CN110705052B

CN110705052B - Equipment model selection method and device

Info

Publication number: CN110705052B
Application number: CN201910877809.5A
Authority: CN
Inventors: 杨艳; 冯毅
Original assignee: China United Network Communications Group Co Ltd
Current assignee: China United Network Communications Group Co Ltd
Priority date: 2019-09-17
Filing date: 2019-09-17
Publication date: 2022-09-13
Anticipated expiration: 2039-09-17
Also published as: CN110705052A

Abstract

The embodiment of the invention provides a device type selection method and a device, relates to the technical field of communication, and solves the problem of how to replace a device of a built base station area with a proper AAU device according to the user requirement of the built base station area. The method comprises the steps of determining SINR according to the mapping relation between SINR and CQI and a first historical CQI; clustering SINR and typical scene simulation data, and determining a central value of each category; determining an SINR interval according to the central value, matching the SINR interval with the equipment type, and determining the corresponding relation between the SINR interval and the equipment type; determining the target SINR of the designated area according to the mapping relation and the second historical CQI; and determining the type of the equipment deployed in the specified area according to the corresponding relation and the target SINR.

Description

Equipment model selection method and device

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a device model selection method and apparatus.

Background

The fifth generation mobile communication technology (5 th-generation, abbreviated as 5G) is a brand new next generation communication system, and makes a comprehensive breakthrough from the device level, first increasing the number of Antenna arrays from 32 of Long Term Evolution (LTE) to 128 and 192 of new Radio interfaces (NR), and then increasing the number of transmission and reception channels of the Radio Remote Unit (AAU) device shown in fig. 1 from 2 transmission (transmit) and T)2 reception (receive) of LTE (RRU) to 8T8R, 16T16R, 32T32R and 64T64R, and further tightly coupling the Antenna with the RRU device to reduce the loss of a large number of arrays and Radio Remote Units (RRU) between RRUs, the antenna gain of the device is improved.

The coverage of multiple scene can be carried out to the multiple passageway technique of 5G, 8TR, 16 TR's equipment can carry out the coverage of a vertical dimension, and 32 TR's equipment can carry out the coverage of 2 vertical dimensions, 64 TR's equipment can carry out the coverage of 4 vertical dimensions, and application scope is wider.

As can be seen from the above, since the AAU in the 5G network has a plurality of device types, how to replace the device in the existing base station area with the appropriate AAU device according to the user requirement of the existing base station area becomes a problem to be solved urgently.

Disclosure of Invention

Embodiments of the present invention provide an apparatus type selection method and apparatus, which solve the problem of how to replace an apparatus in an established base station area with a suitable AAU apparatus according to a user requirement of the established base station area.

In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:

in a first aspect, an embodiment of the present invention provides an apparatus type selection method, including: acquiring typical scene simulation data under a first mobile communication technology, a first historical CQI of a target area under a second mobile communication technology, and a second historical CQI of a designated area under the second mobile communication technology; the typical scene simulation data is obtained by simulating a typical scene under the second mobile communication technology, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas; determining the SINR under the first mobile communication technology according to the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology and the first historical CQI; clustering SINR and typical scene simulation data under a first mobile communication technology, and determining a central value of each category under a second mobile communication technology; determining an SINR interval under the first mobile communication technology according to the central value, matching the SINR interval with the device type, and determining the corresponding relation between the SINR interval and the device type; determining a target SINR of the designated area under the first mobile communication technology according to the mapping relation and the second historical CQI; determining the type of equipment deployed in the specified area according to the corresponding relation and the target SINR; wherein the release time of the first mobile communication technology is later than the release time of the second mobile communication technology.

According to the above solution, when the first mobile communication technology is 5G, the second mobile communication technology is 4G, the device type includes 16TR device, 32TR device, and 64TR device, and the designated area is an established base station area, the device type selection method provided in the embodiment of the present invention obtains the typical scene simulation data of 5G and the first historical CQI of the target area at 4G, determines the SINR of 5G according to the mapping relationship between the SINR of 5G and the CQI of 4G and the first historical CQI, clusters the SINR of 5G and the typical scene simulation data, determines the central value of each category under 5G, thereby determining the SINR interval of 5G according to the central value, and matches the SINR interval with the device type to determine the corresponding relationship between the SINR interval and the device type; because 4G has been built in the designated area (which may be an area of a built base station where equipment replacement is to be performed, and is referred to as an area where equipment replacement is to be performed), the target SINR of the designated area is obtained by using the existing second historical CQI of the 4G, and then the type of equipment deployed in the designated area is determined through the target SINR and the corresponding relationship, so that equipment type selection is completed; the problem of how to change the equipment of the established base station area into proper AAU equipment according to the user requirement of the established base station area is solved.

In a second aspect, an embodiment of the present invention provides an apparatus for device model selection, including: an obtaining unit, configured to obtain typical scene simulation data in a first mobile communication technology, a first historical CQI of a target area in a second mobile communication technology, and a second historical CQI of a specified area in the second mobile communication technology; the typical scene simulation data is obtained by simulating a typical scene under the second mobile communication technology, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas; a processing unit, configured to determine an SINR in the first mobile communication technology according to a mapping relationship between an SINR in the first mobile communication technology and a CQI in the second mobile communication technology and the first historical CQI acquired by the acquiring unit; the processing unit is also used for clustering SINR under the first mobile communication technology and the typical scene simulation data acquired by the acquisition unit and determining the central value of each category under the second mobile communication technology; the processing unit is further used for determining an SINR interval under the first mobile communication technology according to the central value, matching the SINR interval with the device type and determining the corresponding relation between the SINR interval and the device type; the processing unit is further used for determining a target SINR of the designated area under the first mobile communication technology according to the mapping relation and the second historical CQI acquired by the acquisition unit; the processing unit is further used for determining the type of equipment deployed in the specified area according to the corresponding relation and the target SINR; wherein the release time of the first mobile communication technology is later than the release time of the second mobile communication technology.

In a third aspect, an embodiment of the present invention provides an apparatus for device model selection, including: communication interface, processor, memory, bus; the memory is used for storing computer executable instructions, the processor is connected with the memory through the bus, and when the device selection device runs, the processor executes the computer executable instructions stored by the memory so as to enable the device selection device to execute the method provided by the first aspect.

In a fourth aspect, an embodiment of the present invention provides a computer storage medium comprising instructions which, when run on a computer, cause the computer to perform the method as provided in the first aspect above.

It is to be understood that any one of the above-mentioned device selection apparatuses is configured to perform the method according to the first aspect, and therefore, the beneficial effects that can be achieved by the apparatus selection apparatus refer to the method according to the first aspect and the beneficial effects of the solution according to the following embodiments, which are not described herein again.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 shows the distribution of the number of LTE antenna arrays and the number of several typical NR devices in the prior art;

fig. 2 is a schematic diagram of Massive MIMO in the prior art when performing wireless coverage;

fig. 3 is a network architecture diagram of a device model selection method according to an embodiment of the present invention;

FIG. 4 is a flowchart illustrating a method for device model selection according to an embodiment of the present invention;

FIG. 5 is a second flowchart of an apparatus model selection method according to an embodiment of the present invention;

fig. 6 is a diagram illustrating a relationship between 5G SINR and 4G CQI of a device model selection method according to an embodiment of the present invention;

FIG. 7 is a third flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;

FIG. 8 is a fourth flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;

FIG. 9 is a diagram illustrating a correspondence relationship between the number of categories and the average contour value of a device model selection method according to an embodiment of the present invention;

FIG. 10 is a diagram illustrating classification according to a class 3 requirement of a device model selection method according to an embodiment of the present invention;

FIG. 11 is a schematic diagram of a profile obtained according to a requirement classified into 3 categories according to a device model selection method provided by an embodiment of the present invention;

FIG. 12 is a schematic structural diagram of an apparatus model selection apparatus according to an embodiment of the present invention;

FIG. 13 is a second schematic diagram of a device model selection apparatus according to an embodiment of the present invention;

fig. 14 is a third schematic structural diagram of an apparatus model selection device according to an embodiment of the present invention.

Reference numerals:

equipment model selection device-10;

an acquisition unit-101; a processing unit-102.

Detailed Description

Embodiments of the present invention will be described below with reference to the accompanying drawings.

For the convenience of clearly describing the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", and the like are used for distinguishing the same items or similar items with basically the same functions and actions, and those skilled in the art can understand that the words "first", "second", and the like are not limited in number or execution order.

In the embodiments of the present invention, words such as "exemplary" or "for example" are used to mean serving as examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "e.g.," an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present relevant concepts in a concrete fashion.

In the description of the embodiments of the present invention, "a plurality" means two or more unless otherwise specified. For example, a plurality of networks refers to two or more networks.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The symbol "/" herein denotes a relationship in which the associated object is or, for example, a/B denotes a or B.

Fig. 2 is a network architecture diagram illustrating a device type selection method according to an embodiment of the present invention, including: a built base station area and equipment model selection device; the device type selection device comprises an acquisition unit and a processing unit, wherein the acquisition unit is used for respectively acquiring typical scene simulation data under a first mobile communication technology, a first historical Channel Quality Indicator (CQI) of a target area under a second mobile communication technology, and a second historical CQI under the second mobile communication technology in a designated area; the typical scene simulation data is obtained by simulating a typical scene under the second mobile communication technology, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas; a processing unit, configured to determine an SINR in the first mobile communication technology according to a mapping relationship between a signal to interference plus noise ratio (SINR) in the first mobile communication technology and a CQI in the second mobile communication technology and the first historical CQI acquired by the acquiring unit; the processing unit is further used for clustering the SINR under the first mobile communication technology and the typical scene simulation data acquired by the acquisition unit and determining the central value of each category under the second mobile communication technology; the processing unit is further used for determining an SINR interval under the first mobile communication technology according to the central value, matching the SINR interval with the device type and determining the corresponding relation between the SINR interval and the device type; the processing unit is further used for determining a target SINR of the designated area under the first mobile communication technology according to the mapping relation and the second historical CQI acquired by the acquisition unit; and the processing unit is further used for determining the type of the equipment deployed in the specified area according to the corresponding relation and the target SINR.

As shown in fig. 3, 5G is a brand-new technical system, and a Massive MIMO technology is used, and the LTE wide beam coverage mode is changed, but a narrow beam is used for coverage, so that the device type of the wireless side device is greatly changed, where the largest change is that the number of receiving channels of the AAU device is greatly changed, and a single 2TR device is changed into a device type with diversified channel numbers such as 64TR, 32TR, 16TR, 8TR, and the like. However, how to replace the equipment of the established base station area with proper AAU equipment according to the user requirements of the established base station area is an important problem for balancing the network deployment cost of operators; in order to solve the above problem, in the device type selection method provided in the embodiment of the present invention, 5G typical scene simulation data and a first historical CQI of a target area in a fourth generation mobile communication technology (the 4th generation mobile communication technology, abbreviated as 4G) are obtained, then a 5G SINR is determined according to a mapping relationship between the 5G SINR and the 4G CQI and the first historical CQI, then the 5G SINR and the typical scene simulation data are clustered, a central value of each category in the 5G is determined, so as to determine a 5G interval according to the central value, and the SINR interval is matched with a device type, so as to determine a corresponding relationship between the SINR interval and the device type; because 4G is already established in the designated area (which can be an area to be replaced by the equipment), the target SINR of the designated area is obtained by using the existing second historical CQI data of 4G, and then the type of the equipment deployed in the designated area is determined through the target SINR and the corresponding relation, so that equipment type selection is completed, and the problem of how to replace the equipment of the established base station area with proper AAU equipment according to the user requirements of the established base station area is solved.

Illustratively, taking the first mobile communication technology as 5G, the second mobile communication technology as 4G, the device types include 16TR device, 32TR device, and 64TR device, and the designated area is an established base station area as an example for description, the specific implementation process is as follows:

example one

An embodiment of the present invention provides an apparatus model selection method, as shown in fig. 4, including:

s10, acquiring typical scene simulation data under a first mobile communication technology, a first historical CQI of a target area under a second mobile communication technology, and a second historical CQI of a designated area under the second mobile communication technology; wherein the typical scene simulation data is obtained by simulating a typical scene under the second mobile communication technology, and the typical scene comprises at least one of dense urban areas, suburban areas and open areas.

It should be noted that, in practical applications, the following relationship exists between the target area and the designated area:

firstly, a designated area belongs to (here, the designated area is based on the division of an administrative division) a target area, such as: the designated area is a Changan area in the city of Xian, and the target area is the city of Xian; or the designated area is a Changan area in the city of Western Ann, and the target area is an Amaran area in the city of Western Ann; or the designated area is the Changan area Guo Du in Xian city, and the target area is the Changan area in Xian city.

When the target area and the designated area have the relationship, the actual requirements of the user in the designated area can be determined more accurately.

Secondly, the designated area does not belong to the target area, such as: the designated area is Changan area in Xian city, and the target area is Beijing city; or, the designated area is the city of Xian, and the target area is the city of Beijing.

When the target area and the designated area have the relationship, the actual requirements of the users in the designated area can be estimated.

Specifically, in an actual application, in order to more accurately determine the actual demand of the user in the designated area, MR data of all urban areas, suburban areas and open areas in the jurisdiction area (i.e. the target area) to which the designated area belongs in a preset time period (for example, the preset time period may be data of an all-day measurement report (abbreviated as MR) of one weekday and one holiday in the last week) may be obtained. Because of the correlation between the coverage and capacity of the base station and the sinr (cqi), we choose 4G of MR data as the decision parameter. The method comprises the following steps:

the following table is header information about 4G CQI in MR:

and selecting MR data in the 4G current network of each cell in the target area within a preset time period (such as 3 months) to determine the distribution condition of CQI (namely first historical CQI). Table 1 shows header information about CQI in MR data.

TABLE 1

The average occurrence of each CQI on weekdays and holidays was calculated by table 1:

wherein, i represents the total times of reporting the full-bandwidth CQI by an air interface, and i belongs to [0, 15 ]]，CQI ^w CQI indicating a working day ^e CQI indicating holidays.

It should be noted that, in practical applications, when acquiring 5G typical scene simulation data, user spot-scattering simulation needs to be performed on the same device type in the same typical scene, the same station height, and the same station spacing, so as to obtain the 5G typical scene simulation data of the device type.

Exemplary, obtaining typical scene simulation data under a first mobile communication technology includes:

fors 16TR device, 32TR device, 64TR device/input multiple device types.

For C, dense urban, suburban, open area (according to the channel model specified in the third Generation Partnership Project (3 rd Generation Partnership Project, 3GPP) TS38.901 standard)/input various typical scenarios.

For 15, 20, 25, 30, 35,/input multiple station heights.

Ford is 100: 100: 3000/input multiple station spacing.

Specifically, in practical applications, when a 5G network simulation based on a device type, a typical scenario, a station height, and a station distance is performed, N times (N is an integer greater than 0, and N is 1000 times in the example) of simulations are performed for each network configuration (the device type, the typical scenario, the station height, and the station distance) (each simulation can only obtain one SINR value because of a single-user scattering point), and then typical scenario simulation data (for example, the typical scenario simulation data is an average value of SINRs of M users under the same network configuration) under each network configuration is determined according to the obtained SINRs of M users (M is an integer greater than 0, and M is 1000 in the example).

It should be noted that, in practical application, the same device type obtains a series of SINRs by traversing the station height and the station spacing in a typical scene, so as to determine typical scene simulation data of the device type according to the series of SINRs.

S11, determining the SINR under the first mobile communication technology according to the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology and the first historical CQI.

Optionally, determining the SINR in the first mobile communication technology according to the mapping relationship between the SINR in the first mobile communication technology and the CQI in the second mobile communication technology and the first historical CQI, as shown in fig. 5, includes:

s110, determining SINR under the first mobile communication technology according to the mapping relation between SINR under the first mobile communication technology and CQI under the second mobile communication technology and the first historical CQI; wherein, the mapping relation comprises:

SINR＝1.9346×CQI-6.799；

wherein SINR represents SINR in the first mobile communication technology, and CQI represents CQI in the second mobile communication technology.

It should be noted that, in practical applications, when the mapping relationship between the SINR in the first mobile communication technology and the CQI in the second mobile communication technology is obtained, the relationship between the 4G and the CQI may be obtained by comparing the relationship between the 4G CQI and the SINR; further combining the relationship between the 4G and the CQI, the corresponding relationship between the CQI of the 4G and the SINR of the 5G is determined, as shown in fig. 6 below, which basically satisfies a linear relationship, and can be estimated by using the following formula:

SINR＝1.9346×CQI-6.799。

and S12, clustering the SINR and the typical scene simulation data under the first mobile communication technology, and determining the central value of each category under the second mobile communication technology.

Optionally, clustering SINR and typical scene simulation data in the first mobile communication technology, and determining a central value of each category, as shown in fig. 7, includes:

s120, clustering the SINR and the typical scene simulation data under the first mobile communication technology according to a K-means clustering algorithm (K-means for short), and determining the central value of each category.

Specifically, in practical application, since both the SINR under the first mobile communication technology and the typical scene simulation data belong to the SINR of 5G, the SINR under the first mobile communication technology and the typical scene simulation data can be directly clustered, so that the calculation is convenient.

Optionally, the SINR and the typical scene simulation data in the first mobile communication technology are clustered according to a k-means clustering algorithm, and a central value of each category is determined, as shown in fig. 8, where the method includes:

s1200, clustering SINRs under the first mobile communication technology according to a k-means clustering algorithm, and determining a first centroid value of each category.

S1201, clustering the typical scene simulation data according to a k-means clustering algorithm, and determining a second centroid value of each category.

S1202, determining the center value of each category according to the first centroid value and the second centroid value corresponding to the same category.

It should be noted that, in practical applications, the k-means algorithm is a very typical clustering algorithm based on distance, and the distance is used as an evaluation index of similarity, that is, the closer the distance between two objects is, the greater the similarity between the two objects is. The algorithm considers clusters to be composed of closely spaced objects, and therefore targets the resulting compact and independent clusters as final targets. The distance formula used is as follows:

where V represents the distance, x, of the SINR of 5G from the center value (also called centroid) of the specified class _j SINR, μ, representing the jth 5G _i Representing the center value of the ith category.

The specific implementation process is as follows:

1. k SINR of 5G are randomly selected as the centroid from N SINR of 5G (including SINR under the first mobile communication technology and/or SINR of typical scene simulation data).

2. The distance V to each center value is measured for each remaining 5G SINR (including the SINR under the first mobile communication technology and the SINR of typical scene simulation data) and is classified as the nearest center value.

3. And recalculating the obtained central values of the various categories.

4. And iterating for 2-3 steps until the new central value is equal to the original central value or smaller than a specified threshold value, and finishing the algorithm.

The procedure implemented with programming is as follows:

inputting: k, data [ n ];

(1) selecting k initial center values, e.g., c [0] ═ data [0], … c [ k-1] ═ data [ k-1 ];

(2) for data [0] … data [ n ], compare with c [0] … c [ k-1], respectively, and mark as i assuming the least difference with ci;

(3) for all points marked as i, recalculating c [ i ] = { the sum of all data [ j ] marked as i }/the number marked as i;

(4) and (3) repeating the steps (2) and (3) until all the changes of the c [ i ] values are smaller than a given threshold value.

In practical application, when clustering is performed on SINR and typical scene simulation data in the first mobile communication technology by k-means, it is not preferable that the more central values (each central value corresponds to one category) are selected, as shown in fig. 9 (the abscissa is the number of categories, and the ordinate is the average profile value), the clustering result obtained when the selected centroids are 2 is optimal, the clustering result obtained when the selected centroids are 3 is slightly poor, and the clustering result obtained when the selected centroids are 4 is slightly poor; but according to the cost of the equipment and the requirement of the multi-antenna multi-channel gain of the 5G Massvie MIMO, clustering results need to be divided into 3-4 types; therefore, based on the requirement of optimal cost performance, when clustering is carried out on SINR and typical scene simulation data under the first mobile communication technology through k-means, 3-4 centroids are selected to obtain 3-4 classes; illustratively, the specific implementation process for clustering SINR and typical scenario simulation data in the first mobile communication technology is as follows:

1. in terms of the classification into 3 classes, the classification case is given in fig. 10 and the contour case is given in fig. 11.

2. Then, a first centroid value of each category based on SINR under the first mobile communication technology is obtained, and a second centroid value of each category based on typical scene simulation data is obtained and recorded as

Wherein the content of the first and second substances,

a first centroid value representing a first class based on SINR at the first mobile communication technology,

a first centroid value representing a second class based on SINR at the first mobile communication technology,

a first centroid value representing a third class based on SINR at the first mobile communication technology,

the representation is based onA second centroid value of the first class of typical scene simulation data, representing a second centroid value based on the second class of typical scene simulation data,

a second centroid value representing a third category based on typical scene simulation data.

3. Then, the obtained first centroid value of each category based on the SINR under the first mobile communication technology and the obtained second centroid value of each category based on the typical scene simulation data are averaged, so that a center coordinate C of each category is obtained _TS (ii) a Wherein the content of the first and second substances,

wherein, S is ∈ [1, 2, 3 ]]And i represents a category.

Illustratively, the center value of the first class is calculated according to

It can be seen that i is equal to 1, the center coordinate of the first class

And S13, determining the SINR interval under the first mobile communication technology according to the central value, matching the SINR interval with the device type, and determining the corresponding relation between the SINR interval and the device type.

It should be noted that, in practical applications, the center value of each category may be obtained through S12, so that SINR intervals may be divided according to the center points; illustratively, the SINR interval may be divided as follows:

then, matching the SINR interval with the equipment type; exemplarily, the corresponding relationship between the device type and the SINR interval may be determined in the following manner; wherein, the corresponding relation includes:

the requirement of the 16TR equipment meets the interval T1;

the requirement of the 32TR equipment meets the T2 interval;

the 64TR device is required to meet the T3 interval.

Specifically, the mobile communication technology to which the SINR interval belongs is the same as the mobile communication technology to which the device type belongs; such as: when all SINR included in the SINR interval is 5G SINR, the mobile communication technology to which the device type belongs is 5G (that is, the device type is the device type of a 5G device).

And S14, determining the target SINR of the designated area under the first mobile communication technology according to the mapping relation and the second historical CQI.

It should be noted that, in practical applications, in order to more accurately determine the actual demand of the user in the designated area, the MR data of the designated area in a preset time period (for example, the preset time period may be the MR data in a whole day of a weekday and a holiday in a last week, since the coverage and capacity of the base station are related to sinr (cqi), 4G of MR data is selected as the decision parameter.

The following table is header information about 4G CQI in MR:

and selecting MR data in the 4G current network of the designated area within a preset time period (such as 3 months) to determine the distribution condition of the CQI. Table 2 shows header information about CQI in the MR data.

TABLE 2

Respectively calculating a second historical CQI of each CQI in a preset time period (e.g. 3 months) through the table 2 _i ：

Wherein i represents the total times of reporting the full-bandwidth CQI by the air interface, and i belongs to [0, 15 ]]，CQI ^week Each representsThe CQI of the day.

Then, according to the mapping relation between the 4G CQI and the 5G SINR and the second historical CQI _i And determining the target SINR.

And finally, determining the type of the equipment deployed in the specified area according to the target SINR and the corresponding relation.

S15, determining the type of the equipment deployed in the designated area according to the corresponding relation and the target SINR; wherein the release time of the first mobile communication technology is later than the release time of the second mobile communication technology.

Specifically, determining the type of the device deployed in the designated area according to the corresponding relationship and the target SINR includes:

selecting the device type according to the SINR interval to which the target SINR belongs; for example, when the target SINR of any cell in the specified area belongs to the T1 interval, it is determined that the device type deployed in the cell is a 16TR device; when the target SINR of any cell in the specified area belongs to a T2 interval, determining that the type of the device deployed in the cell is 32TR device; when the target SINR of any cell in the specified area belongs to the T3 interval, it is determined that the device type deployed in the cell is 64TR device.

It should be noted that, in the device type selection method provided in the embodiment of the present invention, when the first mobile communication technology is 5G, since the release time of the first mobile communication technology is later than the release time of the second mobile communication technology, the second mobile communication technology can only be the mobile communication technology whose release time is earlier than 5G; such as: 4G and third Generation mobile communication technology (3 rd-Generation, 3G for short).

According to the scheme, when the first mobile communication technology is 5G, the second mobile communication technology is 4G, the device type includes 16TR devices, 32TR devices and 64TR devices, and the designated area is an established base station area, the device type selection method provided by the embodiment of the invention obtains the 5G typical scene simulation data and the first historical CQI of the target area in 4G, then determines the 5G SINR according to the mapping relation between the 5G SINR and the 4G CQI and the first historical CQI, then clusters the 5G SINR and the typical scene simulation data, determines the central value of each category under 5G, thereby determining the 5G SINR interval according to the central value, and matches the SINR interval with the device type to determine the corresponding relation between the SINR interval and the device type; because 4G is already built in the designated area (which can be an area to be replaced by equipment), the target SINR of the designated area is obtained by using the existing second historical CQI of 4G, and then the type of equipment deployed in the designated area is determined through the target SINR and the corresponding relation, so that equipment type selection is completed; the problem of how to change the equipment of the established base station area into proper AAU equipment according to the user requirement of the established base station area is solved.

Example two

An embodiment of the present invention provides an apparatus model selection device 10, as shown in fig. 12, including:

an obtaining unit 101, configured to obtain typical scene simulation data in a first mobile communication technology, a first historical CQI of a target area in a second mobile communication technology, and a second historical CQI of a specified area in the second mobile communication technology; the typical scene simulation data is obtained by simulating a typical scene under the second mobile communication technology, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas;

a processing unit 102, configured to determine an SINR in the first mobile communication technology according to a mapping relationship between an SINR in the first mobile communication technology and a CQI in the second mobile communication technology and the first historical CQI acquired by the acquiring unit 101;

the processing unit 102 is further configured to cluster SINR in the first mobile communication technology and the typical scene simulation data acquired by the acquiring unit 101, and determine a central value of each category in the second mobile communication technology;

the processing unit 102 is further configured to determine an SINR interval in the first mobile communication technology according to the central value, match the SINR interval with the device type, and determine a corresponding relationship between the SINR interval and the device type;

the processing unit 102 is further configured to determine a target SINR of the designated area in the first mobile communication technology according to the mapping relationship and the second historical CQI acquired by the acquiring unit 101;

the processing unit 102 is further configured to determine, according to the correspondence and the target SINR, a type of the device deployed in the designated area; wherein the release time of the first mobile communication technology is later than the release time of the second mobile communication technology.

Optionally, the processing unit 102 is specifically configured to determine an SINR in the first mobile communication technology according to a mapping relationship between an SINR in the first mobile communication technology and a CQI in the second mobile communication technology and the first historical CQI acquired by the acquiring unit 101; wherein, the mapping relation comprises:

SINR＝1.9346×CQI-6.799；

Optionally, the processing unit 102 is specifically configured to cluster the SINR in the first mobile communication technology and the typical scene simulation data acquired by the acquiring unit 101 according to a k-means clustering algorithm, and determine a center value of each category.

Optionally, the processing unit 102 is specifically configured to cluster SINR in the first mobile communication technology according to a k-means clustering algorithm, and determine a first centroid value of each category;

the processing unit 102 is specifically configured to cluster the typical scene simulation data acquired by the acquiring unit 101 according to a k-means clustering algorithm, and determine a second centroid value of each category;

the processing unit 102 is specifically configured to determine a center value of each category according to the first centroid value and the second centroid value corresponding to the same category.

Specifically, in practical application, as shown in fig. 13, the obtaining unit in the device model selection apparatus includes a full-network 4G MR data extraction module and a 5G typical scene simulation and data extraction module; the system comprises a whole network 4G MR data extraction module, a first mobile communication technology acquisition module, a second mobile communication technology acquisition module and a third mobile communication technology acquisition module, wherein the whole network 4G MR data extraction module is used for acquiring a first historical CQI of a target area under the second mobile communication technology and a second historical CQI of a specified area under the second mobile communication technology; the 5G typical scene simulation and data extraction module is used for acquiring typical scene simulation data under the first mobile communication technology; the processing unit comprises a 4G CQI mapping module, a 5G equipment selection judgment method selection module and a 5G equipment selection module; the 4G CQI mapping module is used for determining the SINR under the first mobile communication technology according to the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology and the first historical CQI acquired by the whole-network 4G MR data extraction module; the 5G equipment selection judgment method selection module is used for clustering SINR (signal to interference and noise ratio) under the first mobile communication technology and typical scene simulation data acquired by the 5G typical scene simulation and data extraction module and determining a central value of each category under the second mobile communication technology; the 5G device selection decision method selection module is further used for determining an SINR interval under the first mobile communication technology according to the central value, matching the SINR interval with the device type and determining the corresponding relation between the SINR interval and the device type; the 5G equipment selection module is used for determining a target SINR of the designated area under the first mobile communication technology according to the mapping relation and the second historical CQI acquired by the whole network 4G MR data extraction module; the 5G equipment selection module is also used for determining the type of equipment deployed in the specified area according to the corresponding relation and the target SINR; the 4G CQI mapping module is used for determining the SINR under the first mobile communication technology according to the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology and the whole-network CQI acquired by the whole-network 4G MR data extraction module; the 5G equipment selection judgment method selection module is used for clustering SINR (signal to interference and noise ratio) under the first mobile communication technology and typical scene simulation data acquired by the 5G typical scene simulation and data extraction module and determining a central value of each category under the second mobile communication technology; the 5G device selection decision method selection module is further used for determining an SINR interval under the first mobile communication technology according to the central value, matching the SINR interval with the device type and determining the corresponding relation between the SINR interval and the device type; the 5G equipment selection module is used for determining a target SINR of the designated area under the second mobile communication technology according to the mapping relation and the historical CQI acquired by the whole-network 4G MR data extraction module; and the 5G equipment selection module is also used for determining the equipment type deployed in the specified area according to the corresponding relation and the target SINR determined by the 5G equipment selection judgment method selection module.

All relevant contents of each step related to the above method embodiment may be referred to the functional description of the corresponding functional module, and the function thereof is not described herein again.

The device selection apparatus 10 includes, in the case of an integrated module: the device comprises a storage unit, a processing unit and an acquisition unit. A processing unit for controlling and managing actions of the device type selection apparatus, for example, the processing unit is configured to support the device type selection apparatus to execute the procedures S10, S11, S12, S13, S14 and S15 in fig. 4; the acquisition unit is used for supporting the information interaction between the device type selection device and other devices. And a storage unit for storing the program code and data of the device model selection apparatus.

For example, the processing unit is a processor, the storage unit is a memory, and the obtaining unit is a communication interface. The device selection apparatus is shown in fig. 14 and includes a communication interface 501, a processor 502, a memory 503, and a bus 504, and the communication interface 501 and the processor 502 are connected to the memory 503 through the bus 504.

The processor 502 may be a general purpose Central Processing Unit (CPU), a microprocessor, an Application-Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to control the execution of programs according to the present disclosure.

The Memory 503 may be a Read-Only Memory (ROM) or other types of static storage devices that can store static information and instructions, a Random Access Memory (RAM) or other types of dynamic storage devices that can store information and instructions, an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical Disc storage, optical Disc storage (including Compact Disc, disk, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. The memory may be self-contained and coupled to the processor via a bus. The memory may also be integral to the processor.

The memory 503 is used for storing application program codes for executing the present application, and the processor 502 controls the execution. The communication interface 501 is used for information interaction with other devices, for example, with a remote controller. The processor 502 is configured to execute application program code stored in the memory 503 to implement the methods described in the embodiments of the present application.

Further, a computing storage medium (or media) is also provided that includes instructions that when executed perform the method operations performed by the device selection apparatus in the above-described embodiments. Additionally, a computer program product is also provided, comprising the above-described computing storage medium (or media).

It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb flash disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

It can be understood that any one of the above-mentioned device model selection apparatuses is used to execute the method corresponding to the above-mentioned embodiment, and therefore, the beneficial effects that can be achieved by the apparatus model selection apparatus refer to the method of the above-mentioned embodiment one and the beneficial effects of the solution corresponding to the following detailed description, which are not described herein again.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A device model selection method, comprising:

acquiring typical scene simulation data under a first mobile communication technology, a first historical CQI of a target area under a second mobile communication technology, and a second historical CQI of a designated area under the second mobile communication technology; wherein the typical scene simulation data is obtained by simulating a typical scene under a second mobile communication technology, and the typical scene comprises at least one of dense urban areas, suburban areas and open areas;

determining the SINR under the first mobile communication technology according to the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology and the first historical CQI;

clustering the SINR under the first mobile communication technology and the typical scene simulation data, and determining a central value of each category under a second mobile communication technology;

according to the central value, determining an SINR interval under a first mobile communication technology, matching the SINR interval with a device type, and determining a corresponding relation between the SINR interval and the device type;

determining a target SINR of the designated area under the first mobile communication technology according to the mapping relation and the second historical CQI;

determining the type of equipment deployed in the specified area according to the corresponding relation and the target SINR; wherein a release time of the first mobile communication technology is later than a release time of the second mobile communication technology.

2. The device selection method according to claim 1, wherein determining the SINR in the first mobile communication technology according to the mapping relationship between the SINR in the first mobile communication technology and the CQI in the second mobile communication technology and the first historical CQI comprises:

determining the SINR under the first mobile communication technology according to the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology and the first historical CQI; wherein the mapping relationship comprises:

SINR＝1.9346×CQI-6.799；

3. The device selection method according to claim 1, wherein clustering the SINR under the first mobile communication technology and the typical scenario simulation data to determine a center value of each category comprises:

and clustering the SINR and the typical scene simulation data under the first mobile communication technology according to a k-means clustering algorithm, and determining the central value of each category.

4. The device model selection method of claim 3, wherein clustering the SINR under the first mobile communication technology and the typical scene simulation data according to a k-means clustering algorithm, determining a center value of each class comprises:

clustering SINRs under the first mobile communication technology according to a k-means clustering algorithm, and determining a first centroid value of each category;

clustering the typical scene simulation data according to a k-means clustering algorithm, and determining a second centroid value of each category;

and determining the central value of each category according to the first centroid value and the second centroid value corresponding to the same category.

5. An apparatus model selection device, comprising:

an obtaining unit, configured to obtain typical scene simulation data in a first mobile communication technology, a first historical CQI of a target area in a second mobile communication technology, and a second historical CQI of a specified area in the second mobile communication technology; wherein the typical scene simulation data is obtained by simulating a typical scene under a second mobile communication technology, and the typical scene comprises at least one of dense urban areas, suburban areas and open areas;

a processing unit, configured to determine an SINR in a first mobile communication technology according to a mapping relationship between an SINR in the first mobile communication technology and a CQI in a second mobile communication technology and the first historical CQI acquired by the acquiring unit;

the processing unit is further configured to cluster the SINR in the first mobile communication technology and the typical scene simulation data obtained by the obtaining unit, and determine a central value of each category in a second mobile communication technology;

the processing unit is further configured to determine an SINR interval in a first mobile communication technology according to the central value, match the SINR interval with a device type, and determine a corresponding relationship between the SINR interval and the device type;

the processing unit is further configured to determine a target SINR of the designated area in a first mobile communication technology according to the mapping relationship and the second historical CQI acquired by the acquiring unit;

the processing unit is further configured to determine, according to the correspondence and the target SINR, a type of the device deployed in the designated area; wherein a release time of the first mobile communication technology is later than a release time of the second mobile communication technology.

6. The device selection apparatus according to claim 5, wherein the processing unit is specifically configured to determine the SINR in the first mobile communication technology according to a mapping relationship between the SINR in the first mobile communication technology and the CQI in the second mobile communication technology and the first historical CQI acquired by the acquiring unit; wherein the mapping relationship comprises:

SINR＝1.9346×CQI-6.799；

7. The device selection apparatus according to claim 5, wherein the processing unit is specifically configured to cluster the SINR under the first mobile communication technology and the typical scene simulation data obtained by the obtaining unit according to a k-means clustering algorithm, and determine a center value of each category.

8. The device selection apparatus according to claim 7, wherein the processing unit is specifically configured to cluster SINRs under the first mobile communication technology according to a k-means clustering algorithm, and determine a first centroid value for each category;

the processing unit is specifically configured to cluster the typical scene simulation data acquired by the acquiring unit according to a k-means clustering algorithm, and determine a second centroid value of each category;

the processing unit is specifically configured to determine a center value of each category according to the first centroid value and the second centroid value corresponding to the same category.

9. A computer storage medium comprising instructions which, when executed on a computer, cause the computer to perform the device selection method of any one of claims 1 to 4.

10. An apparatus model selection device, comprising: communication interface, processor, memory, bus; the memory is used for storing computer-executable instructions, the processor is connected with the memory through the bus, and when the equipment type selection device runs, the processor executes the computer-executable instructions stored in the memory so as to enable the equipment type selection device to execute the equipment type selection method according to any one of the claims 1-4.