CN110719594A

CN110719594A - Equipment model selection method and device

Info

Publication number: CN110719594A
Application number: CN201910877745.9A
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: 2020-01-21
Anticipated expiration: 2039-09-17
Also published as: CN110719594B

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 select proper AAU (architecture) devices according to the user requirements of a proposed base station area. The method comprises the steps of obtaining historical CQI and a scene map in a designated area; determining SINR according to the first mapping relation and the historical CQI; clustering SINRs, and determining the central value of each category under the second mobile communication technology; 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; simulating a scene map, and determining an accumulative distribution function of SINR; determining a first target SINR according to the cumulative distribution function; determining a second target SINR according to the second mapping relation and the first target SINR; and determining the type of the equipment deployed in the specified area according to the second target SINR and the corresponding relation.

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 5G multiple channel technique, 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 5G has multiple device types, how to select an appropriate AAU device according to the user requirement of the proposed base station area becomes a problem to be solved urgently.

Disclosure of Invention

The embodiment of the invention provides a device type selection method and a device, which solve the problem of how to select proper AAU (architecture) devices according to the user requirements of a proposed 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 a device type selection method, including acquiring a historical CQI of a target area under a first mobile communication technology and a scene map in a designated area; determining SINR under the second mobile communication technology according to a first mapping relation between SINR under the second mobile communication technology and CQI under the first mobile communication technology and historical CQI; clustering SINRs under a second mobile communication technology, and determining a central value of each category under the second mobile communication technology; determining an SINR interval under the second 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; simulating a scene map, and determining an accumulative distribution function of SINR of a specified area under a first mobile communication technology; determining a first target SINR of the designated area under the first mobile communication technology according to the cumulative distribution function; determining a second target SINR of the designated area under the second mobile communication technology according to a second mapping relation between the SINR under the first mobile communication technology and the SINR under the second mobile communication technology and the first target SINR; determining the type of equipment deployed in the designated area according to the second target SINR and the corresponding relation; wherein the release time of the first mobile communication technology is earlier than the release time of the second mobile communication technology.

According to the scheme, when the first mobile communication technology is 4G, the second mobile communication technology is 5G, the device type includes 16TR devices, 32TR devices and 64TR devices, and the designated area is the proposed base station area, the device type selection method provided by the embodiment of the invention can determine the SINR of 5G and the corresponding relation between the SINR interval of 5G and the device type based on the historical CQI of the target area in 4G and the first mapping relation; simulating a scene map of a proposed base station area so as to determine user requirements in the proposed base station area; meanwhile, according to the cumulative distribution function of the SINRs of 4G determined by simulating the scene map of the proposed base station area, the first target SINR of the proposed base station area in 4G can be determined, and further, according to the first target SINR of 4G and the second mapping relation, the second target SINR of the proposed base station area in 5G can be determined, so that the type of equipment deployed in the proposed base station area is determined according to the second target SINR and the corresponding relation, a user can predict the type of the 5G equipment to be deployed in the proposed base station area according to the historical CQI of 4G, and the problem of how to select proper AAU equipment according to the user requirement of the proposed base station area is solved.

In a second aspect, an embodiment of the present invention provides an apparatus for device model selection, including: an acquisition unit, configured to acquire a historical CQI of a target area in a first mobile communication technology and a scene map in a designated area; the processing unit is used for determining the SINR under the second mobile communication technology according to the first mapping relation between the SINR under the second mobile communication technology and the CQI under the first mobile communication technology and the historical CQI acquired by the acquisition unit; the processing unit is further used for clustering SINRs under the second mobile communication technology and determining a central value of each category under the second mobile communication technology; the processing unit is further used for determining an SINR interval under the second 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 also used for simulating the scene map acquired by the acquisition unit and determining the cumulative distribution function of the SINR of the designated area under the first mobile communication technology; the processing unit is further used for determining a first target SINR of the designated area under the first mobile communication technology according to the cumulative distribution function; the processing unit is further used for determining a second target SINR of the designated area under the second mobile communication technology according to a second mapping relation between the SINR under the first mobile communication technology and the SINR under the second mobile communication technology and the first target SINR; the processing unit is further used for determining the type of the equipment deployed in the specified area according to the second target SINR and the corresponding relation; wherein the release time of the first mobile communication technology is earlier 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 SINR of 5G and CQI of 4G in a device model selection method according to an embodiment of the present invention;

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

FIG. 8 is a corresponding 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. 9 is a diagram illustrating classification according to a classification requirement of class 3 in a device type selection method according to an embodiment of the present invention;

FIG. 10 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. 11 is a fourth flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;

FIG. 12 is a fifth flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;

fig. 13 is a schematic diagram of a CDF curve of SINR of 4G obtained by simulating a scene map according to an apparatus model selection method provided in an embodiment of the present invention;

FIG. 14 is a sixth flowchart illustrating an apparatus modeling method according to an embodiment of the present invention;

FIG. 15 is a seventh schematic flowchart of a device model selection method according to an embodiment of the present invention;

FIG. 16 is a schematic structural diagram of an apparatus model selection apparatus according to an embodiment of the present invention;

FIG. 17 is a second schematic structural diagram of an apparatus model selection apparatus according to an embodiment of the present invention;

fig. 18 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 concepts related in a concrete fashion.

In the description of the embodiments of the present invention, the meaning of "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 (representing a target area in the invention), a proposed base station area (representing a designated area in the invention) and a device type selection device; the device type selection device comprises an acquisition unit and a processing unit, wherein the acquisition unit needs to respectively acquire a historical Channel Quality Indicator (CQI) of a target area under a first mobile communication technology and a scene map in a designated area; a processing unit, configured to determine an SINR in the second mobile communication technology according to a first mapping relationship between a signal to interference plus noise ratio (SINR) in the second mobile communication technology and a CQI in the first mobile communication technology and the historical CQI acquired by the acquiring unit; the processing unit is further used for clustering SINRs under the second mobile communication technology and determining a central value of each category under the second mobile communication technology; the processing unit is further used for determining an SINR interval under the second 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 configured to simulate the scene map acquired by the acquisition unit, and determine a Cumulative Distribution Function (CDF) of the SINR under the first mobile communication technology; the processing unit is further used for determining a first target SINR under the first mobile communication technology according to the cumulative distribution function; the processing unit is further configured to determine a second target SINR in the second mobile communication technology according to a second mapping relationship between the SINR in the first mobile communication technology and the SINR in the second mobile communication technology and the first target SINR; and the processing unit is further configured to determine the type of the device deployed in the designated area according to the second target SINR and the corresponding relationship.

As shown in fig. 3, 5G is a brand new technical system, and a Massive MIMO technology is used, so that a LTE wide beam coverage mode is changed, and a narrow beam is used for coverage, so that the device type of the wireless side device is largely changed, where the most varied device type is that the number of receiving channels of the AAU device is largely 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 select a proper equipment type for the area where the 5G base station is to be built becomes a difficult point at present, and is an important problem of 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, the user may determine the corresponding relationship between the SINR interval of 5G and the device type by using the historical CQI of the target area in the fourth generation mobile communication technology (the 4th generation mobile communication technology, which is abbreviated as 4G); then, determining a second target SINR of the designated area under a second mobile communication technology through a scene map in the designated area; further, the type of the equipment to be deployed in the scene where the 5G base station is deployed is determined according to the corresponding relation and the second target SINR, and the problem of how to select appropriate AAU equipment according to the user requirement of the base station area to be built is solved.

Illustratively, taking the first mobile communication technology as 4G, the second mobile communication technology as 5G, the device types include 16TR device, 32TR device, and 64TR device, and the designated area is the proposed base station area, and 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:

s101, acquiring historical CQI of a target area under a first mobile communication technology and a scene map in a designated area.

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.

It should be noted that, 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 (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 (hereinafter, referred to as "MR") of one working day and one holiday in a next 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 within a preset time period (such as 3 months) to determine the distribution condition of the CQI (namely 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 the air interface, and i belongs to [0, 15 ]]，CQI^wCQI indicating a working day^eCQI indicating holidays.

S102, according to the historical CQI and the first mapping relation between the SINR under the second mobile communication technology and the CQI under the first mobile communication technology, the SINR under the second mobile communication technology is determined.

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

s1020, determining the SINR under the second mobile communication technology according to the historical CQI and a first mapping relation between the SINR under the second mobile communication technology and the CQI under the first mobile communication technology; wherein the first mapping relationship comprises:

SINR＝1.9346×CQI-6.799；

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

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

SINR＝1.9346×CQI-6.799。

s103, clustering SINRs under the second mobile communication technology, and determining a central value of each category under the second mobile communication technology.

Optionally, the SINR under the second mobile communication technology is clustered, and a central value of each category under the second mobile communication technology is determined, where fig. 7 includes:

s1030, clustering the SINRs under the second mobile communication technology according to a K-means clustering algorithm (K-means for short), and determining the central value of each category under the second mobile communication technology.

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_jSINR, μ, representing the jth 5G_iRepresenting the center value of the ith category.

The specific implementation process is as follows:

1. k SINRs of 5G are randomly selected from the N SINRs of 5G (the SINR under the second mobile communication technology is determined according to the historical CQI and the first mapping relation between the SINR under the second mobile communication technology and the CQI under the first mobile communication technology).

2. For each remaining 5G SINR (including the SINR determined for the second mobile communication technology based on the first mapping of SINR for the second mobile communication technology to CQI for the first mobile communication technology and the historical CQI), its distance V to each center value is measured and assigned to the category of 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 minimum 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 the SINR under the second mobile communication technology is clustered by k-means, it is not preferable that the more the center values (each center value corresponds to a category) are selected, as shown in fig. 8 (the abscissa is the number of categories, and the ordinate is the average profile value), the clustering result obtained when the selected center values are 2 is optimal, the clustering result obtained when the selected center values are 3 is slightly poor, and the clustering result obtained when the selected center values 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 under the second mobile communication technology through k-means, 3-4 central values are selected so as to obtain 3-4 classes; illustratively, the specific implementation process of clustering the SINRs under the second mobile communication technology is as follows:

1. in terms of the classification into 3 classes, the classification case is given in fig. 9 and the contour case is given in fig. 10.

2. Then, the central value of each category in the clustering algorithm is obtained and is marked as C_T1，C_T2，C_T3。

S104, determining an SINR interval under the second 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 can be obtained through S103, so that SINR intervals can be divided according to the center value; 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 S105, simulating the scene map, and determining the cumulative distribution function of SINR of the specified area under the first mobile communication technology.

Optionally, the scene map includes a three-dimensional (3 Dimensions, 3D for short) map or a planning map; simulating a scene map, and determining a cumulative distribution function of SINRs of a specified area under the first mobile communication technology, as shown in fig. 11 and 12, including:

and S1050, simulating the 3D map, and determining the cumulative distribution function of SINR of the specified area under the first mobile communication technology.

Or,

s1051, simulating the planning graph, and determining the cumulative distribution function of SINR of the specified area under the first mobile communication technology;

wherein the cumulative distribution function comprises:

F(CDF)＝a×SINR³+b×SINR²+c×SINR+d；

wherein, f (cdf) represents the probability of SINR occurrence of the user in the first mobile communication technology, SINR represents SINR in the first mobile communication technology, and a, b, c and d are all constants.

It should be noted that, in practical applications, determining the cumulative distribution function of SINR according to a 3D map or a planning map includes:

1. scene reproduction method

A 3D map with a specified accuracy (e.g., a 3D map with an accuracy of 2 × 2 m) may be obtained, after the 3D map is imported into simulation software (e.g., Atoll), base station parameters are configured, user point scattering simulation is performed, and then a CDF curve of SINR is calculated, as shown in fig. 13:

F(CDF)＝a×SINR³+b×SINR²+c×SINR+d；

wherein a, b, c, d are all constants, SINR represents SINR under the first mobile communication technology, and f (cdf) is the probability of occurrence of the user at the SINR.

2. Scene hypothesis method

The method is suitable for scenes without base station construction, and under the condition that only the information of buildings and other buildings is known, the occupation ratio conditions of different types of penetration loss need to be calculated, and the specific situation is shown in table 2:

TABLE 2

Type of penetration loss	Ratio of penetration loss
		Outdoors (outdoor)
Indoor low penetration loss
		High indoor wear

Illustratively, the outdoor (outdoor) ratio of the penetration loss may be 20%, the indoor ratio of the penetration loss with low penetration loss may be 40%, and the indoor ratio of the penetration loss with high penetration loss may be 40% (obtained from the third Generation Partnership Project (3rd Generation Partnership Project, 3GPP for short) TR 38.901 table 7.4.3-2).

Then, based on the ratio of the transmission loss to the base station parameter of the cell, simulation is performed using system simulation software (such as matlab, etc.), user point spreading simulation is performed, and then a CDF curve of SINR is calculated, as shown in fig. 13 (SINR on the abscissa, f (CDF) on the ordinate):

F(CDF)＝a×SINR³+b×SINR²+c×SINR+d；

wherein, a, b, c, d are constants respectively, SINR represents SINR under the first mobile communication technology, and f (cdf) is the probability of occurrence of the user at the SINR.

Wherein, the ratio of the penetration loss is determined by scene construction of different penetration loss models defined in 38.901 standard, and the parameters of the base station include: simulation scene, Inter-Site Distance (ISD), Site number (total number of surrounding base stations), base station antenna height, channel model (channel model specified in TS38.901 standard according to 3rd generation Partnership Project (3 GPP)), subcarrier spacing, service model, number of users per sector, user distribution, indoor and outdoor user distribution (different transmission loss ratios), user mobility, frequency band, system bandwidth, number of Physical Resource Blocks (PRB), frame structure, Evolved Node B (eNB), transmission power, number of antenna array elements, antenna array element radiation model, and number of transceiving units.

And S106, determining a first target SINR of the designated area under the first mobile communication technology according to the cumulative distribution function.

Optionally, determining a first target SINR of the specified area under the first mobile communication technology according to the cumulative distribution function, as shown in fig. 14, includes:

s1060, determining the specified SINR of the specified area under the first mobile communication technology according to the cumulative distribution function;

s1061, determining an inverse function according to the cumulative distribution function; wherein the inverse function comprises:

SINR′^＝F^-1(CDF)＝(a×SINR³+b×SINR²+c×SINR+d)^-1；

wherein SINR' and F^-1(CDF) each represents an inverse function, SINR represents SINR at the first mobile communication technology, and a, b, c, and d are all constants.

In practical applications, the probabilities of users appearing in different SINRs are different, and in order to reflect the average requirement of users in a specified area as much as possible, the SINR corresponding to the point with the user appearance probability of 50% is selected as the first target SINR of the specified area, as shown in fig. 13.

Specifically, the determining the first target SINR includes:

if F (CDF) is 50%, a × SINR is obtained according to F (CDF)³+b×SINR²+ c × SINR + d, a specified SINR for the point corresponding to the selected user occurrence probability of 50% may be determined.

Then, the specified SINR is substituted into an inverse function, thereby determining a first target SINR.

S1062, determining a first target SINR of the designated area under the first mobile communication technology according to the inverse function and the designated SINR.

S107, according to a second mapping relation between the SINR under the first mobile communication technology and the SINR under the second mobile communication technology and the first target SINR, determining a second target SINR of the designated area under the second mobile communication technology.

Optionally, according to a second mapping relationship between the SINR in the first mobile communication technology and the SINR in the second mobile communication technology and the first target SINR, determining a second target SINR of the designated area in the second mobile communication technology, as shown in fig. 15, including:

s1070, determining a second target SINR of the designated area under the second mobile communication technology according to a second mapping relation between the SINR under the first mobile communication technology and the SINR under the second mobile communication technology and the first target SINR; wherein the second mapping relationship comprises:

SINR″＝SINR′+3；

where SINR "represents the second target SINR and SINR' represents the first target SINR.

S108, determining the type of equipment deployed in the designated area according to the second target SINR and the corresponding relation; wherein the release time of the first mobile communication technology is earlier than the release time of the second mobile communication technology.

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

selecting the device type according to the SINR interval to which the second target SINR belongs; for example, when the second 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 second target SINR of any cell in the specified area belongs to the T2 interval, determining that the type of the device deployed in the cell is 32TR device; and when the second target SINR of any cell in the specified area belongs to the T3 interval, determining that the type of the device 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 the third Generation mobile communication technology (3rd-Generation, abbreviated as: 3G), since the release time of the first mobile communication technology is earlier than that of the second mobile communication technology, the second mobile communication technology can only be the mobile communication technology whose release time is later than 3G; such as: 4G or 5G.

Example two

An embodiment of the present invention provides an apparatus model selection device 10, as shown in fig. 16, including:

an obtaining unit 101 is configured to obtain a historical CQI of a target area under a first mobile communication technology and a scene map in a designated area.

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

The processing unit 102 is further configured to cluster SINRs in the second mobile communication technology, 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 second 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 simulate the scene map acquired by the acquiring unit 101, and determine a cumulative distribution function of SINR of the specified area under the first mobile communication technology;

the processing unit 102 is further configured to determine a first target SINR of the specified area under the first mobile communication technology according to the cumulative distribution function;

the processing unit 102 is further configured to determine a second target SINR of the designated area in the second mobile communication technology according to a second mapping relationship between the SINR in the first mobile communication technology and the SINR in the second mobile communication technology and the first target SINR;

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

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

SINR＝1.9346×CQI-6.799；

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

Optionally, the scene map includes a 3D map or a planning map;

a processing unit 102, configured to specifically simulate the 3D map acquired by the acquiring unit 101, and determine a cumulative distribution function of SINR of the specified area under the first mobile communication technology;

or,

a processing unit 102, configured to specifically simulate the planning map acquired by the acquiring unit 101, and determine an accumulated distribution function of SINR of the specified area in the first mobile communication technology;

wherein the cumulative distribution function comprises:

F(CDF)＝a×SINR³+b×SINR²+c×SINR+d；

Optionally, the processing unit 102 is specifically configured to determine, according to the cumulative distribution function, a specified SINR of the specified area in the first mobile communication technology; a processing unit 102, specifically configured to determine an inverse function according to the cumulative distribution function; wherein the inverse function comprises:

SINR′＝F^-1(CDF)＝(a×SINR³+b×SINR²+c×SINR+d)^-1；

wherein SINR' and F^-1(CDF) each represents an inverse function, SINR represents SINR under the first mobile communication technology, a, b, c and d are all constants;

the processing unit 102 is specifically configured to determine a first target SINR of the specified area under the first mobile communication technology according to the inverse function and the specified SINR.

Optionally, the processing unit 102 is specifically configured to determine a second target SINR of the designated area in the second mobile communication technology according to a second mapping relationship between the SINR in the first mobile communication technology and the SINR in the second mobile communication technology and the first target SINR; wherein the second mapping relationship comprises:

SINR″＝SINR′+3；

Specifically, in an actual application, as shown in fig. 17, the obtaining unit in the device type selection apparatus includes a whole-network 4G MR data extracting module, and the whole-network 4G MR data extracting module is configured to obtain a historical CQI of a target area under a first mobile communication technology and a scene map in a specified area; 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 second mobile communication technology according to a first mapping relation between the SINR under the second mobile communication technology and the CQI under the first mobile communication technology and the historical CQI acquired by the whole-network 4G MR data extraction module; the 5G equipment selection judgment method selection module is used for clustering SINRs under the second mobile communication technology and determining the central value of each category under the second mobile communication technology; the 5G device selection judgment method selection module is also used for determining an SINR interval under the second 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 judgment method selection module is used for simulating a scene map acquired by the whole-network 4G MR data extraction module and determining an accumulative distribution function of SINR of a specified area under the first mobile communication technology; the 5G equipment selection judgment method selection module is also used for determining a first target SINR of the designated area under the first mobile communication technology according to the cumulative distribution function; the 5G device selection decision method selection module is further configured to determine a second target SINR of the designated area in the second mobile communication technology according to a second mapping relationship between the SINR in the first mobile communication technology and the SINR in the second mobile communication technology and the first target SINR; and the 5G equipment selection judgment method selection module is also used for determining the equipment type deployed in the specified area according to the corresponding relation between the second target SINR and 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 the actions of the device selection apparatus, for example, the processing unit is used for supporting the device selection apparatus to execute the processes S101, S102, S103, S104, S105, S106, S107 and S108 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. 18 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 for controlling 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, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic Disc storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program 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 scheme of the 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 logical functional division, and other divisions may be realized in practice, for example, a plurality of 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 may be embodied in the form of a software product, which is stored in a storage medium and includes 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 U disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

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 appended claims.

Claims

1. A method for device model selection, comprising:

acquiring historical CQI of a target area under a first mobile communication technology and a scene map in a designated area;

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

clustering SINRs under the second mobile communication technology, and determining a central value of each category under the second mobile communication technology;

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

simulating the scene map, and determining a cumulative distribution function of SINR of the specified area under the first mobile communication technology;

determining a first target SINR of the designated area under a first mobile communication technology according to the cumulative distribution function;

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

determining the type of equipment deployed in the specified area according to the second target SINR and the corresponding relation; wherein a release time of the first mobile communication technology is earlier 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 second mobile communication technology according to the historical CQI and the first mapping relationship between the SINR in the second mobile communication technology and the CQI in the first mobile communication technology comprises:

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

SINR＝1.9346×CQI-6.799；

3. The device selection method according to claim 1, wherein clustering the SINRs under the second mobile communication technology and determining the center value of each class under the second mobile communication technology comprises:

and clustering the SINR under the second mobile communication technology according to a k-means clustering algorithm, and determining the central value of each category under the second mobile communication technology.

4. The device selection method according to claim 1, wherein the scene map comprises a 3D map or a planning map;

simulating the scene map, and determining a cumulative distribution function of SINRs of the specified area under the first mobile communication technology, including:

simulating the 3D map, and determining a cumulative distribution function of SINR of the specified area under the first mobile communication technology;

or,

simulating the planning graph, and determining the cumulative distribution function of SINR of the specified area under the first mobile communication technology;

wherein the cumulative distribution function comprises:

F(CDF)＝a×SINR³+b×SINR²+c×SINR+d；

5. The device selection method according to any of claims 1-4, wherein determining a first target SINR for the specified area under the first mobile communication technology according to the cumulative distribution function comprises:

determining a specified SINR of the specified area under the first mobile communication technology according to the cumulative distribution function;

determining an inverse function according to the cumulative distribution function; wherein the inverse function comprises:

SINR′＝F^-1(CDF)＝(a×SINR³+b×SINR²+c×SINR+d)^-1；

and determining a first target SINR of the specified area under the first mobile communication technology according to the inverse function and the specified SINR.

6. The device type selection method according to any one of claims 1-4, wherein determining the second target SINR of the designated area in the second mobile communication technology according to the first target SINR and the second mapping relationship between the SINR in the first mobile communication technology and the SINR in the second mobile communication technology comprises:

determining a second target SINR of the designated area under the second mobile communication technology according to a second mapping relation between the SINR under the first mobile communication technology and the SINR under the second mobile communication technology and the first target SINR; wherein the second mapping relationship comprises:

SINR″＝SINR′+3；

7. An apparatus model selection device, comprising:

an acquisition unit, configured to acquire a historical CQI of a target area in a first mobile communication technology and a scene map in a designated area;

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

the processing unit is further configured to cluster SINR in the second mobile communication technology, and determine a central value of each category in the second mobile communication technology;

the processing unit is further configured to determine an SINR interval in a second 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 simulate the scene map acquired by the acquiring unit, and determine an accumulated distribution function of SINR of the designated area under a first mobile communication technology;

the processing unit is further configured to determine a first target SINR of the designated area under a first mobile communication technology according to the cumulative distribution function;

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

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

8. The device selection apparatus according to claim 7, wherein the processing unit is specifically configured to determine the SINR in the second mobile communication technology according to a first mapping relationship between the SINR in the second mobile communication technology and the CQI in the first mobile communication technology and the historical CQI acquired by the acquiring unit; wherein the first mapping relationship comprises:

SINR＝1.9346×CQI-6.799；

9. The device selection apparatus according to claim 7, wherein the processing unit is specifically configured to cluster SINRs under the second mobile communication technology according to a k-means clustering algorithm, and determine a central value of each category under the second mobile communication technology.

10. The device selection apparatus according to claim 7, wherein the scene map comprises a 3D map or a planning map;

the processing unit is specifically configured to simulate the 3D map acquired by the acquiring unit, and determine an accumulated distribution function of SINR of the specified area in a first mobile communication technology;

or,

the processing unit is specifically configured to simulate the planning map acquired by the acquiring unit, and determine an accumulated distribution function of SINR of the specified area in a first mobile communication technology;

wherein the cumulative distribution function comprises:

F(CDF)＝a×SINR³+b×SINR²+c×SINR+d；

11. The device selection apparatus according to any of claims 7 to 10, wherein the processing unit is specifically configured to determine, according to the cumulative distribution function, a specified SINR of the specified area under the first mobile communication technology;

the processing unit is specifically configured to determine an inverse function according to the cumulative distribution function; wherein the inverse function comprises:

SINR′＝F^-1(CDF)＝(a×SINR³+b×SINR²+c×SINR+d)^-1；

the processing unit is specifically configured to determine, according to the inverse function and the specified SINR, a first target SINR of the specified area in the first mobile communication technology.

12. The device selection apparatus according to any of claims 7 to 10, wherein the processing unit is specifically configured to determine a second target SINR for the designated area in the second mobile communication technology according to a second mapping relationship between SINR in the first mobile communication technology and SINR in the second mobile communication technology and the first target SINR; wherein the second mapping relationship comprises:

SINR″＝SINR′+3；

13. 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 1-6.

14. 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 claims 1 to 6.