Information processing device, information processing method, and program

Abstract

An information processing device according to the present disclosure comprises a control unit. The control unit acquires actual measurement data relating to communication characteristics in a communication area. The control unit corrects the actual measurement data on the basis of a measurement error of the actual measurement data. The control unit uses the corrected actual measurement data to generate statistical information on the communication characteristics in the communication area.

Description

Information processing device, information processing method, and program

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

[0002] In wireless communication, there is known a technique for realizing more suitable communication by appropriately controlling radio resources and communication parameters. For example, suitable communication can be realized by adaptively controlling communication parameters according to the state of the 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, thereby enabling the terminal device to estimate the state of the propagation path. Furthermore, the terminal device feeds back the estimated state of the propagation path to the base station, allowing the base station to set suitable communication parameters for the terminal device.

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

[0005] Japanese Patent Application Laid-Open No. 2021-108459

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

[0007] Therefore, when communication is performed using communication parameters determined by a base station, there is a risk that suitable wireless communication will not be possible 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 a 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 this obstacle.

[0008] Therefore, it is desirable to estimate communication characteristics in the real space, such as the presence or absence of obstacles, with higher accuracy.

[0009] Therefore, the present disclosure provides a mechanism that can estimate communication characteristics in the real space with higher accuracy.

[0010] It should be noted that the above problem or object is merely one of multiple problems or objects that can be solved or achieved by multiple embodiments disclosed in this specification.

[0011] The information processing device of the present disclosure includes a control unit. The control unit acquires measured data regarding communication characteristics in a communication area. The control unit corrects the measured data based on a measurement error of the measured data. The control unit generates statistical information regarding the communication characteristics in the communication area using the corrected measured data.

[0012] FIG. 1 is a diagram illustrating an example of radio wave propagation according to a proposed technique of the present disclosure. FIG. 2 is a diagram illustrating an example of communication processing according to an embodiment of the present disclosure. FIG. 3 is a diagram for explaining an example of statistical processing according to an embodiment of the present disclosure. FIG. 4 is a diagram illustrating an example configuration of a communication system according to an embodiment of the present disclosure. FIG. 5 is a diagram illustrating an example configuration of a control station according to an embodiment of the present disclosure. FIG. 6 is a block diagram illustrating an example configuration of a base station according to an embodiment of the present disclosure. FIG. 7 is a block diagram illustrating an example configuration of a terminal device according to an embodiment of the present disclosure. FIG. 8 is a graph illustrating an example of a confidence interval of an average RSRP according to an embodiment of the present disclosure. FIG. 9 is a diagram illustrating an example distribution shape of an observation error according to an embodiment of the present disclosure. FIG. 10 is a diagram illustrating another example distribution shape of an observation error according to an embodiment of the present disclosure. FIG. 11 is a diagram illustrating an example of a cumulative distribution function according to an embodiment of the present disclosure. FIG. 12 is a diagram illustrating another example of a cumulative distribution function according to an embodiment of the present disclosure. FIG. 13 is a diagram illustrating an example scatter plot of actual measurement data according to an embodiment of the present disclosure. FIG. 14 is a diagram illustrating an example estimation result of a linear model according to an embodiment of the present disclosure. FIG. 15 is a sequence diagram illustrating an example flow of a generation processing according to an embodiment of the present disclosure. FIG. 16 is a sequence diagram illustrating an example flow of a determination processing according to an embodiment of the present disclosure. FIG. 17 is a flowchart illustrating an example flow of an information generation processing according to an embodiment of the present disclosure. FIG. 18 is a block diagram illustrating an example hardware configuration of a computer according to the present disclosure.

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

[0014] Furthermore, in this specification and drawings, similar components of the embodiments may be distinguished by adding at least one different alphabet and / or number after the same reference numeral. However, if there is no need to particularly distinguish between similar components, only the same reference numeral is used. For example, multiple components having substantially the same functional configuration may be distinguished as necessary, such as terminal device 400_1 and terminal device 400_2. For example, if there is no need to particularly distinguish between terminal device 400_1 and terminal device 400_2, they will simply be referred to as terminal device 400.

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

[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 a base station and a terminal device.

[0017] To improve communication characteristics, base stations are required to adaptively design communication parameters such as modulation methods 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 transmission efficiency by using a higher-order modulation scheme, whereas when the received power is low, the base station can suppress increases in bit error rate, symbol error rate, etc. by using a lower-order modulation scheme.

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

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

[0021] However, radio wave propagation characteristics (communication characteristics) in wireless communication can vary significantly depending on the presence or absence of obstacles between a base station (transmission point) and a terminal device (reception point). This issue will be explained using FIG. 1.

[0022] 1A and 1B are diagrams illustrating an example of radio wave propagation according to the proposed technique of the present disclosure, in which Fig. 1A is a diagram illustrating an example of radio wave propagation when there is no obstacle 600, and Fig. 1B is a diagram illustrating an example of radio wave propagation when there is an obstacle 600.

[0023] For example, when the base station 300 transmits a transmission power P ｔｘ When there is no obstacle 600 (see FIG. 1(a)), the radio wave can reach a longer distance than when 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 structure characteristics), the positional relationship between the base station, the structure, and the terminal device, etc.

[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 predicted values ​​of communication characteristics are used continuously after being generated, there is a risk that the actual communication characteristics will not be accurately understood.

[0026] To accurately predict time variations in communication characteristics, it is necessary to install radio wave sensors or other devices in fixed locations in communication areas and constantly measure communication characteristics using these sensors. Furthermore, the communication system that generates predicted values ​​of communication characteristics must statistically process the measured data in real time and constantly update the predicted values ​​of communication characteristics.

[0027] However, this method has the drawback that sensors (radio wave sensors) must be installed in fixed locations, which requires costly maintenance. Furthermore, in order to implement this method, the communication system (or the system designer) must identify in advance the locations where communication characteristics fluctuate significantly over time in order to determine the installation locations of the sensors.

[0028] Furthermore, when the amount of actual measurement data is extremely large or the time interval for statistical processing is short, the computational load required for processing becomes heavy, and it may not be possible to quickly calculate the predicted communication characteristics (or statistical information). This problem may become particularly pronounced when processing is performed in real time.

[0029] Furthermore, with this method, every time a predicted communication characteristic 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, which may require the user to reset parameters, etc., every time the user receives 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 the predicted value of communication characteristics (e.g., statistical information) using at least a part of the actual measurement data measured by the terminal device, without installing fixed sensors or the like.

[0031] Furthermore, it is required that the update frequency of the communication characteristic prediction value (for example, statistical information) and the number of actually measured data used for updating are minimized.

[0032] 2 is a diagram illustrating an example of communication processing according to an embodiment of the present disclosure. The communication processing illustrated 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 related to communication characteristics.

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

[0036] The control station 100 executes a correction process using the acquired measured data (step S3). For example, as the correction process, the control station 100 corrects the measured data based on the observation error included in the measured data.

[0037] The control station 100 performs statistical processing using the corrected measurement data (hereinafter also referred to as corrected data) (step S4). The statistical processing includes, for example, generating statistical information on communication characteristics (radio wave propagation characteristics) in the communication area using the corrected data. The statistical processing will be described later with reference to FIG. 3.

[0038] The control station 100 notifies the base station 300 and / or the terminal device 400_1 of statistical information related to the results of the statistical processing (step S5). Note that, although the control station 100 notifies the terminal device 400_1 of the statistical information here, it may also notify the terminal devices 400_2 and 400_3 in the same manner.

[0039] The control station 100 may notify the base station 300 and / or the terminal device 400 of the estimated communication 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] Depending on the statistical processing, the control station 100 may omit notification of statistical information.

[0041] 3 is a diagram illustrating an example of statistical processing according to an embodiment of the present disclosure. The statistical processing according to the embodiment may include a plurality of processes (for example, a first statistical processing and a second statistical processing).

[0042] The control station 100 generates an estimation model based on first corrected data obtained by correcting first actual measurement data in at least one first area R1. This estimation model is, for example, a communication characteristics estimation model used to estimate communication characteristics (radio wave propagation environment).

[0043] The control station 100 generates an estimation model from the first correction data for the first area R1, for example, by machine learning. In Fig. 3, the number of first areas R1 used to generate the estimation model is three or more (first areas R1_1, R1_2, R1_3, ...), but the number of first areas R1 is not limited to this. The number of first areas R1 may be two or less.

[0044] Here, the first correction data corresponds to the correction data described above, i.e., the first correction data is data obtained by correcting the actual measurement data (corresponding to the first actual measurement data described above) measured in each first area R1 by the control station 100 based on the measurement error.

[0045] The control station 100 can generate an estimation model with higher accuracy by generating the estimation model using correction data in which the observation error has been corrected.

[0046] Here, the process in which the control station 100 generates the estimation model may correspond to the statistical process (e.g., the first statistical process) described above. In this case, the control station 100 may or may not notify the base station 300 and / or the terminal device 400 of the estimation model that is the result of the statistical process.

[0047] Next, the control station 100 generates estimated data based on the estimation model and second corrected data obtained by correcting the second actual measurement data in the second area R2. For example, the control station 100 inputs the second corrected data and uses the output obtained from the estimation model as the estimated data.

[0048] The control station 100 may, for example, determine parameters (communication parameters) related to communication in the second area R2 from the estimated data.

[0049] Here, the second correction data corresponds to the correction data described above, i.e., the second correction data is data obtained by correcting the actual measurement data (corresponding to the second actual measurement data described above) measured in the second area R2 by the control station 100 based on the measurement error.

[0050] Furthermore, the process in which the control station 100 generates the estimated data and / or communication parameters may correspond to the statistical process (e.g., the second statistical process) described above. In this case, the control station 100 may or may not notify the base station 300 and / or the terminal device 400 of the estimated data and / or communication parameters that are the results of the statistical process.

[0051] The control station 100 can generate estimated data with higher accuracy by generating estimated data using correction data in which the observation error has been corrected.

[0052] 4 is a diagram illustrating an example of the configuration of a communication system according to an embodiment of the present disclosure. As shown in FIG. 4, the communication system includes a control station 100, a base station 300, and a terminal device 400.

[0053] The control station 100 is a cloud server 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.

[0054] 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.

[0055] 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.

[0056] Alternatively, the control station 100 may be a database device having an information processing function.

[0057] The base station 300 is connected by wireless communication to the terminal devices 400. The number of terminal devices 400 connected to the base station 300 is not limited to one, and may be two or more.

[0058] The communication system may include components other than the above-described control station 100, base station 300, and terminal device 400. For example, the communication system may include a core network.

[0059] Furthermore, examples of radio access technologies (RATs) used for wireless communication between the base station 300 and the terminal device 400 include cellular communication systems such as a 4G system, a 5G system, a 6G system, LTE (Long Term Evolution), and NR (New Radio). Furthermore, this radio access technology is not limited to cellular communication systems. For example, examples of this radio access technology include various wireless communication systems such as wireless LAN, Bluetooth (registered trademark), and LPWA (Low Power Wide Area) systems.

[0060] 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 shown in FIG. 5, the control station 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0061] (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 USB (Universal Serial Bus) interface configured by a USB host controller, a USB port, etc. The communication unit 110 may be a wired interface or a wireless interface. The communication unit 110 functions as 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.

[0062] (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.

[0063] (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 CPU (Central Processing Unit), an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit).

[0064] 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 RAM (Random Access Memory) or the like as a work area. Note that the control unit 130 may also be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or FPGA (Field Programmable Gate Array). A CPU, an MPU, a GPU, an ASIC, and an FPGA can all be considered as controllers.

[0065] <2.3. Configuration Example of Base Station> 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). Furthermore, the base station 300 operates beams that can be identified by 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.

[0066] Note that the base station 300 may be configured as a collection of multiple physical or logical devices. For example, in an embodiment of the present disclosure, the base station 300 may be divided into multiple devices, a baseband unit (BBU) and an RU, and interpreted as a collection of these multiple devices. Additionally or alternatively, in an embodiment of the present disclosure, the base station 300 may be either or both of a BBU and an RU. The BBU and the RU may be connected via a predetermined interface (e.g., eCPRI). Additionally or alternatively, the RU may be referred to as a remote radio unit (RRU) or a radio DoT (RD). Additionally or alternatively, the RU may correspond to a gNB-DU (gNB-DU) described later. Additionally or alternatively, the BBU may correspond to a gNB-CU (gNB-CU) described later. Alternatively, the RU may be connected to a gNB-DU (gNB-DU) described later. Furthermore, the BBU may correspond to a combination of a gNB-CU and a gNB-DU (gNB-DU) described later. Additionally or alternatively, the RU may be a device integrally formed with an antenna. The antennas of the base station 300 (e.g., antennas integrally formed with the RUs) may employ an Advanced Antenna System and support MIMO (e.g., FD-MIMO) and beamforming. In the Advanced Antenna System, the antennas of the base station 300 (e.g., antennas integrally formed with the RUs) may include, for example, 64 transmitting antenna ports and 64 receiving antenna ports.

[0067] Furthermore, multiple 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 Access Network (AN), or an AN node. The RAN in LTE is called an Enhanced Universal Terrestrial RAN (EUTRAN). The RAN in NR is called an NGRAN. The RAN in W-CDMA (UMTS) is called a UTRAN. The base station 300 in LTE is called an Evolved Node B (eNodeB) or eNB. That is, the EUTRAN includes one or more eNodeBs (eNBs). The base station 300 in NR is called a 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, if the base station 300 is an eNB, gNB, or the like, it may be referred to as a 3GPP (registered trademark) access. Additionally or alternatively, if the base station 300 is a wireless access point (e.g., a WiFi (registered trademark) access point), it may be referred to as a non-3GPP access. Additionally or alternatively, the base station 300 may be an optical extension device called an RRH (Remote Radio Head). Additionally or alternatively, if the base station 300 is a gNB, it may be referred to as a combination of the gNB CU (Central Unit) and gNB DU (Distributed Unit) described above, or as either one of them. The gNB CU hosts multiple upper layers (e.g., RRC, SDAP, PDCP) in the Access Stratum for communication with the UE, while the gNB-DU hosts multiple lower layers (e.g., RLC, MAC, PHY) in the Access Stratum.That is, among the messages and information described below, RRC signaling (e.g., various SIBs including MIB and SIB1, RRC Setup message, RRC Reconfiguration message) may be generated by the gNB CU, while DCI and various physical channels (e.g., PDCCH and PBCH) described below 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 the F1 interface described below. 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 the X2 interface. Additionally or alternatively, when multiple base stations 300 are gNBs or a combination of gn-eNBs and gNBs, the devices may be connected via an Xn interface. Additionally or alternatively, when multiple base stations 300 are a combination of gNB CUs and gNB DUs, the devices may be connected via the above-mentioned F1 interface. Messages and information (RRC signaling or DCI information, physical channel) described below may be communicated between multiple base stations 300 (e.g., via the X2, Xn, or F1 interfaces).

[0068] 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., the terminal device 400), the PCell and zero or one or more SCell(s) provided by a Master Node (MN) 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, the PSCell and zero or one or more SCell(s) provided by a Secondary Node (SN) are called a Secondary Cell Group (SCG). Unless special configuration (e.g., PUCCH on SCell) is performed, the physical uplink control channel (PUCCH) is transmitted on the PCell and PSCell, but not on the SCell. Furthermore, radio link failure is detected on the PCell and PSCell, but not on the SCell (it does not need to be detected). Since the PCell and PSCell thus play special roles among the serving cell(s), they are 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 in the UE, and one Bandwidth Part may be used by the UE as an Active BWP. Furthermore, 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.

[0069] 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.

[0070] The base station 300 shown in Figure 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 Figure 6 is a functional configuration, and the hardware configuration may be different. Furthermore, the functions of the base station 300 may be distributed and implemented in multiple physically separated units. For example, as described above, the functions of the base station 300 may be distributed to the CU and DU, or to the CU, DU, and RU.

[0071] The communication unit 310 is a signal processing unit for wireless communication with other wireless communication devices (e.g., terminal device 400 and other base stations 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 compatible with one or more wireless access methods. For example, the communication unit 310 supports both NR and LTE. The communication unit 310 may also 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.

[0072] (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. Note that when the communication unit 310 supports a plurality of radio access methods, each unit of the communication unit 310 may be configured individually for each radio access method. For example, the reception processing unit 311 and the transmission processing unit 312 may be configured individually for LTE and NR.

[0073] The reception processing unit 311 processes uplink signals received via the antenna 313. The reception processing unit 311 operates as a receiver that receives received signals. The reception processing unit 311 includes a radio reception unit 311a, a demultiplexing unit 311b, a demodulation unit 311c, and a decoding unit 311d.

[0074] 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 guard intervals (cyclic prefixes), extraction of frequency domain signals by fast Fourier transform, etc. on the uplink signals. 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 signals output from the radio receiving unit 311a.

[0075] The demodulator 311c demodulates the received signal using a modulation method such as binary phase shift keying (BPSK) or quadrature phase shift keying (QPSK) 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 need to be equidistant. The constellation may be a non-uniform constellation (NUC).

[0076] The decoder 311d performs decoding processing on the coded bits of the demodulated uplink channel. The decoded uplink data and uplink control information are output to the controller 340.

[0077] The transmission processing unit 312 performs transmission processing of downlink control information and downlink data. In this manner, the transmission processing unit 312 is an acquisition unit that acquires bit sequences such as downlink control information and downlink data 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.

[0078] The encoder 312a encodes the downlink control information and downlink data input from the controller 340 using a coding method such as block coding, convolutional coding, or turbo coding. Note that the encoder 312a may also encode using a polar code or a low density parity check code (LDPC code).

[0079] 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.

[0080] The multiplexing unit 312c multiplexes the modulation symbols of each channel and the downlink reference signal and allocates the multiplexed symbols to predetermined resource elements. 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 using a 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.

[0081] (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.

[0082] (Network Communication Unit 330) The network communication unit 330 is a communication interface for communicating with a node located higher 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.

[0083] (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. Note that the control unit 340 may also 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.

[0084] 2.4. Example of the Configuration of the Terminal Device Next, an example of the configuration 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 an example of the configuration of the terminal device 400 according to an embodiment of the present disclosure.

[0085] 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 also 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.

[0086] The terminal device 400 may also be capable of sidelink communication with other terminal devices 400. The terminal device 400 may be able to use 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 (e.g., base station 300 and other terminal devices 400). Alternatively, 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 or visible light (optical wireless).

[0087] The terminal device 400 may simultaneously connect to multiple base stations 300 or multiple cells to perform communication. For example, if one base station 300 can provide multiple cells, the terminal device 400 can perform carrier aggregation by using one cell as a pCell and another cell as an sCell. Furthermore, if multiple base stations 300 can each provide one or multiple cells, the terminal device 400 can use one or multiple cells managed by one base station 300 (MN (e.g., MeNB or MgNB)) as a pCell, or a pCell and sCell(s), and use one or multiple cells managed by the other base station 300 (SN (e.g., SeNB or SgNB)) as a pCell (PSCell), or a pCell (PSCell) and sCell(s), thereby realizing dual connectivity (DC). DC may also be referred to as multi-connectivity (MC).

[0088] 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 using 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 using coordinated multi-point transmission and reception (CoMP) technology.

[0089] 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. Furthermore, the functions of the terminal device 400 may be distributed and implemented in multiple physically separated configurations.

[0090] (Communication unit 410) The communication unit 410 is a signal processing unit for wireless communication with other wireless communication devices (for example, 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 that supports one or more wireless access methods. For example, the communication unit 410 supports both NR and LTE. The communication unit 410 may also support W-CDMA and cdma2000 in addition to NR and LTE. The communication unit 410 may also support communication using NOMA.

[0091] 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 plurality of transmission processing units 412, and a plurality of antennas 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.

[0092] 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 means of the terminal device 400.

[0093] 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.

[0094] (Input / Output Unit 440) The input / output unit 440 is a user interface for exchanging information with a user. For example, the input / output unit 440 is an operation device such as a keyboard, a mouse, operation keys, 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 input / output means (input means, output means, operation means, or notification means) of the terminal device 400. Note that, for example, when the terminal device 400 is a sensor, at least some of the functions (output function and / or input function) of the input / output unit 440 may be omitted.

[0095] (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 the processor executing various programs stored in a storage device inside the terminal device 400 using RAM or the like as a work area. The control unit 450 may also be realized by an integrated circuit such as an ASIC or an FPGA. The CPU, MPU, GPU, ASIC, and FPGA can all be considered as controllers.

[0096] <<3. Technical Features>> In this embodiment, the control station 100 generates an estimation model using actual measurement data (first actual measurement data) of communication characteristics in a first area R1. The control station 100 also uses actual measurement data (second actual measurement data) of communication characteristics in a second area R2 and the estimation model to estimate data (estimated data) of communication characteristics in the second area R2.

[0097] At this time, the control station 100 corrects at least one of the first measured data and the second measured data based on the measurement error.

[0098] For example, the second area R2 is location X (for example, around Tokyo Station). The first area R1 is location A (for example, around Osaka Station), location B (for example, around Kawasaki Station), and location C (for example, around Shimbashi Station). For example, location A corresponds to the first area R1_1. For example, location B corresponds to the first area R1_2. For example, location C corresponds to the first area R1_3.

[0099] Here, the second area R2 may be an area different from the first area R1, or may be the same area as the first area R1. Note that the areas listed here are only examples, and areas other than these may be the first area R1 and / or the second area R2.

[0100] The estimation model is generated by a predetermined method using first correction data obtained by correcting first actual measurement data related to communication characteristics (e.g., radio wave propagation characteristics) at locations A, B, and C. The communication characteristics at location X are generated based on the generated estimation model and second correction data obtained by correcting second actual measurement data related to communication characteristics at location X.

[0101] Furthermore, for example, linear regression analysis and multiple regression analysis are examples of simple methods for generating an estimation model. Furthermore, AI (artificial intelligence, machine learning, deep learning, etc.) may be used as a method for generating an estimation model. Examples of AI include the following techniques: - Deep Learning - Multi-Layer Perceptron (MLP) - Convolutional Neural Network (CNN) - Stochastic Gradient Descent - Decision Tree - Random Forest - Support Vector Machine - k-Nearest Neighbors - Naive Bayes Classifier In addition to these, many other techniques exist. It is desirable to select an appropriate technique taking into consideration the principles of each technique.

[0102] When using AI, the communication characteristics to be estimated are used as the objective variable, and various parameters, data, and information (e.g., communication environment information) described below can also be used as explanatory variables.

[0103] As described above, the control station 100 corrects (calibrates) the estimation data of the estimation model using actual measurement data (e.g., the first actual measurement data and / or the second actual measurement data). Below, seven calibration methods (first calibration method to seventh calibration method) will be described as examples.

[0104] <3.1. Observation Errors in Measured 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 may contain observation errors.

[0105] The statistical properties of the observation error differ depending on the type of terminal device 400, but if actual measurement data with a large mean value or variance of the observation error is used directly in statistical processing of communication characteristics, the accuracy of the statistical processing may deteriorate.

[0106] Therefore, it is desirable for the control station 100 to perform statistical processing after reducing such observation errors as much as possible.

[0107] Therefore, the control unit 130 of the control station 100 according to this embodiment acquires measured data (e.g., first measured data and / or second measured data) relating to communication characteristics in the communication area. The control unit 130 corrects the measured data based on the measurement error of the measured data. The control unit 130 generates statistical information on the communication characteristics in the communication area using the corrected measured data.

[0108] This allows the control station 100 to generate statistical information on communication characteristics with higher accuracy.

[0109] Methods for calibrating (correcting) the observation error can be broadly classified into a method for improving the observation accuracy itself by the terminal device 400, and a method for performing appropriate statistical processing on the actual measurement data including the observation error.

[0110] A method for improving the observation accuracy of the terminal device 400 itself depends on an observation application for measuring communication characteristics that are actually measured by the terminal device 400. In this method, for example, the control station 100 controls the observation application of the terminal device 400 using, for example, an appropriate API, so as to generate measured data with the required accuracy.

[0111] For example, the control station 100 controls the terminal device 400 to observe 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.

[0112] On the other hand, in a method of performing appropriate statistical processing on actual measurement data including observation errors, the control station 100 estimates the moments of the observation errors using a statistical method, for example, and calibrates the observation errors of the actual measurement data by taking the estimated moments into account.

[0113] For example, the control station 100 corrects the measured data based on the results of statistical analysis of the measurement errors. Such methods are described as the first to third calibration methods. Also, for example, the control station 100 generates a correction model based on the results of statistical analysis of the measurement errors, and corrects the measured data in accordance with the generated correction model. Such methods are described as the fourth and fifth calibration methods.

[0114] Furthermore, for example, the control station 100 corrects the measured data using simulation data. This method will be described as a sixth calibration method. Furthermore, for example, the control station 100 determines which measured data to exclude from a plurality of measured data (hereinafter also referred to as a measured data group) in accordance with the observation error. This method will be described as a seventh calibration method.

[0115] (First Calibration Method) For example, when a terminal device 400 with high 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 high observation accuracy. Here, an example of the terminal device 400 with high observation accuracy is a device for observing communication characteristics of a signal, such as a spectrum analyzer.

[0116] Specifically, the control station 100 calibrates (corrects) the measured data based on the average value of the observation error (measurement error). The control station 100 calculates the average value e' of the observation error at the point s using the formula (1). ｓ Estimate.

[0117]

[0118] Here, P ｓ，ｔｕｒｅ is the communication characteristic at point s, and is actual measurement data that does not include measurement errors or has small measurement errors. ｓ，ｔｕｒｅ is actual measurement data observed by the terminal device 400 with high observation accuracy.

[0119] P ｓ，ｉ is the communication characteristic at point s, and is the i-th actual measurement data including the measurement error. ｓ，ｉ is the communication characteristic observed by the terminal device 400 with low observation accuracy. ｓ is the number of measured data at point s.

[0120] Next, the control station 100 performs calibration on the i-th measured data using equation (2) with the measurement error as an offset.

[0121]

[0122] However, in this calibration method, to ensure calibration accuracy, it is desirable that the average value of the observation errors be relatively constant across multiple locations (or the same terminal device 400). It is also desirable that the standard deviation of the observation errors be less than a certain magnitude.

[0123] When the average value of the observation errors is not constant but varies at multiple points (in other words, the standard deviation is equal to or greater than a certain value), it is desirable for the control station 100 to calculate the offset of the observation errors for each average value of the observation errors at each point. For example, when the average value of the observation errors varies over time, the average value of the observation errors may be calculated for each time period. In addition, when the standard deviation of the observation errors is equal to or greater than a certain value and the total number of actual measurement data E ｓ If is a small value equal to or less than a certain value, the measurement data with a large measurement error may be excluded using a method described later.

[0124] In addition to the average value of the observation errors, the control station 100 may also use various other moments such as the median, variance, mode, etc., to calibrate the observation errors.

[0125] Here, the average value e ｓ However, the control station 100 estimates the average value e ｓ For example, the control station 100 may divide the communication area into a plurality of regions (e.g., rectangular grids) and estimate the average value e ｓ may be estimated.

[0126] (Second Calibration Method) As a second calibration method, there is a method of estimating confidence intervals of various moments.

[0127] 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 using, for example, a confidence limit value. Note that, here, the moments represent statistical values ​​such as the mean value and variance.

[0128] 8 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. 8 represents communication characteristics, and the vertical axis represents 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.

[0129] p in FIG. ｍｉｎ is the lower confidence limit for the 95% confidence interval. ｍａｘ is the upper confidence limit for the 95% confidence interval.

[0130] If the measurement error is large, the width of the confidence interval will be wide. Therefore, if 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 in determining whether to update the statistical information.

[0131] (Third Calibration Method) The third calibration method is calibration (correction) based on an estimation of an error distribution. The control station 100 estimates the distribution of the observation errors and corrects the measured data based on the estimated distribution.

[0132] The control station 100 calculates the observation error using the following equation (3).

[0133]

[0134] Here, e ｋ is the observation error of the kth measured data. ｔｕｒｅ is actual measurement data that does not include measurement errors or has small measurement errors. ｔｕｒｅ is actual measurement data observed by the terminal device 400 with high observation accuracy.

[0135] P ｋ is the k-th measured data including the measurement error. ｋ is the communication characteristic observed by the terminal device 400 with low observation accuracy.

[0136] The control station 100 calculates the measurement errors for the E pieces of actual measurement data, and estimates the distribution of the measurement errors using the calculated measurement errors.

[0137] For example, if the distribution shape of the observation errors is known, the control station 100 may calculate statistics of the observation errors and perform fitting. Alternatively, if the distribution shape of the observation errors is not known, the control station 100 may perform fitting by assuming that the distribution of the observation errors has a predetermined distribution shape.

[0138] 9 is a diagram illustrating an example of the distribution shape of the observation error according to the embodiment of the present disclosure. In FIG. 9, the distribution shape of the observation error is shown as a cumulative distribution function. The horizontal axis of FIG. 9 represents the observation error e ｋ , the vertical axis represents the cumulative distribution function.

[0139] 10 is a diagram illustrating another example of the distribution shape of the observation error according to the embodiment of the present disclosure. In FIG. 10, the distribution shape of the observation error is shown as a probability density function. The horizontal axis of FIG. 10 represents the observation error e ｋ , the vertical axis represents the probability density function.

[0140] For example, it is assumed that the distribution of the observation errors is known to follow a normal distribution. In this case, the control station 100 calculates the mean value and standard deviation of the observation errors when calculating the cumulative distribution function or the probability density function. The control station 100 parametrically estimates the distribution of the observation errors by fitting the mean value and standard deviation to the normal distribution.

[0141] On the other hand, the observation error e ｋ In this case, the control station 100 uses the empirical distribution function to obtain the cumulative distribution function. Ｎ is a method for empirically determining the cumulative distribution function, and is expressed by the following equation (4).

[0142]

[0143] Here, N is the number of actual measurement data, e ｋ is the observation error of the kth measured data. ｅｋ (k is a subscript of e) is an indicator function and is defined by the following equation (5).

[0144]

[0145] By using equation (4), the control station 100 can grasp the degree of observation error and the probability of occurrence.

[0146] Furthermore, when frequency sharing (for example, "Dynamic Spectrum Access (DSA)") is performed, actual measurement data may be used to estimate interference power. In this case, the control station 100 needs to estimate the interference power to be larger than the actual value.

[0147] 11 is a diagram illustrating an example of a cumulative distribution function according to an embodiment of the present disclosure. For example, when the control station 100 estimates the measurement error of the interference power using equation (3), the error distribution shown in FIG. 11 is obtained.

[0148] The horizontal axis of FIG. 11 represents the observation error e of the kth measured data. ｋ (e ｋ =P ｔｒｕｅ -P ｋ ) and the vertical axis represents the cumulative distribution function.

[0149] In the example of Fig. 11, the median of the interference power measurement error is 0 (dB). The cumulative distribution function in Fig. 11 indicates that in the range to the right of the median, the control station 100 underestimates the interference power compared to the actual value, meaning that the event occurs with a 50% probability.

[0150] When frequency sharing is performed, the system must be designed so that the probability of underestimation is equal to or less than a predetermined value (for example, 5% to 1%).

[0151] Therefore, the control station 100 may add a margin to each piece of actual measurement data (or the observation error of each piece of actual measurement data) so that the observation error 0 (dB) is between the 95th percentile and the 99th percentile.

[0152] 12 is a diagram illustrating another example of a cumulative distribution function according to an embodiment of the present disclosure, in which the control station 100 adds a margin Δ to the measurement error.

[0153] The horizontal axis of FIG. 12 represents the observation error e of the kth measured data. ｋ (e ｋ =P ｔｒｕｅ -P ｋ ) with a margin added (hereinafter also referred to as the correction error), and the vertical axis represents the cumulative distribution function.

[0154] For example, as shown in Fig. 12, the control station 100 can add a margin Δ to the observation error so that the cumulative distribution function at which the observation error (correction error) is 0 (dB) is between 0.5 and 0.95. That is, in the example of Fig. 12, the control station 100 estimates the cumulative distribution function by adding a margin Δ to the observation error so that the probability of underestimating the interference power from the actual value is 95% or less.

[0155] The control station 100 calculates the margin amount Δ as follows: Δ=0−P ｔｈ Here, P ｔｈ is a value at a predetermined percentile (here, the observation error). For example, if it is desired to set the probability of underestimating the interference power from the actual value to 95% or less, the control station 100 uses the observation error at the 95th percentile.

[0156] The control station 100 adds, for example, a margin Δ to the measurement error. ｋ ec ｋ = e ｋ +Δ=P ｔｒｕｅ -P ｋ It is calculated as +Δ.

[0157] In this way, the control station 100 can calculate a margin amount that keeps the occurrence probability of a certain event below a tolerance value by estimating the distribution of the observation error. As a result, the control station 100 can probabilistically guarantee a predetermined communication performance by taking the calculated margin amount into account in the observation error.

[0158] (Fourth Calibration Method) The fourth calibration method is calibration (correction) using regression analysis. The control station 100 can estimate the observation error using linear regression analysis or the like. The control station 100 corrects the actually measured data based on the results of the regression analysis of the observation error.

[0159] First, the control station 100 performs linear regression analysis on the actual measurement data including the measurement error and the actual measurement data (true values) including no measurement error (or with a small measurement error) to estimate a linear model.

[0160] 13 is a diagram illustrating an example of a scatter plot of measured data according to an embodiment of the present disclosure, in which the vertical axis represents measured data that does not include measurement errors (referred to as true values ​​in FIG. 13 ), and the horizontal axis represents measured data that includes measurement errors (referred to as measured data in FIG. 13 ).

[0161] 14 is a diagram showing an example of an estimation result of a linear model according to an embodiment of the present disclosure, in which the estimated linear model is shown by a dotted line superimposed on the scatter diagram of FIG.

[0162] 13 and 14, for example, assume that 10 pieces of actual measurement data (including the measurement errors) are each uniformly added with an observation error of 10. In this case, the control station 100 performs linear regression analysis based on the actual measurement data to estimate a linear model of y = x - 10.

[0163] For example, the control station 100 estimates the observation error using the estimated linear model for actual measurement data that includes an observation error and whose true value is unknown. The control station 100 corrects the actual measurement data that includes the observation error using the estimated observation error (hereinafter also referred to as the estimated error).

[0164] For example, the control station 100 receives the i-th measured data P ｉ is corrected using the following equation (6): ｉ The i-th corrected measured data (hereinafter also referred to as corrected measured data) is expressed as e ｅ represents the observation error estimated using a linear model.

[0165]

[0166] In a real environment, the measurement error also varies stochastically, so the accuracy of the estimation error varies depending on the standard deviation of the measurement error and the number of data points used in the linear regression analysis.

[0167] For example, the control station 100 acquires the actual measurement data including the measurement error and the true value corresponding to the actual measurement data in the area where the actual measurement data is measured (or an area similar to the area).

[0168] For example, the control station 100 acquires actual measurement data observed by a terminal device 400 with good (high) observation accuracy at the same point as true values, and acquires observation data observed by a terminal device 400 with poor (low) observation accuracy as actual measurement data including observation errors. Note that an example of a terminal device 400 with high observation accuracy is a device for observing signal communication characteristics, such as a spectrum analyzer. An example of a terminal device 400 with low observation accuracy is a device such as a smartphone.

[0169] The control station 100 estimates a linear model using the acquired measured data and true values. After that, when the control station 100 acquires the first measured data and / or the second measured data, it corrects the observation error of the first measured data and / or the second measured data using the estimated linear model. The explanatory variables of the linear model include at least one of the measured data and the true values. The objective variable of the linear model includes the observation error.

[0170] Here, the control station 100 estimates the linear model using linear regression analysis, but the regression model for correcting the observation error may be estimated using a regression analysis other than linear regression analysis. For example, the control station 100 may perform multiple regression analysis, or may estimate a regression model with multiple slopes. A regression model with multiple slopes is effective when the error trend varies depending on the value of the actual measurement data.

[0171] (Fifth Calibration Method) The fifth calibration method is calibration (correction) using machine learning (AI / ML). The control station 100 may correct the measured data using, for example, a model generated using AI / ML. The control station 100 may, for example, set explanatory variables and objective variables and estimate an error model for estimating the observation error using machine learning. Alternatively, the control station 100 may, for example, estimate a correction model for estimating the measured data that does not include the observation error using machine learning.

[0172] Examples of explanatory variables that can be set by the control station 100 include at least one of the following information: - Actual measurement data including measurement errors - True value (actual measurement data that does not include measurement errors or has small measurement errors) - Distance between transmitter and receiver - Frequency - Transmission power - Information on the LOS (Line Of Sight) / NLOS (Non Line Of Sight) environment

[0173] Examples of the objective variable that can be set by the control station 100 include at least one of the following information: - Observation error - True value (actual measurement data that does not include observation error, or actual measurement data with a small observation error)

[0174] The explanatory variables described above are merely examples, and other parameters (information) that affect the observation error may be set as explanatory variables. For example, the standard deviation of the observation error may be set as an explanatory variable.

[0175] (Sixth Calibration Method) The sixth calibration method is calibration (correction) using simulation data. The control station 100 corrects the observation error using simulation data instead of actual measurement data with no (or small) observation error. For example, the control station 100 corrects the true value P ｔｒｕｅ Simulation data P ｓｉｍ The observation error is calculated by replacing

[0176] P ｓｉｍ are communication characteristics obtained by simulation. For example, at least one of the following information can be used by the control station 100 for the simulation: - Ray tracing - Terrain and building data (three-dimensional map information, etc.)

[0177] The control station 100 calculates the true value P ｔｒｕｅ Simulation data P ｓｉｍ By replacing it with, the observation error is calculated and / or the actual measurement data is corrected.

[0178] In this case, the control station 100 uses the simulation data P ｓｉｍ It is necessary to estimate with high accuracy.

[0179] (Seventh Calibration Method) In the first to sixth calibration methods described above, the control station 100 corrects the measured data using the observation error. For example, if the standard deviation of the measurement error is large and the number of measured data is insufficient (less than a predetermined value), the accuracy of the control station 100's correction of the measurement error may be degraded. In this case, it is desirable for the control station 100 to generate statistical information by excluding measured data with large observation error.

[0180] Here, a method will be described in which the control station 100 calibrates the measured data by excluding measured data with large measurement errors from the acquired measured data group.

[0181] (First Exclusion Method) For example, if the difference between a true value (measured data with no or small observation error) and actual measurement data (measured data with an observation error) is equal to or greater than a predetermined tolerance, the control station 100 excludes the actual measurement data from the actual measurement data group. For example, the control station 100 determines whether to exclude the actual measurement data group depending on whether the following conditional expression (7) is satisfied:

[0182]

[0183] Here, γ represents the allowable error (predetermined tolerance). For example, the control station 100 may use the kth measured data P ｋ are excluded to generate statistics.

[0184] The control station 100 may determine whether to correct the observation error using conditional expression (7). That is, the control station 100 may determine not to correct the observation error for the measured data that satisfies conditional expression (7), i.e., the measured data that has not been excluded.

[0185] The allowable error in conditional expression (7) when determining whether to exclude the measured data may be different from the allowable error in conditional expression (7) when determining whether to correct the measurement error.

[0186] For example, the control station 100 may decide not to correct the measured data whose measurement error is smaller than the measurement error of the measured data that has been determined not to be excluded.

[0187] (Second Exclusion Method) For example, the control station 100 may determine the measured data to be excluded based on an allowable standard deviation.

[0188] For example, the control station 100 calculates the standard deviation of the measured data using the measured data of a certain terminal device 400. If the calculated standard deviation exceeds an allowable value, the control station 100 excludes the measured data from the generation of statistical information.

[0189] (Third Exclusion Method) For example, the control station 100 may determine the measured data to be excluded using an outlier test. The control station 100 performs the outlier test using the measured data of a certain terminal device 400, for example.

[0190] Examples of outlier tests include the following: - Smirnoff-Grubbs test - Thompson test - Interquartile range - Methods using the mean and standard deviation of a normal distribution

[0191] In the method using the mean and standard deviation of a normal distribution, for example, the control station 100 performs outlier testing according to μ±nσ of the normal distribution, where μ is the mean, σ is the standard deviation, and n is typically 2 or 3.

[0192] By performing outlier testing, the control station 100 can detect measured data with an unusually large measurement error as an outlier, and can exclude this measured data from the generation of statistical information.

[0193] <<4. Processing Example>> An example of communication processing executed in a communication system will be described below. The communication processing executed in this embodiment includes a generation process for generating an estimation model and a determination process for determining control information including communication parameters.

[0194] 15 is a sequence diagram illustrating an example of the flow of a generation process according to an embodiment of the present disclosure. The generation process illustrated in FIG. 15 is executed by the communication system, for example, before the determination process. The generation process is executed, for example, between the base station 300, the terminal device 400, and the control station 100 in the first area R1.

[0195] The base station 300 transmits a signal to the terminal device 400 (step S101). The terminal device 400 measures this signal and transmits the measurement result, ie, first measured data, to the control station 100 (step S102).

[0196] 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, Ping, and location information of the terminal device 400.

[0197] The control station 100 performs correction based on the measurement error on the first measured data to generate first corrected data (step S103). The control station 100 generates second corrected data using, for example, the first to seventh calibration methods described above.

[0198] The control station 100 generates an estimation model based on the first correction data (step S104). For example, the control station 100 may generate the estimation model by performing machine learning using the simulation data as student data and the first correction data as teacher data. In this way, the control station 100 may use data other than the first correction data to generate the estimation model.

[0199] The terminal device 400 may transmit the first measured data to the control station 100 via the base station 300, or may transmit the first measured 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 first measured data via the cellular network, or may notify the control station 100 of the first measured data via a network other than the cellular network (for example, a LAN).

[0200] In this way, the control station 100 generates an estimation model using the first correction data in which the observation error has been corrected, thereby enabling the generation of a more accurate estimation model.

[0201] 16 is a sequence diagram showing an example of the flow of the determination process according to an embodiment of the present disclosure. The determination process shown in FIG. 16 is executed by the communication system, for example, at a predetermined interval or in response to a specific event. The determination process is executed, for example, between the base station 300, the terminal device 400, and the control station 100 in the second area R2.

[0202] The base station 300 transmits a signal to the terminal device 400 (step S201). The terminal device 400 measures this signal and transmits the measurement result, ie, second measured data, to the control station 100 (step S202).

[0203] Here, examples of information measured by the terminal device 400 include RSRP, RSSI, and RSRQ indicating received power, interference power, SNR, SIR, SINR, throughput, delay amount, Ping, and location information of the terminal device 400.

[0204] The control station 100 performs correction based on the measurement error on the second measured data to generate second corrected data (step S203). The control station 100 generates the second corrected data using, for example, the first to seventh calibration methods described above.

[0205] The control station 100 generates estimated data based on the second correction data and the estimation model (step S204). For example, the control station 100 inputs the second correction data into the estimation model and uses the resulting output as the estimated data.

[0206] For example, the control station 100 may generate the estimated data using the second correction data, in other words, data other than the second actual measurement data (for example, simulation data, etc.). In this way, when the control station 100 does not use the second actual measurement data to generate the estimated data, the processes of steps S201 to S203 described above may be omitted.

[0207] The control station 100 determines communication parameters between the base station 300 in the second area and the terminal device based on the generated estimated data, and generates control information according to the determined communication parameters (step S205).

[0208] For example, based on the estimated data, the control station 100 can determine communication parameters such as the transmission power of the base station 300. The control station 100 generates control information including information such as the determined transmission power.

[0209] The control station 100 transmits the generated control information to the base station 300 and / or the terminal device 400 (step S206).

[0210] The terminal device 400 may transmit the second measured data to the control station 100 via the base station 300, or may transmit the second measured 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 second measured data via the cellular network, or may notify the control station 100 of the second measured data via a network other than the cellular network (for example, a LAN).

[0211] 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 via the base station 300. That is, the control station 100 may notify the terminal device 400 of the control information via the 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).

[0212] <<5. Other>> Here, definitions (explanations) of terms used in the above-described embodiment will be described.

[0213] <5.1. Terminology> <5.1.1. Communication Characteristics> For example, the communication characteristics are any of the following, or a combination thereof: - Characteristics based on radio wave propagation from a transmission point to a reception point (downlink, uplink, and sidelink) - Communication parameters at a transmission point, a reception point, a base station 300, a terminal device 400, and / or a communication node

[0214] The characteristics based on radio wave propagation from a transmitting point to a receiving 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.

[0215] The characteristics related to the received power include information on at least one of the received power, interference power, RSRP, RSRQ (Reference Signal Received Quality), RSSI (Received Signal Strength Indicator), SNR (Signal-to-noise ratio), and SINR (Signal and interference-to-noise ratio).

[0216] The characteristics related to communication speed include information on at least one of downlink throughput, uplink throughput, and sidelink throughput.

[0217] The delay-related characteristics include information about at least one of latency, jitter, and ping value.

[0218] The communication parameters at the transmission point, reception point, base station 300, terminal device 400 and / or communication node include dynamically determined parameters and / or semi-statically determined parameters.

[0219] The dynamically determined parameters include at least one of the following information: - Information about MCS (Modulation and Coding Scheme) - Information about transmission power - Information about beam control - Information about the number of MIMO (Multi-Input Multi-Output) multiplexings

[0220] 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 that can be selected (allowed for 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.

[0221] <5.1.2. Area> The area (location, base) in this embodiment can be given by any one of the following, or a combination thereof. This area corresponds to the communication area, the first area and / or the second area described above. - Coverage area (communication area) of a predetermined base station 300, terminal device 400, or communication node - Site or building owned or managed by a predetermined business operator - Area set in advance by a predetermined business operator or government - Area divided in a predetermined manner

[0222] 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, these 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.

[0223] For example, a base station 300 of a private network or an access point name (APN) of a core network can be set as a communication area for each base station. In other words, the same access point name is set for the same base station (communication area), and bases (communication areas) with different access point names are recognized as different base stations (communication areas).

[0224] An example of a predetermined method for dividing areas is a method based on location information, in which areas are divided into predetermined distances based on location information such as latitude and longitude.

[0225] 5.1.3. Regarding Communication Environment Information The communication environment information may be used, for example, separately from the first measured data, to generate an estimation model. The communication environment information may be used to generate estimation data using the estimation model. In other words, the communication environment information may be an explanatory variable of the estimation model.

[0226] The communication environment information includes at least one of static or quasi-static information and dynamic information. Static or quasi-static information is fixed information or information that is updated infrequently. Note that the static or quasi-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. Note that the dynamic information may be information in a lower communication layer (e.g., a physical layer, etc.).

[0227] The communication environment information includes, for example, at least one of the following information: - Map information - Structure information - Device information related to the base station 300 or the terminal device 400 - Sensing information acquired through a sensing device - Wireless communication information related to wireless communication

[0228] (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.

[0229] The map information includes, for example, topographical information, an office layout diagram, and a premises diagram.

[0230] (Structure Information) The structure information here includes information that affects radio wave propagation, such as reflection, diffraction, and transmission.

[0231] Examples of structures include buildings, walls, plantations, roads, signs, traffic lights, road signs, pillars, buildings, the ground, glass, windows, desks, and cabinets.

[0232] The structure information includes, for example, the position, shape, size, and material of the structure, as well as parameters related to radio wave propagation in the structure (dielectric constant, conductivity, etc.).

[0233] The structure information is generated and constructed based on, for example, the map information described above. In addition, the structure information may be generated and constructed based on information acquired from a sensing device, which will be described later.

[0234] (Device Information Regarding the Base Station 300 or the Terminal Device 400) The device information regarding the base station 300 or the terminal device 400 here includes, for example, at least one of the following pieces of information: - Antenna information regarding the antenna - Capability information regarding the functions and capabilities supported in wireless communication - Shape information regarding the shape and weight of the base station 300 and / or the terminal device 400 - Location information of the fixed base station 300 and / or the fixed terminal device 400

[0235] The antenna information here includes, for example, at least one of the antenna configuration, beam pattern, number of antenna elements, and antenna element configuration of the base station 300 and / or the terminal device 400 .

[0236] (Sensing information acquired through the sensing device) The sensing information acquired through the sensing device here includes object information regarding an object detected through the sensing device, and / or impact information regarding fluctuations and / or impacts on wireless communication caused by the detected object.

[0237] 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.

[0238] Three-dimensional spatial information (e.g., the above-described 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, photogrammetry technology or volumetric capture technology.

[0239] The objects detected by the sensing device include various devices such as the sensing device itself, devices other than the sensing device, and the terminal device 400 that transmits information acquired by the sensing device. The objects detected by the sensing device also include the above-mentioned structures and objects other than structures.

[0240] The terminal device 400 that transmits the sensing information acquired by the sensing device may or may not be equipped with the sensing device. If 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.

[0241] Note that sensing information acquired by sensing (sensing information acquired through a sensing device) may include various sensing information in addition to object detection information. For example, the sensing information acquired by sensing may include beam information (e.g., information on a beam pattern, a beam angle, etc.) related to a beam transmitted from the base station 300 and / or the terminal device 400.

[0242] The sensing device described above can detect moving objects such as people and robots in addition to stationary objects such as structures. The sensing device transmits, for example, information about the detected moving objects as sensing information via the terminal device 400.

[0243] The sensing device may transmit the sensing information when it detects a moving object and / or when it no longer detects a moving object. Alternatively, the sensing device may transmit the sensing information at regular intervals.

[0244] (Wireless communication information related to wireless communication) Here, the wireless communication information related to wireless communication includes, for example, at least one of the following information: - Communication information related to RAT (Radio access technology) and frequency - Information related to the transmission power of the base station 300 or the terminal device 400 - Scenario information related to the communication environment scenario - Constraint information related to the conditions and constraints related to wireless communication available in the local network - Quality information related to the communication quality in wireless communication

[0245] The communication information related to the RAT includes, for example, information related to LTE, NR, wireless LAN, Bluetooth (registered trademark), etc. The communication information related to the frequency includes information related to at least one of a frequency band, a center frequency, and a frequency bandwidth.

[0246] The information relating to 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.

[0247] Scenario information relating to communication environment scenarios includes, for example, information relating to urban areas, suburban areas, depopulated areas (rural areas), indoor offices, indoor factories, and the like.

[0248] 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.

[0249] The restriction information here includes information about conditions and restrictions regarding wireless communication permitted in the local network.

[0250] 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, and the installation location of base station 300.

[0251] These conditions and constraints may be set or defined in advance, and may be determined and / or changed based on information sent from a predetermined server or storage device (e.g., a Spectrum Access System (SAS) server).

[0252] The quality information regarding the communication quality in wireless communication includes, for example, at least one of the following 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

[0253] 5.1.4. Location Information The information relating to location is location information of the base station 300 and / or the terminal device 400. For example, the location information may be included in the first actual measurement data and / or the second actual measurement data.

[0254] The information about the location includes absolute location information such as latitude, longitude, and / or altitude obtained from, for example, a global positioning system (GPS) or a global navigation satellite system (GNSS).

[0255] Alternatively, the information relating to the location includes relative location information obtained by a beacon, UWB (Ultra-Wide Band), or the like.

[0256] The absolute location information or relative location information may be an area divided by a predetermined distance or method.

[0257] The information according to this embodiment (the first and second measured data, etc.) can be information linked to the position information.

[0258] 5.2. Virtual Space Estimation Information The virtual space estimation information includes, for example, information about 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 a simulation.

[0259] The virtual space estimation information (simulation data) may be used when generating an estimation model. The virtual space estimation information may be used to generate estimation data using the estimation model. The virtual space estimation information may be used as an explanatory variable of the estimation model. Alternatively, the virtual space estimation information may be used as a target variable of the estimation model. For example, the virtual space estimation information may be used as student data and teacher data of the estimation model.

[0260] The virtual space estimation information includes, for example, at least one of the following information: Line of Sight (LOS) / Non-Line of Sight (NLOS) information; Simulation information; Calculation information calculated based on the LOS / NLOS information, simulation information, etc.

[0261] (LOS / NLOS Information) 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.

[0262] 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 the 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.

[0263] The NLOS environment is also called a non-line-of-sight environment. The NLOS environment indicates a situation in which an obstacle 600, such as a structure or a person, is present on the line between the base station 300 and the terminal device 400, preventing the base station 300 and the terminal device 400 from transmitting and receiving direct waves therebetween. In this case, wireless communication between the base station 300 and the terminal device 400 is performed via reflected waves, diffracted waves, and the like, other than direct waves.

[0264] (Simulation Information) The simulation information includes information related to the simulation results 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 related to one or more paths (transmitted waves, arriving waves, rays) acquired by ray tracing simulation.

[0265] This path includes direct waves, reflected waves, diffracted waves, transmitted waves, etc. between the base station 300 and the terminal device 400. Generally, 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 multiple paths, and arrives at a receiving point (e.g., the terminal device 400).

[0266] The path information regarding the path may include, for example, at least one of the following information: - Received power at the receiving point - Transmitted power at the transmitting point - Path loss - Propagation distance - Number of reflections - Number of diffractions - Number of transmissions - Phase fluctuation - Emission angle at the transmitting point - Arrival angle at the receiving point - Arrival order of the path (the order of arrival in time among multiple paths) - Number of paths

[0267] (Calculation Information) The calculation information is information that is generated and calculated based on the above-mentioned LOS / NLOS information, simulation information, etc. The calculation information may include, for example, at least one of the following information: - Path loss at the reception point - Received power - Interference power - RSRP - RSRQ - RSSI - SNR - Downlink throughput - Uplink throughput - Latency - Jitter - Ping value

[0268] Here, an example of a process for generating virtual space estimation information (information generation process) according to this embodiment will be described with reference to Fig. 17. Fig. 17 is a flowchart showing an example of the flow of the information generation process according to an embodiment of the present disclosure.

[0269] The information generation process shown in FIG. 17 can be executed by, for example, the control station 100 when virtual space estimation information (simulation data) is used to generate an estimation model and / or generate estimation data.

[0270] First, the control station 100 constructs a virtual communication environment for an area (first area and / or second area) (step S301).

[0271] 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 of the area. For example, the virtual communication environment includes structures (such as buildings and the ground) within the area.

[0272] Next, the control station 100 performs 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 can use various methods, such as determining whether the environment is a LOS environment or a NLOS environment in a virtual space, or a ray tracing simulation.

[0273] The control station 100 generates virtual space estimation information based on the simulation results (step S303).

[0274] The information 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 that executes the information generation process. Furthermore, the timing of performing the information generation process is not limited to when an estimation model and / or estimation data is generated. The control station 100 may execute the information generation process at any timing.

[0275] 5.3. Regarding Estimation Accuracy For example, when the control station 100 estimates actual communication characteristics using an estimation model, the estimation accuracy may be provided. Alternatively, the estimation accuracy may be provided for the estimation model. This estimation accuracy may be further used when using (utilizing) data estimated using the estimation model of communication characteristics.

[0276] The estimation accuracy of the communication characteristics and / or estimation model in the communication area (second area) can be given by any one of the following or a combination thereof: - Number of first areas R1 - Number of first measured data in the first area R1 - Correction accuracy of the first measured data and / or second measured data - Generation accuracy of the estimation model - Similarity of the communication environments in the first area and the second area - Accuracy of the simulation data in the first area and / or second area - Observation accuracy of the measured data in the first area and / or second area

[0277] Examples of similarity in communication environments include the average height of structures in each area, the density of structures, and the height of base station 300 (including installation location (altitude), building height, antenna height, etc.).

[0278] For example, the accuracy of the simulation data can be determined based on the accuracy (precision, accuracy) of the communication environment information in the simulation for generating the simulation data.

[0279] The observation accuracy of the 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 obtained by the GPS.

[0280] Furthermore, if an observation error specific to the terminal device 400 occurs, information indicating that terminal device 400 can be included in the observation accuracy of the actual measurement data.

[0281] <5.4. Normalization of Measured Data> In this embodiment, when there are a plurality of first areas, the simulation data and / or the first measured data in the first areas are normalized (offsets are provided) by, for example, a predetermined method.

[0282] The predetermined method can be performed based on communication environment information in each first area (for example, information on the transmission power from base station 300, etc.).

[0283] Here, the communication environment information is the information described above, and includes, for example, information on the transmission power of the base station 300, information on the frequency used for communication (carrier frequency, frequency bandwidth, etc.), information on the first area (coverage size, indoor / outdoor information, etc.), etc.

[0284] For example, normalization is performed so that the communication environment information in each first area becomes the same.

[0285] Simulation data of communication characteristics in a specified area (one of multiple first areas) is normalized so that the communication environment information in the specified area is the same as the communication environment information in each of the first areas (the remaining first areas other than the specified area).

[0286] Data on communication characteristics in a predetermined area (one of the plurality of first areas) is estimated taking into account the normalization described above.

[0287] The first measured data of the communication characteristics in a specified area (one of a plurality of first areas) is normalized so that the communication environment information in the specified area is the same as the communication environment information in each of the first areas (the remaining first areas other than the specified area).

[0288] The above-mentioned normalization is performed so that the communication environment information in each first area becomes the communication environment information in the above-mentioned predetermined area (one of the plurality of first areas).

[0289] The normalization is performed, for example, by a device that generates the estimation model (in this embodiment, the control station 100). Note that the device that performs the normalization and the device that generates the estimation model may be different devices.

[0290] A specific example of normalization will be described below.

[0291] For example, consider a case where the communication characteristics of the simulation data and / or the actual measurement data in the first area are downlink RSRP.

[0292] Consider a case where the transmission power of the base station 300 at location A1 is B1 (dBm), the transmission power of the base station 300 at location A2 is B2 (dBm), and the transmission power of the base station 300 at location A3 is B3 (dBm).

[0293] When normalizing these transmission powers to S (dBm), the control station 100 assigns an offset of S-B1 (dBm) to the RSRP at location A1. The control station 100 assigns an offset of S-B2 (dBm) to the RSRP at location A2. The control station 100 assigns an offset of S-B3 (dBm) to the RSRP at location A3. In this way, the control station 100 performs normalization by assigning an offset to each RSRP (each data).

[0294] Here, S (dBm) may be the transmission power of the base station 300 in the second area (location X1) where estimation is performed.

[0295] <<6. Hardware Configuration Example>> Next, a description will be given of a hardware configuration example of the control station 100, base station 300, and terminal device 400 according to each embodiment. The information devices of the control station 100, base station 300, and terminal device 400 described above are realized by, for example, a computer 1000 having a configuration as shown in FIG.

[0296] 18 is a block diagram showing an example hardware configuration of a computer 1000 according to the present disclosure. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM 1300, a hard disk drive (HDD) 1400, a communication interface 1500, and an input / output interface 1600. The components of the computer 1000 are connected via a bus 1050.

[0297] The CPU 1100 operates and controls each component based on programs stored in the ROM 1300 or the HDD 1400. For example, the CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to the various programs.

[0298] The ROM 1300 stores boot programs such as a Basic Input Output System (BIOS) that is executed by the CPU 1100 when the computer 1000 starts up, and programs that depend on the hardware of the computer 1000 .

[0299] HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by CPU 1100 and data used by such programs. Specifically, HDD 1400 is a recording medium that records the proposed program according to the present disclosure, which is an example of program data 1450.

[0300] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (e.g., the Internet). For example, the CPU 1100 receives data from other devices and transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0301] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. For example, the CPU 1100 receives data from an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 also transmits data to an output device such as a display, a speaker, or a printer via the input / output interface 1600. The input / output interface 1600 may also function as a media interface for reading programs recorded on a predetermined recording medium. Examples of media include optical recording media such as a DVD (registered trademark) (Digital Versatile Disc) or a PD (Phase Change Rewritable Disk), magneto-optical recording media such as an MO (Magneto-Optical disk), tape media, magnetic recording media, or semiconductor memory.

[0302] CPU 1100 executes programs loaded onto RAM 1200 to realize functions of control units 130, 340, 450, etc. Furthermore, HDD 1400 stores proposed programs according to the present disclosure and data in storage units 120, 320, 420. Note that CPU 1100 reads and executes program data 1450 from HDD 1400, but as another example, these programs may be acquired from other devices via external network 1550.

[0303] <<7. 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.

[0304] The statistical information according to the above-described embodiments can be used in various processes, controls, use cases, and the like.

[0305] For example, data on communication characteristics estimated in a communication area (for example, predicted values ​​as statistical information) can be used for cell design within the communication area (such as the location of transmission points and setting the maximum transmission power of transmission points).

[0306] This cell design may be performed based on the estimated accuracy of communication characteristics in the communication area. Also, this cell design may be performed by the control station 100 or the base station 300. Alternatively, the cell design may be performed in a core network or the like.

[0307] 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.

[0308] The control of the communication parameters may be further performed based on the estimation accuracy of the communication characteristics in the communication area. Furthermore, 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.

[0309] The statistical information according to this embodiment can be used, for example, to design interference power and separation distance in frequency sharing. The control station 100 estimates interference power using the statistical information on communication characteristics, thereby making effective use of available frequencies in time and space while avoiding interference with the protected system.

[0310] Furthermore, the technology according to this embodiment may be applied to 6G joint communication and sensing.

[0311] For example, in each of the above-described embodiments, the control station 100 performs the generation process and the determination process, but the device that performs these processes is not limited to the control station 100. For example, the base station 300 may perform these processes. In this case, the base station 300 acquires information used to perform the generation process and the determination process from the control station 100 and / or the terminal device 400.

[0312] Alternatively, these processes may be executed by the terminal device 400. In this case, the terminal device 400 acquires information used to execute the generation process and the determination process from the control station 100 and / or the base station 300.

[0313] Furthermore, in each of the above-described embodiments, the same device (for example, the control station 100) executes both the generation process and the determination process, but these processes may be executed by different devices.

[0314] For example, the control station 100 may perform the generation process, and the base station 300 may perform the determination process. In this case, the control station 100 acquires information used in the generation process from the base station 300 and / or the terminal device 400. The base station 300 acquires information used in the determination process from the control station 100 and / or the terminal device 400.

[0315] Alternatively, for example, the control station 100 may perform the generation process, and the terminal device 400 may perform the determination process. In this case, the control station 100 acquires information used in the generation process from the base station 300 and / or the terminal device 400. The terminal device 400 acquires information used in the determination process from the control station 100 and / or the base station 300.

[0316] Alternatively, for example, the base station 300 may perform the generation process, and the terminal device 400 may perform the determination process. In this case, the base station 300 acquires information used in the generation process from the control station 100 and / or the terminal device 400. The terminal device 400 acquires information used in the determination process from the control station 100 and / or the base station 300. Note that the terminal device 400 may perform the generation process, and the base station 300 may perform the determination process.

[0317] Furthermore, the generation process may be distributed among multiple devices. For example, the device that generates the first estimation model and the device that generates the second estimation model may be different. For example, the control station 100 may generate the first estimation model, and the base station 300 may generate the second estimation model.

[0318] In this case, the control station 100 acquires information used to generate the first estimation model from the terminal device 400 or a device other than the terminal device 400. Furthermore, the base station 300 acquires information used to generate the second estimation model from the control station 100 and / or the terminal device 400.

[0319] In this case, the control station 100 may be appropriately replaced with the base station 300 or the terminal device 400. The base station 300 may be appropriately replaced with the control station 100 or the terminal device 400. The terminal device 400 may be appropriately replaced with the control station 100 or the base station 300.

[0320] In the above-described embodiment, the control station 100 is a cloud server or a database device disposed on a network, but the control station 100 is not limited to this. For example, the control station 100 does not have to be disposed on a cloud. For example, the control station 100 may be an edge server. In this case, the control station 100 may be disposed near the base station 300, for example.

[0321] 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 way, at least some of the functions of the control station 100 may be performed by the base station 300 and / or the terminal device 400.

[0322] For example, the control station 100, the base station 300, and the control device that controls the terminal device 400 in the above-described embodiment may be realized by a dedicated computer system or a general-purpose computer system.

[0323] For example, a communication program for executing the above-described operations is stored on 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 on a computer and the above-described processing is executed to configure a control device. In this case, the control device may be a device (e.g., a personal computer) external to the control station 100, the base station 300, and the terminal device 400. Alternatively, the control device may be a device (e.g., a control unit 130, 340, 450) internal to the control station 100, the base station 300, and the terminal device 400.

[0324] The 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 downloaded to a computer. The above-described functions may also be realized by cooperation between an operating system (OS) and application software. In this case, the components other than the OS may be stored on a medium and distributed, or the components other than the OS may be stored in a server device and may be downloaded to a computer.

[0325] 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 using 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 as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0326] Furthermore, the components of each device shown in the figure are conceptual functional components and do 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 depending on various loads, usage conditions, etc.

[0327] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0328] Furthermore, the effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0329] Furthermore, for example, the present embodiment can also be implemented as any configuration that constitutes 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, or the like (i.e., a configuration of a part of an apparatus).

[0330] In this embodiment, a system refers to a collection of multiple components (devices, modules (components), etc.), regardless of whether all of 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.

[0331] Furthermore, 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.

[0332] 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. 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 terminal devices 400 to be installed (maximum number of installations), installation locations, and / or installation directions (horizontal direction, tilt angle, etc.).

[0333] <<8. 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 within the scope of the gist of the present disclosure. Furthermore, components of different embodiments and modifications may be combined as appropriate.

[0334] Furthermore, the effects of each embodiment described in this specification are merely examples and are not intended to be limiting, and other effects may also be obtained.

[0335] The present technology may also be configured as follows. (1) An information processing device including a control unit that acquires measured data regarding communication characteristics in a communication area, corrects the measured data based on a measurement error of the measured data, and generates statistical information on the communication characteristics in the communication area using the corrected measured data. (2) The information processing device according to (1), in which the control unit corrects the measured data based on a statistical analysis result of the measurement error. (3) The information processing device according to (2), in which the control unit corrects the measured data based on an average value of the measurement error. (4) The information processing device according to (2), in which the control unit estimates a distribution of the measurement error and corrects the measured data based on the estimated distribution. (5) The information processing device according to (2), in which the control unit corrects the measured data based on a result of a regression analysis of the measurement error. (6) The information processing device according to (1), in which the control unit corrects the measured data using a correction model. (7) The information processing device according to (6), in which the correction model is generated by machine learning. (8) The information processing device according to (6) or (7), wherein the correction model has at least one of the actual measurement data including the measurement error, the actual measurement data not including the measurement error, information about a receiving point where the actual measurement data was observed and a transmitting point where a signal for observation was transmitted, the transmission power of the signal for observation, and information about the communication environment between the receiving point and the transmitting point as an explanatory variable. (9) The information processing device according to any one of (6) to (8), wherein the correction model has the measurement error included in the actual measurement data or the corrected actual measurement data as a dependent variable. (10) The information processing device according to (1), wherein the control unit corrects the actual measurement data using simulation data related to the communication characteristics in the communication area. (11) The information processing device according to (1), wherein the control unit corrects the actual measurement data by excluding, from the generation of the statistical information, the actual measurement data whose measurement error does not satisfy a predetermined standard. (12) The information processing device according to (11), wherein the control unit excludes the actual measurement data in which the measurement error exceeds a predetermined value from generation of the statistical information.(13) The information processing device according to (11), wherein the control unit calculates a standard deviation of the measured data, and excludes the measured data for which the calculated standard deviation exceeds a predetermined value from generating the statistical information. (14) The information processing device according to (11), wherein the control unit excludes the measured data from generating the statistical information according to a result of an outlier test. (15) The information processing device according to any one of (1) to (14), wherein the statistical information is an estimation model used to estimate communication characteristics in a communication area different from the communication area. (16) An information processing method including: acquiring measured data on communication characteristics in a communication area; correcting the measured data based on a measurement error of the measured data; and generating statistical information on the communication characteristics in the communication area using the corrected measured data. (17) The information processing method according to (16), including correcting the measured data based on a statistical analysis result of the measurement error. (18) The information processing method according to (16), including correcting the measured data using a correction model. (19) The information processing method according to (16), further comprising: correcting the measured data by using simulation data relating to the communication characteristics in the communication area. (20) A program for causing a computer to execute as a control unit: acquiring measured data relating to the communication characteristics in the communication area; correcting the measured data based on a measurement error of the measured data; and generating statistical information of the communication characteristics in the communication area by using the corrected measured data.

[0336] 100 Control station 110, 310, 410 Communication unit 120, 320, 420 Storage unit 130, 340, 450 Control unit 300 Base station 330, 430 Network communication unit 400 Terminal device 440 Input / output unit

Claims

1. An information processing device comprising: a control unit that acquires actual measurement data regarding communication characteristics in a communication area; corrects the actual measurement data based on a measurement error of the actual measurement data; and generates statistical information on the communication characteristics in the communication area using the corrected actual measurement data.

2. The information processing device according to claim 1, wherein the control unit corrects the actual measurement data based on the results of a statistical analysis of the measurement error.

3. The information processing device according to claim 2, wherein the control unit corrects the actual measurement data based on the average value of the measurement errors.

4. The information processing device according to claim 2, wherein the control unit estimates a distribution of the measurement errors and corrects the actual measurement data based on the estimated distribution.

5. The information processing device according to claim 2, wherein the control unit corrects the actual measurement data based on the results of regression analysis of the measurement error.

6. The information processing device according to claim 1, wherein the control unit corrects the actual measurement data using a correction model.

7. The information processing device according to claim 6, wherein the correction model is generated by machine learning.

8. The information processing device described in claim 6, wherein the correction model uses at least one of the following explanatory variables: the actual measurement data including the measurement error; the actual measurement data not including the measurement error; information about the receiving point where the actual measurement data was observed and the transmitting point where the signal for observation was transmitted; the transmission power of the signal for observation; and information about the communication environment between the receiving point and the transmitting point.

9. The information processing device according to claim 6, wherein the correction model uses the measurement error contained in the actual measurement data or the corrected actual measurement data as a response variable.

10. The information processing device according to claim 1, wherein the control unit corrects the actual measurement data using simulation data relating to the communication characteristics in the communication area.

11. The information processing device according to claim 1, wherein the control unit corrects the actual measurement data by excluding from the generation of the statistical information any of the actual measurement data whose measurement error does not satisfy a predetermined standard.

12. The information processing device according to claim 11, wherein the control unit excludes the actual measurement data in which the measurement error exceeds a predetermined value from the generation of the statistical information.

13. The information processing device according to claim 11, wherein the control unit calculates a standard deviation of the actual measurement data, and excludes the actual measurement data for which the calculated standard deviation exceeds a predetermined value from the generation of the statistical information.

14. The information processing device according to claim 11, wherein the control unit excludes the measured data from the generation of the statistical information in accordance with the result of the outlier test.

15. The information processing device according to claim 1, wherein the statistical information is an estimation model used to estimate communication characteristics in a communication area different from the communication area.

16. An information processing method comprising: acquiring actual measurement data regarding communication characteristics in a communication area; correcting the actual measurement data based on a measurement error of the actual measurement data; and generating statistical information on the communication characteristics in the communication area using the corrected actual measurement data.

17. The information processing method according to claim 16, further comprising correcting the actual measurement data based on the results of a statistical analysis of the measurement error.

18. The information processing method according to claim 16, further comprising correcting the actual measurement data using a correction model.

19. The information processing method according to claim 16, further comprising correcting the actual measurement data using simulation data relating to the communication characteristics in the communication area.

20. A program that causes a computer to function as a control unit to acquire actual measurement data regarding communication characteristics in a communication area, correct the actual measurement data based on measurement errors in the actual measurement data, and generate statistical information regarding the communication characteristics in the communication area using the corrected actual measurement data.

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