WO2023135729A1 - Dispositif d'inférence, procédé d'inférence et programme - Google Patents

Dispositif d'inférence, procédé d'inférence et programme Download PDF

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Publication number
WO2023135729A1
WO2023135729A1 PCT/JP2022/001057 JP2022001057W WO2023135729A1 WO 2023135729 A1 WO2023135729 A1 WO 2023135729A1 JP 2022001057 W JP2022001057 W JP 2022001057W WO 2023135729 A1 WO2023135729 A1 WO 2023135729A1
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WO
WIPO (PCT)
Prior art keywords
base station
data
traffic data
station traffic
user
Prior art date
Application number
PCT/JP2022/001057
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English (en)
Japanese (ja)
Inventor
佑紀奈 高野
恵 竹下
Original Assignee
日本電信電話株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by 日本電信電話株式会社 filed Critical 日本電信電話株式会社
Priority to PCT/JP2022/001057 priority Critical patent/WO2023135729A1/fr
Publication of WO2023135729A1 publication Critical patent/WO2023135729A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present invention relates to an estimation device, an estimation method, and a program.
  • Non-Patent Document 1 proposes a method of obtaining an encrypted packet of a user and estimating the application used by the user from the packet.
  • Non-Patent Document 2 proposes a method of predicting the application to be used next time, based on information on the user's current application, place of use, and time of use.
  • Non-Patent Literature 3 treats the communication usage status of users in a base station collectively, It proposes a network management concept based on
  • Non-Patent Document 1 packets for each user are collected and used, but it is difficult for telecommunications carriers to collect such information directly due to privacy issues. Moreover, even if direct collection is possible, the amount of data will increase explosively as the number of users and the amount of communication increase, resulting in the problem of high data acquisition and storage costs. As for non-patent document 2, the same problem occurs because the user's usage application information itself is collected.
  • Non-Patent Document 3 Although the problems in Non-Patent Document 1 and Non-Patent Document 2 can be solved, it is a premise that the user set characteristics in the area corresponding to the base station can be directly acquired. There is still a problem with the method.
  • the present invention has been made in view of the above points, and an object of the present invention is to enable estimation of user set characteristics corresponding to a base station.
  • an estimation device includes an acquisition unit configured to acquire base station traffic data for a certain base station in a certain period; By inputting the base station traffic data acquired by the acquisition unit into a regression model learned based on a plurality of sets of aggregate characteristic data, user aggregate characteristic data for the certain base station in the certain period is estimated.
  • an estimator configured to:
  • FIG. 11 is a flowchart for explaining an example of a processing procedure of a regression model learning process
  • FIG. 4 is a diagram showing an example of UE number distribution of data rates
  • FIG. 10 is a diagram showing an example of the UE number distribution of the elapsed time of the Inactive Timer
  • FIG. 10 is a flowchart for explaining an example of a processing procedure for estimating user set characteristic data
  • an estimating apparatus 10 that estimates user aggregate characteristic data using base station traffic data as an input.
  • base station traffic data refers to statistical network traffic data that reflects the behavior of users' communication usage in the area covered by the base station. That is, the base station traffic data is data indicating the traffic status of the base station.
  • the user set characteristics data is data indicating characteristics of applications or terminals used by a user set under the control of a base station (a user set of terminals connected to a base station).
  • the estimating apparatus 10 first learns a regression model with base station traffic data as input and user set characteristic data as output, based on a plurality of sets of learning data each consisting of base station traffic data and user set characteristic data. .
  • the estimator 10 uses the learned regression model to estimate user aggregate characteristic data based on base station traffic data for a base station at a point in time.
  • FIG. 1 is a diagram showing a hardware configuration example of the estimation device 10 according to the embodiment of the present invention.
  • the estimating device 10 of FIG. 1 has a drive device 100, an auxiliary storage device 102, a memory device 103, a processor 104, an interface device 105, etc., which are connected to each other via a bus B, respectively.
  • a program that implements the processing in the estimation device 10 is provided by a recording medium 101 such as a CD-ROM.
  • a recording medium 101 such as a CD-ROM.
  • the program is installed from the recording medium 101 to the auxiliary storage device 102 via the drive device 100 .
  • the program does not necessarily need to be installed from the recording medium 101, and may be downloaded from another computer via the network.
  • the auxiliary storage device 102 stores installed programs, as well as necessary files and data.
  • the memory device 103 reads and stores the program from the auxiliary storage device 102 when a program activation instruction is received.
  • the processor 104 is a CPU or a GPU (Graphics Processing Unit), or a CPU and a GPU, and executes functions related to the estimation device 10 according to programs stored in the memory device 103 .
  • the interface device 105 is used as an interface for connecting to a network.
  • FIG. 2 is a diagram showing a functional configuration example of the estimation device 10 according to the embodiment of the present invention.
  • the estimation device 10 has a learning data collection unit 11, a model construction unit 12, a base station traffic data acquisition unit 13, and a user group characteristics estimation unit . Each of these units is implemented by processing that one or more programs installed in the estimation apparatus 10 cause the processor 104 to execute.
  • FIG. 3 is a flowchart for explaining an example of a processing procedure of a regression model learning process.
  • the learning data collection unit 11 collects user set characteristic data and base station traffic data corresponding thereto for a plurality of base stations at a plurality of timings.
  • the collected user set characteristic data and the corresponding base station traffic data may be user set characteristic data and base station traffic data generated by network simulation, or user set characteristic data obtained by crowdsourcing or the like. (information indicating the application currently in use) and base station traffic data corresponding to the user set.
  • the "corresponding base station traffic data” refers to the base station traffic data collected at the same timing as the collection timing of the user set characteristics data in relation to the same base station as the target base station for collecting the user set characteristics data. say.
  • the base station traffic data here refers to, for example, downlink (DL)/uplink (UL) data rate UE number distribution, Inactive Timer elapsed time UE number distribution, downlink (DL)/uplink Statistical network traffic data that reflects the behavior of users' communication usage, such as the resource block usage rate of (UL).
  • the base station traffic data may be data containing some (one or more) of these parameters, or may be data containing all of these parameters.
  • the DL/UL data rate UE number distribution corresponds to "DL scheduled IP throughput distribution” and "UL scheduled IP throughput distribution” respectively, and the UE number distribution of the Inactive Timer elapsed time corresponds to "Number of successful RRC connection setups in relation to the time between successful RRC connection setup and last RRCconnection release".
  • An example of the UE number distribution of the data rate is shown in FIG.
  • the DL/UL resource block usage rate corresponds to "DL Total PRB Usage” and "UL Total PRB Usage” and is a value of 0 to 100 [%].
  • base station traffic data depends on the vendor that manufactures the base station, but basically the base station has a data generation cycle, and data is generated at that cycle and timing. For example, if the data generation cycle is one minute, the data rate and resource block usage rate are collected as average values for that one minute, and the Inactive Timer elapsed time is collected as instantaneous values at the collection timing.
  • An example of user set characteristic data is the ratio of the number of users (UE ratio) for each application type under the target base station.
  • UE ratio the ratio of the number of users
  • the ratio of the number of users for each terminal type may be used as the user set characteristic data, or the ratio of the number of users for each combination of the application type and the terminal type may be used.
  • the population ratio is, for example, the value in the period corresponding to the collection period of the base station traffic data corresponding to the user set characteristics data, out of the generation period of the base station traffic data described above. is. For example, if the generation cycle of base station traffic data is one minute, the ratio of the number of users for each application type in one minute during which the base station traffic data corresponding to the user set characteristic data is collected becomes the user set characteristic data.
  • the inventor of the present application believes that the amount of DL data per user increases in areas where video applications are frequently used, the amount of DL data per user decreases in areas where web use is high, and in areas where web conference applications are frequently used
  • the DL/UL data volume per user increases, and in areas where voice usage is high, the DL/UL data volume per user decreases. thought to appear in data items. Therefore, the inventor of the present application considered that the UE number distribution of the DL/UL average data rate can be used as an indication of the DL/UL data amount. This value represents the amount of data that has flowed per subframe, ie the smallest scheduling time unit.
  • the inventor of the present application has another hypothesis that in areas where video is frequently used, connections occur every chunk period, and in areas where audio and web conferences are often used, connections occur frequently, and web use is frequent.
  • connection timing such as connections occurring at sparse timing in the area. Therefore, the inventor of the present application extracted the item of the UE number distribution of the Inactive Timer elapsed time, which represents the elapsed time from the last connection of each user.
  • the inventor of the present application believes that resource consumption is high in areas where there is a large amount of data used for video and web conferencing, and resource consumption is low in areas where there is a large amount of data used for voice and web.
  • resource block usage rate which represents the rate of resource consumption of the base station.
  • the model construction unit 12 uses, as learning data, a plurality of sets of the user set characteristic data and the corresponding base station traffic data collected by the learning data collection unit 11, and receives the base station traffic data as an input.
  • a regression model outputting the set characteristic data is learned (S102). Any regression method such as linear regression or random forest regression can be used as the regression method used here.
  • the model construction unit 12 outputs the learned regression model (for example, outputs the values of the learning parameters of the regression model) (S103).
  • FIG. 6 is a flowchart for explaining an example of the processing procedure for estimating user group characteristic data.
  • the base station traffic data acquisition unit 13 acquires base station traffic data for a period for which user group characteristic data is to be clarified and network base station traffic data in response to an input by the user of the estimation device 10 (S201).
  • the period and the network are specified by the user of the estimation device 10, for example.
  • the network is, for example, any network (or area) among networks (or areas) distinguished by base station unit, sector unit, sector ⁇ carrier (frequency band) unit, or the like.
  • the base station traffic data is composed of the same parameters as the parameters collected by the learning data collection unit 11 .
  • the user aggregate characteristics estimation unit 14 inputs the base station traffic data acquired by the base station traffic data acquisition unit 13 into the learned regression model, thereby estimating the user aggregate characteristics data for the period T of the network. (S202). That is, the user set characteristic estimation unit 14 acquires the user set characteristic data output by the regression model as an estimated value of the user set characteristic data for the period T of the network.
  • the user group characteristic estimation unit 14 outputs the user group characteristic data (S203).
  • the base station traffic data acquisition unit 13 is an example of an acquisition unit.
  • the user group characteristics estimating unit 14 is an example of an estimating unit.
  • the model construction unit 12 is an example of a learning unit.
  • estimation device 11 learning data collection unit 12 model construction unit 13 base station traffic data acquisition unit 14 user group characteristic estimation unit 100 drive device 101 recording medium 102 auxiliary storage device 103 memory device 104 processor 105 interface device B bus

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  • Engineering & Computer Science (AREA)
  • Environmental & Geological Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Ce dispositif d'inférence comprend : une unité d'acquisition configurée de façon à acquérir des données de trafic de station de base dans une certaine période concernant une certaine station de base ; et une unité d'inférence configurée pour inférer des données caractéristiques de groupe d'utilisateurs dans la certaine période concernant la certaine station de base en recevant une entrée des données de trafic de station de base acquises par l'unité d'acquisition d'un modèle de régression obtenu par apprentissage sur la base d'une pluralité d'ensembles de données de trafic de station de base et de données caractéristiques de groupe d'utilisateurs qui sont collectées en tant que données d'apprentissage. Par conséquent, le dispositif d'inférence peut inférer des caractéristiques de groupe d'utilisateurs correspondant à la station de base.
PCT/JP2022/001057 2022-01-14 2022-01-14 Dispositif d'inférence, procédé d'inférence et programme WO2023135729A1 (fr)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013178739A (ja) * 2012-02-07 2013-09-09 Kddi Corp 端末のソフトウェア種別情報を推定する端末情報推定装置、ｄｎｓサーバ、プログラム及び方法
WO2019187296A1 (fr) * 2018-03-29 2019-10-03 日本電気株式会社 Dispositif d'analyse de trafic de communication, procédé d'analyse de trafic de communication, programme, et support d'enregistrement

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013178739A (ja) * 2012-02-07 2013-09-09 Kddi Corp 端末のソフトウェア種別情報を推定する端末情報推定装置、ｄｎｓサーバ、プログラム及び方法
WO2019187296A1 (fr) * 2018-03-29 2019-10-03 日本電気株式会社 Dispositif d'analyse de trafic de communication, procédé d'analyse de trafic de communication, programme, et support d'enregistrement

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