WO2020209472A1 - Wireless terminal device for displaying quality of dynamic wireless network on screen on basis of quality prediction, and operation method therefor - Google Patents

Abstract

A method, according to an embodiment of the present invention, which is a method for operating a wireless terminal device for displaying the quality of a wireless network on a screen, comprises the steps of: collecting variable information of the wireless terminal device; acquiring, according to a quality prediction process using the variable information, quality prediction information for each accessible network; and displaying, on a screen, quality information of the wireless network on the basis of the quality prediction information for each network.

Description

Wireless terminal device for displaying dynamic wireless network quality based on quality prediction and its operation method

The present invention relates to a wireless terminal device and a method of operation thereof. More specifically, the present invention provides a wireless terminal device for displaying a quality prediction-based dynamic wireless network quality on a screen, and an operating method thereof

With the rapid development of electronic and communication technologies, a service using a wireless network is developing into a communication service that transmits and receives data wirelessly, such as packet data.

Accordingly, the data usage of mobile terminals and the number of devices connected to the Internet are increasing exponentially, and the importance of the wireless Internet is becoming more and more prominent in the lives of modern people. If technologies such as VR, AR, IoT, and autonomous driving are actively commercialized with the advent of 5G mobile mobile communication and 6G Wi-Fi (802.11 ax, etc.) in the future, the dependence on the wireless network and expectations for the cost and quality of the network Will continue to increase.

With the increase in wireless Internet demand, wireless networks are provided in various forms, protocols, and communication methods. In particular, base station-based mobile communication networks and WIFI standard-based wireless networks based on AP (ACCESS POINT) Due to infrastructure investment in networks, wireless networks can now be used anywhere in the world.

However, due to the rapid increase in the use of wireless networks, in order to maintain or improve its quality, mobile carriers, etc., are investing excessive infrastructure construction costs to establish new communication networks, which leads to the cost burden of consumers. to be.

Accordingly, wireless network users are choosing and using a wireless network in consideration of cost burden. In particular, a WIFI standard network that has no cost or is cheaper than a high-cost wireless network based on a mobile communication network is intended to be used. However, in the case of a WIFI standard network, there is a problem in that the quality of the network cannot be stably maintained due to decentralized ownership and operation. For example, when connected to a WIFI AP with a weak signal or low performance, wireless Internet may not be used several times a day.

Due to this problem, the user directly observes the access speed of wireless networks, selects and uses the fastest and most cost-effective network, and sets to switch back to the mobile communication network when experiencing a decrease in speed.

However, this method has a problem in that the user cannot always use a fast and efficient network, so that the quality deterioration process is at least temporarily experienced, and the reconnection process and connection delay that occur when the network is switched causes a fast and seamless (SEAMLESS) wireless Internet. There is a problem that it is difficult to use and provide.

In addition, the quality of a wireless network should be determined not only by a simple speed, but also by various variables such as cost, responsiveness, and device performance, but currently, there is a limit in which the network can only be determined by only the signal strength, which is a primary variable experienced by the user.

The present invention was devised to solve the above-described problems, and a quality prediction model according to a network-specific setting variable corresponding to a plurality of accessible wireless networks is determined in advance, and quality prediction information for each network compared to an input variable based on this And a quality prediction information providing device that provides a dynamic wireless network variable access based on quality prediction that can minimize the access delay caused by network switching by allowing the user to always switch to the fastest and most efficient network by calculating An object thereof is to provide a method of operation, a wireless terminal device, and a method of operation thereof.

In addition, the present invention is a method of displaying quality such as speed and bandwidth, as well as a method of displaying the signal strength of a wireless network in a wireless terminal device, and is a dynamic radio based on quality prediction that allows users to accurately recognize the performance of a wireless network. An object of the present invention is to provide a wireless terminal device for displaying network quality and an operation method thereof.

A method according to an embodiment of the present invention for solving the above-described problems, in the operating method of a wireless terminal device capable of accessing a plurality of wireless networks, the method comprising: obtaining a data packet to be transmitted; Collecting variable information of the wireless terminal device and wireless network; Obtaining quality prediction information for each accessible network according to the quality prediction processing of the variable information; Determining a first wireless network to transmit the data packet from among the plurality of wireless networks based on the quality prediction information for each network; And wirelessly transmitting the data packet to a target device using the determined first wireless network.

An apparatus according to an embodiment of the present invention for solving the above-described problems, in a wireless terminal device capable of accessing a plurality of wireless networks, includes: a control unit for obtaining a data packet to be transmitted; A variable information processing unit collecting variable information of the wireless terminal device and wireless network; A quality prediction module that obtains quality prediction information for each accessible network according to the quality prediction processing of the variable information; A network determining unit configured to determine a first wireless network to transmit the data packet from among the plurality of wireless networks based on the network-specific quality prediction information; And a communication unit for wirelessly transmitting the data packet to a target device using the determined first wireless network.

Meanwhile, in the operating method of a terminal device displaying network quality on a screen according to the present invention, in the operating method of a wireless terminal device displaying dynamic wireless network quality on the screen based on quality prediction, the wireless terminal device and the wireless terminal device are connected. Collecting possible wireless network quality parameters; Acquiring quality data of a wireless network accessible to a wireless terminal device based on the collected quality parameters; Generating network quality indication information as a corresponding normalized number or character or a shaped image by classifying the quality data with a predetermined normalized value or classification; Displaying quality indication information of a currently connected wireless network on a screen; Notifying a change in network quality when a change in quality data of the currently connected network exceeds a preset threshold; Includes.

In addition, a wireless terminal device for displaying network quality of the present invention is a wireless terminal device for displaying quality information on a wireless network, comprising: a variable information processing unit for collecting variable information of the wireless terminal device; A quality prediction module that obtains quality prediction information for each accessible network according to a quality prediction process using the variable information; And a network quality display unit configured to display quality information of a currently connected wireless network on a screen based on the quality prediction information for each network. It is characterized by including.

Meanwhile, the method according to an embodiment of the present invention for solving the above-described problems may be implemented with a program for executing the method on a computer and a recording medium in which the program is recorded.

According to an embodiment of the present invention, a quality prediction model according to a set variable for each network corresponding to a plurality of accessible wireless networks may be pre-determined, and based on this, quality prediction information for each network compared to a current input variable may be calculated. Dynamically variable access of wireless networks can be provided by pre-predicting the quality degradation for each network through a process based on various variables.

Accordingly, according to an embodiment of the present invention, it is possible to enable a fast network switch before the user experiences an actual quality deterioration, and minimize an access delay caused by the network switch.

In addition, according to the method of displaying the network quality on the screen, since the wireless terminal device displays the quality of the wireless network in a numerical form or shape, there is an effect that a user of the wireless terminal device can accurately grasp the quality of the wireless network.

In addition, as the quality of the wireless network changes, the changed number or shape is displayed on the wireless terminal device, so that the user of the wireless terminal device can immediately check the quality change of the wireless network, and through this, the quality of other wireless networks is compared. The wireless terminal device user has the effect of using a wireless network of higher quality.

Due to the development of communication technology, there are many types of wireless networks to which wireless terminal devices can connect, and by comparing and analyzing the performance of wireless networks by quality, there is an effect that a wireless network with the best quality or a user's preference can be used.

1 is a conceptual diagram schematically showing an entire system according to an embodiment of the present invention.

2 is a block diagram showing in more detail a wireless terminal device according to an embodiment of the present invention.

3 and 4 are flowcharts illustrating a method of operating a wireless terminal device according to an embodiment of the present invention.

5 is a conceptual diagram schematically illustrating a system based on an apparatus for providing quality prediction information according to an embodiment of the present invention.

6 is a block diagram specifically showing an apparatus for providing quality prediction information according to an embodiment of the present invention.

7 is a flowchart illustrating an operation of an apparatus for providing quality prediction information according to an embodiment of the present invention.

8 is a block diagram illustrating a learning-based quality prediction module according to an embodiment of the present invention in more detail.

9 to 10 are diagrams for explaining a data merging and sharing process of a learning-based quality prediction model according to an embodiment of the present invention.

11 is a block diagram schematically showing the configuration of a wireless terminal device for displaying wireless network quality on a screen according to an embodiment of the present invention.

12A to 12D are exemplary diagrams for displaying the quality of a wireless network on a screen in a wireless terminal device according to an embodiment of the present invention.

13A and 13B are other exemplary diagrams for displaying the quality of a wireless network on a screen in a wireless terminal device according to an embodiment of the present invention.

14 is a flowchart illustrating a process of displaying a quality of a wireless network on a screen in a wireless terminal device according to an embodiment of the present invention.

The following content merely illustrates the principles of the present invention. Therefore, those skilled in the art can implement the principles of the present invention and invent various devices included in the concept and scope of the present invention, although not clearly described or illustrated herein. In addition, it is understood that all conditional terms and examples listed in this specification are, in principle, expressly intended only for the purpose of making the concept of the present invention understood, and are not limited to the embodiments and states specifically listed as such. Should be.

In addition, it is to be understood that all detailed descriptions listing specific embodiments as well as principles, aspects and embodiments of the present invention are intended to include structural and functional equivalents of these matters. It should also be understood that these equivalents include not only currently known equivalents, but also equivalents to be developed in the future, that is, all devices invented to perform the same function regardless of structure.

Thus, for example, the block diagrams herein are to be understood as representing a conceptual perspective of exemplary circuits embodying the principles of the invention. Similarly, all flowcharts, state transition diagrams, pseudocodes, etc. are understood to represent various processes performed by a computer or processor, whether or not the computer or processor is clearly depicted and that can be represented substantially in a computer-readable medium. Should be.

In addition, the explicit use of terms presented as processor, control, or similar concepts should not be interpreted exclusively by referring to hardware capable of executing software, and without limitation, digital signal processor (DSP) hardware, ROM for storing software. It should be understood to implicitly include (ROM), RAM, and non-volatile memory. Other commonly used hardware may also be included.

The above-described objects, features, and advantages will become more apparent through the following detailed description in connection with the accompanying drawings, whereby those of ordinary skill in the technical field to which the present invention pertains can easily implement the technical idea of the present invention. There will be. In addition, in describing the present invention, when it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted.

Hereinafter, a preferred embodiment according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram schematically showing an entire system according to an embodiment of the present invention.

Referring to FIG. 1, a system according to an embodiment of the present invention includes a quality prediction module 200, a wireless terminal device 100, and a connection target device 300, and a wireless terminal device 100 and a connection target device 300 may be configured to be connectable through one or more wireless networks among a plurality of wireless networks 200a, 200b, ... 200n.

First, each wireless network includes a local area network (LAN), a wide area network (WAN), a value added network (VAN), a personal area network (PAN), and a mobile communication network. Mobile radiocommunication network) or a satellite communication network may be implemented as various types of wireless networks, and each base station or access point (AP) device for providing the same may be provided in a remote location.

In particular, in order to provide a local area communication network, the wireless network may include a WIFI wireless network based on a WIFI standard, and each access point may broadcast device information for operating a Wi-Fi service coverage.

Meanwhile, the wireless terminal device 100 may be a wireless terminal device of a user who requests transmission and reception of a wireless data packet by determining a first wireless network 200a to be connected to the access target device 300 among wireless networks. Here, the wireless terminal device 100 may be implemented as various devices such as a smart phone, a tablet computer, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), and smart glasses.

In addition, according to an embodiment of the present invention, the wireless terminal device 100 may collect and transmit a quality model setting variable for each wireless network to the quality prediction module 200, and the quality prediction module 200 Accordingly, a quality prediction model for each wireless network may be determined in advance.

Here, the quality prediction module 200 may obtain a model setting variable and determine a quality prediction learning model that learns and calculates relationship information between various variables, and the learning process is a supervised or non-supervised artificial neural network training process, regression analysis Learning, deep learning analysis, and the like can be exemplified. In addition, the learning model may be a model that is trained and built in advance, is determined according to variable information collected in real time, or is continuously trained and updated by using the variable information in a pre-built learning model. This will be described in more detail later.

In another embodiment, the quality prediction module 200 may obtain a model setting variable and determine a quality prediction model in the form of a function using various variables as input values. The applied function of the corresponding quality prediction model may be a linear function, a logarithmic function, and the like, which will be described in more detail later.

Here, each of the variables may be diversified into physical variable information, constant element information, variable element information, etc. for each communication section for quality prediction.

In addition, the wireless terminal device 100 collects the model input variable of the wireless terminal device 100 in response to the data packet to be transmitted wirelessly, and applies the model input variable to a quality prediction model for each wireless network, Obtains quality prediction information, determines a first wireless network 200a to transmit the data packet from among the plurality of wireless networks based on the quality prediction information for each network, and uses the determined first wireless network May be wirelessly transmitted to the connection target device 300.

Accordingly, the wireless terminal device 100 can more accurately predict in advance the quality deterioration due to changes in model input variables such as network speed and signal strength through a quality prediction model according to preset model setting variables. As a result, it is possible to change the network quickly and efficiently while minimizing the experience of quality deterioration of the user through dynamic variable control of the wireless network.

2 is a block diagram showing in more detail a wireless terminal device according to an embodiment of the present invention.

Referring to FIG. 2, a wireless terminal device 100 according to an embodiment of the present invention includes a control unit 110, a communication unit 120, a model setting variable processing unit 130, a model input variable processing unit 140, and a network determination unit. 150, a quality prediction module 200, a database 160, and a user setting unit 170.

The controller 110 overall controls the operation of each component of the wireless terminal device 100 and execution of functions. For example, the control unit 110 may be implemented as a processor for controlling all or part of the acquisition of packet data to be transmitted, processing of model setting variables, processing of model input variables, network determination, and user setting functions, or a program for executing the same. have.

In addition, all or part of the functions of the control unit 110 may be implemented as a software module installed in the wireless terminal device 100, and the software module operates in an application layer to perform network variable control through network socket binding control, or Alternatively, it is possible to perform network variable control that operates in the OS layer to drive the network interface.

Further, the communication unit 120 connects to the wireless networks 200a, 200b, ... 200n under the control of the control unit 110, or connects to a wired network to connect to/from the network where the connection target device 300 is located. It may include one or more communication modules that enable wireless data packet communication.

In particular, the communication unit 120 may include a wireless Internet module for accessing at least one of the wireless networks 200a, 200b, ... 200n and a wireless Internet protocol, and the wireless Internet module is a WLAN (Wireless LAN) ( Wi-Fi), Wireless broadband (Wibro), World Interoperability for Microwave Access (Wimax), and a network module based on a High Speed Downlink Packet Access (HSDPA) scheme may be exemplified.

In addition, the communication unit 120 may include a short-range communication module for connecting to at least one of the wireless networks 200a, 200b, ... 200n and short-range communication through a short-range communication protocol, and short-range communication technology Examples of examples include Bluetooth, Radio Frequency Identification (RFID), infrared data association (IrDA), Ultra Wideband (UWB), ZigBee, and the like.

On the other hand, the communication unit 120 may include a mobile communication module for mobile communication access to at least one of the wireless networks (200a, 200b, ... 200n) and a mobile communication protocol, for this purpose, a base station on a mobile communication network, an external It is possible to transmit and receive a wireless signal with at least one of the terminal, the connection target device or the server The wireless signal may include a voice call signal, a video call signal, or various types of data according to transmission/reception of text/multimedia messages.

Further, the communication unit 120 can selectively variably control a communication module for wirelessly transmitting data packets under the control of the control unit 110, which is based on the quality prediction information for each network analyzed by the quality prediction module 200. Can be determined accordingly.

To determine the quality prediction information for each network, the quality prediction module 200 collects the model setting variables obtained from the model setting variable processing unit 130, and determines a quality prediction model for each wireless network based on the collected model setting variables. , Can be stored in the database 160.

In addition, the quality prediction module 200 may calculate quality prediction information for each wireless network by collecting model input variables obtained from the model input variable processing unit 140 and applying the collected model input variables to the determined quality prediction model. have. The quality prediction model to which the model input variable is applied may be stored in the database 160.

In one embodiment, the quality prediction module 200 collects the frequency band of the wireless network as a model setting variable, determines a network quality relational expression compared to the signal strength (RSSI) for the corresponding frequency band as an application function of the quality prediction model, , Signal strength can be used as a model input variable to predict the quality of the wireless network. Here, the relationship between signal strength and network quality may include a linear function, a logarithmic function, a sigmoid function, and an arctan function.

In another embodiment, the quality prediction module 200 may use a frequency band of a wireless network as a model setting variable, and determine a network quality relational expression versus signal strength for a corresponding frequency band as a quality prediction learning model. In addition, the relationship learning operation can be performed by using the signal strength and network quality, which are the input and output values of the relational expression as model input variables, as data for regression analysis learning on the relational expression, and learn the signal strength, which is a part of the model input variable. The quality of the wireless network can be predicted by using it as an input value of the relational expression.

Here, the model setting variable processing unit 130 and the model input variable processing unit 140 may collect, refine, and transmit various variables for quality prediction for each wireless network in the quality prediction module 200, which is a quality prediction module. It may be determined differently according to the learning process of 200 or the form of the applied function. As the learning process, artificial intelligence or artificial neural network learning can be exemplified, and variable setting and learning model configuration for this are important.

In addition, the quality prediction module 200 can collect and process model settings and input variables from internal information of the wireless terminal device 100, as well as a data set received from an external device at a remote location as a variable. By collecting and processing data or collecting and processing data input from users as variables, a quality prediction model for each wireless network is generated from variables collected and processed through various routes, and various input variables corresponding to the generated models and situations are generated. You can handle it.

Here, the model setting variable and the model input variable according to an embodiment of the present invention may be composed of basic variable information obtained primarily or variables that can be refined and obtained from the basic variable information, and the determination process of the prediction model W, the learning model generation method and type, the type and type of the applied function, application timing, application target, required output value, etc. can be determined independently and differently from each other.

Such variable information may include, for example, a constant element and a variable element, and the constant element and the variable element may be used as part or all of a model setting variable and a model input variable.

Here, the constant factor is a factor in which the degree of influence on the quality of the wireless communication network is invariant, and the wire cable performance information, the hardware performance information of the AP, and the communication protocol corresponding to the wireless section between the AP and the wireless terminal device (e.g., WIFI network In the case of, standard-based protocols such as 802.11n, 802.11ac, or encryption protocols such as OPEN, WEP, WPA2, and standard-based protocols such as GSM, LTE-A, and LTE for mobile communication networks), frequency band and channel information of the AP , The number of neighboring APs, and the like may be exemplified.

In addition, the variable factor is a factor in which the degree of influence on the quality of the wireless communication network is continuously variable. For example, difference information between the IP of the access target device and the IP of the AP, information on the number of AP access devices, and signal strength (RSSI) ) Information, frequency congestion information, TCP socket destination IP information, RTT information, window size information, transmission/reception data amount, time (TIME) information, time zone information, terminal movement pattern information, etc. Herein, the AP (Access Point) ) May mean a wireless network access point such as a WIFI router or a base station.

Accordingly, the quality prediction module 200 determines a quality prediction model for each wireless network by using the constant element as a quality model setting variable, and generates a quality prediction learning model or a relationship function between the variable element information and the quality information. I can.

In addition, the variable information may be classified and identified in various additional ways, and may be selectively determined according to the type and characteristic of the quality prediction model.

For example, the variable information may include variable information for each communication section. The variable information for each communication section is a physical variable corresponding to at least one of a wired section between a target device and an AP (Access Point) access point, an AP internal section, a wireless section between the AP and a wireless terminal device, and a wireless terminal device internal section. May contain information. Here, the AP access point may mean a wireless network access point such as a WIFI router or a base station.

More specifically, the physical variable information may include wired cable performance information corresponding to a wired section between the connection target device and the AP access point, and difference information between the IP of the connected device and the IP of the AP.

In addition, the physical variable information may include AP hardware capability information and number of connected devices corresponding to the AP internal section.

In addition, it may include at least one of protocol information, signal strength information, frequency band information, and channel/frequency congestion information corresponding to a radio section between the AP and the wireless terminal device.

Here, the protocol information may include a communication protocol, for example, in the case of a WIFI network, a protocol based on 802.11n and 802.11ac standards, and in the case of a mobile communication network, a standard-based protocol such as GSM, LTE-A, and LTE. This can be illustrated.

In addition, since the protocol information may include an encryption protocol, in the case of WIFI, an encryption protocol such as open type, WEP, and WPA2 may be exemplified.

Meanwhile, the constant element and the variable element may include at least one of radio module performance information, radio module characteristic information, and temperature information corresponding to an internal section of the radio terminal device 100, and the model setting variable processing unit 130 And the model input variable processing unit 140 may process the data request and purification process for generating these variables to the control unit 110, the control unit 110 from the process information and sensor data being processed, the requested variable Information may be obtained and provided to the model setting variable processing unit 130 and the model input variable processing unit 140.

In addition, the model input variable processing unit 140 may collect and refine the primary variables transmitted from the wireless terminal device 100 and the communication unit 120 to obtain frequency congestion as basic variable information.

For example, the basic variable information may include the degree of isotropy (effective aperture) of the antenna of the communication unit 120, the inherent gain of the antenna of the wireless terminal device 100 and the AP, and congestion of physical obstacles and surrounding frequencies. In addition, each variable may have a relational expression with the power intensity (milliWatt) of the radio wave emitted from a specific WIFI AP as shown in Equation 1 below.

Here, P _Transmitted is AP-specific emission power information, and may be different according to the AP protocol, spatial trim, modulation, and coding rate. And, G _{Antenna gain1} represents the antenna gain information specific to the AP, A _Effective represents the antenna's inherent effective aperture or isotropic degree of the wireless terminal device 100, and α represents the degree of reduction due to physical obstacles or peripheral frequency congestion in the radio path Can represent.

Here, A _Effective can be obtained through polarization of radio waves, the law of thermodynamics, Rayleigh-Jeans Formula, and Johnson-Nyquist noise calculations. ) Received power can be calculated by arithmetic processing.

Where, G _{Antenna gain2} represents antenna gain information specific to a terminal of the wireless terminal device 100. In addition, if the unit of milliWatt is changed to decibel milliwatt (dBm) by taking log (10*log_10), Equation 2 is summarized as follows.

와

는 AP와 무선 단말 장치(100)의 안테나 고유 특성 정보이며, 파장 λ는 λ=c/f 로 연산되므로, 주파수의 값으로부터 획득될 있다.

는 단말기와 해당 AP 사이 거리에 따라 변하는 값이며

는 경로상 존재하는 물리적 장애물 혹은 주변 주파수 혼잡 등으로 인해 감소되는 감소율에 대한 로그값이다.here,

Wow

Is antenna-specific characteristic information of the AP and the wireless terminal device 100, and since the wavelength λ is calculated as λ=c/f, it can be obtained from the value of the frequency.

Is a value that changes according to the distance between the terminal and the AP

Is a logarithmic value of the reduction rate reduced due to physical obstacles or congestion of surrounding frequencies in the path.

값을 예측할 수 있으며, 이에 따라, 특정 AP와 단말기 간의 RSSI 정보로부터 상기 수학식 3의 해를 연산하면, 주파수 혼잡도인

을 유추 획득할 수 있다. 이에 따라, 모델 입력 변수 처리부(140)는 AP의 MCS(Modulation Coding Scheme) 인덱스에 대응하여 AP의 발산 파워 세기 정보로부터 산출되는 주파수 혼잡도 정보를 모델 입력 변수로 수집 및 정제할 수 있다.For example, the model input variable processing unit 140 from the MCS (Modulation Coding Scheme) Index to obtain variable information

The value can be predicted, and accordingly, when the solution of Equation 3 is calculated from the RSSI information between a specific AP and the terminal, the frequency congestion is

Can be obtained by analogy. Accordingly, the model input variable processing unit 140 may collect and refine the frequency congestion information calculated from the radiated power intensity information of the AP as a model input variable in response to the Modulation Coding Scheme (MCS) index of the AP.

In addition, the model setting variable processing unit 130 and the model input variable processing unit 140 collect the frequency band, Modulation Coding Scheme (MCS) Index, Bandwidth, and Guard Interval values as variable information as unique characteristic information for each AP. The minimum signal strength and maximum speed may be inferred, and the quality prediction module 200 may obtain quality prediction information for each wireless network by using the corresponding value as a model setting variable or a model input variable.

Meanwhile, the model input variable processing unit 140 may perform refinement processing from variable information processed through the communication unit 120 to collect RTT, window size, and transmission/reception data amount information of a TCP socket, and a quality prediction module 200 ) Calculates the quality prediction information for each network refined from the processed variable information of the communication unit 120 as a model input variable, and processes the training of a quality prediction model for each wireless network corresponding thereto.

For example, in TCP communication, the maximum throughput of a current socket may be defined in the form of a transmission packet (Packets in Flight), which is as follows.

The number of data sockets used by the communication unit 120 may be plural, and the speed at a specific time may be calculated as the sum or average of the maximum throughput of each socket. Accordingly, the model input variable processing unit 140 calculates the output information for each network as a model input variable through an average operation or a harmonic average operation in which a weight is applied to the amount of output information and transmission/reception data or other elements, and It can process the training of the quality prediction model for each network. Here, the weight may be processed as a separate function, and the weight value may also be used as a decay function-processed value that decays over time.

In addition, the variable information may include speed information at a specific point in time, and the speed information at a specific point in time is limited to the maximum link speed between the AP and the wireless terminal device 100 at the point in time. 130) or the model input variable processing unit 140.

For example, in Equation 5 below, in the model setting variable processing unit 130 or the model input variable processing unit 140, data-weighted average calculation processing of speed information for a specific signal strength (RSSI), but maximum connection This is an example of output when the calculation is processed to be limited to the link speed.

으로 정의될 수 있다.here,

Can be defined as

Accordingly, the quality prediction module 200 may process the learning of a learning model predicting speed quality versus signal strength by using the signal strength (RSSI) and speed collected from the model input variable processor 140 as model input variables. . As the learning process, a linear regression or logistic regression operation may be exemplified.

Accordingly, the quality prediction module 200 may construct a quality prediction model of a corresponding WIFI network for a specific signal strength (RSSI) and provide prediction information corresponding thereto.

Meanwhile, the quality prediction module 200 processes a Decay function based on the velocity samples of the last n data collected in real time through the model input variable processing unit 140 to predict the quality of the mobile communication network. A quality prediction learning model corresponding to the mobile communication network may be generated by acquiring an average value and performing regression learning on performance information corresponding to the moving average value corresponding thereto.

Here, the decay function may be a process such as an exponential moving average, and the quality prediction module 200 assigns a weight to the latest data sample and only a random number of samples Using this, a quality prediction learning model for predicting the quality of a mobile communication network can be generated. Here, the decay function process may include not only an exponential moving average, but also a simple moving average, a cumulative moving average, a weighted moving average, or various processes including the same. .

For example, in the case of an exponential moving average, the quality prediction module 200 calculates the maximum throughput calculated from the RTT, window size, and transmission/reception data amount of the TCP socket processed by the communication unit 120 through the model input variable processing unit 140. By using it as an input value, it is possible to generate a quality prediction learning model corresponding to a mobile communication network by performing regression learning processing of a function for a speed at a specific point in time.

where

Here, if an exponential moving average is calculated using the recent N random number, the approximate value of the coefficient α can be defined as in Equation 8 below.

Meanwhile, as described above, the quality prediction module 200 according to an embodiment of the present invention may predict network quality through a quality prediction model based on an applied function or a quality prediction model based on learning. The shape of the quality prediction model, the shape and type of the applied function, the type of the learning process, etc. may differ according to the model setting variables.

When the quality prediction module 200 uses a quality prediction model based on an applied function, the applied function of the quality prediction model is a linear function based on frequency band and variable information, a linear function based on link speed and variable information, and based on the first frequency band. A log function, a second frequency band-based linear function, and the like may be exemplified.

For example, the applied function of the quality prediction model may include a linear function based on frequency band information and variable information. The frequency band information may be different for each network, such as 5GHz, 2,4GHz, etc., as defined in the WIFI standard. Accordingly, the quality prediction module 200 according to an embodiment of the present invention may determine, as the application function, a linear function for differently determining a variable limitation range according to frequency band information.

For example, the applied function may be a linear function in the form of α + β x RSSI, which represents a speed relation with respect to signal strength (RSSI) and uses constants α and β as variables, and forms different slopes for each frequency band. In order to be able to do so, the range of the minimum and maximum values of β may be differently limited according to the frequency band.

For example, the minimum and maximum value ranges of β may be set wider as the frequency band increases. In the case of the 5 GHz band, the minimum and maximum value ranges of β may be set higher than in the case of 2 GHz.

Also, for example, the quality prediction function may be determined as a linear function based on link speed and variable information. The link speed information may be used to limit the maximum value of a linear function of α + β x RSSI (signal strength) representing the above-described signal strength (RSSI) versus speed relationship.

Accordingly, the quality prediction module 200 according to an embodiment of the present invention may determine a linear function limiting the maximum value of the quality prediction result according to the link speed information as an application function of the quality prediction model.

And, for example, the applied function of the quality prediction model may be determined as a frequency band adaptive function that is selectively determined from a first frequency band-based log function and a second frequency band-based linear function. For example, in the case of a 5GHz band, a signal strength may be weaker than that of 2.4GHz and a speed quality may be rapidly degraded. Accordingly, the application function is a selective adaptive processing function that processes a logarithmic function corresponding to the first frequency band (5 GHz) and a linear function corresponding to the second frequency band (2.4 GHz). Can include.

Meanwhile, the constants α and β of the speed relational expression relative to the signal strength (RSSI) are exemplary values, and may be changed and set according to the type and shape of the acquisition variable.

When the quality prediction model 200 uses a quality prediction model based on learning, an artificial intelligence algorithm may be used as a learning process for generating a quality learning model for each network. Examples of artificial intelligence algorithms include an artificial neural network method in the form of supervised learning such as Logistic or Logarithmic Regression or Multilayer Perceptron (MLP), Support Vector Machine, Bayesian Networks, and Genetic Algorithm. I can.

In addition, an unsupervised learning method such as Dictionary Learning, Independent Component Analysis, Autoencoders, or various types of clustering may be used, and an analysis process such as Decision Trees and Anomaly Detection may be used.

In addition, the quality prediction module 200 may use a Reinforcement Learning algorithm using data collected in real time from each user as a model input variable.

In the case of regression, methods such as Projection, Maximum Likelihood, Generalized Methods of Moments including Ordinary Least Squares method may be exemplified.In the above-described Supervised, Unsupervised, and Reinforcement processing, the least squares method and the gradient descent method Through regression model, backpropagation method, deep learning, entropy minimization process, etc. may be included.

More specifically, the supervised learning and unsupervised learning processes of the quality prediction module 200 may be determined according to a value to be predicted, and quality prediction information, which is a performance index of a wireless network to be predicted, may be output in various forms. As the classification value based on speed, the amount of data available per hour (Mbps, Gbps, etc.), latency, throughput, bandwidth, etc. can be exemplified, and as classification criteria classification values, Good, Normal, Bad, etc. This can be illustrated.

For example, as supervised learning to predict the performance of a specific AP with a specific index (e.g., speed), a classification method can be used, and the performance of a specific access point can be determined by specific criteria (e.g., Good, Normal, Bad) can be exemplified.

Here, Classification is a supervised learning algorithm that classifies output into specific classes for specific inputs, such as Logistic Regression, Naive Bayes, Stochastic Gradient Descent, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, etc. This can be illustrated.

Accordingly, the quality prediction module 200 substitutes the model setting variable and the model input variable into the supervised learning model, so that various APs (e.g., Wi-Fi AP1, Wi-Fi AP2, LTE Cell Site, 5G Cell Site, etc.) ) Can be judged.

In addition, as a non-supervised learning for predicting the quality of a specific AP with a specific index (e.g., speed), clustering can be used, and the quality of a specific access point is classified and classified into clustered random criteria. The way to do it can be illustrated.

Accordingly, the quality prediction module 200 substitutes the model setting variable and the model input variable into the non-supervised learning model, and thus various APs (e.g., Wi-Fi AP1, Wi-Fi AP2, LTE Cell Site, 5G Cell A learning model that can output the result of determining which AP to use among sites, etc.) can be created.

Here, clustering is an unsupervised learning algorithm that divides the output into specific classes for specific inputs.K-means, Fuzzy K-means, Hierarchical clustering, Mixture of Gaussians, Mean-Shift Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), etc. may be exemplified.

Here, the quality prediction module 200 learns using some or all of the model input variables, applies some or all of the model input variables to the input layer of the quality prediction model, and predicts corresponding performance information for each network, The predicted performance information can be output.

Meanwhile, the network determination unit 150 compares the quality prediction information for each network according to preset user preference information or mode setting information, and transmits the optimal network according to the comparison result to a first wireless network to transmit a data packet. You can decide.

More specifically, the network determination unit 150 may determine a wireless network interface to be driven by the communication unit 120 or a network to which a socket generated for data packet transmission is bound according to a result of the comparison determination.

In addition, the network determiner 150 may compare and determine a network to be used based on at least one of quality information for each network, user preference information for speed and cost, and user setting mode information. The comparison determination process may be determined according to either a static determination or a learning determination.

The static determination method is determined, for example, according to variable comparison, and determination according to the user's speed and quality preference setting may be exemplified.

For example, when the user prefers speed, the network determination unit 150 may determine a network having a high quality variable, and when the user prefers cost, determine a WIFI network having a quality higher than a certain standard as the first network. However, if such a network does not exist, it can be processed to determine a mobile communication network. In addition, when quality and preference information is insufficient, the network determination unit 150 may set a plurality of sockets, bind to each connectable network, and determine a socket from which the first response comes earlier as the first network.

In addition, the network determination unit 150 may determine the user preference based on the learning determination, and the network determination unit 150 compensates for negative compensation such as a Wi-Fi Off behavior from a user input and a manual connection to another Wi-Fi ( Behaviors defined as Negative Reward) are learned as an indicator that the user does not prefer a specific network, and the learned user preference information may be used to determine the above-described first network.

In addition, the determination of the first wireless network by the network determination unit 150 may be periodically processed according to a certain time or may be processed at a specific time according to a certain condition in which quality deterioration of the current network is predicted. Through the processing, fast and real-time network change processing is possible. In addition, the communication unit 120 uses the determined first wireless network according to the decision of the network determination unit 150 to transmit the data packet to the access target device 300 Can be transmitted wirelessly.

For such processing, the communication unit 120 can process network control, and processes data transmission through the first wireless network in a manner that directly connects a specific network with hardware or controls the network layer by an application. can do.

In addition, the communication unit 120 may perform rapid termination processing of an existing connected wireless network, or may perform a rapid change or switch processing from the existing connected wireless network to the first wireless network. In addition, the communication unit 120 may process so that only the new connection to the determined first wireless network and data transmission/reception are performed while maintaining the connection of the existing network.

For example, in the case of an application method, the communication unit 120 may process data transmission through the first wireless network by creating a socket and binding to a specific network interface. In addition, the communication unit 120 may generate a plurality of identical sockets and bind them to a plurality of network interfaces, so that one or more Wi-Fi wireless communication networks and one or more cellular wireless communication networks can be used simultaneously.

In addition, the controller 110 may control the communication unit 120 to more quickly process the termination of communication through the existing network and the start of communication of the determined first wireless network. For example, if the existing wireless network is simply deactivated and the first wireless network is activated by a conventional network change process using the prior art, the termination of the socket through the existing wireless network is not immediately processed, and the termination can be confirmed. There is a problem of delaying the switching time.

Accordingly, according to an embodiment of the present invention, the control unit 110 cancels the connection with the existing wireless network through the communication unit 120 to collectively terminate the sockets through the existing wireless network, and the first wireless network You can activate the connection with. When sockets linked to the existing wireless network are collectively terminated, the currently running wireless network-related applications recognize or detect it and regenerate the sockets required to drive the application, so that the application that performed socket communication through the existing wireless network They can quickly resume communication over the first wireless network.

In addition, as described above, the control unit 110 may process a new connection to the first wireless network while being connected to the existing wireless network. In this case, the control unit 110 releases the connection to a separate existing network. Without it, a communication socket through the first wireless network may be created and packets to be transmitted may be retransmitted. For example, the controller 110 connects to another second WIFI network of high quality without disconnecting from the first WIFI while being connected to the first WIFI network, or performs packet transmission through an existing connected mobile communication network. , You can perform the opposite process.

In another embodiment, the controller 110 may directly terminate the socket through the existing wireless network through the communication unit 120 or transmit a reset signal to induce a socket reboot of the application.

For example, the control unit 110 recognizes that applications running on the wireless terminal device 100 require socket reboot by constructing an RST Flag data packet corresponding to a TCP socket and transmitting it to a socket through an existing wireless network, It can be induced to create a socket over the first wireless network to be switched.

In addition, the control unit 110 may temporarily store a data packet requested to be written before termination of the first wireless network connection in the socket through the existing wireless network, and retransmit the stored packet through the switched first wireless network. I can. For example, when a UDP DNS request packet or the like is temporarily stored and a response is not received, the communication unit 120 may be controlled to be retransmitted to a socket newly connected through the first network.

Here, the switched first wireless network is a network different from the existing wireless network, and any type of network may be determined as the first wireless network. Accordingly, the communication protocol of the first wireless network may be the same as or different from the existing wireless network.

Meanwhile, the user setting unit 170 can set a user mode for network switching, and thus mode information may be transmitted to the network determination unit 150 and used as a variable for determining the first network.

For example, the user mode may be any one of a speed mode, a balanced mode, or a cost mode.

The fast mode may be set when a terminal user who has no limit on the use of mobile communication data uses a short-range wireless communication network to amplify the speed. In this case, the user setting unit 170 may set a mobile communication network and a short-range wireless communication network to be mixed, thereby providing a faster speed and quality than a case of using a single wireless communication network.

The balance mode may be a setting in which a terminal user having a limitation in using mobile communication data uses a short-range wireless communication network to amplify speed, but preferentially uses a short-range wireless communication network based on a certain speed or quality. The user setting unit 170 is used only when the speed of the short-range wireless communication network is greater than or equal to the first speed, and when the speed is less than or equal to the first speed, the user setting unit 170 sets the mobile communication network to be mixed and provides the optimum speed and quality according to the situation. can do.

On the other hand, the price mode is a case in which a user with limited mobile communication data use uses a short-range wireless communication network as much as possible to save data, and the user setting unit 170 is a second speed that interferes with Internet use. Connection to the mobile communication network can be made only in the following cases.

3 and 4 are flowcharts illustrating a method of operating a wireless terminal device according to an embodiment of the present invention.

Referring to FIG. 3, first, the wireless terminal device 100 collects a wireless network quality model setting variable through the model setting variable processing unit 130 (S101).

Then, the wireless terminal device 100 performs refinement processing corresponding to the wireless network quality model setting variable through the model setting variable processing unit 130 (S102). Then, the wireless terminal device 100 determines a quality prediction model for each wireless network based on the model setting variable through the quality prediction module 200 (S103).

Thereafter, the wireless terminal device 100 collects and refines the model input variables of the wireless terminal device 100 through the model input variable processing unit 140 (S105).

Then, the wireless terminal device 100 acquires quality prediction information for each accessible network according to the application of the quality prediction model of the model input variable through the quality prediction module 200 (S107).

Thereafter, the wireless terminal device 100 determines an optimal wireless network according to the user mode or preference setting of the wireless terminal through the network determination unit 150 (S109), and the control unit 110 determines data using the determined wireless network. Prepare for packet transmission (S111).

Then, the communication unit 120 transmits the data packet through the determined wireless network (S113).

Here, FIG. 4 is for describing a socket-based wireless network transmission process of the wireless terminal device 100 in more detail, and the controller 110 creates a socket for transmitting a wireless data packet (S151).

Then, the network determination unit 150 compares the network quality prediction information and user preference information that is statically determined or formed by learning, item by item, and determines one or more wireless networks (S153).

Here, the user preference information may be determined by the above-described user mode setting information, and a plurality of wireless networks may be determined.

Thereafter, the communication unit 120 binds and connects the generated socket to a network interface corresponding to the determined wireless network (S155).

Accordingly, the communication unit 120 may wirelessly transmit the data packet through the connected socket (S157).

5 is a conceptual diagram schematically showing a system based on an apparatus for providing quality prediction information according to another embodiment of the present invention, and FIG. 6 is a block diagram showing in detail an apparatus for providing quality prediction information according to an embodiment of the present invention.

5 and 6 illustrate a system in which the apparatus 400 for providing quality prediction information is configured as a separate device at a remote location. Referring to FIG. 5 first, a system according to another embodiment of the present invention includes a quality prediction module ( 200) including a quality prediction information providing device 400, a wireless terminal device 100, and a connection target device 300, and the wireless terminal device 100 and the connection target device 300 include a plurality of wireless networks ( 200a, 200b, ... 200n) may be configured to be connected through one or more wireless networks, and the apparatus 400 for providing quality prediction information includes a plurality of wireless networks 200a, 200b, ... 200n, respectively Can be connected. Also, the apparatus 400 for providing quality prediction information may provide a separate wired network connection.

Here, each of the wired/wireless networks that can be accessed is a local area network (LAN), a wide area network (WAN), a value added network (VAN), and a personal area network (PAN). ), a mobile radiocommunication network, a satellite communication network, and the like, and each base station or access point (AP) device for providing the same may be provided in a remote location.

In particular, in order to provide a local area communication network, the wireless network may include a WIFI wireless network based on a WIFI standard, and each access point may broadcast device information for operating a Wi-Fi service coverage.

Meanwhile, the wireless terminal device 100 may be a wireless terminal device of a user who requests transmission/reception of a wireless data packet by determining a first wireless network 200a to be connected to the access target device 300 among wireless networks. Here, the wireless terminal device 100 may be implemented as various devices such as a smart phone, a tablet computer, a notebook computer, a personal digital assistant (PDA), a portable multimedia player (PMP), and smart glasses.

In addition, according to an embodiment of the present invention, the apparatus 400 for providing quality prediction information may process a quality model setting variable for each wireless network and transmit it to the quality prediction module 200, and the quality prediction module 200 sets the model. Depending on the variable, the quality prediction model for each wireless network may be determined.

Here, the quality prediction module 200 may collect various variables for determining a quality prediction model, and each variable may be diversified into physical variable information, constant element information, variable element information, etc. for each communication section for quality prediction. have.

In addition, the wireless terminal device 100 determines a model input variable of the wireless terminal device 100 in response to a data packet to be transmitted wirelessly, and determines the model input variable for each wireless network provided from the quality prediction information providing device 400. Applying to a quality prediction model, obtains quality prediction information for each accessible network, and determines a first wireless network 200a to transmit the data packet among the plurality of wireless networks based on the quality prediction information for each network, and the The data packet may be wirelessly transmitted to the access target device 300 by using the determined first wireless network.

Here, the quality prediction information providing device 400 may include the above-described quality prediction module 200, and the quality prediction module 200 is processed by the model setting variable processing unit 430 of the quality prediction information providing device 400 The above-described quality prediction model may be determined according to the determined variable, and prediction information may be output according to the model input variable determined by the model input variable processor 440.

To this end, the information service provider 470 may provide a quality prediction model for each wireless network or may provide quality prediction information for each network corresponding to a model input variable according to a request from the wireless terminal device 100.

Accordingly, the wireless terminal device 100 receives the information service from the apparatus 400 for providing quality prediction information, and learns a relationship between at least one variable among network speed, signal strength, or preset model input variables, or a function Based on the application, it is possible to more accurately predict the quality deterioration according to the model input variable in advance, and the dynamic variable control of the wireless network accordingly enables rapid and efficient network change while minimizing the user's experience of quality deterioration. .

More specifically, referring to FIG. 6, the apparatus 400 for providing quality prediction information according to an embodiment of the present invention includes a control unit 410, a communication unit 420, a model setting variable processing unit 430, and a model input variable processing unit ( 440), a quality prediction module 200, a database 460, and an information service provider 470.

The control unit 410 generally controls the operation of each component of the apparatus 400 for providing quality prediction information and execution of functions. For example, the controller 410 may be implemented as a processor for controlling all or part of a model setting variable processing, model input variable processing, and information service providing function, or a program for executing the same.

In addition, the communication unit 420 connects to the wireless terminal device 100 or wireless networks 200a, 200b, ... 200n, or connects to a wired network to enable wired/wireless data packet communication with each wireless network. It may include one or more communication modules.

In particular, the communication unit 420 may include a wireless Internet module for connecting to at least one of the wireless terminal device 100 or wireless networks 200a, 200b, ... 200n through a wireless Internet protocol, and a wireless Internet module Wireless LAN (WLAN) (Wi-Fi), Wireless broadband (Wibro), World Interoperability for Microwave Access (Wimax), and high speed downlink packet access (HSDPA) based network modules may be exemplified.

In addition, the communication unit 120 may include a short-range communication module for short-range communication access to at least one of the wireless terminal device 100 or wireless networks 200a, 200b, ... 200n, and a short-range communication protocol. As a short-range communication technology, Bluetooth, Radio Frequency Identification (RFID), infrared data association (IrDA), Ultra Wideband (UWB), ZigBee, and the like may be exemplified.

Meanwhile, the communication unit 120 may include a mobile communication module for mobile communication connection with at least one of the wireless terminal device 100 or wireless networks 200a, 200b, ... 200n, and For this reason, it is possible to transmit and receive wireless signals with at least one of a base station on a mobile communication network, an external terminal, a connection target device, and a server. The wireless signal may include a voice call signal, a video call signal, or various types of data according to transmission/reception of text/multimedia messages.

In addition, the communication unit 420 may provide the quality prediction information for each network to the wireless terminal device 100 under the control of the controller 410 or may provide prediction model information for determining the quality prediction information for each network. Information may be generated by the quality prediction module 200

In order to determine the quality prediction information for each network, the quality prediction module 200 collects and refines model setting variables obtained from the model setting variable processing unit 430 and the model input variable processing unit 440 and variable information for model input variables. A quality prediction model for each wireless network may be determined and stored in the database 460 based on the processed model setting variable and the model input variable.

Here, the model setting variable processing unit 430 and the model input variable processing unit 440 may collect, process, and transmit various variables for determining a quality prediction model for each wireless network in the quality prediction module 200, and The model input variable may be determined differently according to a learning model of the quality prediction module 200, a learning process, or applied functions. As the learning process, artificial intelligence or artificial neural network learning can be exemplified, and variable setting and learning model configuration for this are important. For this, the quality prediction module 200 and the quality prediction module 200 in the case of the wireless terminal device 100 Since the operation is similar, a detailed description will be omitted.

In addition, the information service providing unit 470 may provide an information service for transmitting the quality prediction model information for each wireless network generated by the quality prediction module 200 to the wireless terminal device 100.

In addition, the information service providing unit 470 may transmit information of the wireless terminal device 100 according to the request of the wireless terminal device 100 to the model input variable processing unit 440, and the model input variable processing unit 440 is a wireless terminal. By determining a model input variable corresponding to the device 100 information and passing the determined model input variable to the quality prediction module 200, the quality prediction module 200 may output the quality prediction information for each network.

In addition, the information service provider 470 may transmit the obtained network-specific quality prediction information to the wireless terminal device 100 through the communication unit 420.

Accordingly, the wireless terminal device 100, which receives the quality prediction information for each network or obtained from the model, compares according to preset user preference information or mode setting information, and transmits a data packet to the optimal network according to the comparison result. It can be determined as the first wireless network to be performed.

In addition, since the wireless terminal device 100 can transmit a wireless data packet through the determined network, a detailed description thereof will be omitted since this is the same as described in FIG. 2.

7 is a flowchart illustrating an operation of an apparatus for providing quality prediction information according to an embodiment of the present invention.

Referring to FIG. 7, the apparatus 400 for providing quality prediction information according to an embodiment of the present invention collects a quality model setting variable for each wireless network through the model setting variable processing unit 430 (S201).

Then, the model setting variable processing unit 430 performs a variable refinement process on the model setting variable in order to determine a quality prediction model (S202).

Then, the quality prediction module 200 of the apparatus 400 for providing quality prediction information determines a quality prediction model for each wireless network based on a preset model setting variable (S203).

Thereafter, the quality prediction information providing apparatus 400 collects and refines the model input variables according to the request of the wireless terminal device 100 through the model input variable processing unit 440 (S205).

Then, the apparatus 400 for providing quality prediction information acquires quality prediction information for each network according to the application of the quality prediction model of the model input variable (S207).

Thereafter, the apparatus 400 for providing quality prediction information provides quality prediction information for each network to the wireless terminal (S209).

In this way, by providing the apparatus 400 for providing quality prediction information, it is possible to provide a dynamic wireless network variable access based on a quality prediction learning model even if the learning-based quality prediction module is not installed inside the wireless terminal device 100, It is possible to reduce infrastructure and cost by enabling the collection of learning data and building a model without consuming power and resources of the wireless terminal device 100.

8 is a block diagram illustrating a learning-based quality prediction module according to an embodiment of the present invention in more detail.

Referring to FIG. 8, the quality prediction module 200 according to an embodiment of the present invention includes at least one input layer processor 210, at least one first intermediate layer processor 220a, 220b, ... 220n, and at least one A plurality of n-th intermediate layer processing units including second intermediate layer processing units 230a, 230b, ... 230n and an output layer processing unit 240 are included, and a feedback weight application unit 240 corresponding to the output layer is selectively selected. Can be included as.

The quality prediction module 200 converts at least one of a quality model input variable and an output variable (quality value) among the variables collected by the model input variable processing unit 440 into respective input layers, and convolutes the processed input layers. It is possible to construct an artificial neural network network that performs routing calculation and outputs it to an output layer related to network quality. For this, each intermediate layer processor may process an individual multiplication operation according to a neural network network, and the feedback weight application unit 245 may selectively perform a weight adjustment process according to an evaluation of an output layer.

Through the iterative processing of such a learning process, a wireless network quality prediction model corresponding to an input variable and an output variable of a quality model for each wireless network can be constructed, and the constructed prediction model is stored in the wireless network quality prediction model storage unit 250. It may be stored or transmitted to a database of the wireless terminal device 100 or the apparatus 400 for providing quality prediction information, and may be used to predict the quality of each network according to the model input variables collected in real time.

In addition, the model input variables and corresponding quality information collected thereafter may be used for additional learning of the constructed wireless network quality prediction model. Accordingly, the wireless network quality prediction model according to an embodiment of the present invention is The quality prediction accuracy may be improved based on the model input variables collected as.

In addition, according to an embodiment of the present invention, based on a predetermined quality setting variable for each wireless network, a wireless network quality prediction model constructed externally according to preliminary learning is stored in the wireless network quality prediction model storage unit 250 in advance. May be. In this case, the quality prediction module 200 according to an embodiment of the present invention may determine a pre-built wireless network quality prediction model in response to the quality setting variable processed by the model setting variable processing unit 130.

The pre-built wireless network quality prediction model may be in the form of a functional model determined by learning, for example. Accordingly, when the quality prediction module 200 uses a quality prediction model based on a quality prediction model application function, the quality prediction model application function may be a learning-based function determined by a pre-built wireless network quality prediction model. The prediction module 200 first determines a wireless network quality prediction model based on the model setting variable in the model setting variable processing unit 130, and then applies the model input variable collected by the model input variable processing unit 140 to provide real-time quality. Although the output information for is obtained, a process of learning the wireless network quality prediction model again may be performed using the model input variable and the output information.

Therefore, the model setting variable according to an embodiment of the present invention may be used to predetermine or create a new wireless network quality prediction model. In addition, the model input variable may be applied to a generated or determined wireless network quality prediction model or used as an input variable for training.

For example, the network determination unit 150 may obtain real-time quality information for each wireless network according to the application of the wireless network quality prediction model according to the model input variable and use it for network determination, and at the same time, the quality prediction module ( 200) is capable of performing additional learning processing of a wireless network quality prediction model based on the input variable and the quality information.

9 to 10 are diagrams for explaining a data merging and sharing process of a learning-based quality prediction model according to an embodiment of the present invention.

The data on the performance of each wireless terminal device 100 and the AP may be used to predict the performance of the AP even if the device is not directly connected. Therefore, merge sharing of prediction models enables more accurate prediction of AP performance.

Accordingly, referring to FIGS. 9 and 10, the learning-based quality prediction model according to an embodiment of the present invention may be formed by merging shared data, and data exchange for this may be performed by the wireless terminal device 100 or It may be processed by the prediction information providing device 400.

First, FIG. 9 shows a case where the learning-based quality prediction module 200 is provided in the wireless terminal device 100 so that the learning-based quality prediction model sharing data is shared among other wireless terminal devices 100a. The quality prediction model data or training data learned in (100) may be shared and merged to be used in another wireless terminal device (100a). For example, data on the performance of the wireless terminal device 100 and APs of each wireless network may be transmitted to other wireless terminal devices 100a.

For example, the wireless terminal device 100 is based on learning data about the performance between the wireless terminal device 100 and the AP generated by the learning-based quality prediction module 200 (for example, speed, specific variables and performance). Function data indicating a related relationship) to the other wireless terminal device 100a connected to the network.

Here, each wireless terminal device may be connected to a data sharing network such as a block chain network, and each wireless terminal device may store learning model data in a shared DB based on a block chain network.

Thereafter, the specific wireless terminal device 100 may request data related to the performance of a specific AP from the shared network to determine a wireless network, and the shared network may request quality related to the performance of the specific AP based on the previously stored merged shared data. Data can be transmitted to the wireless terminal device 100.

In addition, the wireless terminal device 100 receiving performance or quality data of a specific AP may determine and connect to a first wireless network based on this. In addition, when the wireless terminal device 100 collects the additional data of the AP, it may transmit it to the shared network and merge it into the training data for a learning-based quality prediction model.

In the data merging process, the additional data may be merged by weighting according to the similarity between variables of the previously stored data (eg, model, number of connected devices, time zone) and variables of the wireless terminal device 100, The average value, minimum value or maximum value during may be used. The data merging process may be distributed in a shared network, or data merged in the wireless terminal device 100 may be transmitted to the shared network.

In addition, since the use of shared data is not essential, the wireless terminal device 100 may perform learning and prediction processing using only individually collected data.

On the other hand, FIG. 10 shows a case where the learning-based quality prediction module 200 is provided in the quality prediction information providing apparatus 400 so that the learning-based quality prediction model sharing data is shared with other wireless terminal devices 100a. The quality prediction model data or training data learned by the terminal device 100 and other wireless terminal devices 100a may be shared and merged through the quality prediction information providing apparatus 400.

For example, the apparatus 400 for providing quality prediction information collects data on the performance of each AP of each wireless network from the wireless terminal device 100 and other wireless terminal devices 100a, and Learning data (eg, speed, function data representing a relationship between specific variables and performance) can be merged.

In addition, the quality prediction information providing apparatus 400 provides learning model data according to merge learning to the wireless terminal device 100 and other wireless terminal devices 100a connected to the network. The provision of model data can be processed according to a certain period.

For example, in order to predict performance information of a specific AP for determining a wireless network in a specific wireless terminal device 100, model data provided from the information service providing unit 470 of the quality prediction information providing device 400 may be used. have.

When the wireless terminal device 100 collects the additional data of the AP, it may transmit it to the quality prediction information providing device 400 and merge it into training data for a learning-based quality prediction model.

In the data merging process, the additional data may be merged by weighting according to the similarity between variables of the previously stored data (eg, model, number of connected devices, time zone) and variables of the wireless terminal device 100, The average value, minimum value or maximum value during may be used. The data merging process may be processed by the apparatus 400 for providing quality prediction information, or data merged by the wireless terminal device 100 may be transmitted to the apparatus 400 for providing quality prediction information.

Meanwhile, the present invention can provide a method of measuring the quality of a wireless network accessible by the above method and displaying the measured quality of the wireless network on a screen.

11 is a configuration diagram of a dynamic wireless network quality display apparatus based on quality prediction according to another embodiment of the present invention.

The wireless terminal device 100 according to an embodiment of the present invention includes a control unit 110, a communication unit 120, a variable information processing unit 130, a network determination unit 150, a user setting unit 170, and a network quality display unit ( 180), and including a quality prediction information providing device 400 for interworking with the wireless terminal device 100 to provide quality prediction information accessible to the wireless terminal device, and the quality prediction information providing device 400 Includes a quality prediction module 200, a database 160, a user setting unit 170, and a network quality display unit 180.

The control unit 110, the communication unit 120, the model setting variable processing unit 130, the model input variable processing unit 140, the network determination unit 150, the quality prediction module 200, the database 160, and the user setting unit ( 170) has been described in sufficient detail in the description of the network quality prediction terminal device, so its description is omitted.

As a configuration for displaying the quality of the present invention, the wireless terminal device itself may be equipped with a quality prediction model and a variable information processing unit to predict the quality and display the predicted quality information on the screen. A wireless terminal device displaying quality information on a wireless network, comprising: a variable information processing unit (130, 140) collecting variable information of the wireless terminal device; quality prediction for each accessible network according to quality prediction processing using the variable information And a quality prediction module 200 for obtaining information, and a network quality display unit 180 for displaying quality information of a currently connected wireless network on a screen based on the quality prediction information for each network.

The network quality display unit 180 acquires quality information for each wireless network predicted by the quality prediction module 200 through the control unit 110, and generates quality data by quantifying or classifying a preset number, and then generating quality data. The quality indication information is generated by normalized numericalization or classification, and the quality indication information of the connected network is displayed on the screen.When the quality data of the connected network is changed beyond a preset threshold, the quality change is automatically detected. It performs the function of displaying enlarged on the screen or notifying by vibration or alarm.

The network quality display unit 180 checks the quality fluctuation of the connected network, and when fluctuations exceeding the preset upper and lower limit thresholds occur, overlays the quality change information on the screen to display at least one of a text, a shaped image, and a value. It can be displayed to notify quality fluctuations. In addition, quality fluctuations can be notified by alarm or vibration.

In addition, the network quality display unit 180 may automatically pop up a search window for setting up a network connection as a pop-up window when the quality fluctuation is less than a preset lower limit threshold, and display quality information for each network together. In addition, network information having the highest network quality may be selected from a list of accessible wireless networks, and a screen display may be provided to a user to select a network change connection. Alternatively, if the quality falls below the lower limit threshold in the initial setting, a pop-up window can be displayed and the control can be controlled to automatically connect to the highest quality network.

Here, the shape is not limited to an image such as a figure or picture, but may be a phrase such as a letter, and the degree of filling of the same figure or image, number, color, transformation of shape, simple addition or transformation of a bar shape, animation It can also be a moving or fluctuating shape such as. In addition, a numerical value or shape representing the quality of the wireless network may be displayed in combination with the signal strength indication of the existing wireless network.

As an example, the searched Wi-Fi in the Wi-Fi search window may be displayed in a shape in which signal strength and quality are combined (FIG. 12(A)). In another embodiment, a single or a plurality of wireless networks may be displayed in a shape combining signal strength and quality (FIG. 12(B), (C)).

In addition, it is possible to display the quality change of the wireless network by means of vibration, flash, and alarm, as well as numerical values or shapes (Fig. 12(D)).

Meanwhile, when the digitized or classified quality changes, and when a specific screen is being displayed, the network quality display unit 180 may overlay the changed digitized or shaped quality on the specific screen as shown in FIG. 13 to display.

In addition, when the digitized or classified quality changes, the wireless terminal device 100 enlarges the value or shape corresponding to the changed quality, so that the wireless terminal device user can not reduce the quality and disconnect the connection according to the connection status of the wireless network. You can tell in advance that it can be.

Hereinafter, a process of displaying the quality of the wireless network on the screen by the wireless terminal device 100 according to an embodiment of the present invention will be described.

14 is a flowchart illustrating a process of displaying a quality of a wireless network on a screen in a wireless terminal device according to an embodiment of the present invention.

A step (S410) of collecting a quality variable of a wireless terminal device and a wireless network to which the wireless terminal device is accessible, and a step (420) of acquiring quality data of a wireless network to which the wireless terminal device is accessible based on the collected quality variable. And, generating network quality display information as a corresponding normalized value or character or image by classifying the quality data with a predetermined normalized value (S430), and displaying the quality display information of the currently connected wireless network A step S440 of displaying an image, and a step S450 of notifying a change in network quality when a fluctuation of the quality data of the currently connected network exceeds a preset threshold value (S450).

First, in the step of collecting the quality variable (S410), quality parameters of a wireless terminal device and a wireless network that can be accessed are collected. As for the quality variable, when setting a quality relational expression or a quality measurement model, variables that can affect quality are set, and such variable information is collected. In the quality data acquisition step (S420), the quality of a corresponding network may be measured by collecting and measuring a quality application variable and applying it to the quality measurement model or relational expression. The quality measurement model or relational expression can be set experimentally, and various variables can be considered for the model generation variable or the applied variable. For example, simply collect the frequency band of the wireless network as a model setting variable, determine the relationship of network quality versus signal strength (RSSI) for the frequency band as an application function of the quality prediction model, and determine the actual measured signal strength. The quality of the wireless network can be calculated by using it as a model input variable.

The quality display information generation step (S430) is to digitize or classify the quality measurement information to generate display information, wherein the digitization is to digitize the quality measurement information into a value representing the quality level, and the classification is to digitize the quality information. It is divided into levels of and classified so that you can know which level you belong to. By using this, display information is generated as a normalized numerical value or a shaped image, figure, or text corresponding to the quality.

In the step of displaying the network quality (S440), the quality level corresponding to each network may be displayed as a numerical value or a shaped image in the network search window. In the screen connected to the network, a signal at the top of the screen The quality indication 10 may be displayed in parallel with the intensity indication 20. At this time, the quality display may be any one of a numerical display, a character display, and a shaped image display, or a composite display thereof.

In the step of displaying the quality fluctuation on the screen (S450), if the quality data of the currently connected network is checked and fluctuates above a preset upper and lower limit threshold, a quality fluctuation state, for example, "speed It is possible to notify the user of quality fluctuations by displaying the phrase "has increased" and displaying the shaped image and numerical value.

In addition, as shown in FIGS. 14A and 14B, quality display information enlarged on the entire screen may be displayed, or quality may be displayed by overlaying it on a background screen, thereby indicating a quality change state. In addition, it can be notified by a pop-up window, or a quality change can be notified by including an alarm or vibration. In addition, when the quality drops below the set threshold, the network can be automatically changed to a high-quality network and connected, and a network search window that can be accessed is popped up so that the user can select and change the connection, and the quality of each network is displayed. , It is possible to automatically select the network with the best quality and allow the user to select a connection.

When the quality decreases due to quality fluctuations, the user checks the network search window and changes to a network with good quality on the screen displaying the quality status of each network as shown in Fig. 12A, and connects to a network with good quality. Can be maintained. Of course, the network connection variation may be provided by programming to enable automatic variable access.

In addition, in the present invention, the area and size of the numerical and shape images are changed according to the change of numerically or classified data as shown in FIGS. 13A and 3B.

You can also display it.

As described above, the process of displaying the quality of the wireless network on the screen in the wireless terminal device 100 may be implemented as an application program, and the wireless terminal device 100 may be combined with an application program to execute the process. . In other words, the application program or the like is stored and installed in the wireless terminal device 100, so that the process is executed in the wireless terminal device 100.

In the wireless network quality display method, as shown in (A) of FIG. 12, in the search screen for searching for WiFi that the wireless terminal device 100 can access, each corresponding to the search list of WiFi Wi-Fi 1 to Wi-Fi n Mark the quality (10). The quality display can be displayed in various ways such as multi-stage round bar display, multi-stage round-by-quality mark display, numerical display, character display, and the like. Accordingly, a user can select and access a Wi-Fi network that is desired by the user, that is, a Wi-Fi network having good quality, by viewing the quality display for each accessible Wi-Fi network displayed in the search window.

12(B) is an exemplary diagram showing a connection state and a quality state at the top of a screen when a Wi-Fi network is connected. In general, in a wireless terminal device, a signal strength display 20 of a wireless network is displayed using a multi-level vertical bar. In the present invention, in addition to the signal strength display 20, a quality display 10 of a connected network is performed together. The quality display 10 can display all of a shape display, a numerical display, and a character display as shown in FIG. 12B.

FIG. 12C illustrates that the quality display 10 is displayed in shape and the character display ("GOOD") is displayed at the lower end of the signal strength 20.

In addition, FIG. 12D illustrates that, while displaying the network quality and the signal strength, when the quality state is changed, the quality variable display 30 is further included. When the quality of the connected wireless network is changed, the quality variable display 30 may include a slide push-up character and a changed quality display in the push window. When controlling whether or not the variable display 30 is controlled at least, a variable display may be output when a difference of more than a predetermined set value occurs.

In addition, in the present invention, as shown in FIGS. 13A and 13B, the network quality display 10 on the main screen is greatly enlarged to further include the main screen quality display 40. The main screen quality display 40 is a method of displaying only the main screen quality display 40 on the main screen, as shown in FIG. 13(A), and an overlay on the main screen 1, as shown in FIG. 13(B). There is a method of (overlapping) the main screen quality display (40). The main screen quality display 40 may display the main screen quality display 40 when double-touching the quality display 10 at the top of the screen or when the quality view is selected in the setting, or a certain time when the initial network connection status is displayed. It can be displayed, and it can be displayed when the quality status is varied over a certain range. In addition, it is possible to make the main screen quality display 40 disappear by double-touching the main screen quality display 40 or changing a setting.

As described above, according to the present invention, the wireless network quality indicator 10 for each wireless network to which the wireless terminal device 100 can access is displayed for each network in the search window, so that the user can selectively access the network in the optimal quality state. have. In addition, the quality information of the network connected in the network connection state of the wireless terminal device 100 can be displayed in parallel with the signal strength at the top of the screen as shown in FIGS. By performing at least one of display and character display, or a composite display thereof, the user can intuitively check not only the signal strength of the currently connected network but also the quality status.

In addition, when the network quality status is changed, the changed status information as shown in (C) of FIG. 12 can be displayed as a character display and a shaping indicator to inform the user. Therefore, the user can see the changed quality status information and try to connect to another network, and when the network variable connection is automatically set, the quality information of the newly connected network can be easily checked visually.

Meanwhile, the above-described method according to various embodiments of the present invention may be implemented as a program and provided to each server or devices while being stored in various non-transitory computer readable media. Accordingly, the user terminal 100 can access the server or device and download the program.

The non-transitory readable medium refers to a medium that stores data semi-permanently and can be read by a device, not a medium that stores data for a short moment, such as a register, cache, or memory. Specifically, the above-described various applications or programs may be provided by being stored in a non-transitory readable medium such as a CD, DVD, hard disk, Blu-ray disk, USB, memory card, and ROM.

In addition, although the preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the present invention claimed in the claims. In addition, various modifications are possible by those of ordinary skill in the art, and these modifications should not be individually understood from the technical spirit or prospect of the present invention.

Claims (19)

In the operating method of a wireless terminal device for displaying a dynamic wireless network quality based on quality prediction on a screen, Collecting quality parameters of a wireless terminal device and a wireless network to which the wireless terminal device is accessible; Acquiring quality data of a wireless network accessible to a wireless terminal device based on the collected quality parameters; Generating network quality indication information as a corresponding normalized number or character or a shaped image by classifying the quality data with a predetermined normalized value or classification; Displaying quality indication information of a currently connected wireless network on a screen; Notifying a change in network quality when a change in quality data of the currently connected network exceeds a preset threshold; Including A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 1, The step of obtaining the quality data, Comprising the step of calculating quality data of an accessible wireless network by applying the collected quality variable to a wireless network quality relational expression against a preset quality variable A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 1, The quality variable collection step and the quality data acquisition step, The frequency band of the wireless network is collected as a model setting variable, the relationship of network quality versus signal strength (RSSI) for the frequency band is determined as an application function of the quality prediction model, and the signal strength is used as a model input variable to Comprising the step of calculating the quality data of A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 1, Informing of the quality change, Comprising the step of overlaying a text scroll or a shaped image corresponding to the quality change on the screen, displaying the quality change in a pop-up window, or notifying the quality change through at least one or more of an alarm or vibration How to display the quality of a wireless network on the screen. The method of claim 1, Displaying the quality on the screen, In the accessible network search window of the wireless terminal device, comprising the step of performing a quality display for each network in a list of accessible networks A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 1, Displaying the quality on the screen, Including the step of displaying the quality indication of the connected network and the signal strength indication in parallel A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 1, The step of generating the quality indication information, Normalized values, images including figures and figures, texts including symbols or characters, the degree of filling, number, color, and shape of the same figure or image, and simple addition or transformation of a bar shape , Including at least one or more of a moving or changing shape such as an animation, including displaying a quality state A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 1, The quality prediction processing includes calculation processing of the quality prediction information for each network using a quality prediction function predetermined in response to the variable information. A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 8, The quality prediction function includes at least one of a linear function, a log function, a sigmoid function, and an arctangent function capable of representing network quality information in comparison with a signal strength information (RSSI) variable. A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 9, The quality prediction function includes at least one of a linear function based on frequency band information and variable information, a linear function based on link speed and variable information, a first frequency band based log function, and a second frequency band based linear function. A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 10, The linear function based on the frequency band information and the variable information includes a linear function for forming a different slope for each frequency band by differently determining a variable limitation range according to the frequency band information for each network. A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 10, The link speed and variable information-based linear function is a linear function that limits a maximum value range of a quality prediction result according to link speed information for each network. A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 10, The quality prediction function includes a selective adaptive processing function for processing a logarithmic function corresponding to the first frequency band and a linear function corresponding to the second frequency band, Characterized in that the first frequency band is higher than the second frequency band A method of operating a wireless terminal device for displaying wireless network quality on a screen. The method of claim 10, Further comprising the step of setting variable information for applying the quality prediction function A method of operating a wireless terminal device for displaying wireless network quality on a screen. In the wireless terminal device for displaying quality information on a wireless network, A variable information processing unit collecting variable information of the wireless terminal device; A quality prediction module that obtains quality prediction information for each accessible network according to a quality prediction process using the variable information; And A network quality display unit for displaying quality information of a currently connected wireless network on a screen based on the quality prediction information for each network; Including A wireless terminal device for displaying wireless network quality on a screen. The method of claim 15, The quality prediction model, To calculate the quality data of the accessible wireless network by applying the collected quality variables to a relational formula that collects the quality variables of the wireless network accessible to the wireless terminal device through the variable information processing unit and calculates the network quality information compared to the preset quality variables. A wireless terminal device for displaying wireless network quality on a screen. The method of claim 15, The network quality display unit, The measured quality data is normalized or shaped to display quality information of the currently connected wireless network on the screen, In the wireless network search window to which the wireless terminal device can access, display the searchable wireless networks, including each wireless network quality indication, The quality change is notified by checking the quality change of the currently connected wireless network, and when a change of more than a preset threshold occurs, the quality change information is displayed on the screen by displaying at least one of a text, a shaped image, and a value. With A wireless terminal device for displaying wireless network quality on a screen. The method of claim 17, The network quality display unit, Further comprising notifying the quality change by vibration or an alarm A wireless terminal device for displaying wireless network quality on a screen. The method of claim 15, The network quality display unit, Images including figures and pictures, phrases including symbols or characters, the degree of filling, number, color, shape transformation of the same figure or image, simple addition or transformation of a bar shape, movement such as animation Control to display quality status, including at least one or more of A wireless terminal device for displaying wireless network quality on a screen.

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