CN112350756A

CN112350756A - Method and device for optimizing weight parameters of antenna and electronic equipment

Info

Publication number: CN112350756A
Application number: CN201910728522.6A
Authority: CN
Inventors: 帅农村; 陈标; 梁建粦; 钟柱明; 杜铄
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Guangdong Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Guangdong Co Ltd
Priority date: 2019-08-08
Filing date: 2019-08-08
Publication date: 2021-02-09
Anticipated expiration: 2039-08-08
Also published as: CN112350756B

Abstract

The embodiment of the invention discloses a method and a device for optimizing weight parameters of an antenna and electronic equipment, which are used for solving the problems that the configuration of the weight parameters of the existing 3D-MIMO base station antenna usually needs consumed human resources and the manual configuration is difficult to achieve the optimum. The method comprises the following steps: acquiring user distribution conditions and weight parameters of antennas in the coverage range of a three-dimensional multi-input multi-output (3D-MIMO) base station to be optimized; iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the antenna gain maximization principle based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized, the weight parameters of the antennas and a preset 3D-MIMO base station configuration database; and when the antenna gain of the 3D-MIMO base station to be optimized meets the convergence condition of the iterative optimization, configuring the weight parameter of the antenna of the 3D-MIMO base station to be optimized based on the weight parameter corresponding to the antenna gain meeting the convergence condition of the iterative optimization.

Description

Method and device for optimizing weight parameters of antenna and electronic equipment

Technical Field

The embodiment of the invention relates to the technical field of wireless communication, in particular to an optimization method and device of weight parameters of an antenna and electronic equipment.

Background

After the antenna configuration of a Three-Dimensional Multiple-Input Multiple-output (3D-MIMO base station) is completed, the weight parameters of the antenna, such as horizontal wave width, vertical wave width, and the like, can be flexibly adjusted to be suitable for more application scenarios, so that each scenario can provide better service performance for users.

However, in the existing 3D-MIMO base station scenario, the weight parameters of the antennas are often configured based on historical experience and real-time user distribution. After the antenna weight is configured each time, the values of the antenna gain indexes such as the spectrum efficiency, the user experience rate and the like of the configured weight parameters can be determined after the user experiences for a period of time, and then the configuration of the weight parameters is gradually optimized according to the values of the antenna gain indexes.

That is to say, configuration and optimization of weight parameters of an existing 3D-MIMO base station antenna often require a large amount of human resources, and manual configuration is difficult to achieve the optimum. Therefore, how to configure the weight parameters of the better antennas quickly and accurately to improve the user experience still needs further solutions.

Disclosure of Invention

The embodiment of the invention provides an optimization method and device for weight parameters of an antenna and electronic equipment, which are used for solving the problems that a large amount of human resources are consumed for configuration and optimization of the weight parameters of the conventional 3D-MIMO base station antenna, and manual configuration is difficult to achieve the optimal state.

The embodiment of the invention adopts the following technical scheme:

in a first aspect, a method for optimizing weight parameters of an antenna is provided, including:

acquiring user distribution conditions and weight parameters of antennas in the coverage range of a three-dimensional multi-input multi-output (3D-MIMO) base station to be optimized;

iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the antenna gain maximization principle based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized, the weight parameters of the antennas and a preset 3D-MIMO base station configuration database;

when the antenna gain of the 3D-MIMO base station to be optimized meets the convergence condition of the iterative optimization, configuring the weight parameter of the antenna of the 3D-MIMO base station to be optimized based on the weight parameter corresponding to the antenna gain meeting the convergence condition of the iterative optimization;

the preset 3D-MIMO base station configuration database is established based on weight parameters of antennas of a plurality of 3D-MIMO base stations in a historical time period, user distribution conditions corresponding to the weight parameters of the antennas of the plurality of 3D-MIMO base stations and antenna gains corresponding to the weight parameters of the antennas of the plurality of 3D-MIMO base stations; the preset 3D-MIMO base station configuration database comprises weight parameters of a plurality of groups of antennas, and the weight parameters of a group of antennas correspond to a group of user distribution conditions and antenna gains.

In a second aspect, an apparatus for optimizing weight parameters of an antenna is provided, including:

the processor is used for acquiring the user distribution condition and the weight parameters of the antennas in the coverage range of the three-dimensional multi-input multi-output 3D-MIMO base station to be optimized; iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the antenna gain maximization principle based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized, the weight parameters of the antennas and a preset 3D-MIMO base station configuration database; when the antenna gain of the 3D-MIMO base station to be optimized meets the convergence condition of the iterative optimization, configuring the weight parameter of the antenna of the 3D-MIMO base station to be optimized based on the weight parameter corresponding to the antenna gain meeting the convergence condition of the iterative optimization;

In a third aspect, an electronic device is provided, including:

a memory storing computer program instructions;

a processor which, when executed by the processor, implements the method of optimizing weight parameters for an antenna according to the first aspect.

In a fourth aspect, a computer-readable storage medium is provided,

the computer-readable storage medium comprises instructions which, when run on a computer, cause the computer to perform the method of optimizing weight parameters of an antenna according to the first aspect.

The embodiment of the invention adopts at least one technical scheme which can achieve the following beneficial effects:

the embodiment of the invention can obtain the user distribution condition and the weight parameters of the antennas in the coverage range of the 3D-MIMO base station to be optimized, iteratively optimize the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the user distribution condition, the weight parameters of the antennas and a preset 3D-MIMO base station configuration database in the principle of maximizing the antenna gain, and configure the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the weight parameters corresponding to the antenna gain meeting the convergence condition of iterative optimization when the antenna gain of the 3D-MIMO base station to be optimized meets the convergence condition of the iterative optimization. The preset 3D-MIMO base station configuration database is pre-established based on the weight parameters of the antennas of the plurality of 3D-MIMO base stations in the historical time period, the user distribution conditions corresponding to the weight parameters of the antennas of the plurality of 3D-MIMO base stations and the values of the antenna gain indexes corresponding to the weight parameters of the antennas of the plurality of 3D-MIMO base stations, so that the optimization times in the weight parameter optimization process of the antennas of the 3D-MIMO base stations can be effectively reduced.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a schematic implementation flow diagram of a method for optimizing weight parameters of an antenna according to an embodiment of the present disclosure;

fig. 2 is a schematic diagram of types of weight parameters of antennas of a 3D-MIMO base station according to an embodiment of the present disclosure;

fig. 3 is a schematic diagram of an iterative optimization process of weight parameters of an antenna according to an embodiment of the present disclosure;

fig. 4 is a schematic diagram of an implementation process of iterative optimization of weight parameters of an antenna according to an embodiment of the present disclosure;

fig. 5 is a schematic diagram of coverage ranges before and after adjustment of weight parameters of antennas of a 3D-MIMO base station according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of an apparatus for optimizing weight parameters of an antenna according to an embodiment of the present disclosure;

fig. 7 is a schematic diagram of a hardware structure of an electronic device according to another embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to the specific embodiments of the present specification and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step are within the scope of the present application.

In order to solve the problems that the configuration of the weight parameters of the existing 3D-MIMO base station antenna usually requires consumed human resources and the manual configuration is difficult to achieve the optimal value, embodiments of the present specification provide an optimization method of the weight parameters of the antenna. The execution subject of the method provided by the embodiments of the present disclosure may be, but is not limited to, a personal computer, a server, and the like, which can be configured to execute at least one of the method apparatuses provided by the embodiments of the present disclosure.

For convenience of description, the following description will be made of an embodiment of the method, taking an execution subject of the method as a server capable of executing the method as an example. It is understood that the implementation of the method by the server is merely an exemplary illustration and should not be construed as a limitation of the method.

Specifically, an implementation flow diagram of a method for optimizing weight parameters of an antenna according to one or more embodiments of the present specification is shown in fig. 1, and includes:

step 110, obtaining user distribution conditions and weight parameters of antennas in the coverage area of the 3D-MIMO base station to be optimized;

as shown in fig. 2, a schematic diagram of the category of the weight parameters of the antennas of the 3D-MIMO base station provided in the embodiment of the present disclosure includes an azimuth angle shown in fig. 2(a), a horizontal wave width shown in fig. 2(b), a vertical tilt angle shown in fig. 2(c), and a vertical wave width shown in fig. 2 (D). In the embodiments of the present description, the setting mode of the weight parameter corresponding to the 8T (8-way transmission) or 4T (4-way transmission) base station device in the existing 3D-MIMO base station scenario may be referred to for the azimuth angle and the vertical tilt angle. In the embodiments of the present description, iterative optimization is performed on the weight parameters of the antennas of the 3D-MIMO base station to be optimized mainly for the horizontal wave width shown in fig. 2(b) and the vertical wave width shown in fig. 2 (D).

Step 120, iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the principle of antenna gain maximization based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized, the weight parameters of the antennas and a preset 3D-MIMO base station configuration database;

the preset 3D-MIMO base station configuration database is established based on weight parameters of the antennas of the plurality of 3D-MIMO base stations in a historical time period, user distribution conditions corresponding to the weight parameters of the antennas of the plurality of 3D-MIMO base stations and antenna gains corresponding to the weight parameters of the antennas of the plurality of 3D-MIMO base stations; the preset 3D-MIMO base station configuration database comprises weight parameters of a plurality of groups of antennas, and the weight parameters of a group of antennas correspond to a group of user distribution conditions and antenna gains.

As shown in table 1, examples of combinations of weight parameters of antennas provided in the embodiments of the present specification include combinations of weight parameters of 13 groups of antennas, where each group of weight parameters includes two types of weight parameters, i.e., horizontal wave width and vertical wave width. As shown in table 2, for user distribution and antenna gain corresponding to the combination of the weight parameters of the antennas provided in the embodiment of the present specification, the coverage area of each group of antennas of the 3D-MIMO base station may correspond to the coverage area of multiple beams (i.e., beam 1 to beam N), and users active in the coverage area of the beams are the number of beam i users shown in table 2, where i ∈ [1, N ]. It should be understood that the user distribution and the antenna gain corresponding to the weight parameter of each group of antennas are obtained based on actual measurement data of the antennas of the plurality of 3D-MIMO base stations in the historical time period, which will not be specifically shown here, and the specific measurement value should be based on the actually measured value.

Table 1 example of combinations of weight parameters

Combination serial number of weight parameter	Horizontal wave width	Vertical wave width
			1	90	8
2	65	8
			3	45	8
4	25	8
			5	90	17
6	65	17
			7	45	17
8	25	17
			9	15	17
10	65	35
			11	45	35
12	25	35
			13	15	35

TABLE 2 user distribution and antenna gain corresponding to combinations of weight parameters

Optionally, in order to accurately characterize the antenna gain, the indicator for characterizing the antenna gain may include at least one of:

spectral efficiency;

a user experience rate;

radio Resource Control (RRC) connection success rate;

the handover success rate.

Optionally, in the embodiments of the present description, whether iterative optimization is further required may be measured according to a difference between values of antenna gains corresponding to weight parameters after two adjacent iterative optimizations, and an optimization target value, that is, a second preset threshold, may also be preset to measure whether iterative optimization is further required. Specifically, the convergence condition of the iterative optimization includes at least one of:

the absolute value of the difference between the antenna gain values corresponding to the weight parameters after two adjacent iterative optimizations is less than or equal to a first preset threshold;

and the value of the antenna gain of the 3D-MIMO base station to be optimized after iterative optimization reaches a second preset threshold value.

Optionally, in order to quickly maximize the antenna gain of the 3D-MIMO base station to be optimized, when the weight parameters of the antennas include horizontal wave width and vertical wave width, iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the principle of maximizing the antenna gain based on the user distribution condition in the coverage area of the 3D-MIMO base station to be optimized, the weight parameters of the antennas, and a preset 3D-MIMO base station configuration database, including:

constructing a two-dimensional coordinate graph based on a preset 3D-MIMO base station configuration database, wherein the two-dimensional coordinate graph comprises a plurality of points, and one point corresponds to a weight parameter of a group of antennas;

acquiring a point matched with a weight parameter of an antenna in a coverage range of a 3D-MIMO base station to be optimized from a two-dimensional coordinate graph, and taking the point as an initial point;

acquiring a weight parameter of an antenna corresponding to a point adjacent to the initial point;

and iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the principle of antenna gain maximization based on the weight parameters of the antennas corresponding to the points adjacent to the initial point and the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized.

As shown in fig. 3, for the embodiment of the present specification, a two-dimensional graph is constructed by taking the example of the combination of the weight parameters shown in table 1 as an example, where an abscissa is a horizontal wave width and an ordinate is a vertical wave width. Assuming that the weight parameters of the antennas in the coverage area of the 3D-MIMO base station to be optimized are before iterative optimization, the horizontal wave width corresponding to the combination 8 (i.e., point 8) is 25 and the vertical wave width is 17, then the initial point obtained from fig. 3 is point 8.

As can be seen from fig. 3, the points adjacent to point 8 include: point 9, point 12, point 4, and point 7, then iteratively optimize the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the weight parameters of the antennas corresponding to the points adjacent to the initial point and the user distribution condition within the coverage of the 3D-MIMO base station to be optimized in the principle of maximizing the antenna gain, and specifically iteratively optimize the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the weight parameters of the antennas corresponding to point 9, point 12, point 4, and point 7 and the user distribution condition within the coverage of the 3D-MIMO base station to be optimized in the principle of maximizing the antenna gain.

Optionally, iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the weight parameters of the antennas corresponding to the points adjacent to the initial point and the user distribution condition within the coverage area of the 3D-MIMO base station to be optimized in a principle of maximizing antenna gain, including:

acquiring the user distribution condition of a cell adjacent to the coverage area of the 3D-MIMO base station to be optimized;

acquiring a difference between the received strengths of the reference signals from the target users and a difference between the received strengths of the reference signals from the first target beam and the second target beam;

determining the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized after iterative optimization based on the difference between the reference signal received strength from the target user and the difference between the reference signal received strength from the first target beam and the second target beam;

iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized after iterative optimization and the weight parameters of the antennas of the 3D-MIMO base station to be optimized after iterative optimization in the principle of maximizing the antenna gain;

the target user is any one of a user in the coverage range of the 3D-MIMO base station to be optimized and a user of an adjacent cell, the first target beam is from the coverage range of the 3D-MIMO base station to be optimized, the second target beam is from the adjacent cell, and the first target beam corresponds to the second target beam.

Continuing to take fig. 3 as an example, the distribution conditions of users in cells adjacent to the coverage area of the 3D-MIMO base station to be optimized may be respectively obtained, and when it is respectively assumed that the weight parameters of the antennas of the 3D-MIMO base station to be optimized are adjusted to the weight parameters of the antennas corresponding to point 9, point 12, point 4, and point 7, the distribution conditions of users in the coverage area of the 3D-MIMO base station to be optimized are predicted; and based on the user distribution and the antenna gain (i.e. the configuration database of the 3D-MIMO base station) corresponding to the combination of the weight parameters shown in table 2, the antenna gains when the weight parameters of the antenna of the 3D-MIMO base station to be optimized are adjusted to the weight parameters of the antennas corresponding to the point 9, the point 12, the point 4, and the point 7 are respectively evaluated, and the point with the largest value of the antenna gain is selected from the antenna gains (assuming the point 7).

Then, with the point 7 as a starting point, respectively predicting the distribution situation of users in the coverage range of the 3D-MIMO base station to be optimized when the weight parameters of the antennas of the 3D-MIMO base station to be optimized are adjusted to the weight parameters of the antennas corresponding to the point 11, the point 3 and the point 6; and based on the user distribution and the antenna gain (i.e. the configuration database of the 3D-MIMO base station) corresponding to the combination of the weight parameters shown in table 2, respectively evaluating the antenna gain when the weight parameters of the antenna of the 3D-MIMO base station to be optimized are adjusted to the weight parameters of the antennas corresponding to the point 11, the point 3, and the point 6, from which the value of the antenna gain is the largest (assuming that the point 6 is the point 6). At this time, if the absolute value of the difference between the value of the antenna gain at the point 6 and the value of the antenna gain at the point 7 is less than or equal to the first preset threshold, the iterative optimization is stopped, otherwise, the iterative optimization is continued with the point 6 as a starting point.

Then, with the point 6 as a starting point, respectively predicting the distribution situation of users in the coverage range of the 3D-MIMO base station to be optimized when the weight parameters of the antennas of the 3D-MIMO base station to be optimized are adjusted to be the weight parameters of the antennas corresponding to the point 10, the point 5 and the point 2; and based on the user distribution and the antenna gain (i.e. the configuration database of the 3D-MIMO base station) corresponding to the combination of the weight parameters shown in table 2, respectively evaluating the antenna gain when the weight parameter of the antenna of the 3D-MIMO base station to be optimized is adjusted to the weight parameter of the antenna corresponding to the point 10, the point 5, and the point 2, from which the value of the antenna gain is the largest (assuming the point 10). At this time, if the absolute value of the difference between the value of the antenna gain at the point 10 and the value of the antenna gain at the point 6 is less than or equal to the first preset threshold, the weight parameter of the antenna corresponding to the point 6 may be used as the iterative optimization target, or the weight parameter of the antenna corresponding to the point 10 may be used as the iterative optimization target.

Step 130, when the antenna gain of the 3D-MIMO base station to be optimized meets the convergence condition of the iterative optimization, configuring the weight parameter of the antenna of the 3D-MIMO base station to be optimized based on the weight parameter corresponding to the antenna gain meeting the convergence condition of the iterative optimization;

optionally, since the iterative optimization process is in a prediction stage, and the weight parameter after the iterative optimization is not issued to the base station device of the 3D-MIMO base station to be optimized, in order to verify the weight parameter after the iterative optimization, after the weight parameter corresponding to the antenna gain satisfying the convergence condition of the iterative optimization is configured based on the weight parameter corresponding to the antenna gain satisfying the convergence condition of the iterative optimization when the antenna gain of the 3D-MIMO base station to be optimized satisfies the convergence condition of the iterative optimization, the method further includes:

after a first preset time period, obtaining a value of a first antenna gain corresponding to a weight parameter of an antenna to be optimized after the optimization of the 3D-MIMO base station;

and updating a preset 3D-MIMO base station configuration database based on the value of the first antenna gain corresponding to the optimized weight parameter.

Optionally, in order to further optimize the weight parameter of the iterative optimization to ensure that the weight parameter of the antenna of the 3D-MIMO base station to be optimized is in an optimal state, when the antenna gain of the 3D-MIMO base station to be optimized satisfies the convergence condition of the iterative optimization, after configuring the weight parameter of the antenna of the 3D-MIMO base station to be optimized based on the weight parameter corresponding to the antenna gain satisfying the convergence condition of the iterative optimization, the embodiment of the present specification may further perform further iterative optimization on the weight parameter of the antenna of the 3D-MIMO base station to be optimized, and the method further includes:

acquiring the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized after a first preset time period;

and iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the principle of antenna gain maximization based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized after the first preset time period, the optimized weight parameters of the antennas and the updated preset 3D-MIMO base station configuration database.

Optionally, in order to further verify the weight parameters of the antennas after the further iterative optimization, after iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the user distribution condition in the coverage area of the 3D-MIMO base station to be optimized after the first preset time period, the weight parameters of the optimized antennas, and the updated preset 3D-MIMO base station configuration database in a principle of maximizing antenna gain, the method further includes:

after a second preset time period, obtaining a value of a second antenna gain corresponding to a weight parameter of the antenna to be optimized after the optimization of the 3D-MIMO base station;

and if the absolute value of the difference value between the first antenna gain value and the second antenna gain value is smaller than or equal to a preset threshold value, taking the weight parameter of the antenna corresponding to the first antenna gain value as the optimization target of the 3D-MIMO base station to be optimized.

It should be understood that, if the value of the second antenna gain is greater than the value of the first antenna gain, and the absolute value of the difference between the value of the first antenna gain and the value of the second antenna gain is greater than the preset threshold, the weight parameter of the antenna corresponding to the value of the second antenna gain is used as the optimization target of the 3D-MIMO base station to be optimized.

As shown in fig. 4, a schematic diagram of an implementation process of iterative optimization of weight parameters of antennas of a 3D-MIMO base station provided in an embodiment of the present specification includes:

s41, obtaining user distribution conditions and weight parameters of antennas in the coverage area of the 3D-MIMO base station to be optimized;

s42, iteratively optimizing weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the principle of antenna gain maximization from a preset 3D-MIMO base station configuration database;

s43, determining whether the antenna gain after the iterative optimization meets the convergence condition of the iterative optimization; if yes, executing S44, and if not, executing S42;

s44, issuing weight parameters of the antenna after iterative optimization to the 3D-MIMO base station equipment to be optimized;

s45, obtaining the optimization effect of the feedback of the 3D-MIMO base station to be optimized, namely the value of the antenna gain;

s46, updating a preset 3D-MIMO base station configuration database based on the feedback optimization effect of the 3D-MIMO base station to be optimized;

s47, determining whether the antenna gain after iterative optimization meets the expected target; if yes, the iterative optimization process is ended, and if not, the step S42 is executed.

As shown in fig. 5, for a schematic diagram of coverage areas before and after adjusting weight parameters of an antenna of a 3D-MIMO base station to be optimized according to an embodiment of the present disclosure, in fig. 5, a beam coverage area with a horizontal wave width of 65 ° and a beam coverage area with a horizontal wave width of 90 ° are included, if the weight parameters of the antenna of the 3D-MIMO base station to be optimized are adjusted from a horizontal wave width of 65 ° to a horizontal wave width of 90 °. The prediction process for a user who may cut into the coverage area when adjusted to 90 ° of the horizontal bandwidth includes:

(1) and acquiring the difference between the receiving intensities of the reference signals of the users in the coverage range of the 3D-MIMO base station to be optimized, and performing linear fitting by a least square method to obtain a trend curve of user number distribution.

(2) And when the horizontal wave width is adjusted to be 65 degrees and 90 degrees, acquiring the number of users absorbed by the beam change from the trend curve according to the reference signal receiving intensity of the beam in the cell adjacent to the coverage range of the 3D-MIMO base station to be optimized, the reference signal receiving intensity of the beam in the coverage range of the 3D-MIMO base station to be optimized and the difference between the receiving intensities of the two corresponding beams. The number of users can be referred to the diagonally shaded area shown in fig. 5, i.e., the number of users covered by the area 1.

The prediction process for the users who may be cut out of the coverage area when the horizontal wave width is adjusted to 90 degrees comprises the following steps:

(1) when the horizontal wave width is adjusted to 65 ° to 90 °, the reference signal received strength of the BEAM stage is reduced to Δ RSRP _ BEAM _ N, where N represents the BEAM number.

(2) And acquiring a difference delta RSRP _ USER _ N of the reference signal receiving strength of USERs in the coverage range of the 3D-MIMO base station to be optimized.

(3) And if the difference delta RSRP _ USER _ N of the reference signal received strength of the USERs in the coverage area of the 3D-MIMO base station to be optimized is less than the BEAM level delta RSRP _ BEAM _ N, indicating that the USERs are switched off, and counting the number of the USERs. The number of users can be referred to the number of users covered by the area shaded in grid form shown in fig. 5, i.e., the area 2.

The process of obtaining the user distribution situation in the coverage area of the 3D-MIMO base station to be optimized is as follows:

(1) and summing and averaging the measured number of users of each wave beam to calculate the average number of users of the cell.

(2) In order to reduce the matching times, the number of users measured by each beam is sorted, and the beams larger than the average number of users in a cell participate in matching.

(3) And performing user number distribution matching on the 3D-MIMO base station configuration database according to the number of the users measured by each beam. The difference between the number of the users measured by the wave beams and the number of the users in the experience base is the optimal matching.

(4) And the wave beam with the largest number of users is preferentially matched, the wave beam with the largest number of users is selected to be optimal on the basis of the previous matching, and the process is repeated until the wave beams exceeding the average number of users in the cell are traversed.

(5) As the table below illustrates, assume that beam 1 measures the number of users 220 and beam 2 measures the number of users 160. The beam 1 is firstly matched with a 3D-MIMO base station configuration database to obtain the optimal user number 200, and then the optimal user number 180 of the beam 2 is matched on the basis that the beam 1 selects the optimal user number.

Fig. 6 is a schematic structural diagram of an apparatus 600 for optimizing weight parameters of an antenna according to an embodiment of the present disclosure. In a software implementation, the apparatus 600 for optimizing weight parameters of an antenna may include a processor 601, where:

the processor 601 is configured to obtain a user distribution condition and a weight parameter of an antenna within a coverage range of a three-dimensional multiple-input multiple-output (3D-MIMO) base station to be optimized; iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the antenna gain maximization principle based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized, the weight parameters of the antennas and a preset 3D-MIMO base station configuration database; when the antenna gain of the 3D-MIMO base station to be optimized meets the convergence condition of the iterative optimization, configuring the weight parameter of the antenna of the 3D-MIMO base station to be optimized based on the weight parameter corresponding to the antenna gain meeting the convergence condition of the iterative optimization;

Optionally, in an embodiment, the convergence condition of the iterative optimization includes at least one of:

Optionally, in an embodiment, when the weight parameter of the antenna includes a horizontal wave width and a vertical wave width, the processor 601 is configured to:

constructing a two-dimensional coordinate graph based on the preset 3D-MIMO base station configuration database, wherein the two-dimensional coordinate graph comprises a plurality of points, and one point corresponds to the weight parameter of a group of antennas;

acquiring a point matched with the weight parameter of the antenna in the coverage range of the 3D-MIMO base station to be optimized from the two-dimensional coordinate graph as an initial point;

and iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the weight parameters of the antennas corresponding to the points adjacent to the initial point and the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized by using the principle of maximizing the antenna gain.

Optionally, in an embodiment, the processor 601 is configured to:

determining the user distribution situation in the coverage range of the 3D-MIMO base station to be optimized after iterative optimization based on the difference between the reference signal received strength from the target user and the difference between the reference signal received strength from the first target beam and the second target beam;

iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized according to the principle of maximizing the antenna gain based on the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized after the iterative optimization and the weight parameters of the antennas of the 3D-MIMO base station to be optimized after the iterative optimization;

the target user is any one of a user in the coverage range of the 3D-MIMO base station to be optimized and a user of the adjacent cell, the first target beam is from the coverage range of the 3D-MIMO base station to be optimized, the second target beam is from the adjacent cell, and the first target beam corresponds to the second target beam.

Optionally, in an embodiment, the processor 601 is configured to:

after a first preset time period, obtaining a value of a first antenna gain corresponding to the weight parameter of the antenna to be optimized after the optimization of the 3D-MIMO base station;

and updating the preset 3D-MIMO base station configuration database based on the value of the first antenna gain corresponding to the optimized weight parameter.

Optionally, in an embodiment, the processor 601 is configured to:

acquiring the user distribution condition in the coverage range of the 3D-MIMO base station to be optimized after the first preset time period;

Optionally, in an embodiment, the processor 601 is configured to:

after a second preset time period, obtaining a value of a second antenna gain corresponding to the weight parameter of the antenna to be optimized after the optimization of the 3D-MIMO base station;

Optionally, in an embodiment, the indicator for characterizing the antenna gain includes at least one of:

spectral efficiency;

a user experience rate;

the radio resource control RRC is connected into power;

the handover success rate.

The apparatus 600 for optimizing weight parameters of an antenna can implement the method in the embodiments of the methods shown in fig. 1 to 5, which specifically refers to the method for optimizing weight parameters of an antenna in the embodiments shown in fig. 1 to 5, and is not described again.

Fig. 7 is a schematic structural diagram of an electronic device provided in an embodiment of the present specification. Referring to fig. 7, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 7, but this does not indicate only one bus or one type of bus.

And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.

The processor reads a corresponding computer program from the nonvolatile memory to the memory and then runs the computer program to form the optimization device of the weight parameter of the antenna on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:

The method for optimizing the weight parameters of the antenna disclosed in the embodiment of fig. 1 in this specification may be applied to a processor, or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in one or more embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in hardware, in a software module executed by a hardware decoding processor, or in a combination of the hardware and software modules executed by a hardware decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

The electronic device may further perform the method for optimizing the weight parameter of the antenna in fig. 1, which is not described herein again.

Embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 1, and are specifically configured to:

The computer-readable storage medium may further perform a method for optimizing the weight parameter of the antenna in fig. 1, which is not described herein again.

Of course, besides the software implementation, the electronic device in this specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.

In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present disclosure should be included in the scope of protection of one or more embodiments of the present disclosure.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Claims

1. A method for optimizing weight parameters of an antenna is characterized by comprising the following steps:

2. The method of claim 1, wherein the convergence condition for the iterative optimization comprises at least one of:

3. The method according to claim 1 or 2, wherein when the weight parameters of the antennas include horizontal wave width and vertical wave width, iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the user distribution condition in the coverage area of the 3D-MIMO base station to be optimized, the weight parameters of the antennas, and a preset 3D-MIMO base station configuration database, in a principle of antenna gain maximization, includes:

4. The method of claim 3, wherein iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the weight parameters of the antennas corresponding to the points adjacent to the initial point and the distribution of users in the coverage area of the 3D-MIMO base station to be optimized based on the antenna gain maximization principle comprises:

5. The method of claim 1, wherein after configuring the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the weight parameters corresponding to the antenna gains satisfying the convergence condition of the iterative optimization when the antenna gains of the 3D-MIMO base station to be optimized satisfy the convergence condition of the iterative optimization, the method further comprises:

6. The method of claim 5, wherein the method further comprises:

7. The method according to claim 6, wherein after iteratively optimizing the weight parameters of the antennas of the 3D-MIMO base station to be optimized based on the user distribution within the coverage of the 3D-MIMO base station to be optimized after the first preset time period, the optimized weight parameters of the antennas, and the updated preset 3D-MIMO base station configuration database on the basis of the antenna gain maximization, the method further comprises:

8. The method of any of claims 1-7, wherein the indicator for characterizing the antenna gain comprises at least one of:

spectral efficiency;

a user experience rate;

the radio resource control RRC is connected into power;

the handover success rate.

9. An apparatus for optimizing weight parameters of an antenna, comprising:

10. An electronic device, comprising:

a memory storing computer program instructions;

a processor which, when executed by the processor, implements a method of optimizing weight parameters for an antenna according to any one of claims 1-8.