Information processing device, terminal device, and information processing method

Abstract

To provide a mechanism that can more appropriately update statistical information.SOLUTION: An information processing device of the present disclosure includes a control unit. The control unit updates first statistical information according to a difference between the first statistical information on communication characteristics generated using first actual measurement data in a communication area and second statistical information on communication characteristics generated using second actual measurement data in the communication area.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, a terminal device, and an information processing method. [Background technology]

[0002] In wireless communication, there is known a technique for realizing more suitable communication by appropriately controlling wireless resources and communication parameters. For example, suitable communication can be realized by adaptively controlling communication parameters according to the state of a propagation path between a base station and a terminal device.

[0003] For example, a base station transmits a known signal and a terminal device receives the known signal, whereby the terminal device can estimate the state of a propagation path. Furthermore, the terminal device feeds back the estimated state of the propagation path to the base station, whereby the base station can set suitable communication parameters for the terminal device.

[0004] In addition, if there is no feedback from the terminal device regarding the conditions of the propagation path, the base station can recognize the average conditions of the propagation path by using a statistical propagation model (e.g., a path loss model, an interference model, etc.) according to the distance from the terminal device. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2021-108459 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, radio wave propagation in wireless communication varies greatly depending on, for example, the presence or absence of obstacles between a base station, which is a transmitting point, and a terminal device, which is a receiving point. In addition, when considering interference with adjacent cells and surrounding base stations, the base station determines communication parameters based on statistical information such as a propagation model so as to minimize the interference.

[0007] Therefore, when communication is performed using communication parameters determined by the base station, there is a risk that suitable wireless communication cannot be performed depending on obstacles that exist in the space where the base station and the terminal device actually perform wireless communication (hereinafter also referred to as real space).For example, when the base station determines the minimum transmission power to minimize interference, if an obstacle exists between the base station and the terminal device, there is a risk that the signal transmitted by the base station will not reach the terminal device due to the obstacle.

[0008] To avoid this, it is desirable to update statistical information such as the propagation model. However, if statistical information is updated too frequently, the frequency of updating communication parameters increases. Therefore, it is desirable to update statistical information more appropriately.

[0009] Therefore, the present disclosure provides a mechanism for more appropriately updating statistical information.

[0010] It should be noted that the above problem or objective is merely one of several problems or objectives that can be solved or achieved by several embodiments disclosed in this specification. [Means for solving the problem]

[0011] The information processing device of the present disclosure includes a control unit that updates first statistical information of a communication characteristic generated using first actual measurement data in a communication area and second statistical information of the communication characteristic generated using second actual measurement data in the communication area according to a difference between the first statistical information. [Brief description of the drawings]

[0012] [Figure 1]FIG. 1 is a diagram illustrating an example of radio wave propagation according to a proposed technique of the present disclosure. [Diagram 2] FIG. 11 is a diagram illustrating an example of a communication process according to an embodiment of the present disclosure. [Diagram 3] FIG. 11 is a diagram illustrating an example of statistical processing according to an embodiment of the present disclosure. [Figure 4] FIG. 1 is a diagram illustrating a configuration example of a communication system according to an embodiment of the present disclosure. [Diagram 5] FIG. 2 is a diagram illustrating a configuration example of a control station according to an embodiment of the present disclosure. [Figure 6] FIG. 2 is a block diagram illustrating a configuration example of a base station according to an embodiment of the present disclosure. [Figure 7] FIG. 2 is a block diagram illustrating a configuration example of a terminal device according to an embodiment of the present disclosure. [Figure 8] 1 is a graph illustrating an example of a first probability distribution according to an embodiment of the present disclosure. [Figure 9] 11 is a graph illustrating an example of a second probability distribution according to an embodiment of the present disclosure. [Figure 10] 11 is a graph showing an example of a first estimation model according to an embodiment of the present disclosure. [Figure 11] 11 is a graph showing an example of a second estimation model according to an embodiment of the present disclosure. [Figure 12] FIG. 13 is a diagram illustrating an example of a fluctuation in communication characteristics according to an embodiment of the present disclosure. [Figure 13] FIG. 13 is a diagram illustrating another example of fluctuations in communication characteristics according to an embodiment of the present disclosure. [Figure 14] FIG. 13 is a diagram illustrating another example of fluctuations in communication characteristics according to an embodiment of the present disclosure. [Figure 15] FIG. 13 is a diagram illustrating another example of fluctuations in communication characteristics according to an embodiment of the present disclosure. [Figure 16] FIG. 13 is a diagram illustrating another example of fluctuations in communication characteristics according to an embodiment of the present disclosure. [Figure 17] 5A to 5C are diagrams illustrating an example of first actual measurement data and second actual measurement data according to an embodiment of the present disclosure. [Figure 18] 13A to 13C are diagrams illustrating other examples of the first actual measurement data and the second actual measurement data according to the embodiment of the present disclosure. [Figure 19] FIG. 11 is a diagram illustrating an example of selection of first actual measurement data according to an embodiment of the present disclosure. [Figure 20] FIG. 11 is a diagram illustrating an example of selection of first actual measurement data according to an embodiment of the present disclosure. [Figure 21] FIG. 11 is a diagram illustrating an example of selection of first actual measurement data according to an embodiment of the present disclosure. [Figure 22] 1 is a graph illustrating an example of a confidence interval for an average according to an embodiment of the present disclosure. [Diagram 23] FIG. 11 is a sequence diagram illustrating an example of a flow of a communication process according to an embodiment of the present disclosure. [Figure 24] 11 is a flowchart illustrating an example of a flow of an update process according to an embodiment of the present disclosure. [Diagram 25] 11 is a flowchart illustrating an example of a flow of a generation process according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0014] In addition, in this specification and drawings, similar components of the embodiments may be distinguished by adding at least one of different alphabets and numbers after the same reference numeral. However, when there is no need to particularly distinguish between similar components, only the same reference numerals are used. For example, multiple components having substantially the same functional configurations are distinguished as necessary, such as terminal device 400_1 and terminal device 400_2. For example, when there is no need to particularly distinguish between terminal device 400_1 and terminal device 400_2, they are simply referred to as terminal device 400.

[0015] One or more embodiments (including examples, modifications, and application examples) described below can be implemented independently. Meanwhile, at least a part of the embodiments described below may be implemented in appropriate combination with at least a part of another embodiment. These embodiments may include novel features different from each other. Thus, these embodiments may contribute to solving different purposes or problems and may provide different effects.

[0016] <<1. Introduction>> <1.1.Background> In wireless communications, it is known that communication characteristics such as received power, throughput, interference power, SIR (Signal-to-Interference power ratio), and SINR (Signal-to-Interference and Noise power Ratio) are strongly dependent on the radio wave propagation characteristics between the base station and the terminal device.

[0017] To improve communication characteristics, base stations are required to adaptively design communication parameters such as modulation schemes and coding rates according to the radio wave propagation characteristics around the communication area.

[0018] For example, when the received power at a terminal device is high, the base station can improve the transmission efficiency by using a higher-order modulation scheme, whereas when the received power is low, the base station can suppress an increase in the bit error rate, symbol error rate, etc. by using a lower-order modulation scheme.

[0019] Conventionally, predictions of radio wave propagation characteristics have utilized empirical models such as the Okumura-Hata model. Empirical models are constructed by statistically processing actual measurement data in representative environments such as large cities. Empirical models are used to estimate global characteristics of radio wave propagation (path loss, etc.) from information such as the distance between the transmitter and receiver.

[0020] On the other hand, in a real environment, radio wave propagation characteristics fluctuate probabilistically due to shadowing, multipath fading, etc. Since it is difficult for empirical models to predict propagation characteristics with such probabilistic fluctuations, a new propagation prediction method to replace the empirical models is required.

[0021] <1.2.Challenges> However, radio wave propagation characteristics (communication characteristics) in wireless communication may vary significantly depending on the presence or absence of obstacles between a base station (transmission point) and a terminal device (reception point). This point will be described with reference to FIG.

[0022] Fig. 1 is a diagram showing an example of radio wave propagation according to the proposed technique of the present disclosure. Fig. 1(a) is a diagram showing an example of radio wave propagation when there is no obstacle 600. Fig. 1(b) is a diagram showing an example of radio wave propagation when there is an obstacle 600.

[0023] For example, when the base station 300 transmits tx If there is no obstacle 600 (see FIG. 1(a)), the radio waves will reach a greater distance than if there is an obstacle 600 (see FIG. 1(b)).

[0024] It is known that the amount of radio wave attenuation caused by such an obstacle 600 varies in a complex manner depending on the density and height of the structure (hereinafter also referred to as the structure characteristics), the positional relationship between the base station, the structure, and the terminal device, and the like.

[0025] In particular, in urban areas and other environments where people and obstacles are moving rapidly, time fluctuations in communication characteristics cannot be ignored. Therefore, if a communication characteristics prediction value that has been generated once is used continuously as is, there is a risk that the actual communication characteristics cannot be accurately grasped.

[0026] In order to accurately predict the time variation of communication characteristics, it is necessary to install radio wave sensors or the like in fixed locations in communication areas and to constantly measure the communication characteristics using the sensors. Furthermore, the communication system that generates the predicted values ​​of communication characteristics must perform real-time statistical processing of the measured data and constantly update the predicted values ​​of communication characteristics.

[0027] However, this method has the disadvantage that the sensor (radio wave sensor) needs to be installed in a fixed location, and the maintenance of the sensor is costly. In addition, in order to implement this method, the communication system (or the system designer) needs to know in advance the points where the communication characteristics fluctuate greatly over time in order to decide the installation location of the sensor.

[0028] Furthermore, when the amount of measured data is extremely large or the time interval for statistical processing is short, the computation load required for processing becomes heavy, and it may not be possible to quickly calculate the communication characteristic prediction value (or statistical information). This problem may become more pronounced when processing is performed in real time.

[0029] Moreover, in this method, every time a communication characteristic predicted value is updated, the device that generated the predicted value needs to notify the user (e.g., a base station or a terminal device) of the predicted value. Also, there is a risk that the user will have to reset parameters, etc., every time they receive a notification.

[0030] Considering the above, in order to efficiently predict the time variation of communication characteristics, it is required that the communication system calculates a communication characteristic prediction value (e.g., statistical information) using at least a portion of the actual measurement data measured by the terminal device, without installing fixed sensors, etc.

[0031] In addition, it is required that the update frequency of the communication characteristic predicted value (eg, statistical information) and the number of actually measured data used for the update are as small as possible.

[0032] <1.3. Overview of proposed technology> Fig. 2 is a diagram showing an example of communication processing according to an embodiment of the present disclosure. The communication processing shown in Fig. 2 is executed in a communication system. The communication system includes a control station 100, a base station 300, and terminal devices 400_1 to 400_3.

[0033] First, the base station 300 transmits a signal to the terminal devices 400_1 to 400_3 (step S1). This signal is, for example, a reference signal for measuring communication characteristics.

[0034] The terminal devices 400_1 to 400_3 measure signals to generate measured data, and transmit the generated measured data to the control station 100 (step S2). The measured data includes, for example, information on communication characteristics.

[0035] At this time, the terminal device 400 may transmit the actual measurement data to the control station 100 via the base station 300, or may transmit the actual measurement data to the control station 100 without passing through the base station 300. That is, the terminal device 400 may transmit the actual measurement data to the control station 100 via a cellular network. Alternatively, the terminal device 400 may transmit the actual measurement data to the control station 100 via Wi-Fi (registered trademark), the Internet, or the like. Alternatively, the terminal device 400 may transmit the actual measurement data to the control station 100 by being directly connected to the control station 100 via a cable.

[0036] The control station 100 executes statistical processing using the acquired measured data (step S3). The statistical processing will be described later with reference to FIG.

[0037] The control station 100 notifies the base station 300 and / or the terminal device 400_1 of statistical information related to the result of the statistical processing (step S4). Note that, although the control station 100 notifies the terminal device 400_1 of the statistical information here, it may be configured to notify the terminal devices 400_2 and 400_3 in the same manner.

[0038] Here, the statistical processing is processing using at least actual measurement data. For example, the statistical processing according to this embodiment includes processing for estimating radio wave propagation characteristics between the base station 300 and the terminal device 400.

[0039] The control station 100 may notify the base station 300 and / or the terminal device 400 of the estimated radio wave propagation characteristics as statistical information. Alternatively, the control station 100 may notify the base station 300 and / or the terminal device 400 of communication parameters (e.g., control information for communication between the base station 300 and the terminal device 400) calculated based on the estimated radio wave propagation characteristics as statistical information.

[0040] 3 is a diagram illustrating an example of statistical processing according to an embodiment of the present disclosure. The statistical processing illustrated in FIG.

[0041] The control station 100 generates first statistical information based on first measured data in the communication area (step S11). This first statistical information is information used to estimate, for example, communication characteristics (radio wave propagation environment), and includes information on at least one of a probability distribution of the communication characteristics, an estimation model of the communication characteristics, an average value, and a predicted value of the communication characteristics.

[0042] Next, the control station 100 generates second statistical information based on second actual measurement data in the communication area (step S12). This second statistical information is, for example, information used to estimate communication characteristics (radio wave propagation environment) and may include the same information as the first statistical information.

[0043] The control station 100 performs an update determination as to whether or not to update the first statistical information, depending on the difference between the first statistical information and the second statistical information (step S13). For example, when the difference between the first statistical information and the second statistical information satisfies a predetermined condition, such as the difference being equal to or greater than a threshold, the control station 100 determines to update the first statistical information.

[0044] The control station 100, which has determined to update the first statistical information, updates the first statistical information (step S14). For example, the control station 100 updates the first statistical information by changing the first statistical information to the second statistical information.

[0045] In this way, the control station 100 updates the first statistical information in accordance with the difference between the first statistical information and the second statistical information, which enables the control station 100 to more appropriately update the first statistical information.

[0046] For example, the control station 100 updates the first statistical information when a significant difference (fluctuation) is found between the first statistical information and the second statistical information. For example, the control station 100 determines that there is a significant difference when the difference (difference) between the first statistical information and the second statistical information satisfies a predetermined condition. An example of the predetermined condition is when the difference (difference) between the first statistical information and the second statistical information exceeds a threshold (tolerance).

[0047] This statistical processing can be performed even in an environment where the antenna configuration, transmission power, and transmission frequency of base station 300 are changed.

[0048] When there is no distinction between the first statistical information and the second statistical information, they will simply be referred to as statistical information. When there is no distinction between the first measured data and the second measured data, they will simply be referred to as measured data.

[0049] The statistical information may be, for example, a probability distribution of communication characteristics. In this case, the control station 100 estimates a first probability distribution (corresponding to the first statistical information) of the communication characteristics in the communication area by using the first measured data. At this time, the control station 100 may estimate the first probability distribution at an arbitrary point in the communication area.

[0050] The control station 100 estimates a second probability distribution (corresponding to second statistical information) of the communication characteristics in the communication area by using the second measured data. When the first probability distribution is estimated at an arbitrary point, the control station 100 estimates the second probability distribution at the same point.

[0051] The control station 100 calculates the difference between the first probability distribution and the second probability distribution. For example, the control station 100 calculates the difference between the mean value, variance, or other n-th moment of the first probability distribution and the second probability distribution.

[0052] The control station 100 determines whether or not there is a significant difference between the two distributions based on the calculated difference. If there is a significant difference between the two distributions, the control station 100 updates the first probability distribution. For example, the control station 100 updates the first probability distribution to the second probability distribution.

[0053] In this method, the control station 100 estimates a probability distribution. Therefore, the control station 100 can detect changes in any n-th moment in addition to the average value and variance of the communication characteristics. In this method, it is desirable for the control station 100 to secure a sufficient number of first measured data and second measured data.

[0054] The statistical information may be, for example, an estimation model of a communication characteristic. Here, the estimation model of the communication characteristic is, for example, a model in which an output y is uniquely determined when an input x is given, such as a linear function. The estimation model here is different from a model that outputs a probability density or a cumulative probability.

[0055] In this case, the control station 100 estimates a first estimation model (corresponding to first statistical information) of communication characteristics in the communication area by using the first actual measurement data. At this time, the control station 100 may estimate the first estimation model at an arbitrary point in the communication area.

[0056] The control station 100 estimates a second estimation model (corresponding to second statistical information) of communication characteristics in the communication area using the second measured data. When the first estimation model is estimated at an arbitrary point, the control station 100 estimates the second estimation model at the same point.

[0057] The control station 100 calculates the difference between the first estimation model and the second estimation model. For example, the control station 100 calculates the difference between the parameters of the first estimation model and the second estimation model.

[0058] The control station 100 determines whether there is a significant difference between the parameters of both distributions from the calculated difference. If there is a significant difference between the parameters of both distributions, the control station 100 updates the first estimation model. For example, the control station 100 updates the first estimation model to the second estimation model.

[0059] In this method, the control station 100 can update the estimation model with a smaller amount of data than the above-mentioned method of detecting changes in the probability distribution. On the other hand, in this method, it is difficult to detect fluctuations in moments higher than the mean and variance.

[0060] The statistical information may be time-series data and predicted values ​​of communication characteristics.

[0061] In this case, the control station 100 estimates the first time series data and the first predicted value (corresponding to the first statistical information) of the communication characteristics in the communication area by using the first measured data. At this time, the control station 100 may estimate the first time series data and the first predicted value at an arbitrary point in the communication area.

[0062] Note that the point in this embodiment may be any point within the communication area, or may be an area having a predetermined area (an area smaller than the communication area).

[0063] The control station 100 estimates second time series data and a second predicted value (corresponding to second statistical information) of the communication characteristics in the communication area using the second measured data. When the first time series data and the first predicted value are estimated at an arbitrary point, the control station 100 estimates second time series data and a second predicted value at the same point.

[0064] The control station 100 calculates the difference between the first time series data and the second time series data. For example, the control station 100 integrates the first time series data and the second time series data, and detects a change point in the integrated data (hereinafter also referred to as integrated data).

[0065] When a change point is detected, the control station 100 determines that there is a significant difference between the first time-series data and the second time-series data, and updates the first predicted value. For example, the control station 100 updates the first predicted value to the second predicted value.

[0066] In this method, the control station 100 can update the predicted value with a smaller number of actual measurement data compared to the above-mentioned method of detecting changes in the probability distribution or changes in the parameters of the estimation model. On the other hand, if a small number of actual measurement data is used, the predicted value may be erroneously updated due to the influence of outliers. It is desirable for the control station 100 to determine whether to update the predicted value with an appropriate number of actual measurement data.

[0067] In this embodiment, the control station 100 replaces the first statistical information with the second statistical information as the update of the first statistical information, but the update of the first statistical information is not limited to this. For example, the control station 100 may calculate statistical information using at least a part of the first actual measurement data and at least a part of the second actual measurement data as the update of the first statistical information.

[0068] In this case, the control station 100 may change the number of first actual measurement data and second actual measurement data used for updating, depending on the difference between the first statistical information and the second statistical information, for example.

[0069] For example, the control station 100 selects the first actual measured data and the second actual measured data such that the number of second actual measured data increases as the difference between the first statistical information and the second statistical information increases. On the other hand, the control station 100 selects the first actual measured data and the second actual measured data such that the number of first actual measured data increases as the difference between the first statistical information and the second statistical information decreases.

[0070] In this way, the control station 100 may select the first actual measurement data and the second actual measurement data at a ratio according to the difference between the first statistical information and the second statistical information, and update the first statistical information with the selected actual measurement data. In this case, the determination of whether to update the first statistical information may be omitted. Alternatively, the threshold value used to determine whether to update the first statistical information may be set to a value lower (leading to a smaller difference) than in the update method of replacing the first statistical information with the second statistical information.

[0071] The update determination frequency of the statistical information (the frequency of detecting changes in the statistical information) is determined by the number of first actual measured data and second actual measured data. Alternatively, the update determination frequency of the statistical information may be determined by the accuracy of the statistical information. The update determination frequency of the statistical information may be determined by the observation accuracy (measurement accuracy) of the first actual measured data and second actual measured data. The update determination frequency of the statistical information may be determined according to the acquisition frequency of the second actual measured data.

[0072] When the observation error (measurement error) of the actual measurement data is large, there is a method for improving the observation accuracy of the terminal device 400. This method is a method for changing the implementation of the observation (measurement) function of the communication characteristics in the terminal device 400, and can be realized by the control station 100 acquiring the actual measurement data from the terminal device 400 using an appropriate API (Application Programming Interface) or the like.

[0073] Furthermore, the control station 100 may calibrate the measurement error by performing appropriate statistical processing on the actual measurement data including the measurement error.

[0074] An update reference value may be set for determining whether or not to update the statistical information. For example, when the statistical information is an average value, the control station 100 may set a permissible fluctuation value for the average value in advance, and update the average value when the difference between the average value of the first actual measurement data and the average value of the second actual measurement data exceeds the permissible value.

[0075] When calculating first statistical information of communication characteristics (e.g., a first estimation model of communication characteristics) using the first measured data, the control station 100 may determine the number of first measured data items to use for calculation. For example, the control station 100 changes the number of first implementation data items used for time averaging and calculates the number at which the calculated time average value becomes stationary. The control station 100 calculates the first statistical information using the calculated number of first implementation data items.

[0076] Here, the communication characteristics are characteristics based on radio wave propagation from a transmitting point to a receiving point. Examples of the communication characteristics include at least one of characteristics related to received power, interference power, communication speed (throughput), and characteristics related to delay. Other examples of the communication characteristics include SNR (signal-to-noise ratio), SIR, SINR, INR (interference-to-noise ratio), etc.

[0077] The control station 100 may generate statistical information using simulation data in addition to actual measurement data. For example, the control station 100 may estimate communication characteristics using a ray tracing simulation or the like. This estimation may be performed in real time. In addition, examples of input data for the simulation include topography and building information.

[0078] In this way, the control station 100 according to this embodiment can perform radio wave propagation prediction in the time domain in addition to the spatial domain. This allows the control station 100 to accurately predict radio wave propagation characteristics even if the surrounding environment, such as obstacles, changes over time. The base station 300 and the terminal device 400 can use the radio wave propagation characteristics predicted by the control station 100 in wireless parameter design.

[0079] Furthermore, when the statistical information of the communication characteristics changes, the control station 100 updates the statistical information. This allows the control station 100 to reduce the frequency of updating the statistical information. In this way, the control station 100 can update the statistical information more appropriately.

[0080] Furthermore, the control station 100 can determine whether to update the statistical information by using the statistical information at any point in the communication area.

[0081] When the presence or absence of structures within a communication area changes, the communication characteristics fluctuate, but significant fluctuations in the communication characteristics do not occur at all points within the communication area. Fluctuations in the communication characteristics occur at limited points around the structures.

[0082] Therefore, the control station 100 may extract a point where the communication characteristics have changed and determine whether to update the statistical information on the communication characteristics at that point. This allows the control station 100 to grasp the time fluctuation of the communication characteristics with the minimum number of updates required.

[0083] <<2. Example of communication system configuration>> <2.1. Example of overall configuration of communication system> 4 is a diagram illustrating a configuration example of a communication system according to an embodiment of the present disclosure. As illustrated in FIG. 4, the communication system includes a control station 100, a base station 300, and a terminal device 400.

[0084] The control station 100 is a cloud server that is connected to the base stations 300 via a network (not shown). The number of base stations 300 connected to the control station 100 is not limited to one, and may be two or more.

[0085] The control station 100 controls communication between the base station 300 and the terminal device 400, for example, by notifying the base station 300 of communication parameters.

[0086] For example, the control station 100 may be an information processing device that controls a dynamic spectrum access (DSA) system. In this case, the control station 100 may control radio resources and communication parameters for each base station 300 connected to the DSA. Here, the radio resources refer to resources in at least one of time, frequency, MIMO layer, and spatial domain used for wireless communication.

[0087] Alternatively, the control station 100 may be a database device having an information processing function.

[0088] The base station 300 is connected to the terminal device 400 via wireless communication. The number of terminal devices 400 connected to the base station 300 is not limited to one, and may be two or more.

[0089] The communication system may include components other than the above-mentioned control station 100, base station 300, and terminal device 400. For example, the communication system may include a core network.

[0090] In addition, examples of radio access technologies (RATs) used for radio communication between the base station 300 and the terminal device 400 include cellular communication systems such as 4G systems, 5G systems, 6G systems, LTE (Long Term Evolution), and NR (New Radio). In addition, the radio access technologies are not limited to cellular communication systems. For example, examples of the radio access technologies include various radio communication systems such as wireless LAN, Bluetooth (registered trademark), and LPWA (Low Power Wide Area) systems.

[0091] <2.2. Example of control station configuration> 5 is a diagram illustrating a configuration example of a control station 100 according to an embodiment of the present disclosure. As described above, the control station 100 is, for example, an information processing device that estimates communication characteristics of a communication system. As illustrated in FIG. 5, the control station 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0092] (Communication unit 110) The communication unit 110 is a communication interface for communicating with other devices (e.g., the base station 300). The communication unit 110 may be a network interface or a device connection interface. For example, the communication unit 110 may be a LAN (Local Area Network) interface such as a NIC (Network Interface Card), or a Universal Serial Bus (USB) interface configured with a USB host controller, a USB port, and the like. The communication unit 110 may be a wired interface or a wireless interface. The communication unit 110 functions as a communication means of the control station 100. The communication unit 110 communicates with the base station 300 under the control of the control unit 130.

[0093] (Storage unit 120) The storage unit 120 is a data readable / writable storage device such as a dynamic random access memory (DRAM), a static random access memory (SRAM), a flash memory, a hard disk, etc. The storage unit 120 functions as a storage unit of the control station 100.

[0094] (Control unit 130) The control unit 130 is a controller that controls each unit of the control station 100. The control unit 130 is realized by a processor such as a central processing unit (CPU), a micro processing unit (MPU), or a graphics processing unit (GPU).

[0095] For example, the control unit 130 is realized by a processor executing various programs stored in a storage device inside the control station 100 using a RAM (Random Access Memory) or the like as a working area. The control unit 130 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The CPU, MPU, GPU, ASIC, and FPGA can all be considered as controllers.

[0096] <2.3. Example of base station configuration> Next, the base station 300 will be described. The base station 300 is a communication device that operates a cell and provides wireless communication services to one or more terminal devices 400 located within the coverage of the cell. The cell is operated according to any wireless communication method such as LTE or NR. The base station 300 is connected to a core network. The core network is connected to a packet data network (not shown) via a gateway device (not shown). The base station 300 also operates a beam that can be identified by an SSB (Synchronization Signal / PBCH Block), and can transmit and receive data to and from one or more terminal devices 400 via one or more beams.

[0097] In addition, the base station 300 may be configured with a set of multiple physical or logical devices. For example, in the embodiment of the present disclosure, the base station 300 may be divided into multiple devices, a BBU (Baseband Unit) and a RU, and may be interpreted as a set of these multiple devices. Additionally or alternatively, in the embodiment of the present disclosure, the base station 300 may be either or both of a BBU and a RU. The BBU and the RU may be connected by a predetermined interface (e.g., eCPRI). Additionally or alternatively, the RU may be called a Remote Radio Unit (RRU) or a Radio DoT (RD). Additionally or alternatively, the RU may be compatible with a gNB-DU, which will be described later. Additionally or alternatively, the BBU may be compatible with a gNB-CU, which will be described later. Alternatively, the RU may be connected to a gNB-DU, which will be described later. Furthermore, the BBU may be compatible with a combination of a gNB-CU and a gNB-DU, which will be described later. Additionally or alternatively, the RU may be a device formed integrally with an antenna. The antenna of base station 300 (e.g., an antenna formed integrally with an RU) may employ an advanced antenna system and support MIMO (e.g., FD-MIMO) and beamforming. In the advanced antenna system, the antenna of base station 300 (e.g., an antenna formed integrally with an RU) may include, for example, 64 transmitting antenna ports and 64 receiving antenna ports.

[0098] Also, a plurality of base stations 300 may be connected to each other. One or more base stations 300 may be included in a radio access network (RAN). That is, the base station 300 may simply be referred to as a RAN, a RAN node, an AN (Access Network), or an AN node. The RAN in LTE is called EUTRAN (Enhanced Universal Terrestrial RAN). The RAN in NR is called NGRAN. The RAN in W-CDMA (UMTS) is called UTRAN. The LTE base station 300 is called eNodeB (Evolved Node B) or eNB. That is, the EUTRAN includes one or more eNodeBs (eNBs). Also, the NR base station 300 is called gNodeB or gNB. That is, the NGRAN includes one or more gNBs. Furthermore, the EUTRAN may include a gNB (en-gNB) connected to a core network (EPC) in an LTE communication system (EPS). Similarly, the NGRAN may include an ng-eNB connected to a core network 5GC in a 5G communication system (5GS). Additionally or alternatively, when the base station 300 is an eNB, gNB, etc., it may be referred to as 3GPP Access. Additionally or alternatively, when the base station 300 is a wireless access point (Access Point) (e.g., a WiFi (registered trademark) access point), it may be referred to as Non-3GPP Access. Additionally or alternatively, the base station 300 may be a radio extension device called an RRH (Remote Radio Head). Additionally or alternatively, when the base station 300 is a gNB, the base station 300 may be referred to as a combination of the gNB CU (Central Unit) and gNB DU (Distributed Unit) described above, or any of these. The gNB CU hosts multiple upper layers (e.g., RRC, SDAP, PDCP) of the Access Stratum for communication with the UE. On the other hand, the gNB-DU hosts multiple lower layers (e.g., RLC, MAC, PHY) of the Access Stratum.That is, among the message information described later, RRC signaling (e.g., MIB, various SIBs including SIB1, RRCSetup message, RRCReconfiguration message) may be generated by the gNB CU, while DCI and various physical channels (e.g., PDCCH, PBCH) described later may be generated by the gNB-DU. Alternatively, among the RRC signaling, some configuration (setting information), such as IE:cellGroupConfig, may be generated by the gNB-DU, and the remaining configuration may be generated by the gNB-CU. These configurations (setting information) may be transmitted and received via an F1 interface described later. The base station 300 may be configured to be able to communicate with other base stations 300. For example, when multiple base stations 300 are eNBs or a combination of an eNB and an en-gNB, the base stations 300 may be connected to each other via an X2 interface. Additionally or alternatively, when multiple base stations 300 are gNBs or a combination of a gn-eNB and a gNB, the devices may be connected to each other via an Xn interface. Additionally or alternatively, when the multiple base stations 300 are a combination of gNB CU and gNB DU, the devices may be connected via the F1 interface described above. Message information (RRC signaling or DCI information, physical channel) described below may be communicated between the multiple base stations 300 (e.g., via X2, Xn, F1 interfaces).

[0099] Furthermore, as described above, the base station 300 may be configured to manage multiple cells. A cell provided by the base station 300 is called a serving cell. The serving cell includes a PCell (Primary Cell) and an SCell (Secondary Cell). When dual connectivity (e.g., EUTRA-EUTRA Dual Connectivity, EUTRA-NR Dual Connectivity (ENDC), EUTRA-NR Dual Connectivity with 5GC, NR-EUTRA Dual Connectivity (NEDC), NR-NR Dual Connectivity) is provided to a UE (e.g., a terminal device 400), a PCell and zero or more SCell(s) provided by a MN (Master Node) are called a Master Cell Group. Furthermore, the serving cell may include a PSCell (Primary Secondary Cell or Primary SCG Cell). That is, when dual connectivity is provided to a UE, a PSCell and zero or more SCell(s) provided by a SN (Secondary Node) are called a Secondary Cell Group (SCG). Unless a special configuration (e.g., PUCCH on SCell) is made, the physical uplink control channel (PUCCH) is transmitted on the PCell and PSCell, but not on the SCell. Radio Link Failure is also detected on the PCell and PSCell, but not on the SCell (does not need to be detected). As such, the PCell and PSCell have a special role among the Serving Cell(s), and are therefore also called Special Cells (SpCells). One cell may be associated with one Downlink Component Carrier and one Uplink Component Carrier. Furthermore, the system bandwidth corresponding to one cell may be divided into multiple bandwidth parts.In this case, one or more Bandwidth Parts (BWPs) may be configured for the UE, and one Bandwidth Part may be used by the UE as an Active BWP. In addition, the radio resources (e.g., frequency band, numerology (subcarrier spacing), slot format (Slot configuration)) that the terminal device 400 can use may differ for each cell, each component carrier, or each BWP.

[0100] 6 is a block diagram showing a configuration example of a base station 300 according to an embodiment of the present disclosure. The base station 300 is a wireless communication device that wirelessly communicates with a terminal device 400. The base station 300 is a type of communication device. The base station 300 is also a type of information processing device.

[0101] The base station 300 shown in Fig. 6 includes a communication unit 310, a storage unit 320, a network communication unit 330, and a control unit 340. Note that the configuration shown in Fig. 6 is a functional configuration, and the hardware configuration may be different from this. Also, the functions of the base station 300 may be distributed and implemented in multiple physically separated configurations. For example, as described above, the functions of the base station 300 may be distributed to a CU and a DU, or a CU, a DU, and a RU.

[0102] The communication unit 310 is a signal processing unit for wireless communication with other wireless communication devices (for example, a terminal device 400 and another base station 300). The communication unit 310 operates under the control of the control unit 340. When the other wireless communication device is a terminal device 400, the communication unit 310 may be a wireless transceiver that supports one or more wireless access methods. For example, the communication unit 310 supports both NR and LTE. The communication unit 310 may support W-CDMA and cdma2000 in addition to NR and LTE. The communication unit 310 may also support communication using NOMA. When the other wireless communication device is another base station 300, the communication unit 310 may be an X2 interface, an Xn interface, or an F1 interface.

[0103] (Communication unit 310) The communication unit 310 includes a reception processing unit 311, a transmission processing unit 312, and an antenna 313. The communication unit 310 may include a plurality of reception processing units 311, a plurality of transmission processing units 312, and a plurality of antennas 313. When the communication unit 310 supports a plurality of radio access methods, each unit of the communication unit 310 may be configured separately for each radio access method. For example, the reception processing unit 311 and the transmission processing unit 312 may be configured separately for LTE and NR.

[0104] The reception processing unit 311 processes an uplink signal received via the antenna 313. The reception processing unit 311 operates as a receiving unit that receives a received signal. The reception processing unit 311 includes a radio receiving unit 311a, a demultiplexing unit 311b, a demodulating unit 311c, and a decoding unit 311d.

[0105] The radio receiving unit 311a performs down-conversion, removal of unnecessary frequency components, control of amplification level, orthogonal demodulation, conversion to a digital signal, removal of a guard interval (cyclic prefix), extraction of a frequency domain signal by fast Fourier transform, etc. on the uplink signal. The demultiplexing unit 311b separates uplink channels such as a PUSCH (Physical Uplink Shared Channel) and a PUCCH (Physical Uplink Control Channel) and an uplink reference signal from the signal output from the radio receiving unit 311a.

[0106] The demodulator 311c demodulates the received signal using a modulation method such as BPSK (Binary Phase Shift Keying) or QPSK (Quadrature Phase Shift Keying) for the modulation symbols of the uplink channel. The modulation method used by the demodulator 311c may be 16QAM (Quadrature Amplitude Modulation), 64QAM, or 256QAM. In this case, the signal points on the constellation do not necessarily have to be equidistant. The constellation may be a non-uniform constellation (NUC).

[0107] The decoder 311d performs a decoding process on the coded bits of the demodulated uplink channel. The decoded uplink data and uplink control information are output to the controller 340.

[0108] The transmission processing unit 312 performs a transmission process of the downlink control information and the downlink data. In this manner, the transmission processing unit 312 is an acquisition unit that acquires, for example, a bit sequence of the downlink control information, the downlink data, and the like from the control unit 340. The transmission processing unit 312 includes an encoding unit 312a, a modulation unit 312b, a multiplexing unit 312c, and a radio transmission unit 312d.

[0109] The encoder 312a encodes the downlink control information and downlink data input from the controller 340 using a coding method such as block coding, convolution coding, turbo coding, etc. The encoder 312a may also encode the downlink control information and downlink data using a polar code or a low density parity check code (LDPC code).

[0110] The modulation unit 312b modulates the coded bits output from the coding unit 312a using a predetermined modulation method such as BPSK, QPSK, 16QAM, 64QAM, or 256QAM. In this case, the signal points on the constellation do not necessarily have to be equidistant. The constellation may be a non-uniform constellation.

[0111] The multiplexing unit 312c multiplexes the modulation symbols of each channel and the downlink reference signal, and places the multiplexed symbols in a predetermined resource element. The radio transmitting unit 312d performs various signal processing on the signal from the multiplexing unit 312c. For example, the radio transmitting unit 312d performs processing such as conversion from the time domain to the frequency domain by fast Fourier transform, addition of a guard interval (cyclic prefix), generation of a baseband digital signal, conversion to an analog signal, quadrature modulation, up-conversion, removal of unnecessary frequency components, and power amplification. The signal generated by the transmission processing unit 312 is transmitted from the antenna 313.

[0112] (Storage unit 320) The storage unit 320 is a data readable / writable storage device such as a DRAM, an SRAM, a flash memory, a hard disk, etc. The storage unit 320 functions as a storage unit of the base station 300.

[0113] (Network communication unit 330) The network communication unit 330 is a communication interface for communicating with a node located at a higher level on the network (e.g., a core network). For example, the network communication unit 330 may be a LAN interface such as a NIC. Additionally or alternatively, the network communication unit 330 may be an S1 interface or an NG interface for connecting to a core network node. The network communication unit 330 may be a wired interface or a wireless interface. The network communication unit 330 functions as a network communication means of the base station 300.

[0114] (Control unit 340) The control unit 340 is a controller that controls each unit of the base station 300. The control unit 340 is realized by a processor (hardware processor) such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). For example, the control unit 340 is realized by the processor executing various programs stored in a storage device inside the base station 300 using a RAM (Random Access Memory) or the like as a working area. The control unit 340 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The CPU, MPU, ASIC, and FPGA can all be considered as controllers.

[0115] <2.4. Example of terminal device configuration> Next, a configuration example of the terminal device 400 according to an embodiment of the present disclosure will be described with reference to Fig. 7. Fig. 7 is a block diagram showing a configuration example of the terminal device 400 according to an embodiment of the present disclosure.

[0116] The terminal device 400 is a wireless communication device that wirelessly communicates with the base station 300. The terminal device 400 is, for example, a mobile phone, a smart device (smartphone or tablet), a PDA (Personal Digital Assistant), or a personal computer. The terminal device 400 may be a device such as a commercial camera equipped with a communication function, an M2M (Machine to Machine) device, or an IoT (Internet of Things) device.

[0117] The terminal device 400 may be capable of sidelink communication with other terminal devices 400. The terminal device 400 may be capable of using an automatic retransmission technique such as Hybrid Automatic Repeat reQuest (HARQ) when performing sidelink communication. The terminal device 400 may be capable of non-orthogonal multiple access (NOMA) communication with the base station 300. The terminal device 400 may also be capable of NOMA communication in communication (sidelink) with other terminal devices 400. The terminal device 400 may also be capable of low power wide area (LPWA) communication with other communication devices (for example, the base station 300 and other terminal devices 400). In addition, the wireless communication used by the terminal device 400 may be wireless communication using millimeter waves. The wireless communication (including sidelink communication) used by the terminal device 400 may be wireless communication using radio waves, or wireless communication using infrared rays or visible light (optical wireless).

[0118] The terminal device 400 may simultaneously connect to multiple base stations 300 or multiple cells to perform communication. For example, when one base station 300 can provide multiple cells, the terminal device 400 can perform carrier aggregation by using a certain cell as a pCell and using another cell as an sCell. Also, when multiple base stations 300 can provide one or multiple cells, the terminal device 400 can realize DC (Dual Connectivity) by using one or multiple cells managed by one base station 300 (MN (e.g., MeNB or MgNB)) as a pCell, or pCell and sCell(s), and using one or multiple cells managed by the other base station 300 (SN (e.g., SeNB or SgNB)) as a pCell (PSCell), or pCell (PSCell) and sCell(s). DC may be referred to as MC (Multi Connectivity).

[0119] When a communication area is supported via cells of different base stations 300 (multiple cells having different cell identifiers or the same cell identifier), the multiple cells can be bundled together by Carrier Aggregation (CA), Dual Connectivity (DC), or Multi-Connectivity (MC) technology to enable communication between the base station 300 and the terminal device 400. Alternatively, the terminal device 400 can communicate with the multiple base stations 300 via the cells of the different base stations 300 by Coordinated Multi-Point Transmission and Reception (CoMP) technology.

[0120] The terminal device 400 in Fig. 7 includes a communication unit 410, a storage unit 420, a network communication unit 430, an input / output unit 440, and a control unit 450. Note that the configuration shown in Fig. 7 is a functional configuration, and the hardware configuration may be different from this. Also, the functions of the terminal device 400 may be distributed and implemented in multiple physically separated configurations.

[0121] (Communication unit 410) The communication unit 410 is a signal processing unit for wireless communication with other wireless communication devices (e.g., the base station 300 and other terminal devices 400). The communication unit 410 operates under the control of the control unit 450. The communication unit 410 may be a wireless transceiver compatible with one or more wireless access methods. For example, the communication unit 410 supports both NR and LTE. The communication unit 410 may support W-CDMA and cdma2000 in addition to NR and LTE. The communication unit 410 may also support communication using NOMA.

[0122] The communication unit 410 includes a reception processing unit 411, a transmission processing unit 412, and an antenna 413. The communication unit 410 may include a plurality of reception processing units 411, a transmission processing unit 412, and an antenna 413. The configurations of the communication unit 410, the reception processing unit 411, the transmission processing unit 412, and the antenna 413 are similar to those of the communication unit 310, the reception processing unit 311, the transmission processing unit 312, and the antenna 313 of the base station 300.

[0123] The storage unit 420 is a data readable / writable storage device such as a DRAM, an SRAM, a flash memory, a hard disk, etc. The storage unit 420 functions as a storage unit of the terminal device 400.

[0124] The network communication unit 430 is a communication interface for communicating with other devices connected via a network. For example, the network communication unit 430 is a LAN interface such as a NIC. The network communication unit 430 may be a wired interface or a wireless interface. The network communication unit 430 functions as a network communication means of the terminal device 400. The network communication unit 430 communicates with other devices under the control of the control unit 450.

[0125] (Input / output section 440) The input / output unit 440 is a user interface for exchanging information with the user. For example, the input / output unit 440 is an operation device such as a keyboard, a mouse, an operation key, or a touch panel that allows the user to perform various operations. Alternatively, the input / output unit 440 is a display device such as a liquid crystal display (LCD) or an organic electroluminescence display (OLED). The input / output unit 440 may be an audio device such as a speaker or a buzzer. The input / output unit 440 may also be a lighting device such as an LED (Light Emitting Diode) lamp. The input / output unit 440 functions as an input / output means (input means, output means, operation means, or notification means) of the terminal device 400. Note that, in a case where the terminal device 400 is a sensor, at least a part of the functions (output function and / or input function) of the input / output unit 440 may be omitted.

[0126] (Control unit 450) The control unit 450 is a controller that controls each unit of the terminal device 400. The control unit 450 is realized by a processor such as a CPU, an MPU, or a GPU. For example, the control unit 450 is realized by a processor executing various programs stored in a storage device inside the terminal device 400 using a RAM or the like as a working area. The control unit 450 may be realized by an integrated circuit such as an ASIC or an FPGA. The CPU, the MPU, the GPU, the ASIC, and the FPGA can all be considered as controllers.

[0127] <<3. Technical features>> In this embodiment, the control station 100 first uses actual measurement data in a certain communication area to extract points where the communication characteristics have significant time fluctuations. The control station 100 updates the statistical information on the communication characteristics only for the extracted points.

[0128] This enables the control station 100 to grasp the time fluctuations of the radio wave propagation characteristics with the minimum necessary update frequency.

[0129] It is not necessary for the control station 100 to perform part of the processing, such as extracting points and generating statistical information. For example, a user may extract points. Alternatively, the control station 100 may determine whether to update based on statistical information generated by another device (not shown).

[0130] In this way, at least a part of the processing according to the present embodiment may be performed by a device other than the control station 100. For example, at least a part of the processing according to the present embodiment may be executed in the base station 300 and / or the terminal device 400. The base station 300 and / or the terminal device 400 may function as an information processing device.

[0131] As described above, the statistical information on the communication characteristics includes at least one of the probability distribution of the communication characteristics, the communication characteristics estimation model, the time-series data of the communication characteristics, and the predicted value of the communication characteristics, for example.

[0132] As described above, the first statistical information of the communication characteristics is generated based on the first actual measurement data in at least one communication area. After that, the control station 100 generates the statistical information of the communication characteristics (second statistical information) again using the second actual measurement data in the same area.

[0133] In this embodiment, the control station 100 detects a fluctuation between the first statistical information and the second statistical information. If a significant fluctuation is detected, the control station 100 updates the statistical information of the communication characteristics.

[0134] Moreover, one communication area refers to an area having a certain size, such as the vicinity of Tokyo Station, the vicinity of Kawasaki Station, etc. The communication area may be an area (cell) constituted by one base station 300, or an area constituted by multiple base stations 300.

[0135] Simple methods for generating statistical information on communication characteristics include, for example, linear regression analysis, multiple regression analysis, estimation of empirical distribution functions, fitting to existing probability distributions, time series analysis, mean estimation, and median estimation.

[0136] In addition, AI / ML (artificial intelligence, machine learning, deep learning) may be used to generate the statistical information. Examples of AI / ML include the following techniques: -Deep Learning -Multilayer Perceptron (MLP) -Convolutional Neural Networks (CNN) -Stochastic Gradient Descent -Decision Tree -Random Forest -Support Vector Machine -k-Nearest Neighbors -Naive Bayes Classifier There are many other methods besides these. It is desirable to select an appropriate method by taking into consideration the principles of each method.

[0137] When using AI / ML, the communication characteristics to be estimated are used as the objective variables, and various parameters, data, and information (e.g., communication environment information) described below may be further used as explanatory variables.

[0138] As described above, the control station 100 updates the statistical information on the communication characteristics by using the actual measurement data of the communication area. Hereinafter, five methods (first method to fifth method) will be described as examples of this update method.

[0139] <3.1. 1st method> As a first method for updating the statistical information of the communication characteristics, there is an example of updating when the statistical information is a probability distribution of the communication characteristics.

[0140] In this method, the control station 100 updates the probability distribution in response to changes in the probability distribution of the communication characteristics. For example, the control station 100 updates the probability distribution when the probability distribution of an arbitrary point s satisfies the following formula (1).

[0141]

number

[0142] Here, F 1,s is the first probability distribution estimated using the first measured data at an arbitrary point s. F 2,s is a second probability distribution estimated at an arbitrary point s using the second measured data.

[0143] Also, D F (F 1,s ,F 2,s ) is the first probability distribution F 1,s and the second probability distribution F 2,s This is a function that calculates the difference between and . F is the first probability distribution F 1,s and the second probability distribution F 2,s is the tolerance.

[0144] The control station 100 satisfies the formula (1), that is, the first probability distribution F 1,s and the second probability distribution F 2,s The difference between and is the tolerance γ F If larger, the first probability distribution F 1,s and the second probability distribution F 2,s It is determined that there is a significant difference between and.

[0145] Fig. 8 is a graph showing an example of a first probability distribution according to an embodiment of the present disclosure. Fig. 9 is a graph showing an example of a second probability distribution according to an embodiment of the present disclosure. The horizontal axis of the graphs in Fig. 8 and Fig. 9 indicates communication characteristics. The vertical axis indicates a probability density function.

[0146] When the surrounding environment of an arbitrary point s in the communication area changes, the probability distribution fluctuates as shown in Figures 8 and 9. The control station 100 detects the fluctuation (change) of this probability distribution using, for example, equation (1), and updates the first probability distribution.

[0147] The control station 100 estimates the probability distribution by, for example, estimating an empirical distribution function or a probability density function, or fitting to an existing probability distribution.

[0148] For example, when making an estimation based on an empirical distribution function using n pieces of measured data, the control station 100 estimates the probability distribution using the following equation (2).

[0149]

number

[0150] Here, d is the realized value of the communication characteristics. di is the i-th measured data. di≦d is the indicator function and is defined by the following equation (3).

[0151]

number

[0152] Furthermore, for example, when making an estimation based on a probability density function, the control station 100 estimates the probability distribution using the following equation (4).

[0153]

number

[0154] Here, F(d,d+Δd) is the probability that the random variable X satisfies d≦X≦d+Δd, where Δd represents a small interval. Note that since the above is a continuous probability distribution, if the realized value is a discrete value, a probability mass function can be used to estimate the probability distribution.

[0155] Furthermore, for example, when making an estimation based on fitting to an existing probability distribution, the content of the calculation differs depending on the parameters of the probability distribution.

[0156] For example, the shape of a normal distribution is determined by two parameters, the mean and variance. Therefore, the control station 100 can calculate the mean and variance from the measured data to fit the normal distribution and estimate the probability distribution.

[0157] However, this method is not effective if the probability distribution of communication characteristics at an arbitrary point deviates from the existing distribution. When using this method, evidence that the probability distribution of communication characteristics follows the existing distribution is required, based on prior information or empirical rules.

[0158] D shown in formula (1) F (F 1,s ,F 2,s ) can be calculated using, for example, the Kullback-Leibler divergence. Alternatively, the control station 100 may calculate D F (F 1,s ,F 2,s ) in F 1,s ,F 2,s may be replaced with various moments of the probability distribution for calculation.

[0159] For example, the moment may be the average value of the first measured data and the second measured data. In this case, γ F is the significant difference between the two means. Of course, instead of the mean, the median, mode, etc. can be used as the moment.

[0160] (Update example) Next, a method for updating the communication characteristic statistical information when formula (1) is satisfied will be described. First, consider the case where a specific scalar value such as RSRP=-100 dBm is stored (estimated) as the first predicted value (first statistical information) of the communication characteristic at an arbitrary point s.

[0161] In this case, for example, a method of updating using a forgetting factor is considered. For example, the control station 100 updates the average value PA at an arbitrary point s using a forgetting factor based on the following formula (5): s Calculate.

[0162]

number

[0163] Here, the PA in equation (5) 1,s is the first average value of the communication characteristics at an arbitrary point s, and is estimated from the first measured data. 2,s is a second average value of the communication characteristics at an arbitrary point s, and is estimated from the second measured data. β is a forgetting factor, which is a parameter that adjusts the extent to which the influence of the first measured data is reflected in the average value.

[0164] The control station 100 may calculate the average value using the first measured data and the second measured data without using the forgetting factor. Although the formula (5) shows the case where the control station 100 uses the average value, the control station 100 may use the median value, the instantaneous value, or the like.

[0165] Alternatively, as another example of an updating method, the control station 100 may simply update the first statistical information of the communication characteristics to an average value, a median value, or the like, using the second measured data. This method is the same as when β=1 in equation (5). In this way, the control station 100 can update the first statistical information by using the second statistical information as the first statistical information.

[0166] Alternatively, as another example of an update method, a weighted average according to time is calculated. In this case, the control station 100 updates the statistical information by calculating a weighted average according to time based on the following formula (6).

[0167]

number

[0168] where d t is the measured data at time t. t is the weight at time t. T represents the period for which the weighted average is calculated. For example, w t Equation (6) can be defined so that is large.

[0169] Furthermore, when the communication characteristics at an arbitrary point s are expressed by a probability distribution, the control station 100 can update the statistical information, for example, by updating the second probability distribution as the first probability distribution.

[0170] Alternatively, the control station 100 may estimate a third probability distribution using the first measured data and the second measured data, and update the third probability distribution as the first probability distribution.

[0171] In this case, the control station 100 may determine the ratio of the first measured data and the second measured data used to estimate the third probability distribution.

[0172] For example, when it is desired to increase the influence of the first actual measured data, the control station 100 may reduce the number of second actual measured data and increase the proportion of the first actual measured data. On the other hand, when it is desired to increase the influence of the second actual measured data, the control station 100 may reduce the number of first actual measured data and increase the proportion of the second actual measured data.

[0173] Alternatively, for example, the control station 100 may determine the ratio of the first actual measurement data to the second actual measurement data in accordance with the difference between the first probability distribution and the second probability distribution.

[0174] According to the first method, the control station 100 updates the statistical information of the communication characteristics when, for example, formula (1) is satisfied. Therefore, the control station 100 can suppress an increase in the number of updates of the statistical information, and can appropriately update the statistical information.

[0175] <3.2.Second method> As a second method for updating the statistical information of the communication characteristics, there is an example of updating when the statistical information is an estimated model of the communication characteristics.

[0176] In this method, the control station 100 updates the estimation model of the communication characteristics in response to parameter changes in the estimation model. For example, the control station 100 updates the estimation model when the estimation model of an arbitrary point s satisfies the following formula (7).

[0177]

number

[0178] Here, Y 1,s is a first estimated model of communication characteristics at an arbitrary point s, and is estimated based on the first measured data. Y 2,s is a second estimation model of the communication characteristics at an arbitrary point s, and is estimated based on the second measured data. Note that the estimation model here is a linear function, a quadratic function, an elementary function, or the like, and is different from the above-mentioned probability distribution.

[0179] D M (F 1,s ,F 2,s ) is the first estimation model of the communication characteristics Y 1,s And the second estimation model Y 2,s For example, Y 1,s =ax,Y 2,x When expressed as =bx, calculate the difference between coefficients a and b. In addition, depending on the parameters of the function, M (F 1,s ,F 2,s ) can be defined.

[0180] Also, γ M is the first estimation model Y 1,s and the second estimation model Y 2,s is the tolerance for the difference between γ M is the allowable value for the difference between coefficients a and b when calculating the difference.

[0181] The control station 100 satisfies the equation (7), that is, the first estimation model Y 1,s And the second estimation model Y 2,s The difference (difference) between is the tolerance γ M If larger, the first estimated model Y 1,s And the second estimation model Y 2,s It is determined that there is a significant difference between and.

[0182] Fig. 10 is a graph showing an example of a first estimation model according to an embodiment of the present disclosure. Fig. 11 is a graph showing an example of a second estimation model according to an embodiment of the present disclosure. The horizontal axis of the graphs in Fig. 10 and Fig. 11 indicates the transmission / reception distance. The vertical axis indicates the communication characteristics.

[0183] When the surrounding environment of an arbitrary point s in the communication area changes, the estimation model fluctuates as shown in Fig. 10 and Fig. 11. The control station 100 detects the fluctuation (change) of the estimation model by using, for example, equation (7), and updates the first estimation model.

[0184] The first estimation model may be updated in the same manner as when the statistical information is a probability distribution. That is, the control station 100 may update the first estimation model by replacing the second estimation model with the first estimation model. Alternatively, the control station 100 may update the first estimation model by estimating a new estimation model using a predetermined ratio of the first actual measurement data and the second actual measurement data.

[0185] <3.3. Third method> As a third method for updating the statistical information of the communication characteristics, there is an example in which the statistical information is updated by detecting a transition point of the time series data. Here, the time series data is, for example, integrated data obtained by arranging the first actual measurement data and the second actual measurement data in a time series and integrating them.

[0186] When detecting fluctuations in communication characteristics based on time-series data, as in this method, it is desirable to distinguish between the following cases 1 and 2. (Case 1) A fixed static obstacle such as a building has been removed or a new one has been installed. (Case 2) A moving obstacle such as a person or a bus was temporarily present between the transmitting and receiving points.

[0187] Fig. 12 is a diagram illustrating an example of a fluctuation in communication characteristics according to an embodiment of the present disclosure. In Fig. 12, an example of a fluctuation in communication characteristics in case 1 is illustrated.

[0188] 12, the communication characteristics may change significantly from a change point at a certain time t (the time when the state of a static obstacle changes). In other words, when the state of a static obstacle changes, the fluctuation of the communication characteristics may satisfy a predetermined condition, for example, exceeding a predetermined threshold.

[0189] 13 is a diagram illustrating another example of the fluctuation in communication characteristics according to an embodiment of the present disclosure, in which an example of the fluctuation in communication characteristics in case 2 is illustrated.

[0190] In the example shown in Fig. 13, the communication characteristics may change significantly from time t1 to time t2, which is a short period T (the period during which a moving obstacle exists between the transmitting and receiving points). In other words, during the period during which a moving obstacle exists between the transmitting and receiving points, the communication characteristics may change to satisfy a predetermined condition, for example, exceeding a predetermined threshold. Furthermore, after time t2, the communication characteristics may return to the state before time t1.

[0191] Therefore, the fluctuation in communication characteristics in Case 2 can be considered as a kind of outlier.

[0192] The change points and outliers are detected using different methods. Here, the first to third detection methods will be described as examples of the detection methods.

[0193] (Detection of change points) As a first method for detecting change points, there is a method using Change Finder. In this method, the control station 100 performs two-stage learning using Change Finder to detect outliers and change points separately. This learning method can also be applied to the detection of outliers, which will be described later.

[0194] As a second method for detecting change points, a recurrent neural network and the R package changepoint can be used.

[0195] As a third method for detecting a change point, the control station 100 updates the statistical information of the communication characteristics at an arbitrary point s when the following inequality (8) is satisfied.

[0196]

number

[0197] where d t is the time series data at time t. γ T represents the threshold value of the communication characteristic.

[0198] 14 is a diagram illustrating another example of the fluctuation in communication characteristics according to an embodiment of the present disclosure, in which an example of the fluctuation in communication characteristics in case 1 is illustrated.

[0199] In the example of FIG. 14, the control station 100 has a communication characteristic of γ T At time t when γ=γ1 or more, it is determined that the communication characteristics have changed significantly, and the statistical information on the communication characteristics is updated.

[0200] As a variation of the third detection method, there is a method of providing hysteresis to the detection of fluctuations in communication characteristics, which allows the control station 100 to reduce erroneous detection of change points due to instantaneous fluctuations in communication characteristics.

[0201] Specifically, the control station 100 detects a change point of the communication characteristics by using inequality (9) instead of inequality (8).

[0202]

number

[0203] Here, H is a hysteresis value, and may be set to either a positive or negative value.

[0204] 15 is a diagram illustrating another example of the fluctuation in communication characteristics according to an embodiment of the present disclosure, in which an example of the fluctuation in communication characteristics in case 1 is illustrated.

[0205] Here, the communication characteristic threshold γ T is γ TIn addition, the control station 100 has a communication characteristic of γ T If the value is equal to or greater than +H, it is determined that the communication characteristics have changed significantly, and the statistical information on the communication characteristics is updated.

[0206] In the example of FIG. 15, the communication characteristic is determined by changing the threshold value γ T However, the control station 100 does not determine that the communication characteristics have changed significantly. T At time t13 when the communication characteristic becomes equal to or greater than +H, it is determined that the communication characteristic has changed significantly, and the statistical information on the communication characteristic is updated.

[0207] When hysteresis is not taken into consideration, the control station 100 determines whether the communication characteristic is greater than or equal to the threshold γ T = γ2 or more, it is determined that the communication characteristics have changed.

[0208] The communication characteristic is the threshold γ T = γ2 or more, the communication characteristics do not satisfy stationarity. Here, stationarity refers to a state in which the expectation value and autocovariance of the time series data are constant regardless of time. If such non-stationary data is processed, the variance of the communication characteristics statistics information may become large.

[0209] On the other hand, when hysteresis is taken into consideration, the control station 100 has a communication characteristic of γ T It is determined that the communication characteristics have changed at time t13 when the signal level becomes equal to or higher than +H. In this case, the communication characteristics after time t13 generally satisfy stationarity.

[0210] In this way, the control station 100 detects fluctuations (changing points) in communication characteristics with hysteresis, thereby making it possible to further reduce erroneous detection of fluctuations.

[0211] Another variation of the third detection method is a method in which the control station 100 takes elapsed time into consideration when detecting a change in communication characteristics. With this method, the control station 100 can reduce erroneous detection of a change point due to an instantaneous change in communication characteristics.

[0212] In this case, the control station 100 calculates η from the time when the above-mentioned formula (8) or formula (9) is first satisfied (hereinafter, also referred to as the initial change point). T If formula (8) or formula (9) is satisfied for at least the time, the statistical information of the communication characteristics at the arbitrary point s is updated.

[0213] Fig. 16 is a diagram showing another example of fluctuations in communication characteristics according to an embodiment of the present disclosure. Fig. 16 shows an example of fluctuations in communication characteristics in case 1. Here, the control station 100 detects a change point using equation (8), in other words, determines whether to update statistical information.

[0214] As shown in FIG. 16, at time t21, the communication characteristic exceeds the threshold γ T The control station 100 detects time t21 as the initial change point. After that, the control station 100 detects time t21 and η T The communication characteristic remains constant until time t22, which is the threshold value γ T It is determined whether the above is continuing.

[0215] In the example of FIG. 16, after the time t21, which is the initial change point, the communication characteristic is changed to the threshold value γ T As a result, the control station 100 performs η T At time t22 after the lapse of time, it is determined that the communication characteristics have changed, that is, it is determined that the statistical information on the communication characteristics should be updated.

[0216] In this way, the control station 100 detects whether the communication characteristics meet a predetermined condition (e.g., the threshold value γ T When the above conditions are met, false detection of fluctuations can be further reduced by detecting fluctuations (change points) in communication characteristics.

[0217] (Outlier detection) As an outlier detection method, for example, the control station 100 estimates parameters of an Augmented Reality (AR) model using a Sequentially Discounting Augmented Reality (SDAR) algorithm. After that, the control station 100 calculates an outlier score from a logarithmic loss using a probability density function obtained by the estimation, and detects the outlier.

[0218] As another method of detecting an outlier, the control station 100 calculates an exponentially weighted moving average. If the difference between the average value and the communication characteristic exceeds a threshold, the control station 100 regards the communication characteristic that exceeds the threshold as an outlier.

[0219] Alternatively, the control station 100 may detect outliers using other methods, such as interquartile range, Z score, clustering method (such as k-means), machine learning (such as LSTM (Long Short-Term Memory)), confidence interval, etc.

[0220] Note that the statistical information can be updated in the same manner as when the statistical information is a probability distribution, an estimation model, or a scalar value.

[0221] That is, the control station 100 may update the first statistical information by replacing the second statistical information (e.g., the second predicted value) with the first statistical information (e.g., the first predicted value). Alternatively, the control station 100 may update the first statistical information by estimating new statistical information (e.g., a predicted value) using a predetermined ratio of the first actual measurement data and the second actual measurement data.

[0222] <3.4. 4th method> A fourth method for updating statistical information on communication characteristics is to use AI / ML and image data.

[0223] In this case, the control station 100 detects changes in structures and communication environments in the communication area by inputting images of the communication area to the AI / ML, for example. When the control station 100 detects changes in structures and communication environments, it updates the statistical information of communication characteristics.

[0224] Examples of images of the communication area include satellite images, images from cameras installed within or near the communication area, etc. A representative AI / ML that can be used in this method is CNN (Convolutional Neural Network).

[0225] Note that the statistical information can be updated in the same manner as in the first to third methods, and therefore a description thereof will be omitted here.

[0226] <3.5. 5th method> A fifth method for updating statistical information on communication characteristics is to use information other than actual measurement data.

[0227] For example, the control station 100 may update the statistical information on the communication characteristics by using communication environment information, which will be described later, as information other than the actual measurement data.

[0228] For example, the control station 100 may obtain the latest topographical data and structural data and check whether there are any changes in the presence or absence of obstacles within the communication area. If a significant change in the communication characteristics is recognized before and after the change, the control station 100 updates the statistical information on the communication characteristics.

[0229] Alternatively, the control station 100 may utilize parameter information (e.g., location, transmission power, antenna characteristics, etc.) of the base station 300. When communication characteristics change due to a change in the parameter information of the base station 300, the control station 100 updates statistical information on the communication characteristics.

[0230] The control station 100 may also obtain information about the vicinity of the communication area from a camera or a sensor, and check whether there are any changes, such as the presence or absence of obstacles.

[0231] Note that the statistical information can be updated in the same manner as in the first to third methods, and therefore a description thereof will be omitted here.

[0232] <3.6. Threshold setting> As described above, the control station 100 determines to update the first statistical information when, as a predetermined condition, for example, the difference between the first statistical information and the second statistical information is greater than a threshold or is equal to or greater than a threshold. Here, a method for setting the thresholds in the above-mentioned formulas (1), (7), and (8) among the thresholds used by the control station 100 for update determination will be described.

[0233] The threshold is determined taking into consideration a tolerance for satisfying a desired communication characteristic. For example, it is assumed that the tolerable interference level is specified as −100 dBm, and the control station 100 estimates the average interference power to be −105 dBm using the first actual measurement data.

[0234] In this case, since the difference between the allowable interference level and the average interference power is 5 dB, the control station 100 sets the update determination threshold for the average interference power to 5 dB. If the control station 100 sets a value larger than 5 dB as the update determination threshold, there is a risk that the average interference power will exceed the allowable interference level.

[0235] In this way, the control station 100 sets the threshold value according to the desired communication characteristics, so that the communication system can perform communication so as to satisfy the desired communication characteristics.

[0236] <3.7. About the measured data> As described above, the actual measurement data is data related to communication characteristics when the terminal device 400 observes a signal from the base station 300. The actual measurement data may include time information (for example, a timestamp) related to the time when the terminal device 400 observed the signal.

[0237] The control station 100 selects the first measured data and the second measured data from the measured data acquired from the terminal device 400 .

[0238] When detecting the time variation of the communication characteristics in the above-mentioned third detection method, the control station 100 treats the actual measurement data as time-series data. In this case, there are several possible methods for the control station 100 to set the first actual measurement data and the second actual measurement data.

[0239] FIG. 17 is a diagram illustrating an example of the first actual measurement data and the second actual measurement data according to an embodiment of the present disclosure.

[0240] As shown in FIG. 17, the control station 100 receives the measured data from the terminal device 400 from time t0 to time t m-1 For example, m (m is a natural number) pieces of measured data between time t m After (time t m , time t m+1 , time t m+2 The measured data between (1, 2, 3) is treated as the second measured data.

[0241] In this manner, the control station 100 can treat, for example, at least a portion of the actual measurement data as first actual measurement data, and the remaining actual measurement data as second actual measurement data.

[0242] FIG. 18 is a diagram illustrating another example of the first actual measurement data and the second actual measurement data according to the embodiment of the present disclosure.

[0243] As shown in FIG. 18, the control station 100 detects the actual measurement data measured by the terminal device 400 from time t0 to time t m-1 In addition, the control station 100 treats m pieces of actual measurement data between time t m From time t m+n-1 The n (n is a natural number) pieces of measured data between are treated as the second measured data.

[0244] In this manner, the control station 100 can treat, for example, at least a portion of the actual measurement data as first actual measurement data, and treat at least a portion of the remaining actual measurement data as second actual measurement data.

[0245] If the number of first actual measurement data and second actual measurement data is small, the accuracy of the statistical information of the communication characteristics may be degraded. Therefore, it is desirable for the control station 100 to select an appropriate number of first actual measurement data and second actual measurement data.

[0246] An example of a method for selecting the first measured data will be described below with reference to Figs. 19 to 21. Figs. 19 to 21 are diagrams showing an example of selecting the first measured data according to an embodiment of the present disclosure. The upper diagrams of Figs. 19 to 21 are diagrams showing an example of measured data that is time-series data. Figs. 19 to 21 are diagrams showing average values ​​(average values ​​of communication characteristics) of the first measured data candidates selected by the control station 100.

[0247] The control station 100 first selects one of the measured data as a first measured data candidate. In the example of Fig. 19, the control station 100 selects the measured data at time t0 as the first measured data candidate. Next, the control station 100 calculates the average value of the communication characteristics of the selected first measured data candidate.

[0248] 19 to 21 show values ​​(steady values) at which the average value of the communication characteristics is in a steady state, but this steady state is determined according to the average value of the communication characteristics of the first measured data candidates, and is not a predetermined value. For the sake of simplicity, FIGS. 19 to 21 show the steady state of the average value of the communication characteristics that is determined later by increasing the number of first measured data candidates.

[0249] 19, the control station 100 selects only one first measured data candidate and calculates the average value of the communication characteristics only once. Therefore, the control station 100 does not (cannot) determine whether the calculated average value is in a steady state or not.

[0250] Next, the control station 100 adds one of the measured data to the first measured data candidate. In the example of Fig. 20, the control station 100 adds the measured data at time t1 to the first measured data candidate.

[0251] Next, as shown in the lower diagram of FIG. 20, the control station 100 calculates the average value of the communication characteristics of the first measured data candidate (measured data at time t0 and time t1).

[0252] The control station 100 determines whether the currently calculated average value of the first actual measurement data candidates is close to the previously calculated average value of the first actual measurement data candidates. In other words, the control station 100 determines whether the change (difference) in the average value of the first actual measurement data candidates is less than a threshold value.

[0253] In this case, it is assumed that the average value of the first actual measurement data candidate calculated this time is not close to the average value of the first actual measurement data candidate calculated last time. In this case, the control station 100 determines that the average value of the first actual measurement data candidate is not in a steady state, and continues to select the first actual measurement data candidate.

[0254] Next, the control station 100 adds one of the measured data to the first measured data candidate. In the example of Fig. 20, the control station 100 adds the measured data at time t1 to the first measured data candidate.

[0255] Next, as shown in the lower diagram of FIG. 20, the control station 100 calculates the average value of the communication characteristics of the first measured data candidate (measured data at time t0 and time t1).

[0256] The control station 100 determines whether the currently calculated average value of the first actual measurement data candidates is close to the previously calculated average value of the first actual measurement data candidates. In other words, the control station 100 determines whether the change (difference) in the average value of the first actual measurement data candidates is less than a threshold value.

[0257] In this case, it is assumed that the average value of the first actual measurement data candidate calculated this time is not close to the average value of the first actual measurement data candidate calculated last time. In this case, the control station 100 determines that the average value of the first actual measurement data candidate is not in a steady state, and continues to select the first actual measurement data candidate.

[0258] Similarly, the control station 100 adds one piece of actual measurement data to the first actual measurement data candidate. In the example of Fig. 21, the control station 100 adds the actual measurement data at time t2 to the first actual measurement data candidate.

[0259] Next, as shown in the lower diagram of FIG. 21, the control station 100 calculates the average value of the communication characteristics of the first measured data candidate (measurement data at time t0, time t1, and time t2).

[0260] The control station 100 determines whether the currently calculated average value of the first actual measurement data candidates is close to the previously calculated average value of the first actual measurement data candidates. In other words, the control station 100 determines whether the change (difference) in the average value of the first actual measurement data candidates is less than a threshold value.

[0261] If the average value of the first actual measured data candidate calculated this time is not close to the average value of the first actual measured data candidate calculated last time, the control station 100 determines that the average value of the first actual measured data candidate is not in a steady state and continues selecting the first actual measured data candidate.

[0262] If the average value of the first actual measurement data candidate calculated this time is close to the average value of the first actual measurement data candidate calculated last time, the control station 100 determines that the average value of the first actual measurement data candidate is in a steady state and ends the selection of the first actual measurement data candidate. The control station 100 sets the first actual measurement data candidate as the first actual measurement data.

[0263] The control station 100 may select the remaining actual measurement data that was not selected as the first actual measurement data from among the acquired actual measurement data as the second actual measurement data. Alternatively, the control station 100 may select the second actual measurement data in the same manner as the first actual measurement data.

[0264] Further, in the above embodiment, the control station 100 selects the time-series data one by one in ascending order of time as the first actual measurement data candidates, but the selection of the first actual measurement data candidates by the control station 100 is not limited to this.

[0265] For example, the control station 100 2pAlternatively, the control station 100 may select the first measured data candidates one by one from the measured data (p is an integer equal to or greater than 0) in ascending order of time. That is, in this case, the control station 100 selects the first measured data candidates one by one. In this manner, the control station 100 may select the first measured data candidates at intervals.

[0266] Furthermore, the number of actual measurement data selected by the control station 100 as the first actual measurement data candidate is not limited to 1, and may be 2 or more. For example, the control station 100 may determine the number of actual measurement data to select according to the average value of the first actual measurement data candidates or the difference between the average values.

[0267] This enables the control station 100 to select a more appropriate number of first actual measurement data and second actual measurement data.

[0268] <3.8. Observation errors in actual data> When a terminal device 400, such as a smartphone, that is not manufactured for the purpose of observing communication characteristics generates measured data, there is a risk that the measured data contains observation errors.

[0269] The statistical properties of the observation error differ depending on the type of terminal device 400, but if actual measurement data with a large average value or variance of the observation error is used directly to calculate statistical information on communication characteristics, the accuracy of the statistical information may deteriorate.

[0270] For this reason, it is desirable for the control station 100 to predict communication characteristics (calculate statistical information on communication characteristics) after reducing such observation errors as much as possible.

[0271] Methods for calibrating the measurement error can be broadly classified into a method for improving the measurement accuracy of the terminal device 400 itself and a method for performing appropriate statistical processing on the actual measurement data including the measurement error.

[0272] A method for improving the observation accuracy of the terminal device 400 itself depends on an observation application for measuring communication characteristics by the terminal device 400. In this method, for example, the control station 100 controls the observation application of the terminal device 400 by using, for example, an appropriate API so as to generate measured data with a required accuracy.

[0273] For example, the control station 100 controls the terminal device 400 so that the control station 100 observes the actual measurement data using an appropriate observation application, thereby enabling the control station 100 to further reduce the observation error contained in the actual measurement data.

[0274] On the other hand, in a method of performing appropriate statistical processing on actual measurement data including measurement errors, the control station 100 estimates the moments of the measurement errors by a statistical method and calibrates the measurement errors of the actual measurement data by taking the estimated moments into account. Representative calibration methods will be described below.

[0275] (First calibration method) For example, when a terminal device 400 with good observation accuracy is present around the observation point of the actual measurement data to be calibrated, the control station 100 calibrates the actual measurement data to be calibrated using the actual measurement data observed by the terminal device 400 with good observation accuracy. Here, an example of the terminal device 400 with good observation accuracy is a device for observing communication characteristics of a signal, such as a spectrum analyzer.

[0276] Specifically, the control station 100 calculates the average value e of the measurement error at the point s using the formula (10). s Estimate.

[0277]

number

[0278] Here, P s,ture is the communication characteristic at point s, and is actual measurement data that does not include measurement error or has small measurement error. In other words, P s,ture is actual measurement data observed by the terminal device 400 with high observation accuracy.

[0279] P s,i is the communication characteristic at point s, which is the i-th measured data including measurement error. In other words, P s,i is a communication characteristic observed by a terminal device 400 with low observation accuracy. s is the number of measured data at point s.

[0280] Next, the control station 100 performs calibration on the i-th measured data using equation (11) with the measurement error as an offset.

[0281]

number

[0282] However, in this calibration method, in order to ensure calibration accuracy, it is desirable that the average value of the observation error is relatively constant at multiple points. If the average value of the observation error is not constant and varies at multiple points, it is desirable for the control station 100 to calculate the offset of the observation error for each average value of the observation error at each point.

[0283] In addition to the average value of the observation errors, the control station 100 may use the median, variance, or various other moments to calibrate the observation errors.

[0284] (Second calibration method) A second calibration method involves estimating confidence intervals for various moments.

[0285] When the measurement error of the actual measurement data is large, the control station 100 may estimate the confidence intervals of various moments of the communication characteristics at an arbitrary point s. In this case, the control station 100 grasps the reliability of the statistical information of the communication characteristics by using, for example, a confidence limit value. Note that, here, the moment represents a statistical value such as the average value or the variance.

[0286] 22 is a graph showing an example of a confidence interval of the average RSRP according to an embodiment of the present disclosure. The horizontal axis of FIG. 22 is communication characteristics, and the vertical axis is a probability density function (PDF). Here, a schematic diagram showing a 95% confidence interval of the average RSRP at an arbitrary point s is shown.

[0287] p in Figure 22 min is the lower confidence limit for the 95% confidence interval. p max is the upper confidence limit for the 95% confidence interval.

[0288] When the measurement error is large, the width of the confidence interval becomes wider. Therefore, when the width becomes wider than the allowable value, the control station 100 may take measures such as not using the actual measurement data used for estimating the confidence interval for determining whether to update the statistical information.

[0289] <<4. Processing Example>> An example of each process executed in the communication system will be described below.

[0290] <4.1. Communication processing> Fig. 23 is a sequence diagram showing an example of a flow of communication processing according to an embodiment of the present disclosure. The communication processing shown in Fig. 23 is executed by the communication system at a predetermined cycle or when a specific event or the like is triggered, for example.

[0291] The base station 300 transmits a signal to the terminal device 400 (step S101). The terminal device 400 measures this signal and transmits actual measurement data, which is the measurement result, to the control station 100 (step S102).

[0292] Here, examples of information measured by the terminal device 400 include RSRP (Reference Signal Received Power), RSSI (Received Signal Strength Indicator), RSRQ (Reference Signal Received Quality), interference power, SNR, SIR, SINR, throughput, delay amount, Ping, and location information of the terminal device 400.

[0293] The control station 100 executes statistical processing (step S103). The control station 100 may execute, as the statistical processing, for example, estimation of the first statistical information and updating of the first statistical information.

[0294] The control station 100 transmits control information based on the first statistical information to the base station 300 and the terminal device 400 (step S104). Note that the control station 100 may transmit the control information to one of the base station 300 and the terminal device 400.

[0295] The terminal device 400 may transmit the actual measurement data to the control station 100 via the base station 300, or may transmit the actual measurement data to the control station 100 without via the base station 300. That is, the terminal device 400 may notify the control station 100 of the actual measurement data via a cellular network, or may notify the control station 100 of the actual measurement data via a network other than the cellular network (for example, a LAN).

[0296] Furthermore, when transmitting control information to the terminal device 400, the control station 100 may transmit the control information via the base station 300, or may transmit the control information without passing through the base station 300. That is, the control station 100 may notify the terminal device 400 of the control information via a cellular network, or may notify the terminal device 400 of the control information via a network other than the cellular network (for example, a LAN).

[0297] Moreover, the information that the control station 100 transmits to the base station 300 and / or the terminal device 400 as a result of the statistical processing is not limited to the control information. The control station 100 may transmit first statistical information to the base station 300 and / or the terminal device 400.

[0298] Also, here, it is assumed that the terminal device 400 transmits actual measurement data after observing a signal transmitted by the base station 300. Also, it is assumed that the control station 100 executes statistical processing after acquiring the actual measurement data from the terminal device 400.

[0299] However, the transmission of the measured data and / or the execution of the statistical processing may be performed in real time, or after a certain period of time has elapsed since the terminal device 400 observed (measured) the signal.

[0300] For example, the terminal device 400 may use actual measurement data observed during a day to determine whether or not to update the first statistical information estimated by the control station 100 on the previous day. In this manner, the actual measurement data may be collected within a predetermined period. Furthermore, the determination of whether to update the first statistical information may be made every predetermined period (at a predetermined cycle), such as once a day.

[0301] <4.2. First statistical information update process> In the following, an update process of the first statistical information will be described as an example of the statistical process executed by the control station 100.

[0302] Fig. 24 is a flowchart showing an example of the flow of the update process according to the embodiment of the present disclosure. The update process shown in Fig. 24 is executed by the control station 100 at a certain period or when a predetermined condition is satisfied, such as when a predetermined number of actual measurement data are acquired.

[0303] First, the control station 100 acquires first actual measurement data (step S201). The control station 100 may acquire the first actual measurement data from the terminal device 400, or may acquire the first actual measurement data by selecting the first actual measurement data from the actual measurement data acquired from the terminal device 400.

[0304] The control station 100 generates first statistical information using the acquired first actual measurement data (step S202). Note that if the control station 100 already has the first statistical information, steps S201 and S202 may be omitted.

[0305] Next, the control station 100 acquires the second actual measurement data (step S203). The control station 100 may acquire the second actual measurement data from the terminal device 400, or may acquire the second actual measurement data by selecting the second actual measurement data from the actual measurement data acquired from the terminal device 400.

[0306] The control station 100 generates second statistical information using the acquired second actual measurement data (step S204). The timing of generating the first statistical information and the timing of generating the second statistical information may be different. For example, the control station 100 may generate or update the first statistical information, and generate the second statistical information on the following day.

[0307] The control station 100 determines whether or not there is a significant difference between the first statistical information and the second statistical information (step S205).

[0308] For example, when the difference between the first statistical information and the second statistical information satisfies a predetermined condition (e.g., greater than a threshold), the control station 100 determines that there is a significant difference between the first statistical information and the second statistical information. When the control station 100 detects a fluctuation (change) in the statistical information in the first to fifth detection methods described above, for example, it determines that there is a significant difference between the first statistical information and the second statistical information.

[0309] When it is determined that there is no significant difference between the first statistical information and the second statistical information (step S205; No), the control station 100 ends the process without updating the first statistical information.

[0310] On the other hand, if it is determined that there is a significant difference between the first statistical information and the second statistical information (step S205; Yes), the control station 100 updates the first statistical information (step S206) and ends the process.

[0311] <<5. Definition of Terms>> Here, definitions of terms used in the above-mentioned embodiment will be described.

[0312] (Communication characteristics) For example, the communication characteristics may be any of the following or a combination thereof: -Characteristics based on radio wave propagation from the transmitting point to the receiving point (downlink, uplink, and sidelink) communication parameters at the transmission point, reception point, base station 300, terminal device 400 and / or communication node

[0313] The characteristics based on radio wave propagation from a transmission point to a reception point include at least one of a characteristic related to received power, a characteristic related to communication speed (throughput), and a characteristic related to delay.

[0314] The characteristics related to the received power include information on at least one of the received power, the interference power, the RSRP, the RSRQ, the RSSI, the SNR, the SNR, and the SINR.

[0315] The characteristics related to communication speed include information regarding at least one of a downlink throughput, an uplink throughput, and a sidelink throughput.

[0316] The delay-related characteristics include information regarding at least one of a latency, a jitter, and a ping value.

[0317] The communication parameters at the transmission point, the reception point, the base station 300, the terminal device 400 and / or the communication node include dynamically determined parameters and / or semi-statically determined parameters.

[0318] The dynamically determined parameters include at least one of the following information: -Information about MCS (Modulation and Coding Scheme) -Information about transmission power -Beam control information -Information about MIMO multiplexing

[0319] The semi-statically determined parameters include at least one of a range, a maximum value, a minimum value, an average value, and a median value of a parameter selectable (permitted to the base station 300 or the terminal device 400) by the base station 300 or the terminal device 400. An example of a semi-statically determined parameter is the maximum transmission power.

[0320] (About the area) In this embodiment, an area (location, base) can be given by any one of the following or a combination thereof. -Coverage area (communication area) of a given base station 300, terminal device 400, or communication node - Site or building owned or managed by a specified business operator - Areas designated in advance by designated businesses or governments - Areas divided in a prescribed manner

[0321] A communication area includes, for example, coverage that can be connected to one communication node (such as a base station 300 or a terminal device 400). Also, for example, when multiple base stations 300 are installed in one base station, the base stations 300 are connected to one core network. In other words, a base station can be defined as a coverage area covered by at least one base station 300 connected to one core network.

[0322] For example, the base station 300 of the private network or the access point name (APN) of the core network may be set as the communication area for each base. In other words, the same access point name is set for the same base (communication area), and if the access point names are different, they are recognized as different bases (communication areas).

[0323] An example of a predetermined method for dividing areas is a method based on location information, in which areas are divided at predetermined distances based on location information such as latitude and longitude.

[0324] (About communication environment information) The communication environment information includes at least one of static or semi-static information and dynamic information. Static or semi-static information is fixed information or information that is updated infrequently. The static or semi-static information may be information in a higher communication layer (e.g., an application layer, an RRC (Radio Resource Control) layer, etc.). The dynamic information is information that is updated frequently. The dynamic information may be information in a lower communication layer (e.g., a physical layer, etc.).

[0325] The communication environment information includes, for example, at least one of the following pieces of information: -Map information -Structure information -Device information regarding the base station 300 or the terminal device 400 -Sensing information acquired through sensing devices -Radio communication information regarding wireless communications -Quality information regarding communication quality in wireless communication

[0326] (Map information) The map information here is information that allows the positions and sizes of structures, the base station 300, the terminal device 400, etc. to be recognized. The positions may be absolute position information such as latitude and longitude, or may be relative position information within an area.

[0327] The map information includes, for example, topographical information, an office layout diagram, and a floor plan.

[0328] (Structure information) The structural information here includes information that affects radio wave propagation, such as reflection, diffraction, and transmission.

[0329] Examples of structures include buildings, walls, plantations, roads, signs, traffic lights, road signs, pillars, buildings, the ground, glass, windows, desks, and cabinets.

[0330] The structure information includes, for example, the position, shape, size, and material of the structure, and parameters related to radio wave propagation in the structure (dielectric constant, conductivity, etc.).

[0331] The structure information is generated and constructed, for example, based on the above-mentioned map information, etc. In addition, the structure information may be generated and constructed based on information acquired from a sensing device, etc., which will be described later.

[0332] (Device information regarding base station 300 or terminal device 400) Here, the device information regarding the base station 300 or the terminal device 400 includes, for example, at least one of the pieces of information listed below. -Antenna information about antennas -Capability information regarding the functions and capabilities supported by wireless communication Shape information regarding the shape and weight of the base station 300 and / or the terminal device 400 Location information of fixed base stations 300 and / or fixed terminal devices 400

[0333] The antenna information here includes at least one of the following pieces of information: the antenna configuration of the base station 300 and / or the terminal device 400, the beam pattern, the number of antenna elements, and the configuration of the antenna elements.

[0334] (Sensing information obtained through sensing devices) The sensing information acquired through the sensing device here includes object information regarding an object detected through the sensing device, and / or effect information regarding fluctuations and / or effects on wireless communication caused by the detected object.

[0335] Here, the sensing device includes a camera, a sensor, etc. The sensor includes a photoelectric sensor, a fiber sensor, a laser sensor, a color sensor, a proximity sensor, an eddy current type displacement sensor, a contact type displacement sensor, an ultrasonic sensor, an image discrimination sensor, a pressure sensor, a vibration sensor, an inertial measurement sensor, etc.

[0336] Three-dimensional spatial information (e.g., the above-mentioned structure information) is generated from sensing information acquired through a sensing device. For example, when the sensing information is an image or video acquired in real time by a camera, the three-dimensional spatial information is generated in real time using, for example, a photogrammetry technique or a volumetric capture technique.

[0337] The objects detected by the sensing device include various devices such as the sensing device itself, a device other than the sensing device, and a terminal device 400 that transmits information acquired by the sensing device. In addition, the objects detected by the sensing device include the above-mentioned structures and objects other than structures.

[0338] The terminal device 400 that transmits the sensing information acquired by the sensing device may or may not be equipped with the sensing device. When the terminal device 400 and the sensing device are separate devices, it is preferable that the terminal device 400 acquires the sensing information from the sensing device, for example, by wired or wireless communication.

[0339] In addition, the sensing information acquired by sensing (the sensing information acquired through the sensing device) may include various sensing information in addition to the object detection information. For example, the sensing information acquired by sensing includes beam information (e.g., information on the beam pattern, the beam angle, etc.) regarding the beam transmitted from the base station 300 and / or the terminal device 400.

[0340] The above-mentioned sensing device can detect moving objects such as people and robots in addition to stationary objects such as structures. The sensing device transmits, for example, information on the detected moving object as sensing information via the terminal device 400.

[0341] The sensing device may transmit the sensing information when it detects a moving object and / or when it no longer detects a moving object, or may transmit the sensing information at regular intervals.

[0342] (Radio communication information regarding radio communication) The wireless communication information relating to wireless communication here includes, for example, at least one of the pieces of information listed below. -Communication information related to RAT (Radio access technology) and frequencies -Scenario information on communication environment scenarios - Information on restrictions regarding conditions and restrictions on wireless communication available on the local network -Information regarding the transmission power of the base station 300 or the terminal device 400

[0343] The communication information regarding the RAT includes, for example, information regarding LTE, NR, wireless LAN, Bluetooth (registered trademark), etc. The communication information regarding the frequency includes information regarding at least one of a frequency band, a center frequency, and a frequency bandwidth.

[0344] The information regarding the transmission power of the base station 300 or the terminal device 400 includes, for example, information (ss-PBCH-BlockPower) indicating the transmission power of an SS / PBCH (Synchronization Signal and Physical Broadcast CHannel) block included in an SS / PBCH (System information block type 1) included in SIB1 (System information block type 1), which is control information broadcast from the base station 300.

[0345] The scenario information regarding the communication environment scenario includes, for example, information regarding urban areas, suburban areas, rural areas, indoor offices, indoor factories, and the like.

[0346] The scenario information may further include information on a radio wave propagation model (e.g., a path loss model) corresponding to the communication environment scenario. The radio wave propagation model may correspond to each of a LOS environment and a NLOS environment.

[0347] The restriction information here includes information on conditions and restrictions regarding wireless communication permitted in the local network.

[0348] These conditions and constraints may include, for example, information about available RATs, areas where wireless communication is possible (geographical information (such as two-dimensional planar information and / or spatial information including three-dimensional height)), an upper limit on the amount of interference power outside the area, maximum transmit power that can be transmitted, transmittable frequency information, transmittable time information, the installation location of base station 300, and the like.

[0349] These conditions and constraints are, for example, set and defined in advance, and can be determined and / or changed based on information notified from a predetermined server or storage device (for example, a Spectrum Access System (SAS) server, etc.).

[0350] The quality information regarding the communication quality in wireless communication includes, for example, at least one of the following pieces of information measured or estimated by the terminal device 400 in wireless communication. - Received power -Interference power -RSRP -RSRQ -RSSI -SNR -Downlink throughput -Uplink throughput -Latency -jitter -Ping value

[0351] (About virtual space estimation information) The virtual space estimation information includes, for example, information on radio wave propagation of at least one of the base station 300 and the terminal device 400. The virtual space estimation information is, for example, data (information) obtained by simulation. The virtual space estimation information can be used as necessary when refining an estimation model.

[0352] The virtual space estimation information includes, for example, at least one of the pieces of information listed below. -LOS / NLOS information -Simulation information - Calculated information based on LOS / NLOS information, simulation information, etc.

[0353] (LOS / NLOS information) The LOS / NLOS information is information indicating whether the environment between the base station 300 and the terminal device 400 is an LOS environment or an NLOS environment.

[0354] The LOS environment is also called a line-of-sight environment. The LOS environment indicates a situation in which there are no obstacles 600, such as structures or people, on a straight line between the base station 300 and the terminal device 400, and the base station 300 and the terminal device 400 can transmit and receive direct waves between them. In this case, wireless communication between the base station 300 and the terminal device 400 is performed through reflected waves, diffracted waves, and the like in addition to direct waves.

[0355] The NLOS environment is also called a non-line-of-sight environment. The NLOS environment indicates a situation in which there is an obstacle 600, such as a structure or a person, on the straight line between the base station 300 and the terminal device 400, and the base station 300 and the terminal device 400 cannot transmit or receive direct waves between them. In this case, wireless communication between the base station 300 and the terminal device 400 is performed through reflected waves, diffracted waves, and the like other than direct waves.

[0356] (Simulation information) The simulation information includes information on a simulation result of radio wave propagation in wireless communication between the base station 300 and the terminal device 400. The simulation information includes, for example, path information on one or more paths (propagating waves, arriving waves, rays) acquired by a ray tracing simulation.

[0357] This path includes a direct wave, a reflected wave, a diffracted wave, a transmitted wave, and the like between the base station 300 and the terminal device 400. In general, there are various structures between the base station 300 and the terminal device 400, so a signal (radio wave) transmitted from a transmitting point (e.g., the base station 300) travels through various routes, becomes a plurality of paths, and arrives at a receiving point (e.g., the terminal device 400).

[0358] The path information regarding the path may include, for example, at least one of the following information: - Received power at the receiving point -Transmission power at the transmission point -Path loss - Propagation distance - Number of reflections - Number of diffractions - Number of passes -Phase variation - Angle of emission at the transmitting point - Angle of arrival at the receiving point - Arrival order of the pass (in what order among multiple passes the pass arrived) - Number of passes

[0359] (Calculation information) The calculated information is information that is generated and calculated based on the above-mentioned LOS / NLOS information, simulation information, etc. The calculated information may include, for example, at least one of the following pieces of information: - Path loss at the receiving point - Received power -Interference power -RSRP -RSRQ -RSSI -SNR -Downlink throughput -Uplink throughput -Latency -jitter -Ping value

[0360] Here, an example of a generation process of simulation data according to the present embodiment will be described with reference to Fig. 25. Fig. 25 is a flowchart showing an example of the flow of the generation process according to the embodiment of the present disclosure.

[0361] The generation process shown in FIG. 25 may be performed, for example, by the control station 100 when using simulation data to generate or update statistical information.

[0362] First, the control station 100 creates a virtual communication environment for an area (a predetermined area, an area for model generation) (step S301).

[0363] For example, the virtual communication environment is a three-dimensional virtual space of the area. For example, the virtual communication environment is generated based on communication environment information in the area. For example, the virtual communication environment includes structures (such as buildings and the ground) in the area.

[0364] Next, the control station 100 executes a radio wave propagation simulation between the base station 300 and the terminal device 400 in a virtual communication environment (step S302). The radio wave propagation simulation may use various methods such as LOS environment / NLOS environment determination in a virtual space or ray tracing simulation.

[0365] The control station 100 generates virtual space estimation information based on the simulation results (step S303).

[0366] The generation process may be executed by a device other than the control station 100. In this case, the control station 100 acquires virtual space estimation information (simulation data) from the device executing the generation process. The execution timing of the generation process is not limited to when the statistical information is generated and / or updated. The control station 100 may execute the generation process at any timing.

[0367] (About location information) The information on the location is the location information of the base station 300 and / or the terminal device 400. The information on the location includes absolute location information such as latitude, longitude, and / or altitude acquired by, for example, a Global Positioning System (GPS) or a Global Navigation Satellite System (GNSS).

[0368] Alternatively, the information regarding the location includes relative location information obtained from a beacon, UWB (Ultra-Wide Band), or the like.

[0369] The absolute location information or relative location information may be an area separated by a predetermined distance or method.

[0370] Note that the information according to this embodiment can be information linked to location information.

[0371] (About the estimation accuracy) For example, when the control station 100 estimates the actual communication characteristics using statistical information on the communication characteristics, the estimation accuracy may be given. This estimation accuracy may be further used when using (utilizing) data estimated by an estimation model of the communication characteristics.

[0372] The estimation accuracy of the communication characteristics in the communication area may be given by any one of the following or a combination thereof. - The number of first and second measured data -Accuracy of the first and second measurement data -Accuracy of simulation data

[0373] The measurement accuracy of the first measured data and the second measured data may include at least one of the accuracy of the communication characteristics and the accuracy of the location information. For example, the error of the measured data may include the measurement error of the RSRP and / or the error of the location information by the GPS.

[0374] Furthermore, when a measurement error specific to the terminal device 400 occurs, information indicating the terminal device 400 can be included in the measurement accuracy of the first actual measurement data and the second actual measurement data.

[0375] For example, the accuracy of the simulation data may be determined based on the accuracy (precision, accuracy) of the communication environment information in the simulation for generating the simulation data.

[0376] <<6. Other embodiments>> The processing according to each of the above-described embodiments may be implemented in various different forms other than the above-described embodiments.

[0377] The statistical information according to the above-described embodiments can be used in various processes, controls, use cases, and the like.

[0378] For example, data on communication characteristics estimated in a communication area (for example, predicted values ​​as statistical information) can be used for cell design (location of transmission points, setting of maximum transmission power of transmission points, etc.) in the communication area.

[0379] This cell design may be performed based on the estimation accuracy of communication characteristics in the communication area. Also, this cell design may be performed in the control station 100 or in the base station 300. Alternatively, the cell design may be performed in a core network or the like.

[0380] For example, data on communication characteristics estimated in a communication area (e.g., predicted values ​​as statistical information) can be used to control communication parameters (transmission power, MCS (Modulation and coding scheme), beams, etc.) of transmitting points and / or receiving points within the communication area.

[0381] The control of the communication parameters may be further performed based on the estimation accuracy of the communication characteristics in the communication area. Moreover, the control of the communication parameters may be performed by the control station 100 or the base station 300. Alternatively, the control of the communication parameters may be performed by the core network or the like.

[0382] The statistical information according to the present embodiment can be used, for example, in designing interference power and separation distance in frequency sharing. The control station 100 estimates interference power using statistical information on communication characteristics, thereby making it possible to effectively use available frequencies in time and space while avoiding interference with the system to be protected.

[0383] In addition, the technology according to this embodiment may be applied to 6G joint communication and sensing.

[0384] In the above-described embodiment, the terminal device 400 and the base station 300 transmit various information (for example, real space measurement information, communication environment information, information used by the control station 100 for estimation, etc.) to the control station 100. Conditions regarding the transmission of information by the terminal device 400 and the base station 300 may be set or specified individually. These conditions include, for example, the timing of transmitting information, the trigger conditions for transmission, the period, etc.

[0385] For example, when the terminal device 400 transmits object detection information, the information may be transmitted at the timing when the terminal device 400 detects the object. That is, the detection of an object by the terminal device 400 is used as a trigger to cause the terminal device 400 to transmit the detection information to the control station 100. In other words, the terminal device 400 does not need to transmit the detection information to the control station 100 while it does not detect an object.

[0386] Also, for example, when the terminal device 400 transmits quality information regarding communication quality, the terminal device 400 may transmit the quality information at a constant cycle. Alternatively, the terminal device 400 may determine whether to transmit the quality information depending on the difference from the quality information transmitted previously.

[0387] Specifically, when the quality information is compared with previously transmitted quality information and is equal to or greater than a predetermined value (exceeds the predetermined value), the terminal device 400 transmits the quality information to the control station 100. In other words, when the quality information is compared with previously transmitted quality information and is less than a predetermined value (is equal to or greater than the predetermined value), the terminal device 400 does not need to transmit the quality information to the control station 100.

[0388] When transmitting quality information, the terminal device 400 may transmit to the control station 100 information indicating a difference from the previous quality information.

[0389] In the above-described embodiment, the control station 100 is a cloud server or a database device arranged on a network, but the control station 100 is not limited to this. For example, the control station 100 does not have to be arranged on a cloud. For example, the control station 100 may be an edge server. In this case, the control station 100 may be arranged near the base station 300, for example.

[0390] Alternatively, the base station 300 and / or the terminal device 400 may estimate and / or update the statistical information instead of the control station 100. In this case, the base station 300 and / or the terminal device 400 functions as an information processing device that estimates and / or updates the statistical information. In this manner, at least a part of the functions of the control station 100 may be executed by the base station 300 and / or the terminal device 400.

[0391] For example, the control device that controls the control station 100, the base station 300, and the terminal device 400 in the above-described embodiment may be realized by a dedicated computer system or a general-purpose computer system.

[0392] For example, a communication program for executing the above-mentioned operations is stored in a computer-readable recording medium such as an optical disk, a semiconductor memory, a magnetic tape, or a flexible disk and distributed. Then, for example, the program is installed in a computer and the above-mentioned processing is executed to configure a control device. In this case, the control device may be a device (for example, a personal computer) external to the control station 100, the base station 300, and the terminal device 400. Also, the control device may be a device (for example, the control units 130, 340, 450) internal to the control station 100, the base station 300, and the terminal device 400.

[0393] The above-mentioned communication program may also be stored in a disk device provided in a server device on a network such as the Internet, and may be made available for downloading to a computer. The above-mentioned functions may also be realized by cooperation between an OS (Operating System) and application software. In this case, the parts other than the OS may be stored on a medium and distributed, or the parts other than the OS may be stored in a server device and made available for downloading to a computer.

[0394] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0395] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0396] Furthermore, the above-described embodiments and modifications can be appropriately combined as long as the processing contents are not contradictory.

[0397] Furthermore, the effects described in this specification are merely examples and are not limiting, and other effects may also be obtained.

[0398] Furthermore, for example, the present embodiment can be implemented as any configuration constituting an apparatus or system, such as a processor as a system LSI (Large Scale Integration), a module using multiple processors, a unit using multiple modules, a set in which other functions are added to a unit, and the like (i.e., a configuration of a part of an apparatus).

[0399] In this embodiment, a system means a collection of multiple components (devices, modules (parts), etc.), regardless of whether all the components are in the same housing. Therefore, multiple devices housed in separate housings and connected via a network, and a single device in which multiple modules are housed in a single housing, are both systems.

[0400] Also, for example, this embodiment can have a cloud computing configuration in which one function is shared and processed jointly by a plurality of devices via a network.

[0401] In the above-described embodiments, the control station 100 determines the control information of the base station 300 and / or the terminal device 400, but the present invention is not limited to this. Each of the above-described embodiments can be used for the purpose of determining and designing transmission parameters and / or reception parameters, the number of base stations 300 and / or the terminal devices 400 to be installed (maximum number of installations), installation locations, and / or installation directions (horizontal direction, tilt angle, etc.).

[0402] <<7. Conclusion>> Although the embodiments of the present disclosure have been described above, the technical scope of the present disclosure is not limited to the above-described embodiments, and various modifications are possible without departing from the gist of the present disclosure. In addition, components of different embodiments and modifications may be appropriately combined.

[0403] Furthermore, the effects of each embodiment described in this specification are merely examples and are not limiting, and other effects may also be provided.

[0404] The present technology can also be configured as follows. (1) a control unit that updates the first statistical information according to a difference between first statistical information of a communication characteristic generated using first actual measurement data in a communication area and second statistical information of the communication characteristic generated using second actual measurement data in the communication area. An information processing device comprising: (2) The information processing device according to (1), wherein the control unit updates the first statistical information when there is a significant difference between the first statistical information and the second statistical information. (3) The information processing device according to (1) or (2), wherein the control unit updates the first statistical information when a difference between the first statistical information and the second statistical information satisfies a predetermined condition. (4) The information processing device according to any one of (1) to (3), wherein the control unit updates the first statistical information by changing the first statistical information to the second statistical information. (5) The information processing device according to any one of (1) to (3), wherein the control unit updates the first statistical information by using at least a part of the first actual measurement data and at least a part of the second actual measurement data. (6) The control unit is selecting actual measurement data to be used for updating the first statistical information from the first actual measurement data and the second actual measurement data according to the difference; updating the first statistical information using the selected actual measurement data; An information processing device as described in (1). (7) The information processing device according to any one of (1) to (6), wherein the first statistical information and the second statistical information include information regarding at least one of a probability distribution of the communication characteristics, an estimation model for estimating the communication characteristics, a predicted value, an average, and a variance. (8) the first statistical information and the second statistical information are probability distributions of the communication characteristics; The control unit compares at least one of a mean value, a variance, and an n-th moment of the first statistical information and the second statistical information. An information processing device according to any one of (1) to (7). (9) The first statistical information and the second statistical information provide a unique output for an input. and The control unit compares parameters of the first statistical information and the second statistical information. An information processing device according to any one of (1) to (7). (10) The control unit detects the difference between the first statistical information and the second statistical information from integrated data obtained by integrating the first actual measurement data and the second actual measurement data. An information processing device according to any one of (1) to (7). (11) The information processing device according to (10), wherein the control unit detects the difference between the first statistical information and the second statistical information by detecting a change point in the integrated data. (12) The information processing device according to (11), wherein the control unit imparts hysteresis to a threshold value for detecting the change point of the integrated data. (13) The information processing device according to (11) or (12), wherein the control unit determines that the change point of the integrated data has been detected when a predetermined condition is satisfied in a predetermined section of the integrated data. (14) The information processing device according to any one of (1) to (13), wherein the control unit updates the first statistical information in accordance with image information of the communication area. (15) The information processing device according to any one of (1) to (14), wherein the control unit updates the first statistical information according to at least one of environmental information of the communication area and information on a base station communicating in the communication area. (16) The information processing device according to any one of (1) to (15), wherein a threshold value set according to the required communication characteristics is used to detect the difference between the first statistical information and the second statistical information. (17) the first measured data is at least a part of time-series data measured in the communication area, The second actual measurement data is at least a part of the time-series data excluding the first actual measurement data. The information processing device according to any one of (1) to (16). (18) The information processing device described in (17), wherein the control unit uses the first measured data candidate as the first measured data when statistical information of the first measured data candidate calculated while changing the number of first measured data candidates reaches a steady state. (19) The information processing device according to (17) or (18), wherein the control unit uses the second measured data candidate as the second measured data when statistical information of the second measured data candidates calculated while changing the number of second measured data candidates reaches a steady state. (20) The information processing device according to any one of (1) to (19), wherein a measurement error of the first actual measurement data, which is measured by an apparatus having low measurement accuracy, is corrected using the first actual measurement data, which is measured by an apparatus having high measurement accuracy, among the first actual measurement data. (twenty one) The information processing device according to any one of (1) to (20), wherein the second actual measurement data, which is measured by an apparatus with low measurement accuracy, is used to correct a measurement error in the second actual measurement data, which is measured by an apparatus with high measurement accuracy, among the second actual measurement data. (twenty two) An information processing device according to any one of (1) to (21), wherein if the reliability of the communication characteristics estimated using actual measured data in the communication area does not satisfy a predetermined condition, data excluding the actual measured data is used as the first actual measured data and the second actual measured data. (twenty three) the reliability of the communication characteristic is a confidence interval of a statistical value of the communication characteristic; The information processing device according to (22), wherein, when the width of the confidence interval is equal to or greater than a predetermined value, the data excluding the actual measurement data used to estimate the confidence interval is used as the first actual measurement data and the second actual measurement data. (twenty four) The information processing device according to any one of (1) to (23), wherein the communication characteristic is a characteristic based on radio wave propagation from a transmission point to a reception point. (twenty five) The information processing device according to any one of (1) to (24), wherein at least a part of the first statistical information and the second statistical information is generated using simulation data of the communication area. (26) The information processing device according to (25), wherein the simulation data is data obtained by a simulation based on communication environment information relating to the communication area. (27) A control unit that notifies the information processing device of measurement data measured in the communication area. Equipped with The information processing device includes: updating the first statistical information according to a difference between first statistical information on communication characteristics generated using first actual measurement data including at least a part of the measurement data and second statistical information on the communication characteristics generated using second actual measurement data including at least a part of the measurement data; Terminal device. (28) updating the first statistical information according to a difference between first statistical information of a communication characteristic generated using first actual measurement data in a communication area and second statistical information of the communication characteristic generated using second actual measurement data in the communication area; An information processing method comprising: (29) notifying an information processing device of measurement data measured in the communication area; Including, The information processing device includes: updating the first statistical information according to a difference between first statistical information on communication characteristics generated using first actual measurement data including at least a part of the measurement data and second statistical information on the communication characteristics generated using second actual measurement data including at least a part of the measurement data; Information processing methods. [Explanation of symbols]

[0405] 100 Control Station 110,310,410 Communications Department 120,320,420 storage section 130,340,450 Control section 300 base stations 330,430 Network Communication Department 400 Terminal Equipment 440 Input / output section

Claims

1. a control unit that updates the first statistical information according to a difference between first statistical information of a communication characteristic generated using first actual measurement data in a communication area and second statistical information of the communication characteristic generated using second actual measurement data in the communication area. An information processing device comprising:

2. The information processing device according to claim 1 , wherein the control unit updates the first statistical information when there is a significant difference between the first statistical information and the second statistical information.

3. The information processing device according to claim 1 , wherein the control unit updates the first statistical information by changing the first statistical information to the second statistical information.

4. The information processing apparatus according to claim 1 , wherein the control unit updates the first statistical information by using at least a part of the first actual measurement data and at least a part of the second actual measurement data.

5. the first statistical information and the second statistical information are probability distributions of the communication characteristics, The control unit compares at least one of an average value, a variance, and an n-th moment of the first statistical information and the second statistical information. The information processing device according to claim 1 .

6. the first statistical information and the second statistical information are an estimation model that produces a unique output for an input, The control unit compares parameters of the first statistical information and the second statistical information. The information processing device according to claim 1 .

7. the control unit detects the difference between the first statistical information and the second statistical information from integrated data obtained by integrating the first actual measurement data and the second actual measurement data. The information processing device according to claim 1 .

8. The information processing device according to claim 7 , wherein the control unit detects the difference between the first statistical information and the second statistical information by detecting a change point in the integrated data.

9. The information processing device according to claim 8 , wherein the control unit imparts hysteresis to a threshold value for detecting the change point of the integrated data.

10. The information processing device according to claim 8 , wherein the control unit determines that the change point of the integrated data has been detected when a predetermined condition is satisfied in a predetermined section of the integrated data.

11. The information processing device according to claim 1 , wherein the control unit updates the first statistical information in accordance with at least one of environmental information of the communication area and information on a base station communicating in the communication area.

12. The information processing apparatus according to claim 1 , wherein a threshold value set in accordance with the required communication characteristic is used to detect the difference between the first statistical information and the second statistical information.

13. the first actual measurement data is at least a part of time-series data measured in the communication area, The second actual measurement data is at least a part of the time-series data excluding the first actual measurement data. The information processing device according to claim 1 .

14. 14. The information processing device according to claim 13, wherein the control unit uses the first actual data candidate as the first actual data when statistical information of the first actual data candidate calculated while changing the number of first actual data candidates reaches a steady state.

15. 14. The information processing device according to claim 13, wherein the control unit uses the second actual data candidate as the second actual data when statistical information of the second actual data candidate calculated while changing the number of second actual data candidates reaches a steady state.

16. The information processing device according to claim 1 , wherein, when the reliability of the communication characteristics estimated using the actual measured data in the communication area does not satisfy a predetermined condition, data other than the actual measured data is used as the first actual measured data and the second actual measured data.

17. A control unit that notifies the information processing device of measurement data measured in the communication area. Equipped with The information processing device includes: updating the first statistical information according to a difference between first statistical information on communication characteristics generated using first actual measurement data including at least a part of the measurement data and second statistical information on the communication characteristics generated using second actual measurement data including at least a part of the measurement data; Terminal device.

18. updating the first statistical information according to a difference between first statistical information of a communication characteristic generated using first actual measurement data in a communication area and second statistical information of the communication characteristic generated using second actual measurement data in the communication area; An information processing method comprising:

19. notifying an information processing device of measurement data measured in the communication area; Including, The information processing device includes: updating the first statistical information according to a difference between first statistical information on communication characteristics generated using first actual measurement data including at least a part of the measurement data and second statistical information on the communication characteristics generated using second actual measurement data including at least a part of the measurement data; Information processing methods.

Images