Communication quality estimation system, communication quality estimation method, and program

Abstract

This communication quality estimation system has: a learning unit configured to learn a model for, on the basis of a plurality of data items that are recorded every time when at least one terminal becomes connected to and performs communication with a given base station and that include sets of communication qualities and wireless environment information items of the terminal having performed the communication, receiving input of the wireless environment information item and outputting a communication quality; and an estimation unit configured to, by inputting to the model having been trained a wireless environment information item of a terminal for which the given base station is a candidate for connection, estimate a communication quality obtained when the terminal is connected to the given base station. Accordingly, the system can estimate the communication quality when being connected to a given base station.

Description

[Technical Field]

[0001] The present invention relates to a communication quality estimation system, a communication quality estimation method, and a program. [Background technology]

[0002] Public wireless LAN services using wireless LAN are available. When wireless LAN is available on smartphones and other devices, wireless LAN is the preferred communication method.

[0003] Generally, a connection to a wireless LAN is made when the received power of a beacon signal broadcast by a base station (AP) is stronger than a certain level. Also, when multiple APs with received power stronger than a certain level are observed, the AP with the strongest received power is selected as the connection destination (Non-Patent Document 1).

[0004] The number of terminals connected to the base station and the channel resource utilization rate (Channel Utilization) are included in the wireless LAN beacon signal and are broadcast in advance, and the terminal receives this and decides whether to connect (Non-Patent Document 2, 9.4.2.27, BSS load element). [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Maneesha V. Ramesh; MS Nisha, "Design of Optimization Algorithm for WLAN AP Selection during Emergency Situations", 2011 IEEE 3rd International Conference on Communication Software and Networks [Non-patent document 2] IEEE Wireless LAN Specification (IEEE std 802.11-2020) Summary of the Invention [Problem to be solved by the invention]

[0006] In Non-Patent Document 1, the desired quality (throughput, etc.) may not be achieved after connection because the determination is made based only on radio wave strength without knowing surrounding interference, AP congestion, or the bandwidth of the network above the AP. Furthermore, recent wireless LANs have introduced throughput improvement technologies that use signal processing in the physical layer, such as beamforming and MIMO transmission, and the received power often does not match the throughput.

[0007] In Non-Patent Document 2, if the quality information reported is correct, it is possible to know the degree of congestion at the base station before connection, but it is not possible to directly predict the throughput that can be obtained.

[0008] The present invention has been made in view of the above points, and an object of the present invention is to make it possible to estimate communication quality when connected to a certain base station. [Means for solving the problem]

[0009] In order to solve the above problem, a communication quality estimation system includes a learning unit configured to learn a model that inputs radio environment information and outputs communication quality based on a plurality of data sets including pairs of radio environment information and communication quality of one or more terminals that have performed communication, the data sets being recorded each time the terminals connect to a certain base station and perform communication; and an estimation unit configured to input radio environment information of a terminal that has selected the certain base station as a connection candidate into the trained model, thereby estimating communication quality when the terminal connects to the certain base station; a terminal that inquires of the estimation unit about the communication quality of a connectable base station, and determines whether to connect to the base station based on the communication quality estimated by the estimation unit using radio environment information of the terminal as an input to the model; It has. [Effects of the Invention]

[0010] It is possible to estimate the communication quality when connected to a certain base station. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a diagram illustrating an example of the configuration of a communication quality estimation system according to an embodiment of the present invention. [Figure 2] FIG. 10 is a diagram for explaining an estimator. [Figure 3] 1 is a diagram illustrating an example of a hardware configuration of a communication quality estimation server 10 according to an embodiment of the present invention. [Figure 4] 1 is a diagram illustrating an example of a functional configuration of a communication quality estimation system according to an embodiment of the present invention. [Figure 5] 10 is a flowchart illustrating an example of a processing procedure for generating an estimator. [Figure 6] 10 is a flowchart illustrating an example of a processing procedure for standardizing a feature amount. [Figure 7] 10 is a flowchart illustrating an example of a processing procedure for learning processing of an estimator. [Figure 8] 10 is a flowchart illustrating an example of a processing procedure for estimating communication quality. DETAILED DESCRIPTION OF THE INVENTION

[0012] In this embodiment, in order to enable a terminal to detect the communication quality (throughput) obtained when connected to a base station (AP) of a wireless LAN (Local Area Network) before connecting to the wireless LAN and determine whether or not to use the wireless LAN, machine learning is used to estimate the communication quality from past performance and information on the surrounding wireless environment, and the estimated value is notified to the terminal.

[0013] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a diagram showing an example of the configuration of a communication quality estimation system according to an embodiment of the present invention. Fig. 1 shows one or more terminal devices 30, a plurality of APs 40, a communication quality estimation server 10, and a communication quality measuring server 20.

[0014] The terminal device 30 is a terminal capable of communication using a wireless LAN. For example, a smartphone, a tablet terminal, a PC (Personal Computer), etc. may be used as the terminal device 30.

[0015] The AP 40 is a base station of a wireless LAN, and connects the terminal device 30 to a network N1 such as the Internet via the wireless LAN.

[0016] The communication quality measuring server 20 is one or more computers that measure the communication quality between the terminal device 30 and the network N1 via a wireless LAN (that is, connected to the AP 40) and communicates with the terminal device 30.

[0017] The communication quality estimation server 10 is one or more computers that estimate (predict) the communication quality when the terminal device 30 is connected to one of the APs 40.

[0018] First, the terminal device 30 connects to any one of the APs 40, communicates with the communication quality measurement server 20, and measures the quality of the communication (communication quality). The terminal device 30 also acquires information indicating the wireless environment of the terminal device 30 at the time of measuring the communication quality (hereinafter referred to as "wireless environment information"). The terminal device 30 transmits (uploads) data (hereinafter referred to as "observation data") consisting of a set of the acquired wireless environment information and information indicating the measured communication quality (hereinafter referred to as "quality information") to the communication quality estimation server 10. The wireless environment information includes, for example, one or more parameters, such as identification information of the currently connected AP 40 (hereinafter referred to as the "target AP"), the bandwidth used by the target AP, the current date and time, the RSSI (Received Signal Strength Indicator) of the target AP, the channel usage rate of the target AP, the number of neighboring APs using the same channel as the target AP (the same channel), the channel usage rate of each neighboring AP, and the RSSI of each neighboring AP. In this embodiment, a case will be described in which the wireless environment information includes all of these parameters, but some parameters may be omitted or other parameters may be added.

[0019] Here, the term "neighboring AP" refers to an AP 40 other than the target AP from which the terminal device 30 can scan the surrounding wireless LAN and receive radio waves, regardless of whether the channel is the same as that of the target AP. The number of neighboring APs and the RSSI of the neighboring APs can be obtained by the scan. The channel usage rate of the neighboring APs can also be obtained from the beacon signals and probe response signals of the neighboring APs.

[0020] The quality information is information including the values ​​of each parameter (hereinafter referred to as "quality parameters") such as throughput (uplink, downlink), delay, jitter, and packet loss resulting from communication. Some parameters may be missing from the quality information, and other parameters may be added.

[0021] The communication quality estimation server 10 stores the received observation data in a database. The wireless environment information includes identification information of the target AP. The communication quality estimation server 10 stores the multiple pieces of observation data uploaded from the multiple terminal devices 30 in the database.

[0022] The communication quality estimation server 10 then extracts the observation data for each base station from the database and generates an estimator for each base station and each quality parameter through machine learning, such as a neural network. As shown in FIG. 2, the estimator is a model (such as a neural network) that receives feature quantities of wireless environment information as input and outputs estimated values ​​of communication quality. The feature quantities of the wireless environment information are a group of parameters that are input to the estimator in FIG. 2. That is, the feature quantities include the bandwidth used by the currently connected AP 40 (target AP), time zone information, RSSI of the target AP, channel utilization rate of the target AP, number of neighboring APs on the same channel as the target AP, average channel utilization rates of neighboring APs on other channels (channels different from the channel of the target AP), and average RSSI of each neighboring AP, all of which can be easily derived from the wireless environment information. Note that an estimator is generated for each base station and each quality parameter, so a given estimator outputs (estimates) any one of throughput (uplink, downlink), delay, jitter, and packet loss.

[0023] When the terminal device 30 finds a connectable AP 40 (hereinafter referred to as a "candidate AP"), it transmits wireless environment information of the terminal device 30 at that time to the communication quality estimation server 10 via a network (a network separate from the wireless LAN (e.g., a mobile communication network)) to inquire about an estimated value of communication quality (hereinafter referred to as an "estimated quality"). The communication quality estimation server 10 estimates communication quality by inputting features of the wireless environment information into an estimator corresponding to the AP 40, and transmits the estimated quality to the terminal device 30.

[0024] The terminal device 30 that has received the estimated quality can determine whether or not to connect to the candidate AP based on, for example, whether or not the desired communication quality can be obtained.

[0025] The communication quality measuring server 20 and the communication quality estimating server 10 may be realized using different computers or may be realized using the same computer. When realized using different computers, the communication quality measuring server 20 and the communication quality estimating server 10 may be installed at different locations on the network N1.

[0026] Fig. 3 is a diagram showing an example of the hardware configuration of the communication quality estimation server 10 according to the embodiment of the present invention. The communication quality estimation server 10 in Fig. 3 includes a drive device 100, an auxiliary storage device 102, a memory device 103, a processor 104, and an interface device 105, which are all interconnected via a bus B.

[0027] A program that realizes processing in the communication quality estimation server 10 is provided by a recording medium 101 such as a CD-ROM. When the recording medium 101 storing the program is set in the drive device 100, the program is installed from the recording medium 101 to the auxiliary storage device 102 via the drive device 100. However, the program does not necessarily have to be installed from the recording medium 101, but may be downloaded from another computer via a network. The auxiliary storage device 102 stores the installed program as well as necessary files, data, etc.

[0028] When an instruction to start the program is received, the memory device 103 reads out the program from the auxiliary storage device 102 and stores it. The processor 104 is a CPU or a GPU (Graphics Processing Unit), or a CPU and a GPU, and executes functions related to the communication quality estimation server 10 in accordance with the program stored in the memory device 103. The interface device 105 is used as an interface for connecting to a network.

[0029] FIG. 4 is a diagram illustrating an example of a functional configuration of a communication quality estimation system according to an embodiment of the present invention.

[0030] 4, the communication quality measuring server 20 has a quality measuring unit 21. The quality measuring unit 21 is realized by a process in which one or more programs installed in the communication quality measuring server 20 are executed by a processor of the communication quality measuring server 20 (the processor 104 when the communication quality measuring server 20 is realized by using the same computer as the communication quality estimating server 10).

[0031] The communication quality estimation server 10 has an observation data receiving unit 11, a learning unit 12, and an estimation unit 13. Each of these units is realized by a process in which one or more programs installed in the communication quality estimation server 10 are executed by a processor 104. The communication quality estimation server 10 also uses an observation data storage unit 14 and a learning parameter storage unit 15. Each of these storage units can be realized using, for example, an auxiliary storage device 102, or a storage device connectable to the communication quality estimation server 10 via a network.

[0032] The terminal device 30 has a quality measurement unit 31, a radio environment observation unit 32, an observation data transmission unit 33, and an estimated quality acquisition unit 34. Each of these units is realized by processing that one or more programs installed in the terminal device 30 cause a processor of the terminal device 30 to execute.

[0033] The quality measurement unit 31 of the terminal device 30 connects to any one of the APs 40, communicates with the communication quality measurement server 20, and measures the quality of the communication (communication quality) between the quality measurement unit 21 and the terminal device 30 to obtain quality information.

[0034] The radio environment observing unit 32 acquires radio environment information when the quality measuring unit 31 measures the communication quality or when the communication quality estimation server 10 is inquired about the estimated quality.

[0035] The observation data transmitter 33 transmits to the communication quality estimation server 10 observation data including the quality information acquired by the quality measurement unit 31 and the radio environment information acquired by the radio environment observation unit 32 when measuring the communication quality.

[0036] The observation data receiving unit 11 of the communication quality estimation server 10 receives the observation data and records the observation data in the observation data storage unit 14. Therefore, the observation data is recorded in the observation data storage unit 14 every time the communication quality is measured by each terminal device 30.

[0037] The learning unit 12 uses the group of observation data recorded in the observation data memory unit 14 to learn an estimator for each AP 40 and for each quality parameter, and records the learning results (the values ​​of the learning parameters of the estimator) in the learning parameter memory unit 15.

[0038] For example, before connecting to a wireless LAN, the estimated quality acquisition unit 34 of the terminal device 30 inquires of the communication quality estimation server 10 about the estimated quality of a connection candidate AP 40 (target AP). At this time, the estimated quality acquisition unit 34 transmits to the communication quality estimation server 10 the wireless environment information of the terminal device 30 acquired by the wireless environment observation unit 32 at that time.

[0039] In response to an inquiry from the estimated quality acquisition unit 34, the estimation unit 13 of the communication quality estimation server 10 inputs the radio environment information transmitted from the estimated quality acquisition unit 34 in response to the inquiry into an estimator corresponding to the target AP, thereby estimating the communication quality corresponding to the radio environment information. The estimation unit 13 transmits the estimated value of the communication quality (estimated quality) output by the estimator to the estimated quality acquisition unit 34 that originated the inquiry.

[0040] The following describes the processing procedure executed by the communication quality estimation server 10. Fig. 5 is a flowchart for explaining an example of the processing procedure for generating an estimator. The processing procedure in Fig. 5 is executed periodically or at multiple times in response to a predetermined event (for example, an operation by an administrator of the communication quality estimation server 10).

[0041] In step S101, the learning unit 12 extracts a learning data group for each AP 40 and for each quality parameter from a collection of multiple pieces of observation data (pairs of wireless environment information and communication quality information) stored in the observation data storage unit 14 during a period from the previous learning (or the start of collection of observation data if learning is the first time) to the present time (hereinafter referred to as the "target period"). At this time, the learning unit 12 extracts features from the wireless environment information. For example, if the number of APs 40 is n and the number of quality parameters is m, (n × m) learning data groups (hereinafter referred to as "learning data sets") are extracted. A learning data set corresponding to a certain AP 40 and a certain quality parameter is a collection of learning data consisting of pairs of features of wireless environment information including identification information of the AP 40 and the quality parameter associated with the wireless environment information.

[0042] Next, the learning unit 12 standardizes each feature of each learning data set for each learning data set (i.e., for each AP 40 and quality parameter) (S102). As described above, each feature is the bandwidth used by the target AP, time zone information, RSSI of the target AP, channel usage rate of the target AP, number of neighboring APs on the same channel as the target AP, average channel usage rates of neighboring APs on other channels (channels different from the channel of the target AP), and average RSSI of each neighboring AP.

[0043] Next, the learning unit 12 executes a learning process of the estimator for each learning data set (that is, for each AP 40 and quality parameter) (S103).

[0044] Next, step S102 will be described in detail. Fig. 6 is a flowchart illustrating an example of the processing procedure for standardizing features. In Fig. 6, steps S201 to S203 are executed for each training dataset. Hereinafter, the training dataset to be processed will be referred to as the "target training dataset."

[0045] In step S201, the learning unit 12 calculates the average value μ (average value for each feature) in the target learning data set for each feature. The average value μ of a certain feature can be calculated based on the following formula.

[0046]

number

[0047] Next, the learning unit 12 calculates the variance σ 2 Calculate the variance of a feature (σ). 2 can be calculated based on the following formula:

[0048]

number

[0049]

number

[0050] When steps S201 to S203 have been executed for all the learning data sets, the processing procedure in FIG. 6 ends.

[0051] Next, step S103 in Fig. 5 will be described in detail. Fig. 7 is a flowchart for explaining an example of the processing procedure for the learning process of an estimator. In Fig. 7, steps S301 and S302 are executed for each learning data set. Hereinafter, the learning data set to be processed will be referred to as the "target learning data set." Furthermore, the estimator corresponding to the target learning data set will be referred to as the "target estimator."

[0052] In step S301, the learning unit 12 inputs the feature quantities contained in the learning data to the target estimator for each piece of learning data included in the target learning dataset, and updates the learning parameters of the target estimator so as to reduce the loss between the estimation quality output by the estimator and the value of the quality parameter contained in the learning data. Note that if learning has been performed on the target estimator in the past, the learning unit 12 sets the values ​​of the learning parameters stored in the learning parameter storage unit 15 for the target estimator as initial values, and then executes step S301.

[0053] Next, the learning unit 12 records the values ​​of the learning parameters of the trained target estimator in the learning parameter storage unit 15 in association with the identification information of the AP 40 corresponding to the target training data and the identification information of the quality parameters (S302).

[0054] When steps S301 and S302 have been executed for all the learning data sets, the processing procedure in FIG. 7 ends.

[0055] Next, a process for estimating communication quality using a trained estimator will be described.

[0056] FIG. 8 is a flowchart illustrating an example of a processing procedure for estimating communication quality.

[0057] When a certain terminal device 30 transmits information (hereinafter referred to as "inquiry information") indicating an inquiry about the estimated quality of a connection candidate AP 40 (hereinafter referred to as "candidate AP") to the communication quality estimation server 10, the estimation unit 13 receives the inquiry information (S401). The inquiry information includes radio environment information related to the candidate AP. The radio environment information related to the candidate AP includes identification information of the candidate AP, the bandwidth used by the candidate AP, the current date and time, the RSSI of the candidate AP, the channel usage rate of the candidate AP, the number of neighboring APs on the same channel as the candidate AP, the channel usage rate of each neighboring AP, and the RSSI of each neighboring AP.

[0058] Next, the estimation unit 13 constructs a trained estimator by setting the values ​​of the learning parameters stored in the learning parameter storage unit 15 for each candidate AP and quality parameter in a model as an estimator (S402). Thus, the number of estimators constructed is equal to the number of quality parameters.

[0059] Next, the estimation unit 13 extracts features from the wireless environment information included in the inquiry information (S403). The features include the bandwidth used by the candidate AP, time zone information, the RSSI of the candidate AP, the channel usage rate of the candidate AP, the number of neighboring APs on the same channel as the candidate AP, the average channel usage rates of neighboring APs on other channels (channels different from the channel of the candidate AP), and the average RSSI of each neighboring AP.

[0060] Next, the estimation unit 13 inputs each feature amount extracted in step S403 to each estimator for each quality parameter (S404).

[0061] Next, the estimation unit 13 acquires the values ​​output by the respective estimators as the estimated values ​​(estimated quality) of the quality parameters corresponding to the estimators (S405).

[0062] Next, the estimation unit 13 transmits the estimated quality for each quality parameter related to the candidate AP to the inquiring terminal device 30 (S406).

[0063] As described above, according to the present embodiment, it is possible to estimate the communication quality when connected to a certain base station. In consideration of the feature quantities used at this time, it is possible to estimate the quality taking into account the influence of changes in the degree of network congestion depending on the time of day and the degree of interference from surrounding APs 40.

[0064] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to such specific embodiments, and various modifications and variations are possible within the scope of the gist of the present invention as described in the claims. [Explanation of symbols]

[0065] 10 Communication quality estimation server 11 Observation data receiver 12 Learning Department 13 Estimation part 14 Observation data storage unit 15 Learning parameter memory section 20 Communication quality measurement server 21 Quality measurement department 30 Terminal Equipment 31 Quality measurement department 32 Radio Environment Observation Department 33 Observation data transmission unit 34 Estimated quality acquisition section 40 AP 100 Drive device 101 Recording media 102 Auxiliary storage device 103 Memory Device 104 processors 105 Interface Device B Bus

Claims

1. a learning unit configured to learn a model that inputs radio environment information and outputs communication quality based on a plurality of data including a pair of radio environment information and communication quality of one or more terminals that have performed communication, the pair being recorded each time the terminals connect to a certain base station and perform communication; an estimation unit configured to estimate communication quality when a terminal that has the certain base station as a connection candidate connects to the certain base station by inputting radio environment information of the terminal to the trained model; a terminal that inquires of the estimation unit about the communication quality of a connectable base station, and determines whether to connect to the base station based on the communication quality estimated by the estimation unit using radio environment information of the terminal as an input to the model; A communication quality estimation system comprising:

2. the learning unit is configured to learn the model for each of a plurality of base stations based on a plurality of data items recorded for each of the plurality of base stations each time one or more terminals connect to the base station and perform communication, the data items including a pair of radio environment information and communication quality of the terminals at the time of performing the communication; the estimation unit is configured to estimate communication quality when a certain terminal is connected to a base station by inputting radio environment information of the certain terminal into the trained model corresponding to a base station that the certain terminal is a connection candidate to.

2. The communication quality estimation system according to claim 1.

3. The wireless environment information of the terminal includes a bandwidth used by a first base station to which the terminal is connected, a current date and time, an RSSI of the first base station, a channel utilization rate of the first base station, the number of second base stations that are on the same channel as the first base station and from which the terminal can receive radio waves, the channel utilization rates of each of the second base stations, and any of the second base stations.

3. The communication quality estimation system according to claim 1 or 2.

4. a learning procedure for learning a model that inputs radio environment information and outputs communication quality based on a plurality of data including pairs of radio environment information and communication quality of one or more terminals that have performed communication, the data being recorded each time the terminals connect to a certain base station and perform communication; an estimation step of estimating communication quality when a terminal that has the certain base station as a connection candidate is connected to the certain base station by inputting wireless environment information of the terminal into the trained model; The computer executes a determination step in which a terminal inquiring of the computer about the communication quality of a connectable base station determines whether to connect to the base station based on the communication quality estimated by the estimation step, using radio environment information of the terminal as an input to the model; A communication quality estimation method comprising:

5. A program causing a computer to function as the learning unit and the estimation unit of the communication quality estimation system according to claim 1.

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