Information processing device, communication system, and information processing method

Abstract

An information processing device according to the present disclosure comprises a control unit. The control unit classifies, into at least one group, measurement site candidates at which data pertaining to communication characteristics is measured in a prescribed area. The control unit selects, from among candidates in correspondence to the group, measurement sites at which data is to be measured.

Description

Information processing device, communication system, and information processing method

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

[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] To avoid this, it is desirable to determine communication parameters using highly accurate statistical information. To generate this statistical information, for example, statistical processing is performed using actual measurement data in a specified area. To improve the accuracy of the statistical information, it is necessary to improve the accuracy of the statistical processing. To improve the accuracy of the statistical processing, a large amount of actual measurement data has been required.

[0009] However, in order to measure a large amount of actual measurement data, the number of times the data is measured or the number of terminal devices that perform the measurements must be large. This results in a problem of a large measurement load on the terminal devices. Furthermore, measuring a large amount of actual measurement data results in a problem of a large communication load because the terminal devices must transmit a large amount of the measured actual measurement data.

[0010] Therefore, the present disclosure proposes an information processing device, a communication system, and an information processing method that can generate highly accurate statistical information while reducing the load associated with measuring actual measurement data.

[0011] 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.

[0012] The information processing device of the present disclosure includes a control unit that classifies candidate measurement points for measuring data related to communication characteristics in a predetermined area into at least one group, and selects the measurement points for measuring the data from the candidate points according to the group.

[0013] 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 a communication process according to an embodiment of the present disclosure. FIG. 3 is a diagram illustrating an example of a selection process according to an embodiment of the present disclosure. FIG. 4 is a diagram illustrating an example of a measurement point according to an embodiment of the present disclosure. FIG. 5 is a diagram illustrating an example of a measurement point according to an embodiment of the present disclosure. FIG. 6 is a diagram illustrating an example of a configuration of a wireless communication system according to an embodiment of the present disclosure. FIG. 7 is a block diagram illustrating an example of a configuration of a base station according to an embodiment of the present disclosure. FIG. 8 is a block diagram illustrating an example of a configuration of a terminal device according to an embodiment of the present disclosure. FIG. 9 is a diagram illustrating an example of a configuration of a control station according to an embodiment of the present disclosure. FIG. 10 is a diagram illustrating an example of a generation process and an estimation process according to an embodiment of the present disclosure. FIG. 11 is a diagram illustrating an example of a first selection method according to an embodiment of the present disclosure. FIG. 12 is a diagram illustrating an example of a second selection method according to an embodiment of the present disclosure. FIG. 13 is a diagram illustrating an example of a third selection method according to an embodiment of the present disclosure. FIG. 14 is a diagram illustrating another example of the fourth selection method according to an embodiment of the present disclosure. FIG. 15 is a diagram illustrating an example of a fifth selection method according to an embodiment of the present disclosure. FIG. 16 is a flowchart illustrating an example of a flow of information generation processing according to an embodiment of the present disclosure. FIG. 17 is a diagram illustrating an example of a hardware configuration of a device, etc.

[0014] 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.

[0015] 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.

[0016] 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.

[0017] <<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.

[0018] 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.

[0019] 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.

[0020] 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 global characteristics of radio wave propagation (path loss, etc.) from information such as the distance between transmitter and receiver.

[0021] 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 empirical models.

[0022] 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.

[0023] 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.

[0024] 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)).

[0025] When determining communication parameters using the above-mentioned statistical propagation model, the base station must determine the communication parameters to minimize interference with neighboring cells and surrounding base stations, because the statistical propagation model differs from the actual propagation path conditions.

[0026] Here, the statistical propagation model is generated by limiting the communication environment to typical propagation environments such as free space, urban areas, etc. Therefore, if the statistical propagation model is used to estimate radio wave strength in a specific environment, the estimation accuracy may be degraded.

[0027] Therefore, in conventional interference design, base stations take into account fluctuations in radio wave strength due to obstacles and add a large margin to the interference power to avoid interference with adjacent cells and surrounding base stations.

[0028] In this way, if communication parameters are determined with a large margin added in order to minimize interference, the utilization efficiency of radio resources may be limited, which may cause a significant degradation of communication performance.

[0029] Furthermore, when the terminal device estimates the propagation path conditions and provides feedback, the base station 300 can determine more accurate communication parameters according to the actual transmission path conditions. However, the base station 300 can only grasp the propagation path conditions at the location of the terminal device that provided the feedback.

[0030] Therefore, when a terminal device moves, it is not possible to grasp the propagation path conditions at the destination more accurately, and it is also not possible to grasp the propagation path conditions at a location where no terminal device is present more accurately.

[0031] Furthermore, when a large number of terminal devices each feed back information on the propagation path conditions, the communication resources required for the feedback become overhead, which becomes a factor in reducing the efficiency of radio resource utilization in the entire communication system.

[0032] Furthermore, depending on the use case, there is a risk that the base station may not receive sufficient feedback on the propagation path conditions from the terminal device. For example, there may be cases where the base station receives no feedback at all or only a small amount of feedback from the terminal device.

[0033] In such a situation (environment, location, area), the communication system is required to estimate the propagation path conditions of the terminal device and / or the base station with high accuracy. In other words, the communication system is required to generate statistical information for estimating the propagation path conditions of the base station with high accuracy.

[0034] Here, examples of statistical information include a propagation path model, an estimation model based on machine learning, etc. These models can be generated using, for example, actual measurement data measured in a predetermined area.

[0035] The greater the amount of measured data, the higher the accuracy of the statistical information. However, a large amount of measured data increases the load on the device that performs the measurement and the communication load for transmitting the measured data.

[0036] <1-3. Overview of Proposed Technology> Fig. 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] The control station 100 executes statistical processing using the acquired actual measurement data (step S3). 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 S4). 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 a similar manner.

[0041] The statistical processing here is processing using at least actual measurement data. For example, the statistical processing according to this embodiment includes processing for estimating radio wave propagation characteristics (communication characteristics) between the base station 300 and the terminal device 400.

[0042] The control station 100 may notify the base station 300 and / or the terminal device 400 of the estimated radio wave propagation characteristics as statistical information. Alternatively, the control station 100 may notify the base station 300 and / or the terminal device 400 of communication parameters (e.g., control information for communication between the base station 300 and the terminal device 400) calculated based on the estimated radio wave propagation characteristics as statistical information.

[0043] In order to estimate radio wave propagation characteristics with higher accuracy, it is important to determine at which point the measured data should be used for statistical processing. Therefore, the communication system according to this embodiment executes a selection process to obtain measured data that can be used for statistical processing with higher accuracy.

[0044] 3 is a diagram illustrating an example of a selection process according to an embodiment of the present disclosure. The selection process illustrated in FIG. 3 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.

[0045] As shown in Fig. 3, the control station 100 classifies measurement point candidates (hereinafter also referred to as location candidates) in a predetermined area into at least one group (step S11). Here, the predetermined area is an area to be subjected to statistical processing. In the example of Fig. 3, it is assumed that the predetermined area is the communication range (e.g., a cell) of the base station 300.

[0046] The location candidates are any locations within a predetermined area. In the example of Fig. 3, the control station 100 selects locations P11 to P13, P21, and P22 as location candidates.

[0047] The control station 100 classifies the points P11 to P13, P21, and P22 into at least one group. In the example of Fig. 3, the control station 100 classifies the points P11 to P13 into a first group and the points P21 and P22 into a second group.

[0048] The control station 100 may classify the location candidates according to, for example, the communication characteristics at each point P or the position of the point P. Note that the control station 100 may estimate the communication characteristics at each point P by, for example, simulation or the like.

[0049] Next, the control station 100 selects measurement points from the classified location candidates (step S12). For example, the control station 100 selects measurement points for each classified group. In the example of Fig. 3, the control station 100 selects points P_11 and P_13 included in the first group as measurement points. The control station 100 also selects point P_22 included in the second group as measurement point.

[0050] The control station 100 notifies the base station 300 of the selected measurement point (step S13). The base station 300 requests the terminal device 400 at the notified measurement point to measure actual measurement data. Note that here, the base station 300 requests the terminal device 400 to measure actual measurement data according to the measurement point notified by the control station 100, but instead, the control station 100 may request the terminal device 400 at the measurement point to measure actual measurement data.

[0051] In the example of FIG. 3, the base station 300 requests the terminal device 400_1 at the point P_11 and the terminal device 400_3 at the point P_22 to measure actual measurement data.

[0052] Here, in this embodiment, the terminal device 400 being at point P means that the terminal device 400 is located within a certain area including point P. In this embodiment, even if the terminal device 400 is at point P, it is assumed that the terminal device 400 is not necessarily located at point P.

[0053] When multiple terminal devices 400 are located within a certain area including point P, base station 300 may request measurement of actual measurement data from terminal device 400 that is closest to point P. Alternatively, base station 300 may randomly select a terminal device 400 to request measurement of actual measurement data.

[0054] The number of terminal devices 400 to which the base station 300 requests measurement of actual measurement data at point P is not limited to one. For example, the base station 300 may request measurement of actual measurement data from multiple terminal devices 400 located at point P.

[0055] Furthermore, even if the control station 100 selects a point P as a measurement point, if there is no terminal device 400 at that point P (for example, P_13 in FIG. 3), the base station 300 will not collect actual measurement data at that point P_13.

[0056] The terminal device 400_1, which has been requested to measure the actual measurement data, measures the actual measurement data and notifies the control station 100 of the result via the base station 300 (step S14). Similarly, the terminal device 400_3, which has been requested to measure the actual measurement data, measures the actual measurement data and notifies the control station 100 of the result via the base station 300 (step S15).

[0057] In this way, the control station 100 selects a measurement point where the actual measurement data is measured, and the control station 100 can acquire the actual measurement data at a point according to the statistical processing to be executed. The control station 100 can acquire the actual measurement data at a point according to, for example, a predetermined area or the characteristics of the base station 300.

[0058] For example, it is assumed that the control station 100 generates statistical information using actual measurement data at all of the location candidates, that is, all of the location candidates are measurement locations.

[0059] 4 is a diagram illustrating an example of measurement points according to the present disclosure. In this example, all candidate points are measurement points. In this case, the control station 100 acquires actual measurement data measured at many measurement points.

[0060] This causes problems such as an increase in the measured load on the terminal device 400, a shortage of communication resources, and an increase in the processing load on the control station 100 that generates statistical information.

[0061] On the other hand, the control station 100 according to this embodiment does not acquire actual measurement data from all of the location candidates, but selects measurement points from the location candidates.

[0062] 5 is a diagram illustrating an example of measurement points according to an embodiment of the present disclosure, in which the control station 100 selects measurement points from the candidate points (see FIG. 4 ) based on the selection process described above.

[0063] By executing the selection process according to this embodiment, the control station 100 can select measurement points, thereby reducing the number of actual measurement data and generating statistical information with higher accuracy using less actual measurement data.

[0064] <<2. Configuration Example of a Communication System>> <2-1. Overall Configuration Example of a Communication System> Fig. 6 is a diagram illustrating a configuration example of a wireless communication system according to an embodiment of the present disclosure. The wireless communication system illustrated in Fig. 6 includes a control station 100, core networks 200A and 200B, and a base station 300A. １ , 300A ２ , 300B １ , 300B ２ and the terminal device 400A １ , 400A ２ , 400B １ , 400B ２ And, it is equipped with.

[0065] The control station 100 connects to a core network 200A in a local network N1_A through the network N1_P. The control station 100 connects to a core network 200B in a local network N1_B through the network N1_P.

[0066] The network N1_P is, for example, a communication network such as a local area network (LAN), a wide area network (WAN), a cellular network, a fixed telephone network, a regional Internet Protocol (IP) network, or the Internet. The network N1_P may include a wired network or a wireless network. The network N1_P may also be a data network connected to a core network. The data network may be a service network of a telecommunications carrier, for example, an IP Multimedia Subsystem (IMS) network. The data network may also be a private network such as an in-house network. Note that, although only one network N1_P is shown in the example of FIG. 6, the number of networks N1_P is not limited to one.

[0067] 6 shows two local networks N1_A and N1_B, the number of local networks is not limited to two. The number of local networks may be one, or three or more.

[0068] In the local network N1_A, the core network 200A includes a base station 300A. １ , 300A ２ The number of base stations 300A connected to the core network 200A is not limited to two. The number of base stations 300A may be one, or three or more.

[0069] Base station 300A １ terminal device 400A １ The base station 300A is connected to the base station 300A by wireless communication. ２ terminal device 400A ２ The number of terminal devices 400A connected to the base station 300A is not limited to one, but may be two or more. １ Terminal device 400A connected to １ and the number of base stations 300A ２ Terminal device 400A connected to ２ The number of may be different from.

[0070] The configuration of the local network N1_B is the same as that of the local network N1_A, and therefore a description thereof will be omitted.

[0071] For example, the control station 100 is an information processing device that controls a dynamic spectrum access (DSA) system. The control station 100 can control radio resources and communication parameters for at least one of the local networks N1_A and N1_B, the core networks 200A and 200B, and the base stations 300A and 300B connected to the DSA. Here, the radio resources refer to resources in at least one of the time, frequency, MIMO layer, and spatial domain used for wireless communication.

[0072] The core networks 200A and 200B do not necessarily have to be installed. In this case, the control station 100 is directly connected to the base stations 300A and 300B.

[0073] The core network 200 may be located in one of the control station 100 and the base station 300. The core network 200 may also be located in both the control station 100 and the base station 300 in a distributed manner.

[0074] The local networks N1_A and N1_B are also called private networks, and are networks whose communication coverage (an example of a communication zone) is, for example, within a predetermined area or within a premises. In the local networks N1_A and N1_B, only pre-registered terminal devices 400 can connect to at least one of the base station 300, the control station 100, and the core network 200.

[0075] 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.

[0076] In the example of FIG. 6, the control station 100 is connected to the local networks N1_A and N1_B, but the network to which the control station 100 is connected may be a public network to which subscribers can connect.

[0077] As mentioned above, the core network 200 may be omitted or may be located within the control station 100 and / or base station 300.

[0078] Therefore, in the following, for the sake of simplicity, the wireless communication system is assumed to be a system omitting the core network 200. That is, the wireless communication system of this embodiment is assumed to include a control station 100, a base station 300, and a terminal device 400.

[0079] <2-2. Example of Base Station Configuration> Next, the base station 300 will be described. The base station 300 is a communication device that operates a cell and provides wireless communication services to one or more terminal devices 400 located within the coverage of the cell. The cell is operated according to any wireless communication method, such as LTE or NR. The base station 300 is connected to a core network 200. The core network 200 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.

[0080] Note that the base station 300 may be configured as a collection of multiple physical or logical devices. For example, in this embodiment, the base station 300 may be divided into multiple devices, a baseband unit (BBU) and an RU, and may be interpreted as a collection of these multiple devices. Additionally or alternatively, in this embodiment, 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 be compatible with a gNB-DU (gNB-CU) (described later). Additionally or alternatively, the BBU may be compatible with 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 be compatible with 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.

[0081] 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 3GPP Access (3GPP is a registered trademark). Additionally or alternatively, if the base station 300 is a wireless access point (access point) (e.g., a Wi-Fi (registered trademark) access point), it may be referred to as 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).

[0082] Furthermore, as described above, the base station 300 may be configured to manage multiple cells. A cell provided by the base station 300 is called a serving cell. The serving cell includes a PCell (Primary Cell) and an SCell (Secondary Cell). When dual connectivity (e.g., EUTRA-EUTRA Dual Connectivity, EUTRA-NR Dual Connectivity (ENDC), EUTRA-NR Dual Connectivity with 5GC, NR-EUTRA Dual Connectivity (NEDC), NR-NR Dual Connectivity) is provided to a UE (e.g., a terminal device 400), a PCell and zero or 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, a 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.

[0083] 7 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.

[0084] The base station 300 shown in Figure 7 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 7 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 configurations. 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.

[0085] 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.

[0086] The communication unit 310 includes a reception processing unit 311, a transmission processing unit 312, and an antenna 313. The communication unit 310 may include a plurality of reception processing units 311, a plurality of transmission processing units 312, and a plurality of antennas 313. When the communication unit 310 supports a plurality of radio access methods, each unit of the communication unit 310 may be configured 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] The decoder 311d performs a decoding process on the coded bits of the demodulated uplink channel. The decoded uplink data and uplink control information are output to the controller 340.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] The storage unit 320 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 320 functions as a storage means of the base station 300.

[0096] The network communication unit 330 is a communication interface for communicating with a node located higher on the network (e.g., the core network 200). For example, the network communication unit 330 may be a LAN (Local Area Network) interface such as a NIC (Network Interface Card). 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.

[0097] 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 a processor executing various programs stored in a storage device inside the base station 300 using a RAM (Random Access Memory) or the like as a working area. The control unit 340 may 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.

[0098] 2-3. 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. 8. Fig. 8 is a block diagram showing an example of the configuration of the terminal device 400 according to an embodiment of the present disclosure.

[0099] 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.

[0100] 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).

[0101] 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).

[0102] 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.

[0103] The terminal device 400 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. 8 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 components.

[0104] 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.

[0105] 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.

[0106] The configurations of the communication unit 410 , reception processing unit 411 , transmission processing unit 412 , and antenna 413 are similar to those of the communication unit 310 , reception processing unit 311 , transmission processing unit 312 , and antenna 313 of the base station 300 .

[0107] 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.

[0108] 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.

[0109] The input / output unit 440 is a user interface for exchanging information with the user. For example, the input / output unit 440 is an operation device such as a keyboard, a mouse, 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.

[0110] The control unit 450 is a controller that controls each unit of the terminal device 400. The control unit 450 is realized by a processor such as a CPU, an MPU, or a GPU. For example, the control unit 450 is realized by a processor executing various programs stored in a storage device inside the terminal device 400 using 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.

[0111] 9 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 an information processing device that controls, for example, a dynamic spectrum access (DSA) system. As shown in FIG. 9, the control station 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0112] (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 interface such as a NIC, or a Universal Serial Bus (USB) interface configured with 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.

[0113] (Storage Unit 120) The storage unit 120 is a data readable / writable storage device such as a DRAM, an SRAM, a flash memory, a hard disk, etc. The storage unit 120 functions as a storage unit of the control station 100.

[0114] (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, an MPU, or a GPU.

[0115] 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 or the like as a work area. The control unit 130 may also be realized by an integrated circuit such as an ASIC or FPGA. A CPU, MPU, GPU, ASIC, and FPGA can all be considered as controllers.

[0116] The control unit 130 includes a selection unit 131, an acquisition unit 132, a generation unit 133, a determination unit 134, and a notification unit 135. Each block (selection unit 131 to notification unit 135) constituting the control unit 130 is a functional block indicating a function of the control unit 130.

[0117] These functional blocks may be software blocks or hardware blocks. For example, each of the above-mentioned functional blocks may be a software module implemented by software (including a microprogram), or may be a circuit block on a semiconductor chip (die). Of course, each functional block may be a processor or an integrated circuit. The functional blocks may be configured in any manner.

[0118] The control unit 130 may be configured with functional units different from the above-described functional blocks.

[0119] (Selection unit 131) The selection unit 131, for example, selects measurement points from which actual measurement data is acquired. As will be described later, when the control station 100 generates an estimation model using actual measurement data acquired in an estimation area, for example, the control station 100 selects measurement points in the estimation area. Alternatively, when the control station 100 acquires actual measurement data in a calculation area from which estimation data is estimated using an estimation model, the control station 100 selects measurement points in the calculation area.

[0120] In this manner, the control station 100 may select measurement points in a predetermined area (eg, an estimated area and / or a calculated area).

[0121] For example, the selection unit 131 determines candidates for measurement points (point candidates) in a predetermined area. The selection unit 131 classifies the point candidates into at least one group. For each group, the selection unit 131 selects a measurement point from the point candidates included in the group.

[0122] The selection unit 131 notifies the notification unit 135 of information about the selected measurement point. The notification unit 135 notifies the base station 300 of the information about the measurement point. Alternatively, the notification unit 135 requests the terminal device 400 located at the selected measurement point to collect actual measurement data.

[0123] The selection method used by the selection unit 131 will be described in detail later.

[0124] (Acquisition unit 132) The acquisition unit 132 acquires information used by the generation unit 133 via the communication unit 110. For example, the acquisition unit 132 acquires the actual measurement data of the above-mentioned predetermined area from the base station 300 and / or the terminal device 400 via the communication unit 110. Here, the acquisition unit 132 acquires the actual measurement data of the estimated area R_1 from the base station 300 and / or the terminal device 400.

[0125] The acquisition unit 132 receives, via the communication unit 110, information that may be transmitted from at least one of the local network, the core network 200, the base station 300, the terminal device 400, and other communication nodes.

[0126] The acquiring unit 132 outputs the acquired information to the generating unit 133 .

[0127] (Generation Unit 133) The generation unit 133 executes a generation process to generate an estimation model using the actual measurement data acquired by the acquisition unit 132.

[0128] 10 is a diagram illustrating an example of a generation process and an estimation process according to an embodiment of the present disclosure. The generation process is performed by the generation unit 133. The estimation process is performed by the determination unit 134. The estimation process will be described later.

[0129] 10 , the generation unit 133 generates an estimation model based on actual measurement data (first data) in at least one estimation area R_1. This estimation model is a communication characteristics estimation model used to estimate communication characteristics (radio wave propagation environment), for example.

[0130] The control station 100 generates an estimation model from the actual measurement data of the estimation area R_1, for example, by machine learning. In Fig. 10, the number of estimation areas R_1 used to generate the estimation model is three or more (estimated areas R_11, R_12, R_13, ...), but the number of estimation areas R_1 is not limited to this. The number of estimation areas R_1 may be two or less.

[0131] Here, the actual measurement data that the control station 100 uses to generate the estimation model is, for example, data on communication characteristics measured at the measurement point selected by the selector 131.

[0132] The generation unit 133 outputs the generated estimation model to the determination unit 134 (see FIG. 9).

[0133] (Determination unit 134) The determination unit 134 executes an estimation process using an estimation model to estimate estimated data related to communication characteristics in the calculation area R_0. The determination unit 134 determines communication parameters between the base station 300 and the terminal device 400 in the calculation area R_0, for example, using the estimated data.

[0134] As shown in FIG. 10, the determination unit 134 estimates estimated data of communication characteristics in the calculation area R_0 using an estimation model generated based on actual measurement data of communication characteristics measured in the estimation area R_1, for example.

[0135] For example, the determination unit 134 inputs area data related to the calculation area R_0 into the estimation model. The determination unit 134 sets the output of the estimation model when the area data is input as the estimation data. The area data may include, for example, communication environment information, which will be described later.

[0136] The area data may also include actual measurement data for the calculation area R_0. In this case, the number of actual measurement data for the calculation area R_0 may be less than the number of actual measurement data for the estimation area R_1. The measurement points of the actual measurement data for the calculation area R_0 may be points selected by the selection unit 131.

[0137] In this way, the determination unit 134 may estimate the estimated data using the actual measurement data for the calculation area R_0. Alternatively, the determination unit 134 may correct the estimated data using the actual measurement data for the calculation area R_0. The determination unit 134 may correct (re-learn) the estimation model using the actual measurement data for the calculation area R_0.

[0138] The determination unit 134 uses, for example, the estimated data to determine communication parameters between the base station 300 and the terminal device 400 in the calculation area R_0. The determination unit 134 outputs the determined communication parameters to the notification unit 135.

[0139] It should be noted that, here, the generation unit 133 and the determination unit 134 execute the generation process and the estimation process as examples of statistical processing. The statistical processing executed by the generation unit 133 and the determination unit 134 may be processing using actual measurement data, and is not limited to the generation process and the estimation process. For example, one of the generation unit 133 and the determination unit 134 may execute either the generation process or the estimation process as the statistical processing.

[0140] (Notification Unit 135) The notification unit 135 shown in FIG. 9 notifies the base station 300 and / or the terminal device 400 of the communication parameters determined by the determination unit 134.

[0141] <<3. Method for Selecting Measurement Points>> As described above, the control station 100 selects measurement points for measuring actual measurement data in a predetermined area (for example, the estimated area R_1 and / or the calculated area R_0).

[0142] For example, the control station 100 may select a measurement point depending on communication parameters, estimated data, an estimation model, actual measurement data, and the like.

[0143] Below, some examples of the selection method executed by the control station 100 will be described.

[0144] <3-1. First Selection Method> The control station 100 selects measurement points according to, for example, the communication characteristics of a predetermined area. For example, the control station 100 classifies location candidates in the predetermined area into groups according to the communication characteristics. The control station 100 selects measurement points from the location candidates for each classified group.

[0145] In the first selection method, the control station 100 classifies the location candidates according to information indicating a line-of-sight (LOS) environment / non-line-of-sight (NLOS) environment as a communication characteristic.

[0146] 11 is a diagram illustrating an example of a first selection method according to an embodiment of the present disclosure. In FIG. 11, the control station 100 classifies location candidates into three groups: a LOS environment, a NLOS environment, and a LOS / NLOS mixed environment (hereinafter also referred to as a mixed environment). In FIG. 11, location candidates included in the LOS environment group are indicated by squares, location candidates included in the NLOS environment group are indicated by triangles, and location candidates included in the mixed environment group are indicated by circles.

[0147] First, the control station 100 determines location candidates. For example, a predetermined area may be divided into a plurality of grids, and grid points may be determined as location candidates.

[0148] Next, if the determined location candidate is a LOS environment, the control station 100 classifies the location candidate into a first group indicating a LOS environment. If the determined location candidate is a NLOS environment, the control station 100 classifies the location candidate into a second group indicating a NLOS environment. The control station 100 classifies location candidates that are not classified into either the first or second group into a third group indicating a mixed environment.

[0149] Here, there are several methods for determining whether a location candidate is in a LOS environment or a NLOS environment, as follows: - A first determination method using simulation - A second determination method using the shielding factor of the first Fresnel zone - A third determination method according to topography and building data

[0150] To perform these determination methods, the control station 100 may use, for example, communication environment information and / or virtual space estimation information, which will be described later.

[0151] (First Determination Method) For example, the control station 100 determines whether a location candidate is in a LOS environment or an NLOS environment by using a simulation. For example, the control station 100 determines whether the location candidate is in a LOS environment or an NLOS environment by using a ray tracing simulation.

[0152] The control station 100 may determine whether the environment is a LOS environment or an NLOS environment using a propagation model that can determine whether the environment is a LOS environment or an NLOS environment, such as ITU-R P.452.

[0153] (Second Determination Method) For example, the control station 100 calculates the shielding ratio of the first Fresnel zone between the location candidate and the transmission point (base station 300), and determines whether the environment is a LOS environment or a NLOS environment based on the calculation result.

[0154] For example, if the first Fresnel radius is a predetermined value (e.g., equal to or greater than a threshold Th1) in the first Fresnel zone between the location candidate and the transmission point, the control station 100 considers the location candidate to be in a LOS environment. On the other hand, if the first Fresnel radius is a predetermined value (e.g., less than a threshold Th2 (Th2≦Th1)), the control station 100 considers the location candidate to be in a NLOS environment. Furthermore, for example, if the first Fresnel radius is equal to or greater than a threshold Th2 but less than Th1 (Th2 ≠ Th1), the control station 100 considers the location candidate to be in a mixed environment.

[0155] The threshold value may be a percentage of the first Fresnel radius, or may be a specific value (for example, a distance (meters, etc.)).

[0156] (Third Determination Method) For example, the control station 100 determines whether the environment is a LOS environment or a NLOS environment based on topographical data and / or building data between the location candidate and the transmission point (base station 300).

[0157] For example, the control station 100 determines whether the environment is a LOS environment or a NLOS environment depending on whether the path connecting the candidate point (reception point) and the transmission point is blocked by terrain or buildings.

[0158] This determination may be made by the user, in which case the control station 100 determines whether the location candidate is in a LOS environment or a NLOS environment in accordance with the user's determination.

[0159] (Selection of Measurement Point) For example, the control station 100 classifies the location candidates into one or more groups (here, first to third groups), and then selects a measurement point according to the group, i.e., taking into account the group information.

[0160] For example, if the total number of measurement points to be selected is predetermined, the control station 100 selects measurement points so that the ratio of measurement points selected from each group is a predetermined ratio (e.g., a:b:c).

[0161] For example, when the control station 100 selects measurement points from all groups in the same proportion (a=b=c), it selects a number of measurement points from each group equal to the total number of measurement points divided by the number of groups.

[0162] Alternatively, the control station 100 may select measurement points in proportions according to the characteristics of the group. For example, in a NLOS environment or a mixed environment, fluctuations in radio wave propagation characteristics are greater than in a LOS environment.

[0163] Therefore, the control station 100 selects more measurement points from the second and third groups, which are NLOS environments or mixed environments, than from the first group, which is an LOS environment. For example, if the ratio of the number of measurement points selected from the first group, the second group, and the third group is a:b:c, the control station 100 selects measurement points such that a<c≦b.

[0164] Specifically, when the total number of measurement points to be selected is N1, the control station 100 selects N1*a / (a+b+c) measurement points from the first group (LOS environment). The control station 100 also selects N1*b / (a+b+c) measurement points from the second group (NLOS environment). The control station 100 also selects N1*c / (a+b+c) measurement points from the third group (mixed environment). Note that a, b, and c have a relationship of a<c≦b, for example.

[0165] The method for selecting measurement points from each group is arbitrary. For example, the control station 100 may randomly select measurement points from each group.

[0166] Alternatively, the control station 100 may select measurement points from each group depending on the location of the location candidates in a predetermined area. For example, the control station 100 may divide the location candidates into regions of a predetermined area and select measurement points from each group for each region.

[0167] This allows the control station 100 to preferentially select location candidates in a specific region within a predetermined area as measurement locations. Alternatively, the control station 100 can select measurement locations from each group so as to minimize bias within the predetermined area.

[0168] The total number N1 of measurement points described above may be determined in advance, or may be determined by the control station 100 according to predetermined conditions.

[0169] For example, the control station 100 determines the total number N1 according to at least one of the measurement load of the terminal device 400, the communication resources required for transmitting the actual measurement data, the processing load when generating statistical information, etc. The control station 100 may also determine the total number N1 according to the statistical information to be generated, the accuracy of the required statistical information, etc.

[0170] It should be noted that there are two patterns for determining whether the environment is a LOS environment or a NLOS environment: hard decision and soft decision.

[0171] In the case of a hard decision, the control station 100 represents the LOS environment / NLOS environment of the location candidate with a binary value (e.g., 0 or 1). For example, the control station 100 determines that the location candidate is in a LOS environment as 0, and determines that the location candidate is in a NLOS environment as 1.

[0172] In the case of soft decision, the control station 100 expresses the LOS environment / NLOS environment of the location candidate in terms of probability. For example, the control station 100 estimates the probability that the location candidate is in a LOS environment and / or the probability that the location candidate is in a NLOS environment.

[0173] The control station 100 selects measurement locations based on the estimated probability. For example, suppose there are N2 location candidates, and among the N2, there are M1 location candidates (M1 < N2) that have a 99 percent probability of being in an LOS environment. Also, suppose there are M2 location candidates (M2 = N2 - M1) that have a 99 percent probability of being in an NLOS environment.

[0174] For example, since there is little fluctuation in propagation characteristics at a location where the probability of the location being an LOS environment is 99 percent, the control station 100 selects M2 location candidates where the probability of the location being an NLOS environment is 99 percent as measurement locations.

[0175] That is, the control station 100 selects measurement points from a group of candidate points that have a 99 percent probability of being in a LOS environment, and does not select measurement points from a group of candidate points that have a 99 percent probability of being in a NLOS environment.

[0176] For example, in conventional measurement data collection, the control station 100 acquires measurement data measured at all candidate locations. As a result, the number of terminal devices 400 that measure the measurement data increases, and the measurement load on the terminal devices 400 becomes heavy. In addition, the amount of measurement data transmitted to the control station 100 is large, which puts a strain on communication resources.

[0177] On the other hand, the control station 100 according to the present embodiment selects measurement points according to the communication characteristics (here, LOS environment / NLOS environment) of the candidate points, thereby enabling the control station 100 to reduce the number of points at which actual measurement data is measured without reducing the accuracy of the estimation model and / or the estimation accuracy of the estimated data.

[0178] Therefore, the control station 100 can further reduce the measured load of the terminal device 400, the amount of communication resources used to transmit the measured data, and the load of calculating statistical information.

[0179] Alternatively, when measuring actual measurement data at the same number of measurement points as in the past, the control station 100 can generate an estimation model and / or estimate estimation data with higher accuracy.

[0180] Although the control station 100 classifies the location candidates into three groups here, the number of groups to be classified is not limited to 3. The control station 100 may classify the location candidates into two groups, LOS environment / NLOS environment, or may classify the location candidates into four or more groups.

[0181] For example, the control station 100 may classify the location candidates into multiple groups according to the probability of the location candidate being in a LOS environment / NLOS environment. Also, for example, the control station 100 may classify the location candidates so that one location candidate is included in multiple groups.

[0182] Although the control station 100 selects measurement points from the location candidates included in each group in accordance with the ratio (proportion) of the group, the method of selecting measurement points is not limited to this. For example, the measurement points to be selected for each group may be determined in advance. The control station 100 selects a predetermined number of measurement points for each group.

[0183] <3-2. Second Selection Method> As described above, the control station 100 selects measurement points according to, for example, the communication characteristics of a predetermined area. In the first selection method described above, the control station 100 selects measurement points according to the LOS environment / NLOS environment of the predetermined area.

[0184] In the second selection method, the control station 100 estimates communication characteristics at candidate locations by, for example, simulation, and selects measurement locations based on the estimated communication characteristics. In the following, for the sake of simplicity, the communication characteristics are assumed to be received power (e.g., Reference Signal Received Power (RSRP), Received Signal Strength Indicator (RSSI), etc.), but the communication characteristics according to this embodiment are not limited to received power.

[0185] As described above, the control station 100 selects measurement points using simulation. For example, the control station 100 estimates the received power (here, RSRP) at the candidate points using communication environment information and / or virtual space estimation information, which will be described later.

[0186] The control station 100 classifies location candidates in a predetermined area into one or more groups according to the estimated RSRP. The control station 100 selects measurement locations from the location candidates for each group. Note that the location candidates may be determined in the same manner as in the first selection method.

[0187] 12 is a diagram illustrating an example of a second selection method according to an embodiment of the present disclosure, in which the control station 100 classifies the location candidates into three groups according to RSRP.

[0188] For example, the control station 100 classifies the location candidates into a first group where the RSRP is greater than or equal to the threshold Th11, a second group where the RSRP is less than the threshold Th11 and greater than or equal to the threshold Th12 (Th11 > Th12), and a third group where the RSRP is less than the threshold Th12.

[0189] The thresholds Th11 and Th12 may be determined using various methods. For example, the thresholds Th11 and Th12 may be determined based on communication characteristics that are simulation results. For example, the control station 100 may determine the thresholds Th11 and Th12 using statistical information of the simulation results.

[0190] For example, assume that the simulation results show that the maximum RSRP value is −30 dBm, the minimum RSRP value is −120 dBm, and the median RSRP value is −75 dBm. In this case, the control station 100 may set the threshold value Th11 to −50 dBm. That is, the control station 100 classifies location candidates whose RSRP is in the range of −50 dBm to −30 dBm into a first group.

[0191] The control station 100 may add a predetermined margin to the RSRP, taking into account fluctuations due to shadowing components and multipath fading that depend on structures. Furthermore, propagation loss varies depending on frequency. Therefore, the control station 100 may classify the location candidates into groups according to frequency.

[0192] Hereinafter, the RSRP of the location candidates included in the first group will also be referred to as high RSRP, the RSRP of the location candidates included in the second group as medium RSRP, and the RSRP of the location candidates included in the third group as low RSRP.

[0193] In Fig. 12, location candidates included in the first group with high RSRP are indicated by circles, location candidates included in the second group with medium RSRP are indicated by squares, and location candidates included in the third group with low RSRP are indicated by triangles. As shown in Fig. 12, the location candidates closer to base station 300 have higher RSRP, and the further away from base station 300, the lower the RSRP.

[0194] For example, the control station 100 classifies the location candidates into one or more groups (here, first to third groups), and then selects measurement locations according to the group, that is, taking into account the information of the group.

[0195] For example, if the measurement points are concentrated around the transmission point (base station 300), the actual measurement data acquired by the control station 100 may be biased toward data with high RSRP. If actual measurement data with biased RSRP is used in this way, the control station 100 may not be able to generate statistical information with high accuracy.

[0196] Therefore, when the total number of measurement points to be selected is predetermined, the control station 100 selects measurement points so that the ratio of measurement points selected from each group is a predetermined ratio (e.g., a:b:c).

[0197] For example, when the control station 100 selects measurement points from all groups in the same proportion (a=b=c), it selects a number of measurement points from each group equal to the total number of measurement points divided by the number of groups.

[0198] Alternatively, the control station 100 may select measurement points in proportions according to the characteristics of the group.

[0199] For example, in candidate locations close to the base station 300 (for example, candidate locations classified into the first group with high RSRP), the fluctuations in distance attenuation are large.

[0200] Therefore, the control station 100 selects more measurement points from the first group with high RSRP than from the second group with medium RSRP and the third group with low RSRP. For example, if the ratio of the number of measurement points selected from the first group, the second group, and the third group is a:b:c, the control station 100 selects measurement points such that a > b = c.

[0201] Specifically, when the total number of measurement points to be selected is N1, the control station 100 selects N1*a / (a+b+c) measurement points from the first group (high RSRP). The control station 100 also selects N1*b / (a+b+c) measurement points from the second group (medium RSRP). The control station 100 also selects N1*c / (a+b+c) measurement points from the third group (low RSRP). Note that a, b, and c have a relationship of a > b = c, for example.

[0202] Alternatively, the control station 100 may select a measurement point based on statistical information on communication characteristics at the measurement point. For example, the control station 100 may select a measurement point based on a ratio according to the standard deviation of RSRP at the location candidates for each group.

[0203] For example, if the standard deviation of RSRP at the location candidates in the second group is larger than the standard deviation of RSRP at the location candidates in the first and third groups, the control station 100 selects more measurement locations from the second group than from the first and second groups.

[0204] For example, if the ratio of the number of measurement points selected from each of the first group, second group, and third group is a:b:c, the control station 100 selects measurement points so that b>a=c.

[0205] In this way, the control station 100 can calculate statistical information with higher accuracy by selecting more measurement points from groups with larger standard deviations.

[0206] The method for selecting measurement points from each group is arbitrary. For example, the control station 100 can select measurement points from each group using the same method as the first selection method.

[0207] Alternatively, the control station 100 may select measurement points according to the LOS environment / NLOS environment of the location candidates in each group. For example, when selecting N3 measurement points from one group, the control station 100 selects the measurement points so that the ratio of LOS environment / NLOS environment / mixed environment is a1:b1:c1. For example, when the control station 100 selects measurement points so that the number of measurement points that are LOS environment / NLOS environment / mixed environment is equal, a1 = b1 = c1.

[0208] Alternatively, the control station 100 may select measurement points from each group so that many candidate points in NLOS environments are selected.

[0209] In this way, by selecting measurement points using simulation, the control station 100 can further reduce the actual measured load of the terminal device 400, the amount of communication resources used to transmit the actual measured data, and the load of calculating statistical information.

[0210] Alternatively, when measuring actual measurement data at the same number of measurement points as in the past, the control station 100 can generate an estimation model and / or estimate estimation data with higher accuracy.

[0211] Note that, although the control station 100 classifies the location candidates into three groups here, the number of groups to be classified is not limited to three. The control station 100 may classify the location candidates into two groups, or into four or more groups.

[0212] Although the control station 100 selects measurement points from the location candidates included in each group in accordance with the ratio (proportion) of the group, the method of selecting measurement points is not limited to this. For example, the measurement points to be selected for each group may be determined in advance. The control station 100 selects a predetermined number of measurement points for each group.

[0213] Although the control station 100 estimates the communication characteristics (here, the received power) using a simulation, the method for estimating the communication characteristics is not limited to simulation. For example, the control station 100 may estimate the communication characteristics using an existing propagation model such as the extended Hata formula.

[0214] <3-3. Third Selection Method> In the first and second selection methods described above, the control station 100 selected measurement points based on, for example, the communication characteristics of a predetermined area. In the third selection method, for example, the control station 100 divides a predetermined area into one or more regions and selects measurement points based on which region the location candidate is included in.

[0215] For example, in the third selection method, the control station 100 divides a predetermined area according to the distance from a transmission point (base station 300) that serves as a reference point, and selects a measurement point from candidate points included in the divided areas.

[0216] 13 is a diagram illustrating an example of the third selection method according to an embodiment of the present disclosure. In FIG. 13, the control station 100 classifies the location candidates into four groups according to the distance from the base station 300.

[0217] For example, the control station 100 divides a predetermined area into four regions R01 to R04 according to the distance from the base station 300. In the example of Fig. 13, region R01 is a region whose distance from the base station 300 is less than D01. Region R02 is a region whose distance from the base station 300 is equal to or greater than D01 but less than D02. Region R03 is a region whose distance from the base station 300 is equal to or greater than D02 but less than D03. Region R04 is a region whose distance from the base station 300 is equal to or greater than D03 but less than D04.

[0218] The width of each region R, i.e., the distance interval from the base station 300, may be equal or may be different from each other. That is, D01 = D02 - D01 = D03 - D02 = D04 - D03, or D01, D02 - D01, D03 - D02, and D04 - D03 may be different from each other.

[0219] For example, the distance interval from the base station 300 may be a fixed value or a variable value. The control station 100 may change the distance interval from the base station 300 depending on, for example, the generated statistical information or the characteristics of a predetermined area (for example, whether it is an urban area or not).

[0220] Alternatively, the control station 100 may determine the distance intervals from the base station 300 (in other words, the regions R) taking into consideration the path loss characteristics. For example, the control station 100 may set a smaller distance interval for the region R01 that is closer to the base station 300, and a larger distance interval for the region R04 that is farther from the base station 300. Specifically, the control station 100 may set the regions R01 to R04 so that D01 > D02-D01 > D03-D02 > D04-D03.

[0221] Furthermore, the control station 100 may change the distance interval from the base station 300 linearly or exponentially (or logarithmically).

[0222] The control station 100 groups the location candidates by the region in which they are located. For example, the control station 100 classifies the location candidates located in region R01 into a first group. For example, the control station 100 classifies the location candidates located in region R02 into a second group. For example, the control station 100 classifies the location candidates located in region R03 into a third group. For example, the control station 100 classifies the location candidates located in region R04 into a fourth group. Note that the determination of the location candidates may be the same as in the first selection method.

[0223] For example, the control station 100 classifies the location candidates into one or more groups (here, first to fourth groups), and then selects measurement locations according to the group, that is, taking into account the group information.

[0224] For example, if the measurement points are concentrated around the transmission point (base station 300), the actual measurement data acquired by the control station 100 may be biased toward data with high received power (e.g., RSRP). If actual measurement data with biased received power is used in this way, the control station 100 may not be able to generate statistical information with high accuracy.

[0225] Therefore, if the total number of measurement points to be selected is predetermined, the control station 100 selects measurement points so that the ratio of measurement points selected from each group is a predetermined ratio (e.g., a:b:c:d).

[0226] For example, when the control station 100 selects measurement points from all groups in the same proportion (a=b=c=d), it selects a number of measurement points from each group equal to the total number of measurement points divided by the number of groups.

[0227] Alternatively, the control station 100 may select measurement points in proportions according to the characteristics of the group.

[0228] For example, the closer a candidate location is to base station 300, the greater the fluctuation in distance attenuation tends to be, and the farther a candidate location is from base station 300, the smaller the fluctuation in distance attenuation tends to be.

[0229] Therefore, the control station 100 selects measurement points in the order of decreasing number of measurement points from group 1 to group 4. For example, if the ratio of the number of measurement points selected from group 1 to group 3 is a:b:c:d, the control station 100 selects measurement points in the order a>b>c>d.

[0230] Specifically, when the total number of measurement points to be selected is N1, the control station 100 selects N1*a / (a+b+c+d) measurement points from the first group (area R01). The control station 100 also selects N1*b / (a+b+c+d) measurement points from the second group (area R02). The control station 100 selects N1*c / (a+b+c+d) measurement points from the third group (area R03). The control station 100 selects N1*d / (a+b+c+d) measurement points from the fourth group (area R04). Note that a, b, c, and d have a relationship of, for example, a>b>c>d.

[0231] Alternatively, the control station 100 may select measurement points based on the communication characteristics at the measurement points. For example, the control station 100 may select more measurement points from a group corresponding to an area R where the communication characteristics fluctuate greatly, based on the virtual space estimation information, etc. The control station 100 may select fewer measurement points from a group corresponding to an area R where the communication characteristics fluctuate little, based on the virtual space estimation information, for example.

[0232] The method for selecting measurement points from each group is arbitrary. For example, the control station 100 may randomly select measurement points from each group.

[0233] Alternatively, the control station 100 may select a measurement point from each group according to the communication characteristics of the location candidate. For example, the control station 100 may determine whether the location candidate is in a LOS environment or a NLOS environment, and select a measurement point from each group according to the LOS environment or the NLOS environment.

[0234] This allows the control station 100 to select a measurement point according to the communication characteristics of the point candidates.

[0235] In this way, by selecting a measurement point according to the distance from the transmission point, the control station 100 can further reduce the actual measurement load of the terminal device 400, the amount of communication resources used to transmit the actual measurement data, and the calculation load of statistical information.

[0236] Alternatively, when measuring actual measurement data at the same number of measurement points as in the past, the control station 100 can generate an estimation model and / or estimate estimation data with higher accuracy.

[0237] It should be noted that, although the control station 100 classifies the location candidates into four groups here, the number of groups to be classified is not limited to 4. The control station 100 may classify the location candidates into three or fewer groups, or into four or more groups.

[0238] Although the control station 100 selects measurement points from the location candidates included in each group in accordance with the ratio (proportion) of the group, the method of selecting measurement points is not limited to this. For example, the measurement points to be selected for each group may be determined in advance. The control station 100 selects a predetermined number of measurement points for each group.

[0239] <3-4. Fourth Selection Method> In the third selection method described above, the control station 100 divides the predetermined area according to the distance from the base station 300, but the method for dividing the predetermined area is not limited to this. For example, the control station 100 may divide the predetermined area according to angle information (e.g., azimuth angle) from the base station 300.

[0240] For example, in the fourth selection method, the control station 100 divides a predetermined area according to the azimuth angle from the transmission point (base station 300) that serves as the reference point, and selects a measurement point from the candidate points included in the divided area.

[0241] Fig. 14 is a diagram illustrating an example of a fourth selection method according to an embodiment of the present disclosure. In Fig. 14, the control station 100 classifies the location candidates into four groups according to the azimuth angle from the base station 300. Note that the location candidates may be determined in the same manner as in the first selection method.

[0242] For example, the control station 100 divides a predetermined area into four regions R11 to R14 according to the azimuth angle from the base station 300. In the example of FIG. 14 , region R11 is a region where the azimuth angle from the base station 300 is less than θ01 from a predetermined reference. Region R12 is a region where the azimuth angle from the base station 300 is equal to or greater than θ01 but less than θ02. Region R03 is a region where the azimuth angle from the base station 300 is equal to or greater than θ02 but less than θ03. Region R04 is a region where the azimuth angle from the base station 300 is equal to or greater than θ03 but less than θ04. The unit of the azimuth angle may be degrees or radians.

[0243] The angles of the regions R, i.e., the difference in azimuth angle from the base station 300, may be equal intervals or may be different from each other. That is, θ01 = θ02 - θ01 = θ03 - θ02 = θ04 - θ03 may be satisfied, or θ01, θ02 - θ01, θ03 - θ02, and θ04 - θ03 may be different from each other.

[0244] For example, the angle of the region R may be a fixed value or a variable value. The control station 100 may change the angle depending on, for example, the generated statistical information, the characteristics of a predetermined area (e.g., whether it is an urban area), the performance of the base station 300 (e.g., antenna pattern information), etc.

[0245] Here, examples of the antenna pattern information include antenna installation height, tilt angle (Downtilt), horizontal direction (Azimuth), vertical direction (Elevation), antenna peak gain, and antenna model.

[0246] The control station 100 groups the location candidates by the region in which they are located. For example, the control station 100 classifies the location candidates located in region R11 into a first group. For example, the control station 100 classifies the location candidates located in region R12 into a second group. For example, the control station 100 classifies the location candidates located in region R13 into a third group. For example, the control station 100 classifies the location candidates located in region R14 into a fourth group.

[0247] For example, the control station 100 classifies the location candidates into one or more groups (here, first to fourth groups), and then selects measurement locations according to the group, that is, taking into account the group information.

[0248] For example, if the total number of measurement points to be selected is predetermined, the control station 100 selects measurement points so that the ratio of measurement points selected from each group is a predetermined ratio (e.g., a:b:c:d).

[0249] For example, when the control station 100 selects measurement points from all groups in the same proportion (a=b=c=d), it selects a number of measurement points from each group equal to the total number of measurement points divided by the number of groups.

[0250] Alternatively, the control station 100 may select measurement points at a ratio according to the characteristics of the group. For example, the control station 100 may select measurement points from each group at a ratio according to the communication environment information and / or the virtual space estimation information, the antenna pattern information, etc.

[0251] For example, if the antenna beam pattern of the base station 300 has a strong directivity in a specific direction (area R), the control station 100 selects a larger number of measurement points from the group corresponding to that area R than the number of measurement points selected from other groups. The antenna beam pattern may be included in the antenna pattern information, for example.

[0252] Alternatively, the control station 100 may select measurement points in accordance with fluctuations in communication characteristics in addition to or instead of the antenna beam pattern. For example, the control station 100 selects more measurement points from the region R where the antenna has strong directivity and / or the region R where the communication characteristics fluctuate greatly than from other groups.

[0253] Whether or not the fluctuations in the communication characteristics are large can be determined from, for example, the standard deviation of the communication characteristics and the second or higher order moments related to the probability distribution of the communication characteristics.

[0254] The method of selecting measurement points from each group is not limited to the above-described method, and may be any method. For example, the control station 100 may randomly select measurement points from each group.

[0255] Alternatively, the control station 100 may select a measurement point from each group according to the communication characteristics of the location candidate. For example, the control station 100 may determine whether the location candidate is in a LOS environment or a NLOS environment, and select a measurement point from each group according to the LOS environment or the NLOS environment and the received power.

[0256] The control station 100 may also select a measurement point from each group depending on the distance of the point candidate from the base station 300 .

[0257] In this way, by selecting a measurement point according to the distance from the transmission point, the control station 100 can further reduce the actual measurement load of the terminal device 400, the amount of communication resources used to transmit the actual measurement data, and the calculation load of statistical information.

[0258] Alternatively, when measuring actual measurement data at the same number of measurement points as in the past, the control station 100 can generate an estimation model and / or estimate estimation data with higher accuracy.

[0259] Here, the control station 100 selects measurement points from the location candidates included in each group in accordance with the ratio (proportion) of the group, but the method of selecting measurement points is not limited to this. For example, the measurement points to be selected for each group may be determined in advance. The control station 100 selects a predetermined number of measurement points for each group.

[0260] Furthermore, although the control station 100 classifies the location candidates into four groups here, the number of groups to be classified is not limited to four. The control station 100 may classify the location candidates into three or fewer groups, or into four or more groups.

[0261] The control station 100 may also classify the location candidates into groups according to the distance in addition to the azimuth from the base station 300. For example, the control station 100 divides a predetermined area according to the azimuth and distance from a transmission point (base station 300) that serves as a reference point, and selects measurement locations from the location candidates included in the divided areas.

[0262] 15 is a diagram illustrating another example of the fourth selection method according to the embodiment of the present disclosure. In FIG. 15, the control station 100 classifies the location candidates into five groups according to the azimuth angle and distance from the base station 300.

[0263] For example, the control station 100 divides the above-mentioned region R12 into two regions R21 and R22 according to the distance from the base station 300. The other regions R11, R13, and R14 are the same as those in FIG.

[0264] For example, region R21 is a region where the azimuth angle from base station 300 is equal to or greater than θ01 and less than θ02, and the distance from base station 300 is less than D11. Region R22 is a region where the azimuth angle from base station 300 is equal to or greater than θ01 and less than θ02, and the distance from base station 300 is equal to or greater than D11.

[0265] Here, the control station 100 divides the region R12 into two regions according to the distance from the base station 300, but the region may be divided into three or more regions. Also, the control station 100 may divide any of the regions R11, R13, and R14 into two or more regions according to the distance from the base station 300.

[0266] The control station 100 may also divide the regions R11 to R14 into any shape. For example, the control station 100 may further divide the regions R11 to R14 into two or more grid-like regions.

[0267] The control station 100 groups the location candidates by the region in which they are located. For example, the control station 100 classifies the location candidates located in region R11 into a first group. For example, the control station 100 classifies the location candidates located in region R21 into a second group. For example, the control station 100 classifies the location candidates located in region R22 into a third group. For example, the control station 100 classifies the location candidates located in region R13 into a fourth group. For example, the control station 100 classifies the location candidates located in region R14 into a fifth group.

[0268] The method for selecting measurement points from each group is the same as when the region R is divided according to the azimuth angle from the base station 300 (see FIG. 14), and therefore the explanation will be omitted here.

[0269] In this way, the control station 100 can divide a predetermined area into any shape.

[0270] <3-5. Fifth Selection Method> In the third and fourth selection methods described above, the predetermined area is divided according to information related to the base station 300 (for example, the distance and azimuth angle from the base station 300). In the fifth selection method, the predetermined area is divided according to area information.

[0271] For example, the area information may include information about a telecommunications carrier that provides communication services in the area. For example, a technology that provides communication services limited to a specific area is known. This technology is also referred to as a non-public network technology, a local 5G technology, a private network technology, a local area network technology, or the like.

[0272] For example, if at least a part of the predetermined area includes an area where a predetermined telecommunications carrier provides local 5G service, the predetermined area can be divided into a service area where the predetermined telecommunications carrier provides local 5G service and a non-service area where the predetermined telecommunications carrier does not provide local 5G service.

[0273] Here, the service area where a predetermined telecommunications carrier provides local 5G services is also referred to as "own land." In this own land, the local 5G services are provided by the predetermined telecommunications carrier.

[0274] In addition, a non-service area where a specific telecommunications carrier does not provide local 5G services is also referred to as "other-party land." In this other-party land, local 5G services or public network services may be provided by a telecommunications carrier other than the specific telecommunications carrier.

[0275] Furthermore, information about other people's land is referred to as other people's land information. The other people's land information may include, for example, information about the boundary between the provided area and the non-provided area. The above-mentioned area information may include, for example, other people's land information.

[0276] Fig. 16 is a diagram illustrating an example of a fifth selection method according to an embodiment of the present disclosure. In Fig. 16, the control station 100 classifies location candidates into two groups according to other land information from the base station 300. Note that the determination of location candidates may be the same as in the first selection method.

[0277] As shown in FIG. 16, the other person's land information includes information regarding the boundary B1 between the provided area and the non-provided area.

[0278] The base station 300B operated by another operator provides communication services, for example, in a non-service area (other operator's land).

[0279] The base station 300A operated by a predetermined operator provides local 5G services in a service area (own land), for example. At this time, the base station 300A is required to suppress the amount of interference in non-service areas to an allowable value or less.

[0280] Therefore, when a specified area contains a mixture of coverage areas and non-coverage areas, the control station 100 is required to determine communication parameters (an example of statistical information) that will prevent the base station 300A from causing interference to the non-coverage areas that is greater than the allowable value.

[0281] Therefore, in the fifth selection method, the control station 100 divides a predetermined area according to other land information (for example, provided area / non-provided area), and selects a measurement point from the point candidates included in the divided area.

[0282] For example, the control station 100 divides a predetermined area into a service area / non-service area. The control station 100 groups the location candidates according to the area in which they are located (service area / non-service area). For example, the control station 100 classifies the location candidates located in the service area into a first group. For example, the control station 100 classifies the location candidates located in the non-service area into a second group. Note that the determination of the location candidates may be the same as in the first selection method.

[0283] For example, the control station 100 classifies the location candidates into one or more groups (here, first to fourth groups), and then selects measurement locations according to the group, that is, taking into account the group information.

[0284] For example, if it is desired to more reliably keep interference in non-service areas below an acceptable value, the control station 100 increases the number of measurement points in the second group corresponding to the non-service areas to more than the number of measurement points in the first group corresponding to the service areas.

[0285] For example, in this case, if the ratio of the number of measurement points selected from the first group to the number of measurement points selected from the second group is a:b, the control station 100 selects measurement points such that a>b.

[0286] Specifically, when the total number of measurement points to be selected is N1, the control station 100 selects N1*a / (a+b) measurement points from the first group (service area) and N1*b / (a+b) (a>b) measurement points from the second group (non-service area).

[0287] For example, the control station 100 may select a much larger number of measurement points from the second group than from the first group (a>>b, for example, a:b=19, etc.), which allows the control station 100 to determine communication parameters that can more reliably reduce the amount of interference in non-service areas to within the allowable value.

[0288] Alternatively, for example, if it is desired to ensure throughput in the service area while more reliably keeping interference in the non-service area below an allowable value, the control station 100 sets the number of measurement points in the first and second groups to the same ratio.

[0289] For example, in this case, if the ratio of the number of measurement points selected from the first group to the number of measurement points selected from the second group is a:b, the control station 100 selects measurement points such that a=b.

[0290] Alternatively, the control station 100 may select measurement points based on the communication characteristics at the measurement points. For example, the control station 100 may select more measurement points from a group corresponding to an area R where the communication characteristics fluctuate greatly, based on the virtual space estimation information, etc. The control station 100 may select fewer measurement points from a group corresponding to an area R where the communication characteristics fluctuate little, based on the virtual space estimation information, for example.

[0291] The method for selecting measurement points from each group is arbitrary. For example, the control station 100 may randomly select measurement points from each group.

[0292] Alternatively, the control station 100 may select a measurement point from each group according to the communication characteristics of the location candidate. For example, the control station 100 may determine whether the location candidate is in a LOS environment or a NLOS environment, and select a measurement point from each group according to the LOS environment or the NLOS environment.

[0293] Alternatively, the control station 100 may select measurement points from each group according to their distance from the boundary B1. For example, the control station 100 selects, from each group, point candidates that are close to the boundary B1 as measurement points.

[0294] Alternatively, the control station 100 may select measurement points from each group according to the distance and azimuth angle from the base station 300A and / or the base station 300B.

[0295] In this way, by selecting a measurement point according to the distance from the transmission point, the control station 100 can further reduce the actual measurement load of the terminal device 400, the amount of communication resources used to transmit the actual measurement data, and the calculation load of statistical information.

[0296] Alternatively, when measuring actual measurement data at the same number of measurement points as before, the control station 100 can generate an estimation model and / or estimate estimation data with higher accuracy. This allows the control station 100 to more reliably keep the amount of interference in non-service areas below an allowable value. Alternatively, the control station 100 can further improve the throughput in the service area while more reliably keeping the amount of interference in non-service areas below an allowable value.

[0297] Here, the control station 100 classifies the location candidates into two groups, but the number of groups is not limited to two. The control station 100 may classify the location candidates into three or more groups. For example, the control station 100 may divide the service area and / or non-service area into multiple regions R according to the distance from the boundary B1 (or the base station 300).

[0298] Although the control station 100 selects measurement points from the location candidates included in each group in accordance with the ratio (proportion) of the group, the method of selecting measurement points is not limited to this. For example, the measurement points to be selected for each group may be determined in advance. The control station 100 selects a predetermined number of measurement points for each group.

[0299] <<4. Other>> Here, definitions (explanations) of terms used in the above-described embodiment will be described.

[0300] <4-1. Terminology> <4-1-1. 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 the transmission point, reception point, base station 300, terminal device 400, and / or communication node

[0301] 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.

[0302] 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).

[0303] The characteristics related to communication speed include information on at least one of downlink throughput, uplink throughput, and sidelink throughput.

[0304] The delay-related characteristics include information about at least one of latency, jitter, and ping value.

[0305] 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.

[0306] 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

[0307] 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.

[0308] <4-1-2. 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 above-mentioned predetermined area (estimated area R_1 and / or calculated area R_0). - Coverage area (estimated area R_1) 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

[0309] The estimated area R_1 includes, for example, coverage that can be connected to one communication node (such as a base station 300 or a terminal device 400). Furthermore, 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.

[0310] For example, the base station 300 of the private network or the access point name (APN) of the core network may be set for each base as the estimated area R_1. In other words, the same access point name is set for the same base (estimated area R_1), and if the access point name is different, the base is recognized as a different base (estimated area R_1).

[0311] 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.

[0312] <4-1-3. Regarding Communication Environment Information> Communication environment information is information that can be included in actual measurement data. The communication environment information can be used for statistical processing (e.g., generating an estimation model). The communication environment information can be used to generate estimation data using the estimation model. In other words, the communication environment information can be an explanatory variable of the estimation model.

[0313] 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.).

[0314] 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

[0315] (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.

[0316] The map information includes, for example, topographical information, an office layout diagram, and a premises diagram.

[0317] (Structure Information) The structure information here includes information that affects radio wave propagation, such as reflection, diffraction, and transmission.

[0318] Examples of structures include buildings, walls, plantations, roads, signs, traffic lights, road signs, pillars, buildings, the ground, glass, windows, desks, and cabinets.

[0319] 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.).

[0320] 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.

[0321] (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

[0322] 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 .

[0323] (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.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] (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

[0332] 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.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] The restriction information here includes information about conditions and restrictions regarding wireless communication permitted in the local network.

[0337] 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.

[0338] 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).

[0339] 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

[0340] <4-1-4. 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 second data.

[0341] 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).

[0342] Alternatively, the information relating to the location includes relative location information obtained by a beacon, UWB (Ultra-Wide Band), or the like.

[0343] The absolute location information or relative location information may be an area divided by a predetermined distance or method.

[0344] Note that the information (measurement data, etc.) according to this embodiment can be information linked to location information.

[0345] <4-1-5. Regarding the Estimation Model> The estimation model is generated by the control station 100 as an example of statistical processing. The estimation model is generated by the control station 100 based on, for example, actual measurement data of the calculation area R_0. The estimation model takes, for example, actual measurement data of the estimation area R_1 as input and outputs estimated data. The estimated data can be used for calculating communication parameters for the estimation area R_1, etc.

[0346] Methods for generating an estimation model include, for example, statistical analysis methods using linear regression analysis or multiple regression analysis, or methods using so-called artificial intelligence, machine learning, and deep learning such as AI (Artificial Intelligence) / ML (Machine Learning).

[0347] Examples of techniques using AI / ML include the following: - 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

[0348] It should be noted that the method using AI / ML is not limited to the above-mentioned example. There are many methods using AI / ML. Therefore, it is desirable to generate an estimation model using an appropriate method depending on the principles of each method. When an estimation model is generated using a method using AI / ML, the communication characteristics to be estimated can be the objective variable of the estimation model. In addition, various parameters, data, information (e.g., communication environment information), etc. can be used as explanatory variables of the estimation model.

[0349] 4-2. 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.

[0350] The virtual space estimation information may be used as needed when generating an estimation model. For example, the virtual space estimation information may be used in addition to or instead of the actual measurement data to generate an estimation model.

[0351] The virtual space estimation information may be used as an explanatory variable of an estimation model. Alternatively, the virtual space estimation information may be used as a target variable of an estimation model. For example, the virtual space estimation information may be used as student data and teacher data of an estimation model.

[0352] The virtual space estimation information includes, for example, at least one of the following information: - LOS / NLOS information - Simulation information - Calculation information calculated based on the LOS / NLOS information, simulation information, etc.

[0353] (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.

[0354] The LOS environment is also called a line-of-sight environment. The LOS environment indicates a situation in which there are no obstacles 600, such as structures or people, on 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.

[0355] 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.

[0356] (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.

[0357] 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).

[0358] 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

[0359] (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

[0360] 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.

[0361] 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.

[0362] First, the control station 100 constructs a virtual communication environment for the area (calculated area R_0 and / or estimated area R_1) (step S101).

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

[0364] 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 S102). 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.

[0365] The control station 100 generates virtual space estimation information based on the simulation results (step S103).

[0366] 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.

[0367] <4-3. 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.

[0368] The estimation accuracy of the communication characteristics and / or estimation model in the estimation area R_1 can be given by any one of the following or a combination thereof: - Number of calculation areas R_0 - Number of actual measurement data in each calculation area R_0 - Estimation accuracy of LOS environment / NLOS environment at the location candidate - Method of selecting measurement locations - Generation accuracy of the estimation model - Similarity of the communication environments in the calculation area R_0 and the estimation area R_1 - Accuracy of simulation data in the calculation area R_0 and / or the estimation area R_1 - Observation accuracy of actual measurement data in the calculation area R_0 and / or the estimation area R_1

[0369] Examples of similarities 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.).

[0370] 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.

[0371] 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.

[0372] 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.

[0373] <4-4. Use Cases> The statistical information according to the above-described embodiment can be used in various processes, controls, use cases, and the like.

[0374] For example, data on communication characteristics estimated in the estimated area R_1 (e.g., predicted values ​​as statistical information) can be used for cell design within the estimated area R_1 (such as the location of transmission points and setting the maximum transmission power of transmission points).

[0375] This cell design may be performed based on the estimation accuracy of communication characteristics in the estimation area R_1. 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.

[0376] For example, data on communication characteristics estimated in the estimated area R_1 (e.g., predicted values ​​as statistical information) can be used to control communication parameters (transmission power, MCS (Modulation and coding scheme), beams, etc.) of transmission points and / or reception points within the estimated area R_1.

[0377] The control of the communication parameters may be further performed based on the estimation accuracy of the communication characteristics in the estimation area R_1. Moreover, the control of the communication parameters may be performed by the control station 100 or the base station 300. Alternatively, the control of the communication parameters may be performed by the core network or the like.

[0378] 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.

[0379] Furthermore, the technology according to this embodiment may be applied to 6G joint communication and sensing.

[0380] <4-5. Normalization of Measured Data> In this embodiment, when there are a plurality of calculation areas R_0, the simulation data and / or the measured data in the calculation areas R_0 are normalized (offsets are provided) by, for example, a predetermined method.

[0381] The predetermined method can be performed based on communication environment information (for example, information on the transmission power from the base station 300) in each calculation area R_0.

[0382] The communication environment information here 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 (such as the carrier frequency and the frequency bandwidth), and information on the calculation area R_0 (such as the coverage area and indoor / outdoor information).

[0383] For example, normalization is performed so that the communication environment information in each calculation area R_0 is the same.

[0384] Simulation data of communication characteristics in a specified area (one of multiple calculation areas R_0) is normalized so that the communication environment information in the specified area is the same as the communication environment information in each calculation area R_0 (the remaining calculation areas R_0 other than the specified area).

[0385] The data of the communication characteristics in a predetermined area (one of the multiple calculation areas R_0) is estimated taking into account the normalization described above.

[0386] The actual measured data of communication characteristics in a specified area (one of the multiple calculation areas R_0) is normalized so that the communication environment information in the specified area is the same as the communication environment information in each calculation area R_0 (the remaining calculation areas R_0 other than the specified area).

[0387] The above-mentioned normalization is performed so that the communication environment information in each calculation area R_0 becomes the communication environment information in the above-mentioned predetermined area (one of the plurality of calculation areas R_0).

[0388] 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.

[0389] A specific example of normalization will be described below.

[0390] For example, consider a case where the communication characteristics of the simulation data and / or the measured data in the calculation area R_0 are downlink RSRP.

[0391] Consider a case where the transmission power of base station 300 in calculation area R_01 is B1 (dBm), the transmission power of base station 300 in calculation area R_02 is B2 (dBm), and the transmission power of base station 300 in calculation area R_03 is B3 (dBm).

[0392] When normalizing these transmission powers to S (dBm), the control station 100 applies an offset of S-B1 (dBm) to the RSRP in calculation area R_01. The control station 100 applies an offset of S-B2 (dBm) to the RSRP in calculation area R_02. The control station 100 applies an offset of S-B3 (dBm) to the RSRP in calculation area R_03. In this way, the control station 100 performs normalization by applying an offset to each RSRP (each data).

[0393] Here, S (dBm) may be the transmission power of the base station 300 in the estimation area R_1 where estimation is performed.

[0394] 18 is a diagram showing an example of the hardware configuration of a device, etc. The control station 100 described above is realized by, for example, a computer 1000 shown in FIG.

[0395] 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 to each other via a bus 1050.

[0396] 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.

[0397] The ROM 1300 stores boot programs such as a Basic Input Output System (BIOS) executed by the CPU 1100 when the computer 1000 is started, and programs that depend on the hardware of the computer 1000 .

[0398] The HDD 1400 is a computer-readable storage medium that non-temporarily stores programs executed by the CPU 1100 and data used by such programs. Specifically, the HDD 1400 is a storage medium that stores a program for the information processing method according to the present disclosure, which is an example of program data 1450.

[0399] 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.

[0400] 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 input devices such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 also transmits data to output devices 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 stored on a predetermined computer-readable storage medium. Examples of media include optical storage media such as DVDs (Digital Versatile Discs) and PDs (Phase Change Rewritable Discs), magneto-optical storage media such as MOs (Magneto-Optical Discs), tape media, magnetic storage media, and semiconductor memories.

[0401] When the computer 1000 functions as the control station 100 described above, the CPU 1100 of the computer 1000 executes a program loaded onto the RAM 1200 to realize the functions of the control unit 170. The program may be stored in the HDD 1400. The CPU 1100 reads and executes the program data 1450 from the HDD 1400, but as another example, the CPU 1100 may obtain the program from another device via the external network 1550.

[0402] Each of the above components may be configured using general-purpose materials or may be configured using hardware specialized for the function of each component. Such configurations may be changed as appropriate depending on the technical level at the time of implementation.

[0403] <<6. Other Embodiments>> The processing according to the above-described embodiment may be implemented in various different forms other than the above embodiment.

[0404] In the above-described embodiment, the terminal device 400 and the base station 300 transmit various information (for example, real space measurement information, communication environment information, information used by the control station 100 for estimation, etc.) to the control station 100. Conditions related to the transmission of information by the terminal device 400 and the base station 300 can be set or specified individually. These conditions include, for example, the timing of transmitting information, the trigger conditions for transmission, the period, etc.

[0405] For example, when the terminal device 400 transmits object detection information, the information may be transmitted at the timing when the terminal device 400 detects an object. That is, the detection of an object by the terminal device 400 is used as a trigger to cause the terminal device 400 to transmit the detection information to the control station 100. In other words, the terminal device 400 does not need to transmit the detection information to the control station 100 while it does not detect an object.

[0406] Furthermore, for example, when the terminal device 400 transmits quality information regarding communication quality, the terminal device 400 may transmit the quality information at regular intervals. Alternatively, the terminal device 400 may determine whether to transmit quality information based on the difference from the quality information transmitted last time.

[0407] Specifically, when the quality information is equal to or greater than a predetermined value (exceeds the predetermined value) compared to the quality information previously transmitted, the terminal device 400 transmits the quality information to the control station 100. In other words, when the quality information is less than a predetermined value (equal to or greater than the predetermined value) compared to the quality information previously transmitted, the terminal device 400 does not need to transmit the quality information to the control station 100.

[0408] When transmitting quality information, the terminal device 400 may transmit to the control station 100 information indicating the difference from the previous quality information.

[0409] 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.

[0410] Alternatively, the base station 300 and / or the terminal device 400 may estimate 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 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.

[0411] 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.

[0412] 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.

[0413] 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 a combination of 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 may be stored in a server device and downloaded to a computer.

[0414] 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.

[0415] 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.

[0416] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0417] Furthermore, the effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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.).

[0422] <<7. Conclusion>> The effects described in this disclosure are merely examples and are not limited to the disclosed content. Other effects may also be obtained.

[0423] 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.

[0424] The present technology may also be configured as follows. (1) An information processing device including a control unit that classifies candidates for measurement points where data related to communication characteristics are measured in a predetermined area into at least one group, and selects the measurement point where the data is measured from the candidates according to the group. (2) The information processing device according to (1), in which the control unit classifies the candidates into multiple regions obtained by dividing the predetermined area. (3) The information processing device according to (2), in which the regions are divided according to distances from a reference point. (4) The information processing device according to (2) or (3), in which the regions are divided according to azimuth angles from the reference point. (5) The information processing device according to any one of (2) to (4), in which the regions are divided according to providers that provide communication services in the regions. (6) The information processing device according to (1), in which the control unit classifies the candidates into the groups according to communication characteristics in the predetermined area. (7) The information processing device according to (6), wherein the communication characteristics are information indicating a line-of-sight (LOS) environment and / or a non-line-of-sight (NLOS) environment. (8) The information processing device according to (6) or (7), wherein the communication characteristics are received power at the measurement points. (9) The information processing device according to any one of (6) to (8), wherein the communication characteristics are calculated based on a simulation. (10) The information processing device according to any one of (1) to (9), wherein the control unit selects a predetermined number of the measurement points from the candidates classified into the group. (11) The information processing device according to any one of (1) to (10), wherein the control unit randomly selects the measurement points from the candidates classified into the group. (12) The information processing device according to any one of (1) to (5), wherein the control unit selects the measurement points from the candidates classified into the group in accordance with communication characteristics at the candidate points. (13) The information processing device according to any one of (1) to (12), wherein the data is used to estimate communication characteristics in a calculation area. (14) The information processing device according to (13), wherein the predetermined area is different from the calculation area.(15) The information processing device according to (14), wherein the data is used to generate an estimation model used to estimate the communication characteristics in the calculation area. (16) The information processing device according to (13), wherein the predetermined area is the calculation area. (17) The information processing device according to (16), wherein the data is used to correct the estimation model used to estimate the communication characteristics in the calculation area. (18) The information processing device according to (16), wherein the data is used as input data for the estimation model used to estimate the communication characteristics in the calculation area. (19) A communication system comprising: an information processing device that classifies candidates for measurement points at which data related to communication characteristics in a predetermined area into at least one group, and selects the measurement point at which the data is measured from among the candidates according to the group; and a terminal device that measures the data at the measurement point selected by the information processing device. (20) An information processing method including: classifying candidates for measurement points at which data related to communication characteristics in a predetermined area into at least one group, and selecting the measurement point at which the data is measured from among the candidates according to the group. (21) An information processing method executed in a communication system including an information processing device and a terminal device, wherein the information processing device classifies candidates for measurement points where data related to communication characteristics in a predetermined area are measured into at least one group, selects the measurement point where the data is measured from the candidates according to the group, and the terminal device measures the data at the measurement point selected by the information processing device. (22) A terminal device including a control unit that measures data at the measurement point selected by the information processing device, wherein the measurement point is a point selected from the candidates according to the group after the information processing device classifies candidates for the measurement point where the data related to communication characteristics in a predetermined area into at least one group.(23) A measurement method including measuring data at a measurement point selected by an information processing device, wherein the information processing device classifies candidates for the measurement point at which the data related to communication characteristics in a predetermined area is measured into at least one group, and the measurement point is a point selected from the candidates according to the group.

[0425] 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 having a control unit that classifies candidates for measurement points at which data related to communication characteristics in a specified area are measured into at least one group, and selects the measurement point at which the data is measured from among the candidates according to the group.

2. The information processing device according to claim 1, wherein the control unit classifies the candidates into a plurality of regions obtained by dividing the predetermined area.

3. The information processing device according to claim 2, wherein the area is divided according to the distance from a reference point.

4. The information processing device according to claim 2, wherein the area is divided according to an azimuth angle from a reference point.

5. The information processing device according to claim 2, wherein the area is divided according to the carriers that provide communication services in the area.

6. The information processing device according to claim 1, wherein the control unit divides the candidates into the groups according to communication characteristics in the predetermined area.

7. The information processing device according to claim 6, wherein the communication characteristics are information indicating a line of sight (LOS) environment and / or a non-line of sight (NLOS) environment.

8. The information processing device according to claim 6, wherein the communication characteristic is a received power at the measurement point.

9. The information processing device according to claim 6, wherein the communication characteristics are calculated based on a simulation.

10. The information processing device according to claim 1, wherein the control unit selects a predetermined number of the measurement points from the candidates classified into the groups.

11. The information processing device according to claim 1, wherein the control unit randomly selects the measurement point from among the candidates classified into the group.

12. The information processing device according to claim 1, wherein the control unit selects the measurement location from the candidates classified into the group according to communication characteristics at the candidate locations.

13. The information processing device according to claim 1, wherein the data is used to estimate communication characteristics in a calculation area.

14. The information processing device according to claim 13, wherein the predetermined area is different from the calculated area.

15. The information processing device according to claim 14, wherein the data is used to generate an estimation model used in the estimation of the communication characteristics in the calculation area.

16. The information processing device according to claim 13, wherein the predetermined area is the calculated area.

17. The information processing device according to claim 16, wherein the data is used to correct an estimation model used in the estimation of the communication characteristics in the calculation area.

18. A communication system comprising: an information processing device that classifies candidates for measurement points at which data related to communication characteristics in a specified area are measured into at least one group, and selects the measurement point at which the data is measured from among the candidates according to the group; and a terminal device that measures the data at the measurement point selected by the information processing device.

19. An information processing method comprising: classifying candidates for measurement points at which data relating to communication characteristics are measured in a predetermined area into at least one group; and selecting the measurement point at which the data is measured from among the candidates according to the group.

20. An information processing method executed in a communication system including an information processing device and a terminal device, the information processing device classifying candidates for measurement points at which data related to communication characteristics in a specified area are measured into at least one group; selecting the measurement point at which the data is measured from among the candidates according to the group; and the terminal device measuring the data at the measurement point selected by the information processing device.

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