CN110621025B - Equipment model selection method and device - Google Patents
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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. Clustering typical scene simulation data, and determining a central value of each category under a first 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 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; and determining the type of the equipment deployed in the specified area according to the second target SINR value and the corresponding relation.
Description
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 the 5G network 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 an apparatus type selection method, including: acquiring typical scene simulation data and a scene map in a specified area under a first mobile communication technology; the typical scene simulation data is obtained by simulating a typical scene under the first mobile communication technology, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas; clustering typical scene simulation data, and determining a central value of each category under a first 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; simulating the scene map, and determining the cumulative distribution function of SINR of the designated area under the second mobile communication technology; determining a first target SINR of the designated area under the second mobile communication technology according to the cumulative distribution function; determining a second target SINR of the designated area under the first mobile communication technology according to the 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 value and the corresponding relation; 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 scheme, when the first mobile communication technology is 5G, the second mobile communication technology is 4G, the device types comprise 16TR devices, 32TR devices and 64TR devices, and the designated area is the area where the base station is to be built, the device model selection method provided by the embodiment of the invention simulates a 5G typical scene, so that the typical scene simulation data under 5G is obtained; clustering the simulation data to obtain a central value of each category, determining an SINR interval under the first mobile communication technology 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; meanwhile, a cumulative distribution function of SINRs of 4G determined by simulating a scene map of a proposed base station area can determine a first target SINR of the proposed base station area in 4G, and further can determine a second target SINR of the proposed base station area in 5G according to the first target SINR of 4G and a mapping relation, 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 5G equipment to be deployed in the proposed base station area according to 5G typical scene simulation data and 4G typical scene simulation data, and the problem of how to select proper AAU equipment according to the user requirements 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 typical scene simulation data in a first mobile communication technology and a scene map in a specified area; the typical scene simulation data is obtained by simulating a typical scene under the first mobile communication technology, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas; the processing unit is used for clustering the typical scene simulation data acquired by the acquisition unit and determining the central value of each category in the first 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 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 second mobile communication technology; the processing unit is further used for determining a first target SINR of the designated area under the second 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 first mobile communication technology according to the 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 value and the corresponding relation; 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 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. 7 is a schematic diagram of classification according to a classification requirement into 3 classes of a device model selection method according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of contour cases obtained according to the requirement of classification into 3 classes of a device model selection method provided by the embodiment of the invention;
FIG. 9 is a third flowchart illustrating a device model selection method according to an embodiment of the present invention;
FIG. 10 is a fourth flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;
fig. 11 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. 12 is a fifth flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;
FIG. 13 is a sixth flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;
FIG. 14 is a schematic structural diagram of an apparatus model selection apparatus according to an embodiment of the present invention;
FIG. 15 is a second schematic structural diagram of an apparatus model selection apparatus according to an embodiment of the present invention;
fig. 16 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: 5G, a base station area (representing a designated area in the invention) and a device type selection device are proposed; the device type selection device comprises an acquisition unit and a processing unit, wherein the acquisition unit needs to acquire typical scene simulation data under a first mobile communication technology and a scene map in a designated area respectively; the processing unit is used for clustering the typical scene simulation data acquired by the acquisition unit and determining the central value of each category in the first mobile communication technology; the processing unit is further configured to determine a signal to interference plus noise ratio (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 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 second mobile communication technology; the processing unit is further used for determining a first target SINR under the second mobile communication technology according to the cumulative distribution function; the processing unit is further configured to determine a second target SINR in the first mobile communication technology according to the mapping relationship between the SINR in the first mobile communication technology and 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 value and the corresponding relationship.
As shown in fig. 3, 5G is a brand new technical system, a massive antenna technology masivemimo technology is used, 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 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 and 8 TR. 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 model selection method provided in the embodiment of the present invention, multiple 5G typical scenes are simulated, typical scene simulation data under 5G is obtained, meanwhile, simulation data of a fourth-generation mobile communication technology (the 4 generation mobile communication technology, abbreviated as 4G) that needs to deploy the 5G scene is considered, and then, comprehensive analysis and consideration are performed according to the 4G simulation data and the typical scene simulation data, so that a device type that needs to be deployed in a scene in which a 5G base station is deployed is determined, and a problem of how to select a suitable AAU device according to a user requirement of a proposed base station area is solved.
Illustratively, the first mobile communication technology is 5G, the second mobile communication technology is 4G, the device types include 16TR devices, 32TR devices, and 64TR devices, and the designated area is a proposed base station area, which is described as an example, 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, obtaining typical scene simulation data under a first mobile communication technology and a scene map in a designated area; wherein the typical scene simulation data is obtained by simulating a typical scene under the first 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, 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 (3rd 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.
S102, clustering typical scene simulation data, and determining the central value of each category under the first mobile communication technology.
Optionally, clustering the typical scene simulation data, and determining a central value of each category in the first mobile communication technology, as shown in fig. 5, includes:
s1020, clustering the typical scene simulation data according to a K-means clustering algorithm (K-means clustering, for short, K-means), and determining a central value of each category under the first 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 classjSINR, μ, representing the jth 5GiRepresenting the center value of the ith category.
The specific implementation process is as follows:
1. k SINRs of 5G are randomly selected from N pieces of 5G typical scene simulation data to serve as central values.
2. The distance V to each center value is measured for each of the remaining 5G 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 typical scene simulation data is clustered through 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. 6 (the abscissa is the number of categories, and the ordinate is the average contour 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 the clustering result needs to be divided into 3-4 types according to the cost of the equipment and the requirement of the multi-antenna multi-channel gain of the 5G MassvieMO; therefore, starting from the requirement of optimizing cost performance, when clustering is carried out on typical scene simulation data through k-means, 3-4 central values are selected so as to obtain 3-4 classes; for example, the specific implementation process for clustering typical scene simulation data is as follows:
1. in terms of the classification into 3 classes, the classification case is given in fig. 7 and the contour case is given in fig. 8.
2. Then, the central value of each category in the clustering algorithm is obtained and is marked as CT1，CT2，CT3。
S103, 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.
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).
S104, simulating the scene map, and determining the cumulative distribution function of SINR of the specified area under the second mobile communication technology.
Optionally, the scene map includes a three-dimensional (3 Dimensions, 3D for short) map or a planning map; simulating the scene map, and determining the cumulative distribution function of SINR of the specified area under the second mobile communication technology, as shown in fig. 9 and 10, including:
s1040, simulating the 3D map, and determining an accumulative distribution function of SINR of the designated area under the second mobile communication technology;
alternatively, the first and second electrodes may be,
s1041, simulating the planning graph, and determining the cumulative distribution function of SINR of the specified area under the second 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 second mobile communication technology, SINR represents SINR in the second 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. 11:
F(CDF)＝a×SINR3+b×SINR2+c×SINR+d；
wherein, f (cdf) represents the probability of SINR occurrence of the user in the second mobile communication technology, SINR represents SINR in the second mobile communication technology, and a, b, c and d are all constants.
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 conditions are shown in table 1:
TABLE 1
| Type of penetration loss | Ratio of penetration loss |
| Outdoors (outdoor) | |
| Indoor low penetration loss | |
| High indoor wear |
Illustratively, the outdoor (outdoor) penetration loss ratio may be 20%, the indoor low-penetration loss ratio may be 40%, and the indoor high-penetration loss ratio may be 40% (taken from 3GPP tr38.9011 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. 11 (SINR on the abscissa, f (CDF) on the ordinate):
F(CDF)＝a×SINR3+b×SINR2+c×SINR+d；
wherein, f (cdf) represents the probability of SINR occurrence of the user in the second mobile communication technology, SINR represents SINR in the second mobile communication technology, and a, b, c and d are all constants.
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: the method comprises the following steps of simulation scene, station spacing (overall English name: Inter-Site Distance, abbreviated as ISD), station number (total number of surrounding base stations), base station antenna height, channel model, subcarrier spacing, service model, user number per sector, user distribution, indoor and outdoor user distribution (different penetration loss ratios), user mobility, frequency band, system bandwidth, Physical Resource Block (overall English name: Physical Resource Block, abbreviated as PRB) number, frame structure, Evolved Node B (overall English name: Evolved Node B, abbreviated as eNB) transmitting power, antenna array sub number, antenna array sub radiation model and receiving and transmitting unit number.
And S105, determining a first target SINR of the designated area under the second mobile communication technology according to the cumulative distribution function.
Optionally, determining a first target SINR of the designated area under the second mobile communication technology according to the cumulative distribution function, as shown in fig. 12, includes:
and S1050, determining the specified SINR of the specified area under the second mobile communication technology according to the cumulative distribution function.
S1051, 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 under the second mobile communication technology, and a, b, c, and d are constants;
and S1052, determining a first target SINR of the designated area under the second mobile communication technology according to the inverse function and the designated SINR.
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. 11.
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.
The specified SINR is then substituted into an inverse function, thereby determining a first target SINR.
And S106, determining a second target SINR of the designated area under the first mobile communication technology according to the 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.
Optionally, determining a second target SINR of the designated area in the first mobile communication technology according to the 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, as shown in fig. 13, includes:
s1060, determining a second target SINR of the designated area under the first mobile communication technology according to the 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 mapping relation comprises:
SINR″＝SINR′+3；
where SINR "represents the second target SINR and SINR' represents the first target SINR.
S107, determining the type of equipment deployed in the specified area according to the second target SINR value and the corresponding relation; 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 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 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: third Generation mobile communication technology (3rd-Generation, abbreviated as 3G) or 4G.
According to the scheme, when the first mobile communication technology is 5G, the second mobile communication technology is 4G, the device types comprise 16TR devices, 32TR devices and 64TR devices, and the designated area is the area where the base station is to be built, the device model selection method provided by the embodiment of the invention simulates a 5G typical scene, so that the typical scene simulation data under 5G is obtained; clustering the simulation data to obtain a central value of each category, determining an SINR interval under the first mobile communication technology 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; meanwhile, a cumulative distribution function of SINRs of 4G determined by simulating a scene map of a proposed base station area can determine a first target SINR of the proposed base station area in 4G, and further can determine a second target SINR of the proposed base station area in 5G according to the first target SINR of 4G and a mapping relation, 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 5G equipment to be deployed in the proposed base station area according to 5G typical scene simulation data and 4G typical scene simulation data, and the problem of how to select proper AAU equipment according to the user requirements of the proposed base station area is solved.
Example two
An embodiment of the present invention provides an apparatus model selecting device 10, as shown in fig. 14, including:
an acquiring unit 101, configured to acquire typical scene simulation data in a first mobile communication technology and a scene map in a specified area; the typical scene simulation data is obtained by simulating a typical scene under the first mobile communication technology, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas;
the processing unit 102 is configured to cluster the typical scene simulation data acquired by the acquiring unit 101, and determine a central value of each category in the first 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 simulate the scene map acquired by the acquiring unit 101, and determine a cumulative distribution function of SINR of the specified area under the second mobile communication technology;
the processing unit 102 is further configured to determine a first target SINR of the designated area under the second 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 first mobile communication technology according to the first target SINR and a mapping relationship between the SINR in the first mobile communication technology and the SINR in the second mobile communication technology;
the processing unit 102 is further configured to determine, according to the second target SINR value and the corresponding relationship, a device type 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 cluster the typical scene simulation data acquired by the acquiring unit 101 according to a k-means clustering algorithm, and determine a central value of each category in the first mobile communication technology.
Optionally, the scene map includes a 3D map or a planning map;
the processing unit 102 is specifically configured to simulate the 3D map acquired by the acquiring unit 101, and determine an accumulated distribution function of SINR of the specified area under the second mobile communication technology;
alternatively, the first and second electrodes may be,
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 second 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 second mobile communication technology, SINR represents SINR in the second mobile communication technology, and a, b, c and d are all constants.
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 second 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 second mobile communication technology, and a, b, c, and d are constants;
the processing unit 102 is specifically configured to determine a first target SINR of the specified area under the second 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 first mobile communication technology according to a 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 mapping relation comprises:
SINR″＝SINR′+3；
where SINR "represents the second target SINR and SINR' represents the first target SINR.
Specifically, in practical applications, as shown in fig. 15, the obtaining unit in the device type selection apparatus includes a 5G typical scene simulation and data extraction module, where the 5G typical scene simulation and data extraction module is configured to obtain typical scene simulation data in the first mobile communication technology and a scene map in a specified area; the processing unit comprises a 5G equipment selection judgment method selection module and a 5G equipment selection module; the 5G equipment selection judgment method selection module is used for clustering typical scene simulation data acquired by the 5G typical scene simulation and data extraction module and determining the central value of each category in the first 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 simulating the scene map acquired by the 5G typical scene simulation and data extraction module and determining the cumulative distribution function of SINR of the specified area under the second mobile communication technology; the 5G equipment selection module is further used for determining a first target SINR of the designated area under the second mobile communication technology according to the cumulative distribution function; the 5G device selection module is further used for determining a second target SINR of the designated area under the first mobile communication technology according to the 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; and the 5G device selection module is further used for determining the device type deployed in the specified area according to the second target SINR value and the corresponding relation determined by the 5G device 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 and S107 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. 16 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 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 (6)
1. A method for device model selection, comprising:
acquiring typical scene simulation data and a scene map in a specified area under a first mobile communication technology; wherein the typical scene simulation data is obtained by simulating a typical scene under a first mobile communication technology, and the typical scene comprises at least one of dense urban areas, suburban areas and open areas;
clustering the typical scene simulation data, and determining the central value of each category under the first 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;
simulating the scene map, and determining the cumulative distribution function of SINR of the specified area under the second mobile communication technology;
determining a first target SINR of the designated area under a second mobile communication technology according to the cumulative distribution function;
determining a second target SINR of the designated area under the first mobile communication technology according to the 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 value and the corresponding relation; wherein a release time of the first mobile communication technology is later than a release time of the second mobile communication technology; the device types comprise 16TR devices, 32TR devices and 64TR devices;
the clustering the typical scene simulation data and determining the central value of each category under the first mobile communication technology comprises:
clustering the typical scene simulation data according to a k-means clustering algorithm and a used distance formula, and determining a central value of each category under a first mobile communication technology; the distance formula used is:
where V represents the distance between the SINR of 5G and the center value of the specified class, xjSINR, μ, representing the jth 5GiRepresents the center value of the ith category;
the determining a first target SINR of the designated area under a second mobile communication technology according to the cumulative distribution function includes:
determining a specified SINR of the specified area under a second mobile communication technology according to the cumulative distribution function; 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 under the second mobile communication technology, SINR represents SINR under the second mobile communication technology, and a, b, c and d are constants;
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 under the second mobile communication technology, and a, b, c, and d are constants;
and determining a first target SINR of the designated area under the second mobile communication technology according to the inverse function and the designated SINR.
2. The device type selection method of claim 1, wherein determining the second target SINR for the designated area in the first mobile communication technology according to the 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 comprises:
determining a second target SINR of the designated area under the first mobile communication technology according to the 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 mapping relationship comprises:
SINR″＝SINR′+3；
where SINR "represents the second target SINR and SINR' represents the first target SINR.
3. An apparatus model selection device, comprising:
an acquisition unit, configured to acquire typical scene simulation data in a first mobile communication technology and a scene map in a specified area; wherein the typical scene simulation data is obtained by simulating a typical scene under a first mobile communication technology, and the typical scene comprises at least one of dense urban areas, suburban areas and open areas;
the processing unit is used for clustering the typical scene simulation data acquired by the acquisition unit and determining the central value of each category in the first 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 simulate the scene map acquired by the acquiring unit, and determine an accumulated distribution function of SINR of the designated area under a second mobile communication technology;
the processing unit is further configured to determine a first target SINR of the designated area under a second 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 first mobile communication technology according to the first target SINR and a mapping relationship between SINR in the first mobile communication technology and SINR in the second mobile communication technology;
the processing unit is further configured to determine, according to the second target SINR value and the correspondence, a device type 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; the device types comprise 16TR devices, 32TR devices and 64TR devices;
the processing unit is specifically configured to cluster the typical scene simulation data according to a k-means clustering algorithm and a used distance formula, and determine a central value of each category in the first mobile communication technology; the distance formula used is:
where V represents the distance between the SINR of 5G and the center value of the specified class, xjSINR, μ, representing the jth 5GiRepresents the center value of the ith category;
the processing unit is specifically configured to determine, according to the cumulative distribution function, a specified SINR of the specified area in a second 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 under the second mobile communication technology, SINR represents SINR under the second mobile communication technology, and a, b, c and d are constants;
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；
wherein SINR' and F-1(CDF) each represents an inverse function, SINR represents SINR at the second mobile communication technology, and a, b, c, and d are all constants.
4. The device selection apparatus according to claim 3, wherein the processing unit is specifically configured to determine a second target SINR of the designated area in the first mobile communication technology according to a 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 mapping relationship comprises:
SINR″＝SINR′+3；
where SINR "represents the second target SINR and SINR' represents the first target SINR.
5. A computer storage medium comprising instructions which, when run on a computer, cause the computer to perform the device selection method of claim 1 or 2.
6. 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 device type selection device runs, the processor executes the computer-executable instructions stored in the memory so as to enable the device type selection device to execute the device type selection method as claimed in claim 1 or 2.
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