Terminal movement situation estimation device, control method for terminal movement situation estimation device, and control program for terminal movement situation estimation device

Abstract

A terminal movement situation estimation device according to an embodiment of the present invention comprises: an acquisition unit that acquires channel information indicating communication quality between a base station and a wireless communication terminal; a generation unit that generates an estimation model obtained by modeling, by machine learning, a relationship between a plurality of pieces of time-series channel information acquired by the acquisition unit, and the movement speed and the movement direction of the wireless communication terminal which are based on the change of the position information of the wireless communication terminal when the plurality of pieces of time-series channel information are acquired; and an estimation unit that estimates the movement speed and the movement direction of a wireless communication terminal to be subjected to estimation by inputting, to the estimation model, a plurality of pieces of time-series channel information between the wireless communication terminal to be subjected to estimation and the base station.

Description

Terminal movement status estimation device, control method for terminal movement status estimation device, and control program for terminal movement status estimation device

[0001] The present invention relates to a terminal movement status estimation device, a control method for the terminal movement status estimation device, and a control program for the terminal movement status estimation device.

[0002] Conventionally, it has been attempted to estimate the moving speed and moving direction of a moving wireless communication terminal (UE: User Equipment) with respect to a base station (BS: Base Station). For example, Patent Document 1 discloses a base station device that estimates the moving direction of a wireless terminal based on the sign of the amount of change per unit time of the received power of a wireless signal arriving from the wireless terminal by an antenna, and estimates the moving speed of the wireless terminal based on the absolute value of the amount of change.

[0003] Japanese Patent Application Laid-Open No. 2012-95051

[0004] A terminal movement status estimation device according to an embodiment of the present invention includes an acquisition unit that acquires channel information indicating the communication quality between a base station and a wireless communication terminal, a plurality of time-series channel information acquired by the acquisition unit, and a plurality of time-series channel information. A generation unit that generates an estimation model in which the relationship between the moving speed and moving direction of the wireless communication terminal based on the change in the position information of the wireless communication terminal when the information is acquired is modeled by machine learning, and the estimation model inputs a plurality of time-series channel information between the wireless communication terminal to be estimated and the base station, and estimates the moving speed and moving direction of the wireless communication terminal to be estimated. And an estimation unit.

[0005] In the terminal movement status estimation device according to an embodiment of the present invention, the generation unit may learn the relationship for a plurality of wireless communication terminals connected to the base station and generate an estimation model.

[0006] A terminal movement status estimation device according to an embodiment of the present invention further includes an image generation unit that generates channel image information obtained by imaging the channel information acquired by the acquisition unit for each acquired time. In generating the estimation model, the generation unit uses the channel image information as the channel information, and the estimation unit may use the channel image information as the channel information in estimating the moving speed and moving direction of the wireless communication terminal to be estimated.

[0007] In a terminal movement status estimation device according to one embodiment of the present invention, the generation unit may further use location information of a wireless communication terminal to generate an estimation model.

[0008] A control method for a terminal movement status estimation device according to one embodiment of the present invention involves the terminal movement status estimation device performing the following steps: acquiring channel information indicating the communication quality between a base station and a wireless communication terminal; generating an estimation model that models the relationship between the multiple time-series channel information acquired in the acquisition step and the change in the position information of the wireless communication terminal when the multiple time-series channel information was acquired, using machine learning; and inputting the multiple time-series channel information between the wireless communication terminal to be estimated and the base station to the estimation model to estimate the movement speed and direction of the wireless communication terminal to be estimated.

[0009] A control program for a terminal movement status estimation device according to one embodiment of the present invention provides the terminal movement status estimation device with the following functions: a function to acquire channel information indicating the communication quality between a base station and a wireless communication terminal; a function to generate an estimation model that models the relationship between the time-series multiple channel information acquired by the acquisition function and the change in the position information of the wireless communication terminal when the time-series multiple channel information is acquired, using machine learning; and a function to input the time-series multiple channel information between the wireless communication terminal to be estimated and the base station to the estimation model to estimate the movement speed and direction of the wireless communication terminal to be estimated.

[0010] Figures 1(a) and 1(b) illustrate an overview of the estimation of the movement status of a wireless communication terminal according to one embodiment of the present invention. Figures 2(a) and 2(b) illustrate an overview of the estimation of the movement status of a wireless communication terminal according to one embodiment of the present invention. Figure 3 is an example of a schematic diagram of a terminal movement status estimation system according to one embodiment of the present invention. Figure 4 is an example of a functional block diagram of a terminal movement status estimation device according to one embodiment of the present invention. Figure 5 is a flowchart showing an example of operation of a terminal movement status estimation device according to one embodiment of the present invention. Figure 6 is an example of a hard block configuration of a computer capable of realizing a terminal movement status estimation device according to one embodiment of the present invention.

[0011] Hereafter, an embodiment of the invention described herein (also referred to as the present invention) will be explained using the figures. Note that the figures are examples only, and the present invention is not limited to those shown in the figures. For example, the illustrated terminal movement status estimation device, wireless communication terminal, number of base stations, functional block diagram, flowchart, and heat map showing channel information are examples only, and the present invention is not limited to these.

[0012] Conventionally, the estimation of the speed and direction of movement of a moving wireless communication terminal (hereinafter also simply referred to as "terminal") relative to a base station has been performed using information about the approximate direction of the wireless communication terminal based on the exchange of synchronization signals and reference signals between the wireless communication terminal and the base station, or using location information (GPS (Global Positioning System)) obtained from the wireless communication terminal at a higher layer. However, in order to support the high-precision beamforming and handover between base stations required for 5G (5th Generation) and later mobile communication systems, it is essential to know the speed and direction of movement of the wireless communication terminal accurately. With conventional technology, it has been difficult to obtain the speed and direction of movement of a wireless communication terminal in real time, especially in environments where GPS information is unavailable, such as indoors. To address the above problem, according to one embodiment of the present invention, a predictive model is generated that estimates the speed and direction of movement of a wireless communication terminal from channel information, using information about the movement status of the wireless communication terminal (changes in the location information of the wireless communication terminal obtained by GPS, etc., and measurement values ​​of acceleration sensors etc. measured by the wireless communication terminal), and channel information at that time. In one embodiment of the present invention, the channel information used for learning may be represented as an image.

[0013] This will be explained using Figures 1 and 2. Figures 1 and 2 are diagrams illustrating an outline of the estimation of the movement status of a wireless communication terminal according to one embodiment of the present invention.

[0014] Figure 1(a) shows how terminals A 300A and B 300B, which are connected to base station 200, move within the communication range (cell) 500 covered by base station 200. From this point forward, unless otherwise necessary, terminals A 300A and B 300B will be collectively referred to as terminal 300. Figure 1(b) is a diagram illustrating the generation of an estimation model for the movement status of terminal 300, based on channel information between terminal 300 and base station 200, and information regarding the movement status of terminal 300, such as changes in terminal 300's location information.

[0015] The channel information between terminal 300 and base station 200 changes according to the speed and direction of movement of terminal 300. For example, in Figure 1(b), images A1 to A5 and B1 to B5 are images of the channel information between terminals 300A and 300B and base station 200, respectively, and are examples of channel information heatmaps where the horizontal axis is the number of resource blocks (RS) and the vertical axis is the number of receiving antennas. In Figure 1(b), images A1 to A5 and B1 to B5 show how the channel information changes over time.

[0016] According to one embodiment of the present invention, a model for estimating the movement status of terminal 300 (movement status estimation model) may be generated by machine learning with channel information as an explanatory variable and information regarding the movement status of terminal 300 at the time the channel information was acquired as the dependent variable. Here, the information regarding the movement status of terminal 300 may be the change in the position information of terminal 300 at the time the channel information was acquired, or the movement speed and direction of terminal 300 calculated by the acceleration sensor of terminal 300, etc. Alternatively, the information regarding the movement status of terminal 300 may be the change in the position information of terminal 300 itself. Although only two terminals 300 are shown in Figure 1, channel information from more terminals 300 may be used in generating the movement status estimation model for terminal 300. Also, although the same number of channel information entries are shown for terminal A 300A and terminal B 300B in Figure 1(b), the number of channel information entries may differ for each terminal 300.

[0017] Using Figure 2, the estimation of the movement status of terminal 300 using the generated movement status estimation model will be explained. As shown in Figure 2(a), the movement status of terminal C 300C or terminal D 300D moving within the base station 200 may be obtained as an output result by inputting the respective time-series channel information acquired by the base station 200 into the movement status estimation model, as shown in Figure 2(b). In other words, according to one embodiment of the present invention, it is possible to estimate the movement speed and direction of movement relative to the base station for any mobile communication terminal using a model that has been trained to output the movement speed and direction of movement according to the input channel information.

[0018] <System Configuration> Figure 3 shows an example configuration of a terminal movement status estimation system according to one embodiment of the present invention. The terminal movement status estimation system 600 includes a plurality of wireless communication terminals 300, a wireless access network RAN ​​(Radio Access Network) 400, and a core network CN. The RAN 400 includes a base station 200 and a terminal movement status estimation device 100. In this embodiment, a 5G (fifth generation) mobile communication system is assumed, and the RAN 400 may be a RAN to which the specifications defined by the industry group O-RAN Alliance (Open Radio Access Network Alliance) are applied. The base station 200 may also include RU (Radio Unit), DU (Distribution Unit), and CU (Central Unit). The CU / DU may be implemented by vRAN (virtual RAN), which implements each function by software on general-purpose hardware.

[0019] The terminal movement status estimation device 100 may implement a RIC (RAN Intelligent Controller), which is defined in O-RAN as a logical node that automates and optimizes the parameter design, setting, and operation of base stations. The terminal movement status estimation device 100 and the base station 200 are connected to each other via a network for communication. The base station 200 may also have the functions of the terminal movement status estimation device 100.

[0020] According to one embodiment of the present invention, a neural network may be used to generate an AI model (estimation model) for estimating the terminal movement status. More specifically, in one embodiment of the present invention, channel information may be processed as image information as described above, and a CNN (Convolutional Neural Network) may be used as the machine learning algorithm.

[0021] <Functional Configuration> The functions of each functional unit of the terminal movement status estimation device 100 will be explained using Figure 4. The terminal movement status estimation device 100 may include a movement status acquisition unit 110, a channel information acquisition unit 120, a generation unit 130, an estimation unit 140, and an image generation unit 150.

[0022] The movement status acquisition unit 110 acquires information regarding the movement status of the wireless communication terminal 300. The information regarding the movement status may be the time-series location information of the wireless communication terminal 300. Alternatively, the information regarding the movement status may be the speed and direction of movement calculated from the time-series location information. For example, the speed and direction of movement may be acquired from the change in the current position using GPS. That is, the speed of movement may be determined from the distance between two points and the time required to move between those two points, and the direction of movement may be determined from the direction when the two points are connected. Note that the speed and direction of movement as information regarding the movement status may be acquired by the wireless communication terminal 300 or the terminal movement status estimation device 100 by any method other than GPS.

[0023] The channel information acquisition unit 120 acquires channel information indicating the communication quality between the base station 200 and the wireless communication terminal 300. The channel information may be measured at the base station 200, or it may be measured at the wireless communication terminal 300 and notified to the base station 200. The channel information to be measured is not limited to channel state information (CSI). The channel information acquisition unit 120 may acquire channel information transmitted periodically from multiple wireless communication terminals 300 connected to the base station 200, or at times when requested by the base station 200, along with information regarding the movement status of the multiple wireless communication terminals 300.

[0024] The generation unit 130 may generate an estimation model that models the relationship between multiple time-series channel information and the movement speed and direction of the wireless communication terminal 300, which are identified based on the change in the position information of the wireless communication terminal 300 when the multiple time-series channel information is acquired, using machine learning. That is, an estimation model (movement status estimation model) may be generated by machine learning, which takes multiple time-series channel information acquired in the past as input and outputs the movement speed and direction of the wireless communication terminal 300 when the multiple time-series channel information is acquired. The training data used for machine learning may be a combination of a channel image at a certain point in time and the movement speed and direction at that point in time, or a combination of channel images at two certain points in time and the movement speed and direction between those two points in time.

[0025] Here, the generation unit 130 may learn the relationships between multiple wireless communication terminals 300 with different movement speeds and directions and generate an estimation model. That is, the channel information acquisition unit 120 may acquire channel information between each terminal 300 connected to the base station 200 and the base station 200, and the movement status acquisition unit 110 may determine the movement speed and direction from the location information of the terminal 300. Then, the generation unit 130 may generate an estimation model from the channel information of the multiple terminals 300, as well as the movement speed and direction.

[0026] The estimation unit 140 inputs multiple time-series channel information between the wireless communication terminal 300 to be estimated and the base station 200 into the estimation model to estimate the movement speed and direction of the wireless communication terminal 300 to be estimated. That is, for the wireless communication terminal 300 to be estimated, the estimation unit 140 inputs the time-series channel information acquired by the channel information acquisition unit 120 into the estimation model and may output the movement status (movement speed and movement speed) of the wireless communication terminal 300 to be estimated.

[0027] In one embodiment of the present invention, channel information may be processed as predetermined image information. The image generation unit 150 may generate channel image information by imaging the channel information acquired by the channel information acquisition unit 120 for each acquisition time. The image information may be a heat map of channel information, for example, as described in Figures 1 and 2, with the horizontal axis representing the number of resource blocks and the vertical axis representing the number of receiving antennas. However, the image information is not limited to this. This makes it easier to extract features of the channel information.

[0028] Furthermore, the generation unit 130 may also use the location information of the wireless communication terminal 300 to generate the estimation model. Channel information can also vary depending on the surrounding environment where the wireless communication terminal 300 is located (presence or absence of buildings, population density, etc.). Therefore, by further learning the location information as an explanatory variable when generating the estimation model, a more accurate estimation model can be generated.

[0029] <Control Flow of Terminal Movement Status Estimation Device> The control flow of the terminal movement status estimation device 100 described above will be explained using Figure 5. The channel information acquisition unit 120 acquires channel information indicating the communication quality between the base station 200 and the wireless communication terminal 300 (step S11). Next, the generation unit 130 generates an estimation model that models the relationship between the time-series channel information acquired by the channel information acquisition unit 120 and the change in the position information of the wireless communication terminal 300 when the time-series channel information was acquired, using machine learning (step S12). The estimation unit 140 inputs the time-series channel information between the wireless communication terminal 300 and the base station 200 to the estimation model and estimates the movement speed and direction of the wireless communication terminal 300 (step S13).

[0030] <Hardware Configuration> The hardware configuration of the terminal movement status estimation device 100 will now be described. Figure 6 shows an example of a computer hardware configuration that can implement the terminal movement status estimation device 100 in this embodiment. The terminal movement status estimation device 100 includes a processor 101, storage 102, memory 103, input / output interface (input / output I / F) 104, and communication interface (communication I / F) 105. Each component is interconnected via bus B. The terminal movement status estimation device 100 realizes the functions and methods described in this embodiment through the cooperation of these components. For example, when each functional part of the terminal movement status estimation device 100 is realized by software, it is realized by the processor 101 executing instructions contained in a program read from storage 102 into memory 103. That is, in this embodiment, the terminal movement status estimation device 100 functions as a movement status acquisition unit 110, a channel information acquisition unit 120, a generation unit 130, an estimation unit 140, and an image generation unit 150 by the processor 101 executing a program read onto memory 103.

[0031] The processor 101 may include, for example, a central processing unit (CPU), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), a microprocessor, a processor core, a multiprocessor, an ASIC (Application-Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), etc., and may be realized by logic circuits (hardware) or dedicated circuits formed on an integrated circuit (IC (Integrated Circuit) chip, LSI (Large Scale Integration)), etc. It is preferable that the terminal movement status estimation device 100 has a processor 101 with high computing power for processing the large amount of data mentioned above.

[0032] The communication interface 105 is implemented as hardware such as a network adapter, communication software, or a combination thereof, and transmits and receives various types of data with external devices. This communication may be performed via wired or wireless connection, and any communication protocol may be used as long as communication between the devices is possible.

[0033] The input / output interface 104 includes an input device for inputting various operations to the terminal movement status estimation device 100, and an output device for outputting processing results processed by the terminal movement status estimation device 100. The input device includes, for example, hardware keys such as a touch panel, touch display, and keyboard, a pointing device such as a mouse, a camera (operation input via image), and a microphone (operation input via voice). The output device outputs the processing results processed by the processor 101. The output device includes, for example, a touch panel and a speaker.

[0034] The present invention has been described based on various drawings and embodiments, but it should be noted that those skilled in the art will find it easy to make various modifications and alterations based on this disclosure. Therefore, it should be noted that these modifications and alterations are within the scope of the present invention. For example, the functions included in each component, step, etc., can be rearranged in a logically consistent manner, and multiple components or steps, etc., can be combined into one or divided. Furthermore, the configurations shown in the above embodiments may be combined as appropriate. For example, each component described as being provided by the terminal movement status estimation device 100 may be implemented by multiple servers in a distributed manner. Also, the processing described above as being performed by the terminal movement status estimation device 100 may be performed by the base station 200.

[0035] Furthermore, although the above description used 5G mobile communication systems as an example, the present invention is not limited to this and may also be applied to mobile communication systems such as 4G (fourth generation), 6G (sixth generation), and LTE.

[0036] The programs of each embodiment of this disclosure may be provided stored in a storage medium readable by the terminal movement status estimation device. The storage medium is a “non-temporary tangible medium” capable of storing the program. The program includes, for example, software programs and control programs. The storage medium may, where appropriate, include one or more semiconductor-based or other integrated circuits (ICs) (e.g., field-programmable gate arrays (FPGAs), application-specific ICs (ASICs), etc.), hard disk drives (HDDs), hybrid hard drives (HHDs), optical disks, optical disk drives (ODDs), magneto-optical disks, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM drives, secure digital cards or drives, any other suitable storage medium, or two or more suitable combinations thereof. The storage medium may, where appropriate, be volatile, non-volatile, or a combination of volatile and non-volatile.

[0037] Furthermore, the program disclosed herein may be provided to the terminal movement status estimation device 100 via any transmission medium capable of transmitting the program (such as a communication network or broadcast waves).

[0038] Furthermore, each embodiment of this disclosure can also be realized in the form of data signals embedded in a carrier wave, where the program is embodied by electronic transmission. The programs of this disclosure may be implemented using, for example, scripting languages ​​such as JavaScript® and Python, C language, Go language, Swift®, Koltin®, Java®, etc.

[0039] According to each aspect of this disclosure described above, it becomes possible to estimate terminal movement status with greater accuracy, thereby contributing to the achievement of Sustainable Development Goal (SDG) 9, "Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation."

[0040] 100 Terminal movement status estimation device 110 Movement status acquisition unit 120 Channel information acquisition unit 130 Generation unit 140 Estimation unit 150 Image generation unit 500 Cell 200 Base station 300 Wireless communication terminal 600 Terminal movement status estimation system

Claims

1. A terminal movement status estimation device comprising: an acquisition unit that acquires channel information indicating the communication quality between a base station and a wireless communication terminal; a generation unit that generates an estimation model by machine learning that models the relationship between the multiple time-series channel information acquired by the acquisition unit and the change in the position information of the wireless communication terminal when the multiple time-series channel information is acquired, and an estimation unit that inputs the multiple time-series channel information between the wireless communication terminal to be estimated and the base station to the estimation model to estimate the movement speed and direction of the wireless communication terminal to be estimated.

2. The terminal movement status estimation device according to claim 1, wherein the generation unit learns the relationships for a plurality of wireless communication terminals connected to the base station and generates the estimation model.

3. The terminal movement status estimation device according to claim 1, further comprising an image generation unit that generates channel image information by imaging the channel information acquired by the acquisition unit for each acquisition time, wherein the generation unit uses the channel image information as the channel information in generating the estimation model, and the estimation unit uses the channel image information as the channel information in estimating the movement speed and movement direction of the wireless communication terminal to be estimated.

4. The terminal movement status estimation device according to claim 1, wherein the generation unit further uses the location information of the wireless communication terminal in generating the estimation model.

5. A control method for a terminal movement status estimation device, which performs the following steps: acquiring channel information indicating the communication quality between a base station and a wireless communication terminal; generating an estimation model that models the relationship between the multiple time-series channel information acquired in the acquisition step and the change in the position information of the wireless communication terminal when the multiple time-series channel information was acquired, using machine learning; and inputting the multiple time-series channel information between the wireless communication terminal to be estimated and the base station to the estimation model to estimate the speed and direction of movement of the wireless communication terminal to be estimated.

6. A control program for a terminal movement status estimation device that enables the following functions: a function to acquire channel information indicating the communication quality between a base station and a wireless communication terminal; a function to generate an estimation model that models the relationship between the multiple time-series channel information acquired by the acquisition function and the change in the position information of the wireless communication terminal when the multiple time-series channel information is acquired, using machine learning; and a function to input multiple time-series channel information between the wireless communication terminal to be estimated and the base station to the estimation model to estimate the speed and direction of movement of the wireless communication terminal to be estimated.

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