CN110677206A - Equipment model selection method and device - Google Patents

Equipment model selection method and device Download PDF

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CN110677206A
CN110677206A CN201910877799.5A CN201910877799A CN110677206A CN 110677206 A CN110677206 A CN 110677206A CN 201910877799 A CN201910877799 A CN 201910877799A CN 110677206 A CN110677206 A CN 110677206A
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CN110677206B (en
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杨艳
冯毅
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools

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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 replace a device of a built base station area with a proper AAU device according to the user requirement of the built base station area. The method comprises the steps of acquiring typical channel simulation data under a first mobile communication technology and historical CQI in a specified area; according to typical channel simulation data, determining a cumulative distribution function of SINR; determining the median value of each equipment type in the SINR according to the cumulative distribution function; determining an SINR interval according to the SINR median, matching the SINR interval with the device type, and determining the corresponding relation between the SINR interval and the device type; determining a first target SINR of the designated area according to the mapping relation between the SINR and the CQI and the historical CQI; and determining the type of equipment deployed in the specified area according to the first 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 the 5G network has a plurality of device types, how to replace the device in the existing base station area with the appropriate AAU device according to the user requirement of the existing base station area becomes a problem to be solved urgently.
Disclosure of Invention
Embodiments of the present invention provide an apparatus type selection method and apparatus, which solve the problem of how to replace an apparatus in an established base station area with a suitable AAU apparatus according to a user requirement of the established base station area.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides an apparatus type selection method, including: acquiring typical channel simulation data under a first mobile communication technology and historical CQI under a second mobile communication technology in a designated area; the typical channel simulation data is obtained by simulating a typical scene under the first mobile communication technology according to a specified channel model, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas; determining a cumulative distribution function of SINR under the first mobile communication technology according to the typical channel simulation data; determining the SINR median value of each device type under the first mobile communication technology according to the cumulative distribution function; according to the SINR median value, determining an SINR interval under the first mobile communication technology, matching the SINR interval with the device type, and determining the corresponding relation between the SINR interval and the device type; determining a first 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 CQI under the second mobile communication technology and the historical CQI; determining the type of equipment deployed in the specified area according to the first target SINR 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 above scheme, when the first mobile communication technology is 5G, the second mobile communication technology is 4G, the device type includes 16TR devices, 32TR devices and 64TR devices, and the designated area is an established base station area, the device model selection method provided in the embodiment of the present invention may determine typical channel simulation data of 5G by simulating a typical scene of 5G according to a designated channel model, determine an accumulated distribution function of SINR of 5G according to the typical channel simulation data, and further determine a corresponding relationship between an SINR interval of 5G and the device type according to the accumulated distribution function; according to the historical CQI in the established base station area and the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology, the user requirement in the established base station area can be determined; meanwhile, the device type deployed in the designated area is determined according to the first target SINR and the corresponding relation, so that a user can replace the device in the established base station area with a proper device type according to the 5G typical channel simulation data and the historical CQI in the established base station area, and the problem of how to replace the device in the established base station area with a proper AAU device according to the user requirement of the established base station area is solved.
In a second aspect, an embodiment of the present invention provides an apparatus for device model selection, including: an obtaining unit, configured to obtain typical channel simulation data in a first mobile communication technology and a historical CQI in a specified area in a second mobile communication technology; the typical channel simulation data is obtained by simulating a typical scene under the first mobile communication technology according to a specified channel model, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas; the processing unit is used for determining the cumulative distribution function of SINR under the first mobile communication technology according to the typical channel simulation data acquired by the acquisition unit; the processing unit is further used for determining the SINR median value of each device type under the first mobile communication technology according to the cumulative distribution function; the processing unit is further configured to determine an SINR interval in the first mobile communication technology according to the SINR median, 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 used for determining a first 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 CQI under the second mobile communication technology and the historical CQI acquired by the acquiring unit; the processing unit is further used for determining the type of equipment deployed in the specified area according to the first target SINR 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 distribution of SINR of 5G of 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 fourth flowchart illustrating an apparatus model selection method according to an embodiment of the present invention;
fig. 9 is a mapping relationship diagram of SINR and CQI of a device type selection method according to an embodiment of the present invention;
FIG. 10 is a schematic structural diagram of an apparatus model selection apparatus according to an embodiment of the present invention;
FIG. 11 is a second schematic structural diagram of an apparatus model selection apparatus according to an embodiment of the present invention;
fig. 12 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 built 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 typical channel simulation data under a first mobile communication technology and historical channel quality indicators (CQI for short) under a second mobile communication technology in a designated area; a processing unit, configured to determine, according to the typical channel simulation data acquired by the acquiring unit, a Cumulative Distribution Function (CDF) of signal to interference plus noise ratio (SINR) in the first mobile communication technology; the processing unit is further used for determining the SINR median value of each device type under the first mobile communication technology according to the cumulative distribution function; the processing unit is further configured to determine an SINR interval in the first mobile communication technology according to the SINR median, 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 used for determining a first 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 CQI under the second mobile communication technology and the historical CQI acquired by the acquiring unit; and the processing unit is further used for determining the type of the equipment deployed in the specified area according to the first target SINR and the corresponding relation.
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 replace the equipment of the established base station area with proper AAU equipment according to the user requirements of the established base station area is an important problem for balancing the network deployment cost of operators; in order to solve the above problem, in the device type selection method provided in the embodiments of the present invention, a 5G typical scenario is adopted to perform simulation of different device types, inter-station distances, and station heights, and 5G SINR distribution conditions in multiple scenarios are obtained, so that a CDF curve of the 5G SINR can be determined, and then, based on the CDF curve, a corresponding relationship between an SINR interval and a device type can be determined; as the specified area has built a fourth generation mobile communication technology (the 4th generation mobile communication technology, abbreviated as 4G), the existing 4G historical CQI is used to obtain the first target SINR of the specified area, and then the type of the equipment deployed in the specified area is determined through the first target SINR and the corresponding relation, thereby completing equipment type selection and solving the problem of how to replace the equipment of the established base station area with proper AAU equipment according to the user requirement of the established base station area.
Illustratively, taking the first mobile communication technology as 5G, the second mobile communication technology as 4G, the device types include 16TR device, 32TR device, and 64TR device, and the designated area is an established base station area as an example for description, the specific implementation process is as follows:
example one
An embodiment of the present invention provides an apparatus model selection method, as shown in fig. 4, including:
s101, acquiring typical channel simulation data under a first mobile communication technology and historical CQI under a second mobile communication technology in a designated area; the typical channel simulation data is obtained by simulating a typical scene under the first mobile communication technology according to a specified channel model, wherein 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 typical channel simulation data of SINR of 5G, user point scattering simulation needs to be performed on the same device type under the same typical scenario, the same station height, and the same station spacing, so as to obtain the typical channel simulation data of SINR of 5G of the device type.
Illustratively, obtaining typical channel simulation data under a first mobile communication technology comprises:
fors 16TR device, 32TR device, 64TR device/input multiple device types.
For C ═ dense urban, suburban, open area (according to the channel model specified in the third generation Partnership Project (3 GPP) TS38.901 standard)/input of a variety of typical scenarios.
For 15, 20, 25, 30, 35,/input multiple station heights.
Ford is 100: 100: 3000/input multiple station spacing.
5G network simulation based on device types, typical scenes, station heights and station distances is carried out.
For num＝1:1000。
Carrying out single-user random point scattering, acquiring the SINR of each simulation, and recording (s, C, h, d, num, SINR)/note: s is the device type, C is the channel model, h is the station height, d is the station spacing, num is the scattering point number of the user, and SINR is the SINR value of 5G of the user.
Specifically, in practical applications, when 5G network simulation based on a channel model, a device type, a typical scenario, a station height, and a station separation is performed, N times (N is an integer greater than 0, and N is 1000 in the example) simulation is performed for each network configuration (channel model, device type, typical scenario, station height, and station separation) (only one SINR value is obtained for each simulation because of a single-user scattering point), and then typical channel simulation data (for example, typical channel simulation data is an average value of SINRs of M users in the same network configuration) in 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 applications, the same device type obtains an most typical value by analyzing the typical station height and the typical station spacing in a typical scenario, so as to determine typical channel simulation data of the device type according to the most typical value.
And S102, determining the cumulative distribution function of SINR under the first mobile communication technology according to the typical channel simulation data.
Optionally, according to the typical channel simulation data, determining a cumulative distribution function of SINRs under the first mobile communication technology, as shown in fig. 5, includes:
s1020, determining an accumulative distribution function of SINR under the first mobile communication technology according to the typical channel simulation data; wherein the cumulative distribution function comprises:
Figure BDA0002204916430000071
wherein F (CDF) represents the probability of the SINR appearing for the user in the first mobile communication technology, SINRs,C,h,dIndicates SINR under the first mobile communication technology, a, b, c and d are allA constant.
Specifically, in practical application, first, a 5G typical scenario is classified according to (s, C, h, d, num, SINR) obtained by simulation of a specified channel model, that is, s, C, h, d are fixed, and CDF calculation is performed on SINRs of all users under the same network configuration, as shown in fig. 6:
Figure BDA0002204916430000072
where the a, b, c and d scores are also called fitting parameters, determined by the actual cell situation.
S103, determining the SINR median value of each device type under the first mobile communication technology according to the cumulative distribution function.
Optionally, determining a SINR median of each device type under the first mobile communication technology according to the cumulative distribution function, as shown in fig. 7, includes:
and S1030, determining a second target SINR according to the cumulative distribution function.
S1031, determining an inverse function according to the cumulative distribution function; wherein the inverse function comprises:
Figure BDA0002204916430000073
wherein, SINR's,C,h,dAnd F-1(CDF) all represent inverse functions, SINRs,C,h,dIndicating the SINR in the first mobile communication technology, a, b, c and d are all constants.
And S1032, determining the SINR median value of each device type under the first mobile communication technology according to the inverse function and the second target 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 third target SINR of the specified area, as shown in fig. 6.
Specifically, the determining the SINR median includes: taking F (CDF) as 50%, then rootAccording to
Figure BDA0002204916430000081
A third target SINR for selecting a point corresponding to a user occurrence probability of 50% may be determined.
The third target SINR is substituted into a first inverse function so that each device type is median SINR under the first mobile communication technology.
S104, determining an SINR interval under the first mobile communication technology according to the SINR median, matching the SINR interval with the device type, and determining the corresponding relation between the SINR interval and the device type.
Specifically, determining an SINR interval under the first mobile communication technology according to the SINR median, matching the SINR interval with the device type, and determining a correspondence between the SINR interval and the device type includes:
firstly, averaging the SINR median values of each class (each class corresponds to one network configuration) obtained in step S103, and determining an average center value C of each device typeTs(ii) a Wherein the content of the first and second substances,
Figure BDA0002204916430000082
wherein s is the equipment type, C is the channel model, h is the station height, and d is the station spacing; (since the types of devices of the present invention include 16TR device, 32TR device, 64TR device, S takes values of 1, 2, 3.
Then, based on the average center value CTsThe SINR interval of 5G is determined, and for example, the SINR interval may be divided as follows:
Figure BDA0002204916430000083
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, determining a first 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 CQI under the second mobile communication technology and the historical CQI.
Optionally, determining a first 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 CQI in the second mobile communication technology and the historical CQI, as shown in fig. 8, includes:
s1050, determining a first 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 CQI under the second mobile communication technology and the historical CQI; wherein, the mapping relation comprises:
SINR＝1.9346×CQI-6.799；
wherein SINR represents SINR in the first mobile communication technology, and CQI represents CQI in the second mobile communication technology.
It should be noted that, in practical applications, when the mapping relationship between the SINR in the first mobile communication technology and the CQI in the second mobile communication technology is obtained, the relationship between the 4G and the CQI may be obtained by comparing the relationship between the 4G CQI and the SINR; further, the relationship between 4G and CQI is combined to determine the corresponding relationship between 4G CQI and 5G SINR, as shown in fig. 9 below, which substantially satisfies a linear relationship, and can be estimated using the following formula:
SINR＝1.9346×CQI-6.799。
it should be noted that, in an actual application, in order to more accurately determine an actual requirement of a user in the designated area, MR data of the designated area in a preset time period (for example, the preset time period may be full-day Measurement Report (MR) data of one working day and one holiday in a recent week) may be obtained. Because of the correlation between the coverage and capacity of the base station and the sinr (cqi), we choose 4G of MR data as the decision parameter. The method comprises the following steps:
the following table is header information about 4G CQI in MR:
and selecting MR data in the 4G current network of the designated area within a preset time period (such as 3 months) to determine the distribution condition of the CQI. Table 1 shows header information about CQI in MR data.
TABLE 1
Figure BDA0002204916430000101
The historical CQI of each CQI in a preset time period (e.g. 3 months) is calculated respectively through the table 1i：
Figure BDA0002204916430000102
Wherein i represents the total times of reporting the full-bandwidth CQI by the air interface, and i belongs to [0, 15 ]]，CQIweekIndicating the CQI for each day.
Then, according to the mapping relation between 4G CQI and 5G SINR and the historical CQIiAnd determining a first target SINR.
And finally, determining the type of the equipment deployed in the specified area according to the first target SINR and the corresponding relation.
S106, determining the type of equipment deployed in the specified area according to the first target SINR 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; illustratively, when the second target SINR belongs to the T1 interval, it is determined that the device type deployed in the specified area is a 16TR device; when the second target SINR belongs to the interval T2, determining that the type of the device deployed in the specified area is 32TR device; when the second target SINR belongs to the T3 interval, it is determined that the device type deployed in the specified area is a 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 above scheme, when the first mobile communication technology is 5G, the second mobile communication technology is 4G, the device type includes 16TR devices, 32TR devices and 64TR devices, and the designated area is an established base station area, the device model selection method provided in the embodiment of the present invention may determine typical channel simulation data of 5G by simulating a typical scene of 5G according to a designated channel model, determine an accumulated distribution function of SINR of 5G according to the typical channel simulation data, and further determine a corresponding relationship between an SINR interval of 5G and the device type according to the accumulated distribution function; according to the historical CQI in the established base station area and the mapping relation between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology, the user requirement in the established base station area can be determined; meanwhile, the device type deployed in the designated area is determined according to the first target SINR and the corresponding relation, so that a user can replace the device in the established base station area with a proper device type according to the 5G typical channel simulation data and the historical CQI in the established base station area, and the problem of how to replace the device in the established base station area with a proper AAU device according to the user requirement of the established base station area is solved.
Example two
An embodiment of the present invention provides an apparatus model selecting device 10, as shown in fig. 10, including:
an obtaining unit 101, configured to obtain typical channel simulation data in a first mobile communication technology and a historical CQI in a specified area in a second mobile communication technology; the typical channel simulation data is obtained by simulating a typical scene under the first mobile communication technology according to a specified channel model, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas;
a processing unit 102, configured to determine, according to the typical channel simulation data acquired by the acquiring unit 101, an accumulated distribution function of SINR in the first mobile communication technology;
a processing unit 102, further configured to determine a median SINR value of each device type under the first mobile communication technology according to the cumulative distribution function;
the processing unit 102 is further configured to determine an SINR interval in the first mobile communication technology according to the SINR median, match the SINR interval with the device type, and determine a corresponding relationship between the SINR interval and the device type;
the processing unit 102 is further configured to determine a first 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 CQI in the second mobile communication technology and the historical CQI acquired by the acquiring unit 101;
the processing unit 102 is further configured to determine, according to the first 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 later than the release time of the second mobile communication technology.
Optionally, the processing unit 102 is specifically configured to determine, according to the typical channel simulation data acquired by the acquiring unit 101, an accumulated distribution function of SINR in the first mobile communication technology; wherein the cumulative distribution function comprises:
Figure BDA0002204916430000121
wherein F (CDF) represents the probability of the SINR appearing for the user in the first mobile communication technology, SINRs,C,h,dIndicating the SINR in the first mobile communication technology, a, b, c and d are all constants.
Optionally, the processing unit 102 is specifically configured to determine a second target SINR according to the cumulative distribution function;
a processing unit 102, specifically configured to determine an inverse function according to the cumulative distribution function; wherein the inverse function comprises:
Figure BDA0002204916430000122
wherein, SINR's,C,h,dAnd F-1(CDF) all represent inverse functions, SINRs,C,h,dIndicating that SINR under the first mobile communication technology, a, b, c and d are constants;
the processing unit 102 is specifically configured to determine a median SINR value of each device type in the first mobile communication technology according to the inverse function and the second target SINR.
Optionally, the processing unit 102 is specifically configured to determine a first target SINR of the designated area in the first mobile communication technology according to a mapping relationship between an SINR in the first mobile communication technology and a CQI in the second mobile communication technology and the historical CQI acquired by the acquiring unit 101; wherein, the mapping relation comprises:
SINR＝1.9346×CQI-6.799；
wherein SINR represents SINR in the first mobile communication technology, and CQI represents CQI in the second mobile communication technology.
Specifically, in practical applications, as shown in fig. 11, the obtaining unit in the device 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 channel simulation data in the first mobile communication technology and historical CQI in the specified area in the second mobile communication technology; the processing unit comprises a 5G device selection judgment method selection module, a 4G CQI mapping module and a 5G device selection module; the 5G device selection judgment module is used for determining an accumulative distribution function of SINR under the first mobile communication technology according to the typical channel simulation data acquired by the 5G typical scene simulation and data extraction module; the 5G device selection decision method selection module is also used for determining the SINR median value of each device type under the first mobile communication technology according to the cumulative distribution function; 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 SINR median, matching the SINR interval with the device type, and determining a corresponding relation between the SINR interval and the device type; the 4G CQI mapping module is used for determining a first 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 CQI under the second mobile communication technology and the historical CQI acquired by the 5G typical scene simulation and data extraction module; the 5G device selection module is used for determining the device type deployed in the designated area according to the first target SINR 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 and S106 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. 12 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 (10)

1. A method for device model selection, comprising:
acquiring typical channel simulation data under a first mobile communication technology and historical CQI under a second mobile communication technology in a designated area; the typical channel simulation data is obtained by simulating a typical scene under the first mobile communication technology according to a specified channel model, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas;
determining a cumulative distribution function of SINR under the first mobile communication technology according to the typical channel simulation data;
determining the SINR median value of each device type under the first mobile communication technology according to the cumulative distribution function;
according to the SINR median, determining an SINR interval under the first mobile communication technology, matching the SINR interval with the device type, and determining a corresponding relation between the SINR interval and the device type;
determining a first 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 CQI under the second mobile communication technology and the historical CQI;
determining the type of equipment deployed in the specified area according to the first target SINR 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.
2. The device selection method according to claim 1, wherein determining a cumulative distribution function of SINRs for the first mobile communication technology based on the representative channel simulation data comprises:
determining a cumulative distribution function of SINR under the first mobile communication technology according to the typical channel simulation data; wherein the cumulative distribution function comprises:
Figure FDA0002204916420000011
wherein F (CDF) represents the probability of the SINR appearing for the user in the first mobile communication technology, SINRs,C,h,dIndicating that SINR under the first mobile communication technology, a, b, c and d are all constants.
3. The device type selection method of claim 1, wherein determining the SINR median of each device type under the first mobile communication technology according to the cumulative distribution function comprises:
determining a second target SINR according to the cumulative distribution function;
determining an inverse function according to the cumulative distribution function; wherein the inverse function comprises:
Figure FDA0002204916420000021
wherein, SINR's,C,h,dAnd F-1(CDF) all represent inverse functions, SINRs,C,h,dIndicating that SINR under the first mobile communication technology, a, b, c and d are constants;
and determining the SINR median value of each device type under the first mobile communication technology according to the inverse function and the second target SINR.
4. The device selection method according to any one of claims 1 to 3, wherein determining the first target SINR of the designated area under the first mobile communication technology according to the mapping relationship between the SINR under the first mobile communication technology and the CQI under the second mobile communication technology and the historical CQI under the second mobile communication technology in the designated area comprises:
determining a first 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 CQI under the second mobile communication technology and the historical CQI; wherein the mapping relationship comprises:
SINR＝1.9346×CQI-6.799；
wherein SINR represents SINR in the first mobile communication technology, and CQI represents CQI in the second mobile communication technology.
5. An apparatus model selection device, comprising:
an obtaining unit, configured to obtain typical channel simulation data in a first mobile communication technology and a historical CQI in a specified area in a second mobile communication technology; the typical channel simulation data is obtained by simulating a typical scene under the first mobile communication technology according to a specified channel model, wherein the typical scene comprises at least one of dense urban areas, suburban areas and open areas;
a processing unit, configured to determine, according to the typical channel simulation data acquired by the acquiring unit, an accumulated distribution function of SINR in a first mobile communication technology;
the processing unit is further configured to determine a median SINR value of each device type under the first mobile communication technology according to the cumulative distribution function;
the processing unit is further configured to determine an SINR interval in the first mobile communication technology according to the SINR median, 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 determine a first target SINR of the designated area in the first mobile communication technology according to a mapping relationship between an SINR in the first mobile communication technology and a CQI in the second mobile communication technology and the historical CQI acquired by the acquiring unit;
the processing unit is further configured to determine, according to the first target SINR 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.
6. The device selection apparatus according to claim 5, wherein the processing unit is specifically configured to determine a cumulative distribution function of SINRs in the first mobile communication technology according to the typical channel simulation data acquired by the acquiring unit; wherein the cumulative distribution function comprises:
Figure FDA0002204916420000031
wherein F (CDF) represents the probability of the SINR appearing for the user in the first mobile communication technology, SINRs,C,h,dIndicating that SINR under the first mobile communication technology, a, b, c and d are all constants.
7. The device selection apparatus according to claim 5, wherein the processing unit is specifically configured to determine a second target SINR according to the cumulative distribution function;
the processing unit is specifically configured to determine an inverse function according to the cumulative distribution function; wherein the inverse function comprises:
Figure FDA0002204916420000032
wherein, SINR's,C,h,dAnd F-1(CDF) all represent inverse functions, SINRs,C,h,dIndicating that SINR under the first mobile communication technology, a, b, c and d are constants;
the processing unit is specifically configured to determine, according to the inverse function and the second target SINR, a median SINR value of each device type in the first mobile communication technology.
8. The device selection apparatus according to any one of claims 5 to 7, wherein the processing unit is specifically configured to determine a first target SINR of the designated area in the first mobile communication technology according to a mapping relationship between an SINR in the first mobile communication technology and a CQI in the second mobile communication technology and the historical CQI acquired by the acquiring unit; wherein the mapping relationship comprises:
SINR＝1.9346×CQI-6.799；
wherein SINR represents SINR in the first mobile communication technology, and CQI represents CQI in the second mobile communication technology.
9. A computer storage medium comprising instructions which, when executed on a computer, cause the computer to perform the device selection method of any one of claims 1 to 4.
10. An apparatus model selection device, comprising: communication interface, processor, memory, bus; the memory is used for storing computer-executable instructions, the processor is connected with the memory through the bus, and when the equipment type selection device runs, the processor executes the computer-executable instructions stored in the memory so as to enable the equipment type selection device to execute the equipment type selection method according to any one of the claims 1-4.
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