Control device, control method, control system, and program

Abstract

In the present invention, in an environment in which control functions of a base station in wireless communication can be dynamically arranged on one or more processing platforms, a control device has an optimal arrangement function unit that dynamically arranges the control functions on the processing platforms in accordance with arrangement requirements or specific parameters of the control functions specified by a user, and connects the arranged control functions.

Description

Control Device, Control Method, Control System, and Program

[0001] The present invention relates to a technique for wireless quality control in a wireless communication system.

[0002] In a wireless communication system, in order to control wireless quality, it is common to combine various functions (such as propagation, design, grasping, prediction, control, etc.) to realize desired processing. As prior art, the self-organizing network optimization technology SON (Self-Organizing Network) disclosed in Non-Patent Document 1 and the multi-radio proactive control technology Cradio (registered trademark) disclosed in Non-Patent Document 2 are known. In these technologies, each function is implemented as a container and realized as a microservice, enabling flexible combination of functions.

[0003] Ryuichi Takechi, et al., "Optimization Technology for External Wireless Networks: SON", [online], July 2011, NEC, [searched on October 18, 2024], Internet <URL: https: / / www.fujitsu.com / downloads / JP / archive / imgjp / jmag / vol62-4 / paper15.pdf> NTT Access Service System Laboratories, "Multi-Radio Proactive Control Technology Cradio (registered trademark) (Cradio 1.0 System)", 2021, [searched on October 18, 2024], Internet <URL: https: / / www.rd.ntt / as / history / wireless / wi0519.html>

[0004] However, in the prior art, there was no means to define on which execution platform (platform) each function should be placed and dynamically place it on an optimal platform according to the user's usage purpose and processing content to execute the processing. Therefore, it was difficult to achieve optimal wireless quality control according to user requirements such as data confidentiality, processing responsiveness, and high speed.

[0005] This invention has been made in view of the above points, and provides a technology that enables efficient and flexible wireless quality control by dynamically arranging each function that realizes wireless quality control on an optimal execution platform according to user requirements, etc.

[0006] According to the disclosed technology, in an environment in which control functions of a base station in wireless communication can be dynamically arranged on one or more processing boards, a control device is provided which has an optimal arrangement function unit that dynamically arranges the control functions on the processing board according to the arrangement requirements or specific parameters of the control functions specified by the user, and connects the arranged control functions.

[0007] According to the disclosed technology, each function that realizes wireless quality control can be dynamically arranged on the optimal execution platform according to user requirements, etc., enabling efficient and flexible wireless quality control.

[0008] Figure 1 is a diagram illustrating an overview of closed-loop control that realizes quality control of a wireless network. Figure 2 is a diagram illustrating an overview of a conventional system configuration in closed-loop control. Figure 3 is a diagram illustrating an overview of a system configuration according to an embodiment of the present invention. Figure 4 is a diagram illustrating an overview of the optimal placement function unit according to an embodiment of the present invention. Figure 5 is a diagram illustrating the relationship between the group of functions placed by the optimal placement function unit and the execution base. Figure 6 is a diagram illustrating an embodiment of the optimal placement function unit. Figure 7 is an example of the operation of the optimal placement function unit in Embodiment 1. Figure 8 is an example of the operation of the optimal placement function unit in Embodiment 2. Figure 9 is an example of the operation of the optimal placement function unit in Embodiment 3. Figure 10 is a diagram visualizing the control time by the wireless control time analysis unit in Embodiment 3. Figure 11 is an example of a sequence diagram relating to the dynamic placement of each function unit and the measurement / notification of control time by the cooperative function unit. Figure 12 is an example of a sequence diagram relating to the determination of the optimal execution base according to the control time by the wireless control time analysis unit. Figure 13 is an example of a sequence diagram relating to the determination of the optimal execution base following the passage of time / environmental changes by the wireless control time analysis unit. Figure 14 shows an example of the hardware configuration of a control device that performs quality control of a wireless network. Figure 15 shows an example of the functional configuration of a control device that performs quality control of a wireless network.

[0009] Embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are merely examples, and the embodiments to which the present invention is applied are not limited to the embodiments described below.

[0010] In the following explanation, " / " means "and / or" unless otherwise specified, or unless the context makes it clear that it has a different meaning.

[0011] In the following explanation, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, "based on" can mean both "based solely on" and "based at least on." Furthermore, "based on" can sometimes be used interchangeably with "according to."

[0012] In the following explanation, terms may be used interchangeably. For example, "wireless equipment" and "terminal" may be used interchangeably. "Local" and "on-premise" may be used interchangeably. "Predictive measurement," "predictive learning," and "predictive inference" may be called "measurement," "learning," and "inference," respectively. Terms such as "mobile station," "terminal," "device," and "equipment" may be used interchangeably.

[0013] In the following explanation, "wireless quality" refers to "the quality of the wireless network," and "wireless control" refers to "the control of the wireless network."

[0014] The following will first describe the prior art and its problems in detail, and then provide an overview and details of the technology according to the embodiment.

[0015] <Conventional Technology and Challenges> Figure 1 shows an overview of closed-loop control for achieving quality control of wireless networks.

[0016] To optimize the quality of real-world wireless networks, analysis and control are performed in a virtual world called Cradio. The real world may include various wireless networks and various terminals, and may be called a "tenant." The virtual world (Cradio) understands the quality of the tenant's wireless network, performs analysis, and dynamically determines the conditions for moving to action for control or redesign in the real world. The results of the action are then understood again, and the wireless network quality is continuously improved through an optimization loop.

[0017] In short, Cradio optimizes wireless network quality by forming a cycle of understanding (analyzing), predicting, and acting (controlling or redesigning) the wireless network's quality. Cradio achieves the desired processing by coordinating its various functional units for understanding, analyzing, and controlling.

[0018] Figure 2 shows an overview of a conventional system configuration in closed-loop control.

[0019] In the conventional system configuration 1A, the quality of the equipment's wireless network is optimized by various functional units shared with other tenants for monitoring, analyzing, and controlling wireless network quality. To facilitate coordination between these functional units and the real world, the conventional system configuration places a coordinating function between the real world and the virtual world. This coordinating function dynamically controls the activation (auto-scaling) of each functional unit located in the cloud, enabling it to keep up with the constantly changing wireless quality conditions and operate with the optimal amount of resources.

[0020] However, this conventional system configuration 1A has several drawbacks. For example, because each functional unit operates only on the public cloud, it lacks flexibility in terms of processing responsiveness. Also, because each functional unit is shared by all tenants, it is difficult to optimize it to meet the specific requirements of each tenant.

[0021] <Overview of Embodiments> Figure 3 is a diagram showing an overview of the system configuration according to an embodiment of the present invention.

[0022] In the system configuration 1B according to the embodiment of the present invention, a new cooperative function unit 60 is introduced, which includes a wireless control time analysis unit 601 and an optimal placement function unit 602. This enables the optimal placement of each function unit according to the requirements of each tenant and workflow processing through the linking of functions.

[0023] Specifically, each functional unit for understanding, analyzing, and controlling the quality of the wireless network can be placed in the most optimal location from the cloud, MEC (Multi-access Edge Computing), or on-premises. As shown in Figure 3, for example, in Tenant 1, the site design function is placed on-premises, and the control function is placed in MEC.

[0024] An on-premises processing platform is an example of a processing platform with the shortest response time among multiple processing platforms (cloud, MEC, on-premises). Furthermore, an MEC processing platform is an example of a processing platform with a shorter response time than a cloud-based processing platform. Additionally, a cloud-based processing platform is an example of a processing platform with the longest response time among multiple processing platforms (cloud, MEC, on-premises).

[0025] Furthermore, a common ML (Machine Learning) section will be introduced, allowing tenants to share the process of predicting and redesigning wireless network quality using machine learning models such as Recurrent Neural Networks (RNNs).

[0026] Figure 4 is a diagram showing an overview of the optimal arrangement function unit according to an embodiment of the present invention.

[0027] The optimal placement function unit 602 realizes a distributed processing platform for controlling wireless quality while considering cost and functional characteristics. The optimal placement function unit 602 realizes various processes by linking function groups with optimal functional placement according to the requirements of each user and tenant. The optimal placement function unit 602 automatically selects the optimal execution platform according to the time required for controlling wireless quality and the responsiveness of the processing required by the use case, and realizes dynamic placement of function groups according to the requirements of each user and tenant.

[0028] In Figure 4, the common ML section includes, for example, RNNs and Convolutional Neural Networks (CNNs). Tenant A's functional group (Tenant A functional arrangement) includes, for example, a predictive learning unit, a recognition unit, a control unit, a propagation design unit, and a predictive inference unit. Tenant B's functional group (Tenant B functional arrangement) includes, for example, a propagation design unit, a recognition unit, and a control unit.

[0029] Tenant A (Company A) has functional unit placement requirements, for example, that the functional units related to understanding / prediction (understanding unit, predictive learning unit, and predictive inference unit in Figure 4) be placed in the cloud, the functional unit related to control (control unit in Figure 4) be placed in the MEC, and the functional unit related to propagation / design (propagation design unit in Figure 4) be placed locally (on-premise). In addition, the use of RNN is required for prediction. In this case, the optimal placement function unit 602 places each functional unit on the optimal execution platform for Tenant A's functional deployment, according to Tenant A's functional unit placement requirements. Furthermore, the optimal placement function unit 602 operates to coordinate each functional unit by dynamically linking the functional units on each execution platform in Tenant A. In the following explanation, dynamic linking may simply be referred to as linking.

[0030] The wireless control time analysis unit 601, described later, determines an execution platform suitable for control based on the control time (control time). The wireless control time analysis unit 601, described later, works in cooperation with the optimal placement function unit 602 and contributes to operations such as the dynamic placement of function groups by the optimal placement function unit 602.

[0031] Figure 5 shows the relationship between the group of functions arranged by the optimal placement function unit and the execution platform.

[0032] Each function may be deployed on at least one execution platform from among cloud, MEC, and local (on-premises) execution platforms.

[0033] The monitoring function may be located in the cloud or on the MEC. The monitoring function is used in relation to parameters for quality control of the wireless network (wireless control parameters) and requires immediate processing time of several hundred milliseconds to several seconds. Wireless control parameters may include, for example, the bandwidth occupied and transmit power of the local terminal, the bandwidth occupied and transmit power of other terminals, etc.

[0034] The control function may be located in the cloud or MEC. In addition to wireless control parameters, the control function is used in relation to real-time control of wireless network quality (wireless real-time control), network switching control between the cloud and MEC, etc. Wireless real-time control requires processing responsiveness of several milliseconds to tens of milliseconds. In network switching control, high-speed network switching processing requires processing responsiveness of several hundred milliseconds, while low-speed network switching processing requires processing responsiveness of several seconds.

[0035] The propagation design function may be located in the cloud or locally. The propagation design function is used in connection with radio wave propagation estimation or site design and requires immediate processing time ranging from minutes to hours.

[0036] The prediction function may be deployed according to the processing content. For example, the learning / measurement in the prediction function may be deployed in the cloud because it requires immediate processing within a few seconds during slow network switching processes. The inference in the prediction function is relatively lightweight because it uses a pre-trained model, so it may be deployed on the MEC or locally as needed.

[0037] <Example> Next, an example of the operation related to placement / cooperation and closed-loop control by the cooperative function unit 60, which includes the wireless control time analysis unit 601 and the optimal placement function unit 602, will be described.

[0038] ≪Optimal Arrangement Function Unit≫ Figure 6 shows an example of the optimal arrangement function unit.

[0039] As shown in Figure 6, suppose that tenant A's functional unit placement requirements stipulate that control-related functional units be placed on the MEC and propagation-related functional units be placed on-premises (locally). Also, suppose that tenant B's functional unit placement requirements stipulate that analysis-related functional units be placed in the cloud and control-related functional units be placed on the MEC. In this case, the optimal placement function unit 602 included in the cooperative function unit 60 places each functional unit on the optimal execution platform for tenant A, according to the functional unit placement requirements of tenants A and B. Furthermore, this optimal placement function unit 602 operates to coordinate each function by dynamically linking the functional units on each execution platform in each tenant.

[0040] In the following embodiments, we will discuss three more specific embodiments of the optimal placement function unit 602: Embodiment 1, which arranges the function unit for propagation estimation and station location design; Embodiment 2, which arranges the function unit for predictive measurement, predictive learning, and predictive estimation; and Embodiment 3, which arranges the function unit for understanding, analyzing, and controlling wireless network quality.

[0041] [Example 1] The optimal placement function unit 602, which arranges functional units for propagation estimation and location design, may place both functional units on-premise, considering that both functions require long processing times. Alternatively, the optimal placement function unit 602 may dynamically connect the propagation estimation unit and location design unit, which are placed on-premise, and coordinate each function.

[0042] Figure 7 shows an example of the operation of the optimal placement function in Example 1.

[0043] As shown in Figure 7, assume that tenant A's functional unit placement requirements stipulate that the functional units related to propagation / design must be placed on-premises (locally). In this case, the optimal placement functional unit 602 included in the cooperative functional unit 60 places the propagation estimation unit and the site design unit on-premises based on the functional unit placement requirements. Furthermore, this optimal placement functional unit 602 operates to coordinate each function by dynamically linking the functional units on each execution platform in each tenant.

[0044] The propagation estimation unit estimates the propagation characteristics of radio waves in wireless communication and the like. The station placement design unit designs the installation positions and the like of wireless devices such as base stations based on the estimation results of the propagation estimation unit.

[0045] In FIG. 7, the propagation estimation unit and the station placement design unit are arranged on-premises by the optimal placement function unit 602. However, only one of the function units may be arranged on-premises. For example, only the propagation estimation unit may be arranged by the optimal placement function unit 602, and the station placement design unit may not be arranged.

[0046] In order to conceal the processing results by the propagation estimation unit or the station placement design unit, a data encryption unit may be arranged on-premises. The data encryption unit may be connected to a database (DB) including the processing results. The database may be called the data store group 110. The concealment of the processing results is an example of a specific parameter.

[0047] In this way, by arranging the propagation estimation unit and the station placement design unit on-premises by the optimal placement function unit 602, the data necessary for propagation estimation and station placement design can be arranged in the environment where the user is present. In addition, the cost related to the operation of the cloud can be reduced. Furthermore, since the user can utilize each function without going through the network, the confidentiality of data and the like can be ensured.

[0048] [Embodiment 2] The optimal placement function unit 602 that performs function unit placement for prediction measurement, prediction learning, and prediction estimation of the quality of a wireless network may disperse each function unit according to the characteristics of the processing by each function unit. For example, when the network switching process required for a certain use case is fast, the optimal placement function unit 602 may arrange the prediction inference unit in the MEC. For example, when the network switching process required for a certain use case is slow, the optimal placement function unit 602 may arrange the prediction inference unit in the cloud. In addition, the optimal placement function unit 602 may dynamically connect each function unit related to prediction arranged on the execution platform and cooperate each function.

[0049] FIG. 8 is an example of the operation of the optimal placement function unit in Embodiment 2.

[0050] As shown in Figure 8, for example, based on the speed applied to autonomous driving (which may also be called vehicle speed), the functional unit placement requirements specified by tenant A are that the functional unit related to predictive measurement and the functional unit related to predictive learning are to be placed in the cloud. Furthermore, the functional unit related to predictive inference is to be placed in the MEC. In addition, it is a requirement that the RNN of the common ML unit be used for predictive learning. In this case, the optimal placement functional unit 602 included in the cooperative functional unit 60 places the predictive measurement unit and the predictive learning unit in the cloud and the predictive inference unit in the MEC, based on the functional unit placement requirements. Furthermore, this optimal placement functional unit 602 operates to coordinate each function by dynamically linking the above functional units (here, the predictive measurement unit, the predictive learning unit, and the RNN in the common ML unit) on each execution platform in each tenant. Vehicle speed is an example of a specific parameter.

[0051] The predictive measurement unit broadly collects data necessary for predictive learning via the internet and stores the collected data in the data store group 110. Therefore, since data is collected broadly via the internet, the predictive learning unit may be located in the cloud.

[0052] The predictive learning unit performs training using the data collected from the predictive measurement unit. Since it utilizes the data collected from the predictive measurement unit, the predictive learning unit may be located in the cloud. The predictive learning unit generates a predictor for the predictive inference unit to calculate predicted values. This predictor is then downloaded to the predictive inference unit.

[0053] The predictive inference unit is deployed on the execution platform depending on the network switching speed required for a particular use case. Therefore, although Tenant A's functional unit deployment requirements stipulate that the predictive inference unit be deployed on the MEC, depending on the use case, the predictive inference unit may be deployed on the cloud. The predictive inference unit performs inference to calculate predicted values ​​using a predictor downloaded from the predictive learning unit.

[0054] The switch obtains predicted values ​​from the predictive inference unit, which includes a predictor, and switches the network based on the obtained predicted values.

[0055] Although not shown in Figure 8, a movement speed analysis unit may be provided in the cooperative function unit 60 in cooperation with the optimal placement function unit 602 and the predictive inference unit. The movement speed analysis unit may have the same functions as the wireless control time analysis unit 601 in [Example 3] described later. In other words, the movement speed analysis unit may, in cooperation with the optimal placement function unit 602, determine the optimal processing base for the predictive inference unit based on input information (information related to movement speed) from the predictive inference unit temporarily placed in the MEC and the cloud. For example, if the movement speed is high, the predictive inference unit may be placed in the MEC, and if it is low, the predictive inference unit may be placed in the cloud. Whether the movement speed is high or low may be determined based on thresholds that have been input to the movement speed analysis unit in advance as parameters by the user (tenant).

[0056] In this way, by arranging each functional unit on the execution platform using the optimal placement function unit 602, prediction-related processing can be performed on the appropriate execution platform according to the required network switching speed. Furthermore, by dynamically connecting the machine learning processing unit, such as RNN, when the optimal placement function unit 602 dynamically connects each functional unit, flexible wireless network quality control processing can be performed. In addition, since measurement data collection and learning can be performed on the same execution platform (cloud), data transfer overhead can be reduced.

[0057] [Example 3] The optimal placement function unit 602, which arranges the functional units for understanding, analyzing, and controlling wireless network quality, may place all the functional units for understanding, analyzing, and controlling wireless network quality on the MEC, taking into consideration the responsiveness of each process. Alternatively, the optimal placement function unit 602 may place the functional units for controlling wireless network quality on the execution board based on the control time of the control device 30. Here, the control device 30 may be a device that controls wireless control parameters, performs wireless real-time control, etc., and responds with the control results. Furthermore, the optimal placement function unit 602 may dynamically connect the functional units related to understanding, analyzing, and controlling wireless network quality that are placed on the execution board, and coordinate each function.

[0058] Figure 9 shows an example of the operation of the optimal placement function in Embodiment 3.

[0059] As shown in Figure 9, assume that tenant A's functional unit placement requirements stipulate that functional units related to prediction / analysis / control must be placed in the MEC. In this case, the optimal placement functional unit 602 included in the cooperative functional unit 60 places the wireless equipment control unit 40, the wireless quality sensing unit 70, and the wireless quality analysis unit 80 in the MEC based on the functional unit placement requirements. Furthermore, this optimal placement functional unit 602 operates to coordinate each function by dynamically linking the above functional units (in this case, the wireless equipment control unit 40, the wireless quality sensing unit 70, and the wireless quality analysis unit 80) on each execution platform (in this case, the MEC).

[0060] The wireless quality assessment unit 70 collects data (assessment data) to assess the quality of the wireless network. The wireless quality assessment unit 70 may be located in a MEC connected to the terminal information collection device 20 via a closed network in order to collect the assessment data with low latency. The wireless quality assessment unit 70 stores the assessment data in a data store group 110.

[0061] The wireless quality analysis unit 80 analyzes the data (collected data) collected by the wireless quality assessment unit 70. The wireless quality analysis unit 80 may be located in the same MEC as the wireless quality assessment unit 70 in order to analyze the collected data immediately. The collected data may be stored in the data store group 110.

[0062] The wireless device control unit 40 and the wireless device control unit 50 perform various controls on the control device 30 (including control device 30a and control device 30b). The wireless device control unit 40 and the wireless device control unit 50 notify the wireless control time analysis unit 601 of the time required for the control (control time). Here, the control time may be the time from the notification of a control instruction to the control device 30 to the reception of the result response to that notification. The wireless device control unit 40 and the wireless device control unit 50 may be located in the cloud and MEC, respectively, in order to determine the function unit according to the control time of the control device 30.

[0063] The wireless control time analysis unit 601 determines the execution platform for wireless device control based on the control time of the wireless device control unit 40 and the wireless device control unit 50 and the threshold (T%). For example, if the calculated value (let's call it X) of ((control time of wireless device control unit 50) - (control time of wireless device control unit 40)) / (control time of wireless device control unit 50) is greater than the threshold (T%), wireless device control is performed by MEC. On the other hand, if X is less than or equal to the threshold (T%), wireless device control is performed by the cloud.

[0064] In other words, if the relative improvement rate X of control time, based on the control time in the cloud, is greater than the threshold (T%), wireless device control will be performed using MEC. On the other hand, if the relative improvement rate X of control time, based on the control time in the cloud, is less than or equal to the threshold (T%), wireless device control will be performed using the cloud.

[0065] Let's explain with a specific example. In control device 30b, the control time from wireless device control unit 40 (control time in MEC) is 50 milliseconds, the control time from wireless device control unit 50 (control time in the cloud) is 100 milliseconds, and the threshold is T = 25%. In this case, X = (100 - 50) / 100 = 0.5, so X is greater than T%. Therefore, wireless device control of control device 30b is performed in MEC.

[0066] In control device 30a, the control time from wireless device control unit 40 (control time in MEC) is 450 milliseconds, the control time from wireless device control unit 50 (control time in the cloud) is 500 milliseconds, and the threshold is T = 25%. In this case, X = (500 - 450) / 500 = 0.1, so X is less than or equal to T%. Therefore, wireless device control of control device 30a is performed in the cloud.

[0067] The control time in the cloud and the control time in MEC are examples of control parameters.

[0068] Figure 10 is a diagram visualizing the control time by the wireless control time analysis unit in Example 3.

[0069] The length of the arrow in Figure 10 indicates the control time of the wireless device control unit.

[0070] Figure 10(a) shows a case where control using MEC is effective. It can be seen that the control time with MEC is significantly shorter compared to the control time with the cloud. In this case, the wireless control time analysis unit 601 determines that X in the above calculation is greater than T%, and decides to perform subsequent control using MEC.

[0071] Figure 10(b) shows a case where there is no significant difference between control using MEC and control using the cloud. It can be seen that the control time using MEC is not as short as in Figure 10(a) compared to the control time using the cloud. In this case, the wireless control time analysis unit 601 determines that X in the above calculation is less than or equal to T%, and decides to perform subsequent control using the cloud.

[0072] Furthermore, even after determining the execution platform for wireless device control, the execution platform for performing the control may be switched at regular intervals or triggered by changes in the wireless environment. For example, even after the wireless control time analysis unit 601 has decided to perform control using MEC (control by the wireless device control unit 40), it may perform cloud-based control (control by the wireless device control unit 50) at an appropriate timing to confirm whether or not there has been a change in the optimal execution platform for wireless device control.

[0073] In this way, the dynamic deployment of the wireless device control execution platform based on the control time analysis results by the wireless control time analysis unit 601 makes it possible to use MEC resources when it is possible to shorten the control time, and cloud resources otherwise. In other words, it becomes possible to control the quality of the wireless network efficiently.

[0074] ≪Closed-Loop Control≫ Below, we present three examples of closed-loop control that optimize the quality of wireless networks, limited to understanding, analyzing, and controlling wireless equipment for wireless network quality. The first is the dynamic placement of each functional unit by the optimal placement function unit 602 and the measurement / notification of control time by the wireless control time analysis unit 601. The second is the determination of the optimal execution base according to the control time by the wireless control time analysis unit 601. The third is the determination of the optimal execution base that follows the passage of time and environmental changes by the wireless control time analysis unit 601.

[0075] [Example 1] Figure 11 is an example of a sequence diagram related to the dynamic arrangement of each functional unit and the measurement / notification of control time by the cooperative function unit.

[0076] In step S101, the wireless area management officer 10 sets the user request / purpose of use in the cooperative function unit 60.

[0077] In steps S102a and S102b, the optimal placement function unit 602 included in the cooperative function unit 60 dynamically activates each function unit in order to grasp, analyze, and control wireless devices based on wireless network quality. At this time, the functions related to wireless device control may be activated in both the MEC and the cloud.

[0078] As part of loop control for understanding wireless quality, in step S103, the terminal information collection device 20 collects information related to wireless quality (observational data), and in step S104, it periodically transmits the observational data to the wireless quality understanding unit 70. In step S105, the wireless quality understanding unit 70 stores the received observational data in the data store group 110.

[0079] As loop control for wireless quality analysis, in step S106a, the wireless quality analysis unit 80 refers to quality information (observational data) stored in the data store group 110. In step S106b, the data store group 110 responds to the wireless quality analysis unit 80 regarding the referenced quality information. In step S107a, the coordinating function unit 60 polls the wireless quality analysis unit 80 for the execution status and results of the analysis. In step S107b, the wireless quality analysis unit 80 responds to the polling in the coordinating function unit 60. Note that steps S106a and S106b, and steps S107a and S107b may be performed asynchronously.

[0080] For example, in loop control for wireless quality analysis, suppose, as in step S107c, the wireless quality analysis unit 80 detects a fault from the quality information when a condition to be controlled occurs in the observation data collected by the terminal information collection device 20. In this case, in order to respond to the fault, in step S108, the coordinating function unit 60 requests new wireless control parameters, and in step S109, it obtains those wireless control parameters. Then, in step S110, the coordinating function unit 60 performs control, for example, via the cloud-based wireless device control unit 50. In step S111, the wireless device control unit 50, having received an instruction to control the wireless device, performs control on the control device 30. Then, the control device 30 responds to the coordinating function unit 60 with the control result through steps S112, S113, and S114. The wireless device control unit 50 also measures the time from step S110 to step S114 as the control time and notifies the coordinating function unit 60.

[0081] Thus, in closed-loop control that optimizes the quality of wireless networks, the cooperative function unit enables the dynamic arrangement of each function unit and the measurement / notification of control time.

[0082] [Example 2] Figure 12 is an example of a sequence diagram related to the determination of the optimal execution base according to the control time by the wireless control time analysis unit.

[0083] The sequence diagram in Figure 12 is assumed to be a continuation of the sequence diagram in Figure 11.

[0084] In steps S201 to S204, similar to steps S110 to S114 in Figure 11, the cloud's wireless device control unit 50 controls the control device 30 and measures the control time, notifying the coordination function unit 60.

[0085] In steps S205a and S205b, quality information is referenced and a response is made, similar to steps S106a and S106b in Figure 11. In steps S206a and S206b, it is assumed that the wireless quality analysis unit 80 has detected a fault from the quality information, similar to steps S107a and S107c in Figure 11. In this case, in steps S207 and S208, wireless control parameters are requested and acquired, similar to steps S108 and S109 in Figure 11.

[0086] Subsequently, in step S209, the cooperative function unit 60 performs control via the MEC's ​​wireless device control unit 40, unlike in step S201. In step S210, the wireless device control unit 40, having received instructions from the wireless device control unit, performs control on the control device 30. The control device 30 then responds to the cooperative function unit 60 with the control result through steps S211 and S212. The wireless device control unit 40 also measures the time from step S209 to step S212 as the control time and notifies the cooperative function unit 60.

[0087] The wireless control time analysis unit 601 of the cooperative function unit 60 determines which control is optimal as the execution platform based on the control time in the cloud and the control time in the MEC. If the wireless control time analysis unit 601 determines, for example, that the MEC is optimal as the execution platform for controlling the control device 30, then subsequent control will be performed on the determined platform (MEC).

[0088] Thus, in closed-loop control that optimizes the quality of wireless networks, the wireless control time analysis unit determines the optimal execution platform according to the control time.

[0089] [Example 3] Figure 13 is an example of a sequence diagram related to the determination of the optimal execution platform by the wireless control time analysis unit in accordance with the passage of time and environmental changes.

[0090] The sequence diagram in Figure 13 is assumed to be a continuation of the sequence diagram in Figure 12.

[0091] In steps S301a and S301b, quality information is referenced and a response is made, similar to steps S205a and S205b in Figure 12. In steps S302a and S302b, it is assumed that the wireless quality analysis unit 80 has detected a fault from the quality information, similar to steps S206a and S206b in Figure 12. In this case, in steps S303 and S304, wireless control parameters are requested and acquired, similar to steps S207 and S208 in Figure 12.

[0092] Subsequently, in step S305, the cooperative function unit 60 performs control via the cloud's wireless device control unit 50, unlike in step S209. Alternatively, the cooperative function unit 60 may perform control via the MEC's ​​wireless device control unit 40, and then, after a certain period of time has elapsed, perform control via the cloud's wireless device control unit 50. Steps S306 to S308 are the same as steps S202 to S204 in Figure 12. That is, the wireless control time analysis unit 601 of the cooperative function unit 60 reanalyzes the execution base for control of the control device 30 based on the control time by the MEC's ​​wireless device control unit 40 before step S305 and the control time by the cloud's wireless device control unit 50 in step S305. This allows the wireless control time analysis unit 601 to confirm whether there has been any change to the optimal execution base for control.

[0093] Thus, in closed-loop control that optimizes the quality of wireless networks, the wireless control time analysis unit enables the determination of the optimal execution platform in response to the passage of time and environmental changes.

[0094] <Device Configuration> <Hardware Configuration> The cooperative function unit 60, which includes the wireless control time analysis unit 601 and the optimal placement function unit 602 described in this embodiment, can be realized, for example, by having a computer execute a program. This computer may be a physical computer or a virtual machine on the cloud. Furthermore, since this computer performs quality control of the wireless network, it may also be a control device.

[0095] In other words, the control device can be realized by using hardware resources such as the CPU and memory built into the computer to execute a program corresponding to the processing performed by the control device. The program can be recorded on a computer-readable recording medium (such as portable memory), saved, and distributed. It can also be provided via a network such as the Internet or email.

[0096] Other functional units (such as monitoring, analysis, control, and common ML) that run on a cloud / MEC / on-premise infrastructure, in addition to the cooperative function unit 60 described in this embodiment, can also be realized, for example, by having a computer execute a program. This computer may be a physical computer or a virtual machine on the cloud. Furthermore, since this computer processes information related to the quality control of wireless networks, it may also be an information processing device.

[0097] The terminal information collection device 20 described in this embodiment may be, for example, a terminal device such as a smartphone or a mobile phone. The control device 30 described in this embodiment may be, for example, a base station or a router.

[0098] Figure 14 shows an example of the hardware configuration of a control device that performs quality control of a wireless network.

[0099] The control device, which is a computer as shown in Figure 14, includes a drive device 1000, an auxiliary storage device 1002, a memory device 1003, a CPU 1004, an interface device 1005, a display device 1006, an input device 1007, an output device 1008, etc., all of which are interconnected via bus B. The control device may also include a GPU (Graphics Processing Unit).

[0100] The program that enables processing in the control device is provided on a recording medium 1001, such as a CD (Compact Disc)-ROM (Read Only Memory) or a memory card. When the recording medium 1001 containing the program is set in the drive device 1000, the program is installed from the recording medium 1001 to the auxiliary storage device 1002 via the drive device 1000. However, the program does not necessarily have to be installed from the recording medium 1001; it may also be downloaded from another computer via a network. The auxiliary storage device 1002 stores the installed program as well as necessary files and data.

[0101] The memory device 1003 reads and stores a program from the auxiliary storage device 1002 when a program startup command is received. The CPU 1004 implements the functions related to the control device 100 according to the program stored in the memory device 1003. The interface device 1005 is used as an interface for connecting to a network, etc. The display device 1006 displays a GUI (Graphical User Interface) etc. based on a program. The input device 1007 consists of a keyboard and mouse, buttons, or a touch panel etc., and is used to input various operation commands. The output device 1008 outputs the calculation results.

[0102] <Functional Configuration> Figure 15 shows an example of the functional configuration of a control device that performs quality control of a wireless network.

[0103] The control device 100 includes a wireless control time analysis unit 601 and an optimal placement function unit 602.

[0104] The control device 100 may consist of one device (computer) or multiple devices. In either case, the control device 100 may be called a "control system". The control device 100 may also be a virtual machine on the cloud.

[0105] For example, a configuration including at least one of the following is a control device: a control device 100, an information processing device including various functional units for wireless network optimization (such as recognition, analysis, control, and common ML), a terminal device which is a terminal information collection device 20, and a control device 30.

[0106] Each functional unit of the control device 100 is a function or means realized by the CPU (Central Processing Unit) of the control device 100 executing one or more programs stored in an auxiliary storage device or the like, and controlling each piece of hardware of the control device 100.

[0107] The wireless control time analysis unit 601 may determine an execution base suitable for control based on the time (control time) related to the control of the control device 30. The wireless control time analysis unit 601 may also determine an execution base for wireless device control based on the control time of the wireless device control unit 40 and the wireless device control unit 50 and a predetermined threshold.

[0108] The optimal placement function unit 602 may arrange each functional unit to be started on the optimal execution platform according to the requirements of each user / tenant. The optimal placement function unit 602 may also operate to coordinate each functional unit by dynamically linking each functional unit on the execution platform.

[0109] <Summary of Embodiments> Based on the above, the technology described in this embodiment enables workflow processing through the optimal arrangement of each functional unit and the linking of functions according to the requirements of each tenant.

[0110] Each aspect / embodiment described herein may be used individually, in combination, or switched between as needed during execution.

[0111] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0112] Aspects of the present invention are, for example, as follows: <1> A control device having an optimal placement function unit that dynamically places the control functions of a base station in wireless communication on one or more processing boards in accordance with the placement requirements or specific parameters of the control functions specified by the user, and connects the placed control functions. <2> The control device according to <1>, wherein the control functions include a propagation estimation function and a base station design function, and the optimal placement function unit places the propagation estimation function and the base station design function on the processing board with the shortest response time and connects the placed propagation estimation function and the base station design function. <3> The control device according to <1> or <2>, wherein the control function includes a predictive measurement function, a predictive learning function, and a predictive inference function, the optimal placement function unit places the predictive measurement function and the predictive learning function on a first processing board having the longest response time, places the predictive inference function on the first processing board or on a second processing board having a shorter response time than the first processing board, according to the required response time, and connects the placed predictive measurement function, the predictive learning function, and the predictive inference function. <4> The control device according to <3>, further comprising a movement speed analysis unit that determines the placement destination of the predictive inference function by the optimal placement function unit based on information relating to the movement speed input from the predictive inference function, the predictive inference function is placed on the second processing board when the movement speed is above a predetermined threshold, and the predictive inference function is placed on the first processing board when the movement speed is below a predetermined threshold. <5> The control device according to any one of <1> to <4>, wherein the control function includes a wireless quality sensing function, a wireless quality analysis function, and a wireless device control function, the optimal placement function unit places the wireless quality sensing function and the wireless quality analysis function on a second processing board, places the wireless device control function on the second processing board and on a first processing board having a longer response time than the second processing board, connects the placed wireless quality sensing function, the wireless quality analysis function, and the wireless device control function, and further has a wireless control time analysis unit that determines the processing board to which the wireless device control function will be placed based on the control time of the wireless device control function.<6> A control system comprising one or more information processing devices that function as one or more processing platforms, one or more base stations to be controlled, and the control device described in <1>. <7> A program installed in a control device in an environment in which control functions of base stations in wireless communication can be dynamically arranged on one or more processing platforms, the program causing the control device to dynamically arrange the control functions on the processing platform according to the arrangement requirements or specific parameters of the control functions specified by the user, and to perform processing to connect the arranged control functions.

[0113] 20 Terminal information collection equipment 30 Control equipment 40 Wireless equipment control unit (MEC) 50 Wireless equipment control unit (cloud) 60 Coordination function unit 70 Wireless quality assessment unit 80 Wireless quality analysis unit 100 Control device (control system) 110 Data store group 601 Wireless control time analysis unit 602 Optimal placement function unit

Claims

1. A control device having an optimal placement function unit that dynamically places the control functions of a base station in wireless communication on one or more processing boards according to the placement requirements or specific parameters of the control functions specified by the user, and connects the placed control functions, in an environment in which the control functions of a base station in wireless communication can be dynamically placed on one or more processing boards.

2. The control device according to claim 1, wherein the control function includes a propagation estimation function and a location design function, and the optimal placement function unit places the propagation estimation function and the location design function on a processing board with the shortest response time, and connects the placed propagation estimation function and location design function.

3. The control device according to claim 1, wherein the control function includes a predictive measurement function, a predictive learning function, and a predictive inference function, the optimal placement function unit places the predictive measurement function and the predictive learning function on a first processing board having the longest response time, places the predictive inference function on the first processing board or on a second processing board having a shorter response time than the first processing board, according to the required response time, and connects the placed predictive measurement function, the predictive learning function, and the predictive inference function.

4. The control device according to claim 3, further comprising a movement speed analysis unit that determines the placement destination of the prediction inference function by the optimal placement function unit based on information relating to the movement speed input from the prediction inference function, wherein the prediction inference function is placed on the second processing board when the movement speed is above a predetermined threshold, and the prediction inference function is placed on the first processing board when the movement speed is below a predetermined threshold.

5. The control device according to claim 1, wherein the control function includes a wireless quality sensing function, a wireless quality analysis function, and a wireless device control function, the optimal placement function unit places the wireless quality sensing function and the wireless quality analysis function on a second processing board, places the wireless device control function on the second processing board and on a first processing board having a longer response time than the second processing board, connects the placed wireless quality sensing function, the wireless quality analysis function, and the wireless device control function, and further comprises a wireless control time analysis unit that determines the processing board to which the wireless device control function will be placed based on the control time of the wireless device control function.

6. A control system comprising one or more information processing devices that function as one or more processing infrastructures, one or more base stations to be controlled, and the control device described in claim 1.

7. A program installed in a control device in an environment where control functions of a base station in wireless communication can be dynamically arranged on one or more processing boards, the program causing the control device to dynamically arrange the control functions on the processing board and to perform a process of linking the arranged control functions according to the arrangement requirements or specific parameters of the control functions specified by the user.

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