WO2024018586A1 - Système de prédiction de qualité de communication de terminal - Google Patents

Système de prédiction de qualité de communication de terminal Download PDF

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Publication number
WO2024018586A1
WO2024018586A1 PCT/JP2022/028329 JP2022028329W WO2024018586A1 WO 2024018586 A1 WO2024018586 A1 WO 2024018586A1 JP 2022028329 W JP2022028329 W JP 2022028329W WO 2024018586 A1 WO2024018586 A1 WO 2024018586A1
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terminal
information
unmanaged
management
communication quality
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PCT/JP2022/028329
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English (en)
Japanese (ja)
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央也 小野
裕希 坂上
友宏 谷口
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日本電信電話株式会社
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Priority to PCT/JP2022/028329 priority Critical patent/WO2024018586A1/fr
Publication of WO2024018586A1 publication Critical patent/WO2024018586A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements

Definitions

  • the present disclosure relates to a system for predicting communication quality of a terminal.
  • wireless communication lines When using network services, users use communication lines provided by communication carriers. For example, in the case of wireless communication lines, there are standards such as 3GPP 5G NR, LTE, IEEE 802.11 (wireless LAN), and IEEE 802.16 (WiMAX).
  • 3GPP 5G NR Long Term Evolution
  • LTE Long Term Evolution
  • IEEE 802.11 wireless LAN
  • WiMAX IEEE 802.16
  • User terminals can communicate using multiple communication standards. For example, a smartphone can select whether to use LTE, wireless LAN, or Bluetooth. It is also possible to use different access means of different carriers that use the same communication standard. Each of these access methods has different communication quality such as bandwidth and delay, so it is possible to maximize the user's quality of experience (QoE) by appropriately using them depending on the purpose. Become.
  • the access means determination function used by the terminal does not necessarily need to be present in the terminal itself, and can be held by a router or server on the network, or by an external terminal.
  • a message method called ANDSF Access Network Discovery and Selection Function
  • 3GPP 3GPP
  • this type of external control method optimization is performed using the combination of connection destinations of a group of terminals within the control area as a variable, thereby suppressing quality deterioration due to competition in access methods between terminals, and improving quality. This makes it possible to control connection destinations, which leads to improved fairness.
  • Non-Patent Documents 1 to 3 There are several techniques for predicting the communication quality of each terminal using the connection pattern between the terminal and the network as input (see, for example, Non-Patent Documents 1 to 3).
  • the first method is to use a mathematical model.
  • Throughput and the like are analytically predicted from the behavior of the communication protocol based on the SNR (Signal-Noise Ratio), the allocated frequency width, the number of devices connected to the base station device, and the like.
  • the second method is to use network simulation.
  • [Non-Patent Document 3] predicts communication quality by simulating application communication from terminals and servers and simulating the behavior of each packet within a virtual network topology.
  • the third method is based on machine learning regression.
  • Patent Document 4 Patent Document 1] a prediction model of communication quality is learned from simulation results and communication results at an actual terminal, and communication quality for an unknown connection pattern is predicted.
  • the connected control device When using these quality prediction methods, the connected control device performs prediction after collecting information necessary for prediction from terminals and network devices in advance through TCP/IP communication. For example, when making predictions using a mathematical model, it is possible to estimate the upper bound of the realized throughput from Shannon's equation by collecting information on the radio field strength that each terminal receives from each wireless base station. At this time, it is expected that the more detailed the terminal information and network information within the system is acquired, the more accurately the communication results at the actual terminals can be predicted.
  • the destination control device When applying these technologies, it is ideal for the destination control device to manage information on all terminals within the system, but in an actual communication environment, terminals that are not under the control of the destination control device, Using the same radio frequency band may affect the quality of managed terminals. At this time, the connection destination control device cannot grasp the number and location of terminals that are not under management, or the traffic patterns generated, at least using the same method as the terminals under management.
  • the quality prediction method described above is based on the assumption that all terminals in the system are under management, and if interference with unmanaged terminals occurs, the prediction accuracy will deteriorate. Although it is possible to obtain the traffic generated by unmanaged terminals using packet capture, etc., it is only possible to obtain throughput etc. at the moment of capture, and the degree of interference when changing the connection pattern of managed terminals cannot be determined. Expected to be different. For example, even if there is an unmanaged terminal that is generating 4 Mbps of traffic at a certain time based on packet capture, depending on the connection pattern of the management terminal, the unmanaged traffic may be about 3 Mbps depending on the degree of network congestion.
  • communication information at a certain moment through packet capture alone is insufficient as a method to obtain information on unmanaged terminals, and information on the maximum data rate at which traffic can be generated from which position on unmanaged terminals can be obtained. It is desirable to be able to do so.
  • 3GPP TS 24.312 V15.0.0 (2018-06), Access Network Discovery and Selection Function (ANDSF) Management t Object (MO), https://portal. 3gpp. org/desktopmodules/Specifications/SpecificationDetails. aspx? specificationId 1077 I. B. Dhia et al. , “Optimization of access points selection and resource allocation in heterogeneous wireless net ork,”2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communica tions (PIMRC), Montreal, QC, 2017, pp. 1-7. “ns-3
  • the present disclosure aims to make it possible to predict the communication quality obtained by a managed terminal even if there is an unmanaged terminal.
  • the system of the present disclosure includes a management terminal connectable to a network and a device of the present disclosure that acquires communication quality information from the management terminal, and is a system that predicts the communication quality of the management terminal.
  • the apparatus and method of the present disclosure include: Obtaining management terminal information including the geographical location of the management terminal and communication quality information obtained from the management terminal, predicting unmanaged terminal information including the geographical location of the unmanaged terminal based on the management terminal information and the communication quality information; The communication quality of the management terminal is predicted using the predicted unmanaged terminal information.
  • the device of the present disclosure may predict the unmanaged terminal information using an unmanaged terminal prediction model that has previously learned the communication quality obtained by the management terminal when the unmanaged terminal exists.
  • the management terminal information can be represented by a two-dimensional array or a three-dimensional array of pixels corresponding to the geographical location of the management terminal.
  • the unmanaged terminal information is represented by a two-dimensional array or three-dimensional array of pixels corresponding to the geographical location of the unmanaged terminal, and the pixel positions of the managed terminal information and the unmanaged terminal information are The positions may match.
  • the device of the present disclosure may predict the communication quality obtained by the management terminal for each network when the management terminal connects to the network, and determine the network to which the management terminal connects based on the prediction. .
  • the device of the present disclosure acquires the management terminal information and the communication quality information from a plurality of the management terminals, extracts a connection pattern when the plurality of management terminals connect to a network, and selects a connection pattern from among the extracted connection patterns.
  • An arbitrary connection pattern may be determined, and the plurality of management terminals may be connected to the network using the determined connection pattern.
  • the program of the present disclosure is a program for realizing a computer as each functional unit included in the device of the present disclosure, and is a program for causing the computer to execute each step of a method executed by the device of the present disclosure. .
  • the present disclosure even if an unmanaged terminal exists, the communication quality obtained by the managed terminal can be predicted. Therefore, even if there are unmanaged terminals, the present disclosure makes it possible to accurately predict the communication quality obtained by each management terminal in a virtual combination of connection destinations, and to select the optimal combination of connection destinations. It becomes possible.
  • An example of the network configuration of this embodiment is shown.
  • An example of the operation of the connected control device during learning is shown.
  • An example of the operation of the connection destination control device at the time of prediction is shown.
  • An example of the architecture of Conditional GAN is shown.
  • An example of the configuration of a terminal and a connected control device is shown.
  • An example of the learning flow of the unmanaged terminal prediction model is shown.
  • An example of the learning flow of the management terminal quality prediction model is shown.
  • An example of a prediction flow is shown.
  • An example of a dataset generation method is shown.
  • FIG. 1 shows an example of the network configuration of this embodiment.
  • an example is an environment in which the terminals 91#1 to 91#4 can connect to the Internet 93 via two types of networks (sometimes abbreviated as NW) 92#1 and 92#2. shows.
  • the system of the present disclosure includes a connection destination control device 94 that functions as a device according to the present disclosure.
  • the terminals 91#1 to 91#4 there are terminals 91#1 to 91#3 under the control of the destination control device 94 and a terminal 91#4 not under the control of the destination control device 94.
  • the terminals 91#1 to 91#3 are referred to as “management terminals", and the terminal 91#4 is referred to as an "unmanaged terminal".
  • the connection destination control device 94 controls the communication quality obtained by the management terminals 91#1 to 91#3 when the management terminals 91#1 to 91#3 connect to the network 92#1 or 92#2. 92#2, and based on the prediction, determine the network to which the management terminals 91#1 to 91#3 will connect, and connect the plurality of management terminals 91#1 to 91#3 to the network using the determined connection pattern. Connect to. At this time, the connection destination control device 94 lists the patterns in which each of the management terminals 91#1 to 91#3 connects to the network 92#1 or 92#2, and performs each management for any connection pattern extracted from the list. The communication quality achieved by terminals 91#1 to 91#3 is predicted.
  • connection destination control device 94 connects the terminals 91#1 and 91#2 to the network 92#1, connects the terminal 91#3 to the network 92#2, and connects the terminal to the first connection pattern.
  • a second connection pattern can be extracted in which terminal 91#1 is connected to network 92#1 and terminals 91#2 and 91#3 are connected to network 92#2.
  • the connection destination control device 94 derives the communication qualities ⁇ and ⁇ of the first and second connection patterns, respectively, and determines the optimal connection pattern based on the magnitude of the communication qualities ⁇ and ⁇ . Thereby, the system of this embodiment can optimize the connection pattern of each management terminal 91#1 to 91#3.
  • the connection destination control device 94 determines a connection pattern based on the following information. ⁇ Management terminal information of management terminals 91#1 to 91#3 ⁇ Communication quality information of management terminals 91#1 to 91#3 ⁇ Network information of networks 92#1 and 92#2
  • the management terminal information includes the geographical locations of the management terminals 91#1 to 91#3.
  • the management terminal information may include any information regarding the communication quality of the management terminals 91#1 to 91#3, such as data rate, traffic pattern, and relative coordinates with the connected base station.
  • the communication quality information is information regarding the communication quality of the management terminals 91#1 to 91#3, and includes, for example, upstream/downstream throughput, delay, jitter, etc. of the management terminals 91#1 to 91#3.
  • the network information includes the geographic locations of the base stations of networks 92#1 and 92#2.
  • the network information may include the frequency channel and bandwidth used by the base station.
  • the connection destination control device 94 further determines a connection pattern based on the unmanaged terminal information of the unmanaged terminal 91#4.
  • the unmanaged terminal information includes the position of the unmanaged terminal 91#4.
  • the unmanaged terminal information includes any information that affects the communication quality of the unmanaged terminal 91#4 to the managed terminals 91#1 to 91#3, such as data rate, traffic pattern, and relative coordinates with the connected base station. You can stay there.
  • unmanaged terminal information including the geographical location of unmanaged terminal 91#4 is collected by prediction, and the same type of parameters as when collecting management terminal information from management terminals 91#1 to 91#3 are used. obtain.
  • highly accurate quality prediction results for the management terminals 91#1 to 91#3 are obtained, taking into consideration the behavior of the unmanaged terminal 91#4.
  • FIG. 2 shows an example of the operation of the connection destination control device 94 during learning.
  • the connection destination control device 94 inputs certain management terminal information, certain network information, and communication quality information of the management terminal information realized under certain network information, and calculates information such as the geographical location and traffic pattern of the unmanaged terminal 91#4. Uses an unmanaged terminal prediction model that outputs information.
  • the management terminal information and network information used for input are represented by a two-dimensional array or a three-dimensional array of pixels in which geographical locations and pixel locations correspond to each other.
  • the number of arrays matches the number of input parameters used for prediction.
  • the output is represented by a two-dimensional or three-dimensional array of pixels in which the geographic location and pixel location correspond.
  • the number of arrays matches the predicted number of parameters of the unmanaged terminal 91#4.
  • the managed terminal information and the unmanaged terminal information match in pixel position and geographical position.
  • the two-dimensional array includes latitude and longitude dimensions.
  • the three-dimensional array includes latitude, longitude, and height dimensions.
  • a two-dimensional array or three-dimensional array of pixels may be prepared for each type of parameter. In this case, a multidimensional array of four or more dimensions may be used.
  • pixel positions will be represented as a two-dimensional matrix for easy understanding.
  • the management terminal information can be expressed by equation (1).
  • the usage frequency channel of the base station of network 92#1 located at the pixel position of the first row and first column is ⁇ 1
  • the usage frequency channel of the base station of network 92#2 located at the pixel position of the third row and fourth column can be expressed by equation (2).
  • the unmanaged terminal 91#4 with a data rate ⁇ 1 exists at the pixel position of the 1st row and 4th column
  • the unmanaged terminal 91 #4 with a data rate ⁇ 2 exists at the pixel position of the 1st row and 4th column. If it is found that the unmanaged terminal information exists at the pixel position of the fourth row and second column, the unmanaged terminal information can be expressed by equation (3).
  • FIG. 3 shows an example of the operation of the connection destination control device 94 at the time of prediction. Since the unmanaged terminal information obtained by the unmanaged terminal prediction model can be expressed using the same parameters as the managed terminal information, it can be treated in the same way as the managed terminal information, and various communication patterns and connection patterns can be expressed using an arbitrary method. It is possible to predict the communication quality of a terminal. Then, the prediction result becomes the communication quality with interference of the unmanaged terminal 91#4 taken into account.
  • conditional generative adversarial network which is a type of supervised machine learning model.
  • CVAE Conditional Variable Autoencoder
  • Conditional GAN will be described below as an example.
  • FIG. 4 shows an example of the architecture of Conditional GAN.
  • the Conditional GAN two learning devices, a generator 81 and a discriminator 82, coexist.
  • the generator 81 is a neural network that generates a multidimensional array of unmanaged terminal information from management terminal information, network information, and labels.
  • communication quality information obtained by the management terminals 91#1 to 91#3 is used as a label.
  • the discriminator 82 is a neural network that determines whether the input multidimensional array is true data derived from a dataset or false data generated by the generator 81.
  • an unmanaged terminal prediction model that generates unmanaged terminal information is learned so that the unmanaged terminal information generated by the generator 81 cannot be distinguished from real data.
  • the generator 81 can generate more accurate unmanaged terminal information.
  • the learning results of the generator 81 that are determined to be true data by the discriminator 82 are used as an unmanaged terminal prediction model.
  • the learned unmanaged terminal prediction model may be installed in the connection destination control device 94, the connection destination control device 94 itself may be provided with learning functions such as the generator 81 and the discriminator 82.
  • FIG. 5 shows a configuration example of the terminal 91 and the connection destination control device 94.
  • the terminal 91 includes a communication interface (IF) 11 , an application 12 , a location information acquisition section 13 , a terminal information acquisition/notification section 14 , and a communication result notification section 15 .
  • the connection destination control device 94 includes a communication interface (IF) 41, a communication information aggregation unit 42, a data set storage unit 43, an unmanaged terminal prediction model 44, a management terminal quality prediction model 45, a connection pattern optimization program 46, and connection destination control. 47.
  • IF communication interface
  • the terminal 91 and the connection destination control device 94 of the present disclosure can also be realized by a computer and a program, and the program can be recorded on a recording medium or provided through a network.
  • - Communication interface 11 An interface that communicates with the destination control device 94 directly or over a network. The same interface that communicates with the base station of network 92#1 or 92#2 may be used.
  • -Application 12 An arbitrary application provided in the terminal 91.
  • - Location information acquisition unit 13 Acquires the geographical position of the terminal 91 in coordinates using GPS or the like.
  • - Terminal information acquisition/notification unit 14 Acquires location information from the location information acquisition unit 13, acquires the average data rate generated by the application 12, and acquires management terminal information necessary for quality prediction of the management terminal.
  • - Communication result notification unit 15 Acquires communication quality information including what kind of communication quality was obtained (throughput and delay) as a result of the acquired management terminal information, and notifies it to the connection destination control device 94.
  • - Communication interface 41 An interface for communicating with the terminal 91. It may also include an interface for communicating with base stations of networks 92#1 and 92#2.
  • - Communication information aggregation unit 42 Processes the management terminal information and communication quality information acquired from the terminal group and stores it as a data set. At the time of prediction, the processed management terminal information and communication quality information are output to the unmanaged terminal prediction model 44 and the management terminal quality prediction model 45.
  • - Data set storage unit 43 Stores the data set used for learning of Conditional GAN, etc., and passes the data to the unmanaged terminal prediction model 44 during learning.
  • - Unmanaged terminal prediction model 44 Predicts unmanaged terminal information using a learning model such as Conditional GAN.
  • - Management terminal quality prediction model 45 Predicts the communication quality of management terminals 91#1 to 91#3 by inputting management terminal information and predicted unmanaged terminal information. At this time, the management terminal quality prediction model 45 may use network information in addition to the management terminal information and unmanaged terminal information.
  • - Connection pattern optimization program 46 Using the prediction results of the management terminal quality prediction model 45, discovers the optimal connection pattern for the management terminals 91#1 to 91#3. For example, the connection pattern optimization program 46 extracts connection patterns when each management terminal connects to each network 92#1 and 92#2, and determines an arbitrary connection pattern from among the extracted connection patterns.
  • - Connection destination control unit 47 Instructs the management terminals 91#1 to 91#3 to switch to the connection pattern determined by the connection pattern optimization program 46.
  • FIG. 6 shows an example of the learning flow of the unmanaged terminal prediction model 44.
  • the data set required for learning the unmanaged terminal prediction model 44 can be generated by, for example, a network simulator.
  • the network simulator simulates a virtual network in which multiple terminals exist (S11), and the communication information aggregation unit 42 of the connection destination control device 94 aggregates the simulation results (S12) and converts them into a multidimensional array format of data sets. After that (S13), it is stored in the data set storage unit 43 (S14).
  • the unmanaged terminal prediction model 44 learns unmanaged terminal information for each data set stored in the data set storage unit 43 (S15) (S16).
  • S15 data set storage unit 43
  • S16 When using a network simulator, it is possible to generate data on a plurality of unmanaged terminal information using one simulation data, and the generation method will be described later.
  • FIG. 7 shows an example of a learning flow of the management terminal quality prediction model 45.
  • the data set required for learning the management terminal quality prediction model 45 can be generated from the management terminal information and communication quality information of the actual terminal. Learning may be performed by regarding some of the management terminals 91#1 to 91#3 as unmanaged terminals.
  • the management terminals 91#1 to 91#3 communicate with the peripheral terminals at the same time (S21), and the connection destination control device 94 aggregates the management terminal information and communication quality information at that time (S22).
  • the connection destination control device 94 converts the aggregated information into a data set multidimensional array format (S23), and then stores it in the data set storage unit 43 (S24).
  • the management terminal quality prediction model 45 performs learning for each data set stored in the data set storage unit 43 (S25) (S26).
  • FIG. 8 shows an example of a prediction flow.
  • the management terminals 91#1 to 91#3 communicate with peripheral terminals at the same time (S31), and the communication information aggregation unit 42 aggregates the management terminal information and communication quality information at that time (S32).
  • the unmanaged terminal prediction model 44 inputs the management terminal information and communication quality information of the immediately preceding management terminals 91#1 to 91#3, and calculates unmanaged terminals such as the position of the unmanaged terminal 91#4 and the communication traffic pattern to be generated. Predict information (S33).
  • the output of the unmanaged terminal prediction model 44 becomes the input of the managed terminal quality prediction model 45.
  • the management terminal quality prediction model 45 combines the aggregation results of the communication information aggregation unit 42 and the prediction results of the unmanaged terminal prediction model 44 (S34), and uses the same method as the quality prediction of the management terminal when there is no unmanaged terminal,
  • the management terminal/unmanaged terminal is collectively regarded as a management terminal and quality prediction is performed (S35).
  • connection pattern optimization program 46 input data related to the management terminals 91#1 to 91#3 out of the output of the management terminal quality prediction model 45. This is because the connection pattern optimization program 46 can only control the network connection state of the management terminal.
  • FIG. 9 shows an example of a data set generation method.
  • a conceptual diagram of a method for generating a dataset from data that aggregates communication results from a network simulation or an actual terminal is shown.
  • the aggregated data is all arbitrarily set simulation parameters or management terminal information, but this data is divided into a pseudo multidimensional array consisting of management terminals and a multidimensional array consisting of unmanaged terminals,
  • the set of multidimensional arrays is regarded as one piece of data.
  • the multidimensional array of management terminal information is used as a label in the Conditional GAN, and that of unmanaged terminals is used as an input array to the discriminator 82.
  • the management terminal information is expressed by equation (4) and the communication quality information is expressed by equation (5)
  • the first data set expressed by equations (6) to (8) and the equation ( 9) to a second data set expressed by equation (11) are generated.
  • the first data set includes management terminal information represented by formula (6), unmanaged terminal information represented by formula (7), communication quality information of the management terminal represented by formula (8), including.
  • the second data set includes management terminal information represented by formula (9), unmanaged terminal information represented by formula (10), communication quality information of the management terminal represented by formula (11), including.
  • the present disclosure learns in advance unmanaged terminal information including the location of unmanaged terminal 91#4, and when predicting the communication quality of management terminals 91#1 to 91#3, management terminal information Obtain unmanaged terminal information with the same type of parameters as . As a result, the present disclosure can obtain highly accurate quality prediction results for the management terminals 91#1 to 91#3 in consideration of the behavior of the unmanaged terminal 91#4.

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Abstract

Le but de la présente divulgation est de permettre de prédire la qualité de communication qui peut être obtenue au niveau d'un terminal géré, même s'il existe un terminal non géré.　Le dispositif et le procédé de la présente divulgation consistent à : acquérir des informations de terminal gérées concernant un terminal géré et des informations de qualité de communication qui peuvent être obtenues au niveau du terminal géré ; prédire des informations de terminal non gérées sur la base des informations de terminal gérées et des informations de qualité de communication ; et utiliser les informations de terminal non gérées prédites pour prédire la qualité de communication pour le terminal géré.
PCT/JP2022/028329 2022-07-21 2022-07-21 Système de prédiction de qualité de communication de terminal WO2024018586A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017028340A (ja) * 2015-07-15 2017-02-02 日本電信電話株式会社 Ｃｓｍａ／ｃａ通信品質管理システムおよび方法
JP2021022913A (ja) * 2019-07-25 2021-02-18 パナソニック株式会社 制御装置、及び、制御方法
WO2022038760A1 (fr) * 2020-08-21 2022-02-24 日本電信電話株式会社 Dispositif, procédé et programme de prédiction de qualité de communication

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017028340A (ja) * 2015-07-15 2017-02-02 日本電信電話株式会社 Ｃｓｍａ／ｃａ通信品質管理システムおよび方法
JP2021022913A (ja) * 2019-07-25 2021-02-18 パナソニック株式会社 制御装置、及び、制御方法
WO2022038760A1 (fr) * 2020-08-21 2022-02-24 日本電信電話株式会社 Dispositif, procédé et programme de prédiction de qualité de communication

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