-noise ratio based on the normal distribution curve; determining the network coverage factor based on the first probability and the second probability.

[0009] Optionally, after determining the network coverage factor, the method further includes: determining a user coverage factor, wherein the user coverage factor is determined by a coverage excellence ratio and a signal-to-noise ratio excellence ratio of a reference signal received power; and determining the coverage factor based on the network coverage factor and the user coverage factor.

[0010] Optionally, the user perception factor among multiple optimization factors is determined in the following manner: under the parameter information, obtaining the second data in the base station corresponding to the first core network, wherein the second data includes at least an uplink rate and a downlink rate; determining the first perception rate based on the uplink rate and the reference signal received power, and determining the second perception rate based on the downlink rate and the reference signal received power, wherein the first perception rate is used to represent the perceived rate of the average uplink user of each cell, and the second perception rate is used to represent the perceived rate of the average downlink user of each cell; determining the first perception rate excellence ratio and the second perception rate excellence ratio based on conditions satisfied by the uplink rate and the downlink rate; determining the user perception factor based on at least the first perception rate, the second perception rate, the first perception rate excellence ratio and the second perception rate excellence ratio.

[0011] Optionally, the network perception factor among multiple optimization factors is determined in the following manner: under the parameter information, performance indicator data in the base station corresponding to the first core network is obtained, wherein the performance indicator data includes at least one of the following: wireless access success rate, user equipment context drop rate and switching success rate; and the network perception factor is determined at least based on the performance indicator data.

[0012] Optionally, the network coordination factor among multiple optimization factors is determined in the following manner: obtaining terminal data and first engineering parameter data of the base station corresponding to the first core network, and minimized road test data and second engineering parameter data of the base station corresponding to the second core network; determining a first user set based on the terminal data, wherein the first user set is used to represent users whose terminal devices support the first core network and whose switches of the first core network are turned on; screening target data corresponding to users in the first user set from the minimized road test data; determining association information between the first core network and the second core network based on at least the first engineering parameter data and the second engineering parameter data, as well as first position information of the base station corresponding to the first core network and second position information of the base station corresponding to the second core network; determining a neighboring cell relationship between the first core network and the second core network based on the association information and the target data; determining a third mean of the reference signal receiving power corresponding to the neighboring cell of the first core network when the cell covered by the first core network is not included in the first user set based on the neighboring cell relationship, and determining a target perception rate, wherein the target perception rate is determined by the first perception rate and the second perception rate; determining the network coordination factor based on the third mean and the target perception rate.

[0013] According to another aspect of an embodiment of the present application, a device for adjusting beam parameters is also provided, including: an acquisition module, used to acquire current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle; a first determination module, used to determine a first perceived benefit of the beam based on the parameter information, wherein the first perceived benefit is used to indicate the degree of optimization of the beam; an adjustment module, used to adjust the parameter information when the first perceived benefit meets a preset condition, and determine a second perceived benefit based on the adjusted parameter information; a second determination module, used to stop adjusting the parameter information when the second perceived benefit does not meet the preset condition, and determine the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information.

[0014] According to another aspect of the embodiments of the present application, an electronic device is also provided, including: a memory for storing program instructions; a processor, connected to the memory, for executing program instructions to implement the following functions: obtaining current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle; determining a first perceived benefit of the beam based on the parameter information, wherein the first perceived benefit is used to indicate a degree of optimization of the beam; when the first perceived benefit meets a preset condition, adjusting the parameter information, and determining a second perceived benefit based on the adjusted parameter information; when the second perceived benefit does not meet the preset condition, stopping adjusting the parameter information, and determining the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information.

[0015] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned method for adjusting beam parameters by running the computer program.

[0016] In an embodiment of the present application, the current parameter information of the beam is obtained; a first perceptual benefit of the beam is determined based on the parameter information, wherein the first perceptual benefit is used to indicate the degree of optimization of the beam; when the first perceptual benefit meets a preset condition, the parameter information is adjusted, and a second perceptual benefit is determined based on the adjusted parameter information; when the second perceptual benefit does not meet the preset condition, the parameter information is stopped from being adjusted, and the last adjusted parameter information corresponding to the second perceptual benefit is determined as the target parameter information, thereby achieving the purpose of adjusting the parameter information according to the conditions met by the perceptual benefit, thereby realizing the technical effect of automatically adjusting the parameter information, and further solving the technical problem in the prior art that beam optimization relies on expert experience, has a large workload and is inefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for adjusting beam parameters according to an embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for adjusting beam parameters according to an embodiment of the present application;

[0020] Figure 3 is a structural diagram of a device for adjusting beam parameters according to an embodiment of the present application;

[0021] Figure 4 is a schematic diagram of determining a state equation according to an embodiment of the present application;

[0022] Figure 5 It is a flowchart of a beam optimization algorithm according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] First, some nouns or terms that appear in the process of explaining the embodiments of the present application are subject to the following explanations:

[0026] Reference Signal Receiving Power (RSRP) is a key parameter that can represent the strength of wireless signals in LTE networks and one of the physical layer measurement requirements. It is the average value of the signal power received on all REs (resource elements) that carry the reference signal in a symbol.

[0027] The signal-to-noise ratio (SINR) refers to the ratio of the strength of the received useful signal to the strength of the received interference signal (noise and interference); it can be simply understood as the "signal-to-noise ratio".

[0028] Reference Signal Receiving Quality (RSRQ) represents the LTE reference signal reception quality. This metric is mainly used to rank different LTE candidate cells based on signal quality. This measurement is used as an input for handover and cell reselection decisions.

[0029] The basic principle of Minimization Drive Test (MDT) data is to optimize the network based on the measurement reports of commercial terminals.

[0030] There are two main ways to optimize 5G beams in related technologies:

[0031] 1. Digital simulation 5G beam optimization based on electronic maps: According to the 5G wireless network propagation model, use electronic maps to optimize and adjust the 5G beam. In the actual simulation process, high-precision electronic maps are required. At the same time, in order to improve the simulation accuracy, hot spots can be set artificially so that the final beam main lobe direction of the 5G cell covers the user hot spot area and obtains the maximum gain during user use.

[0032] 2. 5G beam optimization based on human experience: By extracting 5G cell beam-level KPI indicators and coverage data from the system platform, we analyze and determine the traffic hotspot areas, and further use test instruments to conduct DT tests to determine weak coverage areas, and then optimize and adjust the 5G beam based on expert experience.

[0033] Although the above solutions can meet the 5G beam optimization work to a certain extent, due to the rapid development of 5G users and the market, the user's business model may also change, so the above solutions cannot meet the actual application needs. Specifically, for the first solution, after completing a simulation beam optimization, if a batch of 5G sites are newly built in the area, then for the 5G cells that have already been connected to the network, their wireless environment has changed, and the original parameter configuration needs to be adjusted accordingly, otherwise it may lead to network performance degradation and user perception degradation. For the second solution, the specific solution of beam optimization relies heavily on the expert experience of the optimizers, and requires a lot of data analysis and on-site DT testing. Each optimization requires a lot of manpower and material resources, and it is difficult to cope with the massive parameter modification work of 5G beam optimization.

[0034] To solve the above problems, the embodiments of the present application provide corresponding solutions, which are described in detail below.

[0035] The method for adjusting beam parameters provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for adjusting beam parameters. Figure 1As shown, the computer terminal 10 (or electronic device 10) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0036] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or electronic device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for adjusting beam parameters in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned method for adjusting beam parameters. 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. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 10 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.

[0038] The transmission module 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 the computer terminal 10. In one example, 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. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0040] It should be noted that, in some optional embodiments, the above Figure 1 The computer device (or electronic device) shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. It should be noted that Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the above-described computer device (or electronic device).

[0041] In the above operating environment, an embodiment of the present application provides an embodiment of a method for adjusting beam parameters. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] Figure 2 is a flow chart of a method for adjusting beam parameters according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0043] Step S202, obtaining current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle, and downtilt angle;

[0044] Step S204, determining a first perceived benefit of the beam according to the parameter information, wherein the first perceived benefit is used to indicate a degree of optimization of the beam;

[0045] Step S206, when the first perceived benefit meets a preset condition, adjusting the parameter information, and determining the second perceived benefit according to the adjusted parameter information;

[0046] Step S208: When the second perceived benefit does not meet the preset condition, stop adjusting the parameter information, and determine the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information.

[0047] In the above steps S202 to S208, the parameter equation γ is established for the four parameters of 5G beam optimization, namely, horizontal beam width W, vertical beam width H, downtilt angle θ, and azimuth angle A. t ={W, H, θ, A}, as the optimization strategy and action, γ0 is the initial parameter equation. Based on the initialization state of the beam, the state equation is established, and the state equation is established according to the coverage factor C, user perception factor U, primary service cell perception factor P, and network coordination factor N. Where t is the time, t={0,1,2,……}, That is the initial state equation. According to the initial state equation under the initial parameters Set the first parameter optimization action and direction, that is, establish the parameter equation γ1, and then obtain the state equation under the corresponding parameter of γ1 According to the equation of state and The perceived benefit S corresponding to the beam in different states can be obtained, which is the first perceived benefit mentioned above, where S = α*C+β*U+δ*P+ε*N, where α, β, δ, and ε are random numbers that conform to the Poisson distribution. Once these four random numbers are determined, they will no longer change in the subsequent calculation process. If the perceived benefit S ≥ 0, it is determined that the perceived benefit meets the preset conditions. At this time, it is considered that the beam can continue to be optimized, and then the four parameters of the above beam optimization are continued to be adjusted, and the parameter equation is established again to determine the second perceived benefit. Repeat the above steps until the second perceived benefit S < 0, it is determined that the second perceived benefit does not meet the preset conditions, then stop adjusting the parameter information, and determine the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information. The beam optimization corresponding to the cell covered by the base station is completed, and the remaining cells are optimized according to the above steps until the beam optimization of all cells is completed. It should be noted that the number of optimization adjustments to the parameter information should be within the preset number of optimizations. If the number of optimizations is exceeded, the optimization of the parameter information is stopped.

[0048] In step S204 of the above method for adjusting beam parameters, determining the first perceived benefit of the beam based on the parameter information specifically includes the following steps: determining multiple optimization factors based on the parameter information; determining the first perceived benefit of the beam based on the multiple optimization factors.

[0049] In an embodiment of the present application, multiple optimization factors, namely the factors in the above-mentioned state equation, specifically include coverage factor C, user perception factor U, main service cell perception factor P, and network coordination factor N. The multiple optimization factors in the corresponding state equation are determined according to parameter information, and the first perception benefit of the beam is determined according to the obtained multiple optimization factors and the calculation formula of the above-mentioned first perception benefit S.

[0050] In step S206 of the above-mentioned method for adjusting beam parameters, adjusting parameter information includes: determining value ranges corresponding to the parameter information respectively to obtain multiple value sets; traversing the multiple value sets, selecting different parameter information from the multiple value sets according to a preset order to obtain a parameter combination; and adjusting the parameter information according to the parameter combination.

[0051] In the embodiment of the present application, the value ranges corresponding to the parameter information are first determined. For example, when setting the initial parameter information, one optional parameter value set can be set as:

[0052] (1) Horizontal beam width W, with a step size of 10°, and values ​​ranging from {10°, 20°, 30°, 40°, 50°, 60°, 70°, 80°, 90°}. It should be noted that the above value range is only for example. In actual situations, the step size of W is configurable. The optimal value range of the horizontal beam width W is [10°, 105°];

[0053] (2) Vertical beam width H, with a step size of 5°, and values ​​ranging from {5°, 10°, 15°, 20°, 25°}. It should be noted that the above value range is only for example. In actual situations, the step size of H is configurable. The optimal value range of the vertical beam width H is [0°, 25°];

[0054] (3) Azimuth angle A, with a step size of 5°, and values ​​ranging from {-20°, -15°, -10°, -5°, 0°, 5°, 10°, 15°, 20°}. It should be noted that the above value range is only for example. In actual situations, the step size of A is configurable. The optimal value range of azimuth angle A is [-25°, 25°];

[0055] (4) Downtilt angle θ, with a step size of 3°, and values ​​ranging from {-9°, -6°, -3°, 0°, 3°, 6°, 9°}. It should be noted that the above value range is only for example. In actual situations, the step size of θ is configurable. The optimal value range of the downtilt angle θ is [-9°, 9°].

[0056] In the above-mentioned method for adjusting beam parameters, the network coverage factor among multiple optimization factors is determined in the following manner: under the parameter information, obtaining the first data in the base station corresponding to the first core network, wherein the first data at least includes the reference signal received power and the signal-to-noise ratio of the main service cell; calculating the first mean of the reference signal received power and the second mean of the signal-to-noise ratio of each cell in the main service cell; determining a normal distribution curve based on multiple first means and multiple second means of the main service cell; determining a first probability of the reference signal received power and a second probability of the signal-to-noise ratio based on the normal distribution curve; determining the network coverage factor based on the first probability and the second probability.

[0057] In an embodiment of the present application, the first core network is a 5G network, the first data is M1 data in the MDT data, and the 4G / 5G MDT data includes: user AGPS (Assisted Global Positioning System) location information data and wireless measurement data of M1-M4, M1: RSRP, RSRQ, SINR, etc. of the main service cell; M2: RSRP, RSRQ, SINR of the neighboring cell; M3: uplink rate / downlink rate; M4: uplink throughput / downlink throughput. The above-mentioned first mean and second mean can be understood as the RSRP mean and SINR mean of all data in the 5G cell. The RSRP mean is used to represent the RSRP coverage rate of the cell, and the SINR mean is used to represent the SINR interference level of the cell. According to the above-mentioned cell-level RSRP mean and SINR mean, the data of the entire network are fitted with a normal distribution, that is, the normal distribution curve is determined. According to the above normal distribution curve, the RSRP probability and SINR probability of the 5G cell are calculated, that is, corresponding to the above first probability and second probability, the network coverage factor C1 = RSRP probability * SINR probability can be defined.

[0058] In the above method for adjusting beam parameters, after determining the network coverage factor, the method also includes the following steps: determining a user coverage factor, wherein the user coverage factor is determined by a coverage excellence ratio of a reference signal received power and a signal-to-noise ratio excellence ratio; and determining the coverage factor based on the network coverage factor and the user coverage factor.

[0059] In the embodiment of the present application, according to the M1 data in the 5G MDT, the 5G cell, the user-level RSRP coverage quality ratio and the SINR quality ratio are calculated. Among them, RSRP coverage quality ratio = sampling point of RSRP>-105dBm / total sampling points, SINR quality ratio = sampling point of SINR>13dB / total sampling points, and the total sampling points are the total number of collected data. Then, the user coverage factor C2 can be defined as RSRP coverage quality ratio * SINR quality ratio. According to C1 and C2, the coverage factor C can be determined as network coverage factor C1 * user coverage factor C2.

[0060] In the above-mentioned method for adjusting beam parameters, the user perception factor among multiple optimization factors is determined in the following manner: under the parameter information, the second data in the base station corresponding to the first core network is obtained, wherein the second data at least includes an uplink rate and a downlink rate; based on the uplink rate and the reference signal received power, the first perception rate is determined, and based on the downlink rate and the reference signal received power, the second perception rate is determined, wherein the first perception rate is used to represent the perceived rate of the average uplink user of each cell, and the second perception rate is used to represent the perceived rate of the average downlink user of each cell; based on the conditions satisfied by the uplink rate and the downlink rate, the first perception rate excellence ratio and the second perception rate excellence ratio are determined; the user perception factor is determined based at least on the first perception rate, the second perception rate, the first perception rate excellence ratio and the second perception rate excellence ratio.

[0061] In the embodiment of the present application, the second data is M3 data in the MDT data, that is, the uplink rate and the downlink rate. Based on the second data, the cell-level user perceived rate and the user-level perceived rate quality ratio can be calculated, which can be specifically expressed as:

[0062] Average user perceived uplink rate at the cell level

[0063] Average user perceived downlink rate at the cell level

[0064] User-level uplink perceived rate quality ratio B1 = uplink rate > 20Mbps sampling points / total sampling points

[0065] User-level downlink perceived rate goodness ratio B2 = downlink rate>20Mbps sampling points / total sampling points

[0066] Among them, A1 corresponds to the first perceived rate, A2 corresponds to the second perceived rate, B1 corresponds to the first perceived rate quality ratio, and B2 corresponds to the second perceived rate quality ratio. Then the user perception factor U can be expressed as:

[0067] User perception factor U = α1*cell-level uplink average user perception rate A1*user-level uplink perception rate quality ratio B1+β1*cell-level downlink average user perception rate A2*user-level downlink perception rate quality ratio B2. Among them, α1 and β1 are the set uplink perception weight ratio and downlink perception weight ratio, and the sum of α1 and β1 is 1. The specific value can be selected according to the actual situation and is not limited here.

[0068] In the above-mentioned method for adjusting beam parameters, the network perception factor among multiple optimization factors is determined in the following manner: under the parameter information, performance indicator data in the base station corresponding to the first core network is obtained, wherein the performance indicator data includes at least one of the following: wireless access success rate, user equipment (User Equipment, referred to as UE) context drop rate and switching success rate; at least based on the performance indicator data, the network perception factor is determined.

[0069] In the embodiment of the present application, the 5G beam-level performance indicator data mainly includes wireless access success rate, UE context drop rate, switching success rate, etc. The above-mentioned network perception factor is the main service cell perception factor, and its specific calculation method is: main service cell perception factor P = (wireless access success rate + switching success rate + UE context drop rate) / 3.

[0070] In the above-mentioned method for adjusting beam parameters, the network coordination factor among multiple optimization factors is determined in the following manner: obtaining terminal data and first engineering parameter data of a base station corresponding to a first core network, and minimized drive test data and second engineering parameter data of a base station corresponding to a second core network; determining a first user set based on the terminal data, wherein the first user set is used to represent users whose terminal devices support the first core network and whose switches of the first core network are turned on; screening target data corresponding to users in the first user set from the minimized drive test data; determining association information between the first core network and the second core network based at least on the first engineering parameter data and the second engineering parameter data, and first location information of a base station corresponding to the first core network and second location information of a base station corresponding to the second core network; determining a neighboring cell relationship between the first core network and the second core network based on the association information and the target data; determining a third mean of reference signal received power corresponding to a neighboring cell of the first core network when the first user set is not included in a cell covered by the first core network, and determining a target perception rate based on the neighboring cell relationship, wherein the target perception rate is determined by the first perception rate and the second perception rate; determining the network coordination factor based on the third mean and the target perception rate.

[0071] In the embodiment of the present application, the engineering parameter data includes the first engineering parameter data and the second engineering parameter data, namely, 5G engineering parameter data and 4G engineering parameter data. The engineering parameter data is required to include the geographical location information (latitude and longitude, site code, etc.) of the base station, which is used to determine the data of the 4 / 5G same site information; PCI and frequency data are used for the correlation analysis of MDT data. 5G terminal data includes user-level terminal data, 5G switch data, etc., which can determine whether the user terminal supports 5G, the 5G switch opening status, etc.

[0072] Based on 5G terminal data, filter the user list T1 that supports 5G terminals and has the 5G switch turned on, that is, the first user set mentioned above, and then filter the MDT data T2 of the user T1 from the 4G MDT data. 4G is the second core network, and T2 is the target data mentioned above, which contains the 5G neighboring area measurement information; according to the 4 / 5G engineering parameter data, establish the 4 / 5G site-level information table T3 with the same site and coverage based on the geographical location information such as longitude and latitude. T3 contains the association information between the 4G network and the 5G network. According to the PCI in the above T3 and the 5G neighboring area PCI information measured and reported in T2, the 4 / 5G neighboring area relationship is established by association; the average RSRP and average perceived rate of the 5G neighboring area measured when the 5G terminal does not reside in the 5G cell can be calculated. The above average RSRP corresponds to the above third mean value, and the average perceived rate corresponds to the above target perceived rate, which is also the average of A1 and A2.

[0073] Based on the above average RSRP and average perceived rate, the network coordination factor N is calculated. The specific calculation method is:

[0074] Network Synergy Factor

[0075] The method for adjusting beam parameters provided in the embodiment of the present application can not only improve the efficiency of 5G beam optimization, but also introduce 4G network data and user-level data in the 5G network quality evaluation to achieve the optimal solution for 5G network performance and user perception. In the embodiment of the present application, first, 5G MDT data is introduced. Through the latitude and longitude information reported by the user, the geographical location distribution of users in the 5G cell can be effectively determined, so that the main lobe covers the user-dense area; second, the user-level perception rate can be calculated based on the MDT data, which is used as an evaluation indicator for beam optimization, and the 5G network performance and user perception can be improved simultaneously during the optimization process; third, 5G terminal data and 4G MDT data are introduced. For 5G terminals that fall back to the 4G network due to coverage reasons, 4G MDT data can be used for association, thereby improving the 5G coverage quality in weak coverage areas.

[0076] The method for adjusting beam parameters provided in the embodiment of the present application performs beam self-optimization based on the measurement report (MDT) and service performance data reported by the user. It has low dependence on high-precision electronic maps and is easy to promote, so it is more practical and scalable. Compared with manual optimization solutions, the method in the present application has the advantage of dynamic adjustment and optimization, and can timely optimize and adjust the beam configuration as the user's service behavior changes.

[0077] Figure 3 is a structural diagram of a device for adjusting beam parameters according to an embodiment of the present application, such as Figure 3 As shown, the device comprises:

[0078] An acquisition module 302 is used to acquire current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle;

[0079] A first determining module 304 is used to determine a first perceived benefit of the beam according to the parameter information, wherein the first perceived benefit is used to indicate the degree of optimization of the beam;

[0080] An adjustment module 306, configured to adjust parameter information when the first perceived benefit meets a preset condition, and determine a second perceived benefit according to the adjusted parameter information;

[0081] The second determination module 308 is used to stop adjusting the parameter information when the second perceived benefit does not meet the preset condition, and determine the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information.

[0082] In the first determination module in the above-mentioned device for adjusting beam parameters, the first perceived benefit of the beam is determined based on the parameter information, which specifically includes the following processes: determining multiple optimization factors based on the parameter information; determining the first perceived benefit of the beam based on the multiple optimization factors.

[0083] In the adjustment module in the above-mentioned device for adjusting beam parameters, the parameter information is adjusted, which specifically includes the following process: determining the value ranges corresponding to the parameter information respectively, and obtaining multiple value sets; traversing the multiple value sets, and selecting different parameter information from the multiple value sets according to a preset order to obtain a parameter combination; and adjusting the parameter information according to the parameter combination.

[0084] In the above-mentioned device for adjusting beam parameters, the network coverage factor among multiple optimization factors is determined in the following manner: under the parameter information, obtaining the first data in the base station corresponding to the first core network, wherein the first data at least includes the reference signal received power and the signal-to-noise ratio of the main service cell; calculating the first mean of the reference signal received power and the second mean of the signal-to-noise ratio of each cell in the main service cell; determining a normal distribution curve based on multiple first means and multiple second means of the main service cell; determining a first probability of the reference signal received power and a second probability of the signal-to-noise ratio based on the normal distribution curve; determining the network coverage factor based on the first probability and the second probability.

[0085] In the above-mentioned device for adjusting beam parameters, after determining the network coverage factor, the device is also used to execute the following process: determine the user coverage factor, wherein the user coverage factor is determined by the coverage excellence ratio of the reference signal receiving power and the signal-to-noise ratio excellence ratio; determine the coverage factor based on the network coverage factor and the user coverage factor.

[0086] In the above-mentioned device for adjusting beam parameters, the user perception factor among multiple optimization factors is determined in the following manner: under the parameter information, the second data in the base station corresponding to the first core network is obtained, wherein the second data at least includes an uplink rate and a downlink rate; based on the uplink rate and the reference signal received power, the first perception rate is determined, and based on the downlink rate and the reference signal received power, the second perception rate is determined, wherein the first perception rate is used to represent the perceived rate of the average uplink user of each cell, and the second perception rate is used to represent the perceived rate of the average downlink user of each cell; based on the conditions satisfied by the uplink rate and the downlink rate, a first perception rate excellence ratio and a second perception rate excellence ratio are determined; and the user perception factor is determined based at least on the first perception rate, the second perception rate, the first perception rate excellence ratio and the second perception rate excellence ratio.

[0087] In the above-mentioned device for adjusting beam parameters, the network perception factor among multiple optimization factors is determined in the following manner: under the parameter information, performance indicator data in the base station corresponding to the first core network is obtained, wherein the performance indicator data includes at least one of the following: wireless access success rate, user equipment context drop rate and switching success rate; and the network perception factor is determined at least based on the performance indicator data.

[0088] In the above-mentioned device for adjusting beam parameters, the network coordination factor among multiple optimization factors is determined in the following manner: obtaining terminal data and first engineering parameter data of the base station corresponding to the first core network, and minimized drive test data and second engineering parameter data of the base station corresponding to the second core network; determining a first user set based on the terminal data, wherein the first user set is used to represent users whose terminal devices support the first core network and whose switches of the first core network are turned on; screening target data corresponding to users in the first user set from the minimized drive test data; determining association information between the first core network and the second core network based on at least the first engineering parameter data and the second engineering parameter data, as well as first position information of the base station corresponding to the first core network and second position information of the base station corresponding to the second core network; determining a neighboring cell relationship between the first core network and the second core network based on the association information and the target data; determining a third mean of the reference signal received power corresponding to the neighboring cell of the first core network when the first user set is not included in the cell covered by the first core network, and determining a target perception rate based on the neighboring cell relationship; wherein the target perception rate is determined by the first perception rate and the second perception rate; determining the network coordination factor based on the third mean and the target perception rate.

[0089] It should be noted that Figure 3 The device for adjusting beam parameters is used to perform Figure 2 The method for adjusting beam parameters is shown in the figure, so the relevant explanations in the above method for adjusting beam parameters are also applicable to the device for adjusting beam parameters, which will not be repeated here.

[0090] In another optional embodiment, the 5G beam optimization system device may specifically include the following modules: a data acquisition module: used to collect 4G working parameters, 5G working parameter data, 4 / 5G MDT data, 5G performance data, etc. involved in the above method; a data storage module: used to store the above data, as well as intermediate state data in the beam optimization execution process, etc.; a state equation calculation module: based on the above data and the multi-dimensional factors involved in the above system method (i.e., the above C, U, P, N), calculate the beam optimization state equation, i.e. Parameter equation calculation module: Based on the above data, calculate the parameter equation, i.e. γ t ={W, H, θ, A}, optimize beam configuration; parameter equation execution module: connect to the base station or other platforms capable of performing the above-mentioned beam parameter optimization, and perform parameter optimization actions.

[0091] Figure 4 is a schematic diagram of determining a state equation according to an embodiment of the present application, such as Figure 4 As shown, the state equation includes four dimensional factors, namely, coverage factor C, user perception factor U, main service cell perception factor (or network perception factor) P, and network coordination factor N, wherein the coverage factor C is determined by the network coverage factor and user coverage factor in the coverage model, the user perception factor U is determined by the cell-level user perception rate (including A1 and A2) and the cell-level user perception rate quality ratio (including B1 and B2) in the user perception model, the main service cell perception factor P is perceived by the cell-level network KPI (including wireless access success rate, switching success rate and UE context drop rate) in the network perception model, and the network coordination factor N is determined by the perception of the 5G terminal on the 4G network in the dual-network coordination model (including average RSRP and average perception rate).

[0092] Figure 5 is a flow chart of a beam optimization algorithm according to an embodiment of the present application, such as Figure 5 As shown, S51, input data, which includes four parameters W, H, θ, A of beam optimization, and also includes C, U, P, N; S52, generate the initial state equation S53, establish the initial parameter equation γ0={W0,H0,θ0,A0}; S54, determine whether the perception benefit S corresponding to the state equation meets the preset conditions, that is, determine whether the perception benefit S is greater than or equal to 0. When the perception benefit S≥0, it is determined that the perception benefit meets the preset conditions. At this time, it is considered that the beam can continue to be optimized, and S55 is executed. If S<0, the optimization parameter information is stopped; S55, generate the parameter equation γ1={W1,H1,θ1,A1}; S56, send and execute the parameter equation γ1, and calculate the state equation S57, determining whether the optimization times have been reached, if so, stopping the optimization or adjusting the parameter information, if not, continuing to execute S54.

[0093] An embodiment of the present application also provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the following method for adjusting beam parameters by running the computer program: obtaining current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle; determining a first perceived benefit of the beam based on the parameter information, wherein the first perceived benefit is used to indicate a degree of optimization of the beam; when the first perceived benefit meets a preset condition, adjusting the parameter information, and determining a second perceived benefit based on the adjusted parameter information; when the second perceived benefit does not meet the preset condition, stopping adjusting the parameter information, and determining the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information.

[0094] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0095] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0098] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0100] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for adjusting beam parameters, characterized in that: include: Acquire current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle, and downtilt angle; Determine a first perceived benefit of the beam according to the parameter information, wherein the first perceived benefit is used to indicate a degree of optimization of the beam; When the first perceived benefit meets a preset condition, adjusting the parameter information, and determining a second perceived benefit according to the adjusted parameter information; When the second perceived benefit does not meet the preset condition, stop adjusting the parameter information, and determine the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information; Wherein, determining the first perceived benefit of the beam according to the parameter information includes: determining a plurality of optimization factors according to the parameter information; determining the first perceived benefit of the beam according to the plurality of optimization factors; Among them, the network coverage factor among the multiple optimization factors is determined in the following manner: under the parameter information, obtaining the first data in the base station corresponding to the first core network, wherein the first data at least includes the reference signal received power and the signal-to-noise ratio of the main service cell; calculating the first mean of the reference signal received power of each cell in the main service cell and the second mean of the signal-to-noise ratio; determining a normal distribution curve based on multiple first means and multiple second means of the main service cell; determining a first probability of the reference signal received power and a second probability of the signal-to-noise ratio based on the normal distribution curve; determining the network coverage factor based on the first probability and the second probability.

2. The method according to claim 1, characterized in that Adjusting the parameter information includes: Determine the value ranges corresponding to the parameter information respectively to obtain multiple value sets; Traversing the multiple value sets, selecting different parameter information from the multiple value sets in a preset order, and obtaining a parameter combination; The parameter information is correspondingly adjusted according to the parameter combination.

3. The method according to claim 1, characterized in that After determining the network coverage factor, the method further includes: Determine a user coverage factor, wherein the user coverage factor is determined by a coverage quality ratio of the reference signal received power and a quality ratio of the signal-to-noise ratio; A coverage factor is determined according to the network coverage factor and the user coverage factor.

4. The method according to claim 1, characterized in that The user perception factor among the multiple optimization factors is determined in the following manner: Under the parameter information, obtain second data in the base station corresponding to the first core network, wherein the second data at least includes an uplink rate and a downlink rate; Determine a first perception rate according to the uplink rate and the reference signal received power, and determine a second perception rate according to the downlink rate and the reference signal received power, wherein the first perception rate is used to represent the perception rate of an average uplink user of each cell, and the second perception rate is used to represent the perception rate of an average downlink user of each cell; Determining a first perceived rate quality ratio and a second perceived rate quality ratio according to conditions satisfied by the uplink rate and the downlink rate; The user perception factor is determined based on at least the first perceived rate, the second perceived rate, the first perceived rate excellence ratio, and the second perceived rate excellence ratio.

5. The method according to claim 1, characterized in that The network awareness factor in the plurality of optimization factors is determined in the following manner: Under the parameter information, obtain performance indicator data in the base station corresponding to the first core network, wherein the performance indicator data includes at least one of the following: wireless access success rate, user equipment context drop rate and handover success rate; The network perception factor is determined based at least on the performance indicator data.

6. The method according to claim 4, characterized in that The network synergy factor among the multiple optimization factors is determined in the following manner: Acquire terminal data and first engineering parameter data of a base station corresponding to the first core network, and minimized drive test data and second engineering parameter data of a base station corresponding to the second core network; Determine a first user set according to the terminal data, wherein the first user set is used to represent users whose terminal devices support the first core network and whose switches of the first core network are turned on; Filtering target data corresponding to users in the first user set from the minimization of drive test data; Determine association information between the first core network and the second core network based at least on the first engineering parameter data and the second engineering parameter data, as well as first location information of a base station corresponding to the first core network and second location information of a base station corresponding to the second core network; Determine, according to the association information and the target data, a neighbor relationship between the first core network and the second core network; Determine, according to the neighboring cell relationship, a third mean of reference signal received power corresponding to a neighboring cell of the first core network when the first user set is not included in the cell covered by the first core network, and determine a target sensing rate, wherein the target sensing rate is determined by the first sensing rate and the second sensing rate; The network cooperation factor is determined according to the third mean value and the target perception rate.

7. A device for adjusting beam parameters, characterized in that: include: An acquisition module, used to acquire current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle; A first determining module, configured to determine a first perceived benefit of the beam according to the parameter information, wherein the first perceived benefit is used to indicate a degree of optimization of the beam; an adjustment module, configured to adjust the parameter information when the first perceived benefit meets a preset condition, and determine a second perceived benefit according to the adjusted parameter information; A second determination module is configured to stop adjusting the parameter information when the second perceived benefit does not meet the preset condition, and determine the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information; Wherein, determining the first perceived benefit of the beam according to the parameter information includes: determining a plurality of optimization factors according to the parameter information; determining the first perceived benefit of the beam according to the plurality of optimization factors; Among them, the network coverage factor among the multiple optimization factors is determined in the following manner: under the parameter information, obtaining the first data in the base station corresponding to the first core network, wherein the first data at least includes the reference signal received power and the signal-to-noise ratio of the main service cell; calculating the first mean of the reference signal received power of each cell in the main service cell and the second mean of the signal-to-noise ratio; determining a normal distribution curve based on multiple first means and multiple second means of the main service cell; determining a first probability of the reference signal received power and a second probability of the signal-to-noise ratio based on the normal distribution curve; determining the network coverage factor based on the first probability and the second probability.

8. An electronic device, characterized in that: include: A memory for storing program instructions; A processor, connected to the memory, is used to execute program instructions to implement the following functions: obtain current parameter information of the beam, wherein the parameter information includes at least one of the following: horizontal beam width, vertical beam width, azimuth angle and downtilt angle; determine a first perceived benefit of the beam based on the parameter information, wherein the first perceived benefit is used to indicate the degree of optimization of the beam; when the first perceived benefit meets a preset condition, adjust the parameter information, and determine a second perceived benefit based on the adjusted parameter information; when the second perceived benefit does not meet the preset condition, stop adjusting the parameter information, and determine the last adjusted parameter information corresponding to the second perceived benefit as the target parameter information, wherein determining the first perceived benefit of the beam based on the parameter information includes: The parameter information is used to determine multiple optimization factors; based on the multiple optimization factors, a first perceived benefit of the beam is determined; wherein the network coverage factor among the multiple optimization factors is determined in the following manner: under the parameter information, first data in a base station corresponding to a first core network is obtained, wherein the first data at least includes a reference signal received power and a signal-to-noise ratio of a primary service cell; a first mean of the reference signal received power of each cell in the primary service cell and a second mean of the signal-to-noise ratio are calculated; a normal distribution curve is determined based on multiple first means and multiple second means of the primary service cell; a first probability of the reference signal received power and a second probability of the signal-to-noise ratio are determined based on the normal distribution curve; and the network coverage factor is determined based on the first probability and the second probability.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the method for adjusting beam parameters according to any one of claims 1 to 6.

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