Control system and control method

Abstract

A server (10) predicts a traffic situation in a specific time zone and a specific area in the future on the basis of prescribed information, and transmits information indicating the prediction result to at least one first base station among a plurality of base stations (30-1 to 30-3) located in an area where traffic is predicted to increase in the specific time zone.

Description

Control system and control method

[0001] The present invention relates to a control system and a control method.

[0002] Fifth generation mobile communication system (5G) services, which can achieve high speeds and large capacities, have been launched. As traffic increases with the spread of 5G, there is a demand for effective use of limited wireless resources. Patent Document 1 describes a method in which a base station (secondary communication permission node) that controls traffic refers to user traffic history and grants secondary use permission for time periods and locations with many available channels, thereby freeing up some available channels and enabling effective use of frequency bands.

[0003] Japanese Patent Application Publication No. 2010-206780

[0004] Traffic may suddenly increase due to unexpected events such as sudden changes in weather, train delays, and large-scale events. A sudden increase in traffic may lead to a shortage of wireless resources, which may result in a deterioration in communication quality for users. However, Patent Document 1 does not mention how to predict how traffic conditions will change in the future and how to utilize the prediction results, leaving room for improvement.

[0005] In order to solve the above problem, a control system according to one embodiment of the present invention is a control system including a control device and a plurality of base stations capable of communicating with the control device, wherein the control device predicts traffic conditions in a specific time period and a specific area in the future based on predetermined information, and transmits information indicating the prediction result to at least one first base station among the plurality of base stations located in an area where traffic is predicted to increase during the specific time period, and the first base station calculates the radio resources required for the traffic predicted to increase based on the information indicating the prediction result, determines a policy for securing the radio resources, and executes the policy in cooperation with other base stations.

[0006] In order to solve the above problem, a control method according to one aspect of the present invention is a control method executed by a control device and at least one first base station among a plurality of base stations that can communicate with the control device, wherein the method executed by the control device includes: a prediction step of predicting a traffic situation in a specific time period and a specific area in the future based on predetermined information; and a transmission step of transmitting information indicating the prediction result to at least one first base station among the plurality of base stations located in an area where traffic is predicted to increase during the specific time period; and the method executed by the first base station includes: a calculation step of calculating radio resources required for the traffic predicted to increase based on the information indicating the prediction result; a decision step of determining a policy for securing the radio resources; and an execution step of executing the policy in cooperation with other base stations.

[0007] By performing cooperative control among multiple base stations based on future traffic conditions and reserving the necessary radio resources, it is possible to prevent radio resources from becoming constrained even in the event of a sudden increase in traffic, thereby preventing a deterioration in the communication quality for users.

[0008] 1 is a diagram illustrating an example of the configuration of a wireless communication system according to an embodiment of the present invention; FIG. 2 is a diagram illustrating an example of functional blocks of a server according to an embodiment of the present invention; FIG. 3 is a diagram illustrating an example of functional blocks of a base station according to an embodiment of the present invention; FIG. 4 is a sequence chart illustrating an example of processing executed by a server and a base station according to an embodiment of the present invention; and FIG. 5 is a block diagram illustrating an example of the configuration of a computer that can be used as a server, a base station, etc.

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same parts are designated by the same reference numerals and the description thereof will be omitted.

[0010] (Configuration of Wireless Communication System) FIG. 1 is a diagram illustrating an example of the configuration of a wireless communication system 1 according to this embodiment.

[0011] 1, the wireless communication system 1 includes a server 10, devices 20-1 to 20-3, base stations 30-1 to 30-3, and user terminals (UEs). The wireless communication system 1 is intended to be applied to a 5G network, but is not limited to this. For example, the wireless communication system 1 may be applied to a future network following the 5G network, a 4G network, or a legacy network prior to the 4G network.

[0012] Each of the base stations 30-1 to 30-3 communicates with a user terminal UE located within a cell 31-1 to 31-3, which is a wireless communication area.

[0013] 1 shows three base stations 30-1 to 30-3 and one user terminal UE for ease of viewing, but the number of base stations connected to the core network 40 is not limited to three and may be two, or four or more. Furthermore, the number of user terminals UE connected to each of the base stations 30-1 to 30-3 may be two or more.

[0014] In the example of FIG. 1, cell 31-1 corresponding to base station 30-1, cell 31-2 corresponding to base station 30-2, and cell 31-3 corresponding to base station 30-3 partially overlap, and the user terminal UE is located in this overlapping area. Therefore, the user terminal UE may be connected to base station 30-1, base station 30-2, or base station 30-3. The user terminal UE may be connected to these base stations by wired or wireless connection. The user terminal UE may also be connected to an SFN cell (Single Frequency Network). An SFN cell is a type of cell arrangement in wireless communication, and is a form of cell in which multiple base stations (here, base stations 30-1 to 30-3) simultaneously provide service using the same frequency. SFN cells enable different base stations using the same frequency to cooperate, ensure signal consistency, and improve communication coverage and quality.

[0015] 1 shows the user terminal UE as a smartphone that can be carried by a user, this is just an example and is not limited to a smartphone. For example, the user terminal UE may be a tablet terminal, a smart watch, a mobile phone, etc. Furthermore, the user terminal UE may be an electronic device attached to a vehicle, etc.

[0016] The server 10, the devices 20-1 to 20-3, and the base stations 30-1 to 30-3 are connected via a core network 40 so as to be able to communicate with each other.

[0017] The core network 40 is a network having functions such as a UPF (User Plane Function), a NEF (Network Exposure Function), a PCF (Policy Control Function), an AMF (Access and Mobility Management Function), and a SMF (Session Management Function), and is used to establish user sessions and transfer user data.

[0018] The server 10 is configured by a general-purpose computer, and may have a processor that executes programs, memory that stores programs and data, an auxiliary storage device (HDD, SSD, etc.), and a network interface.

[0019] The device 20-1 may be a web server that provides weather-related information. Such a device 20-1 may be configured by a general-purpose computer having a processor that executes programs, memory that stores programs and data, an auxiliary storage device, and a network interface. The device 20-1 may also be equipped with a weather information database 21. The weather information database 21 stores various types of weather-related information. Such a weather information database 21 may be a database owned by the Japan Meteorological Agency, a public organization of Japan, or a database owned by a private company. The weather information database 21 may also be a database owned by a public organization or private company of the country to which the user belongs. "Weather-related information" includes, for example, weather forecast information.

[0020] The device 20-2 may be a web server that provides information about trains. The device 20-2 may be configured by a general-purpose computer, similar to the device 20-1. The device 20-2 may also include a train information database 22. The train information database 22 stores information about various trains. Such a train information database 22 may be a database owned by a railway operator in the country to which the user belongs. As an example, the "information about trains" includes train delay information.

[0021] The device 20-3 may be a web server that provides information about events. The device 20-3 may be configured by a general-purpose computer, similar to the device 20-1. The device 20-3 may also include an event information database 23. Information about various events is stored in the event information database 23. Such an event information database 23 may be a database owned by a local government, which is a public institution in Japan, or a database owned by a private company. The event information database 23 may also be a database owned by a public institution or private company in the country to which the user belongs. Examples of "information about events" include information about fireworks displays, cherry blossom viewings, festivals, live music concerts, etc.

[0022] (Functional Blocks of Server) FIG. 2 is a diagram showing an example of functional blocks of the server 10 according to this embodiment.

[0023] The server 10 includes a communication unit 110 , a first control unit 120 , a second control unit 130 , a memory 140 , and a storage unit 150 .

[0024] The communication unit 110 communicates with the devices 20-1 to 20-3 and the base stations 30-1 to 30-3 via the core network 40.

[0025] The memory 140 temporarily stores various programs executed by the first control unit 120 and the second control unit 130 and various data referenced by these programs.

[0026] The storage unit 150 stores information to be read, written, referenced, etc. by the first control unit 120 and the second control unit 130. As an example, the storage unit 150 stores a training dataset 151 and an inference model 152.

[0027] The training dataset 151 is a dataset containing training data (labeled data used in machine learning). Machine learning is a technique for learning features contained in input data and generating a "model" that predicts results corresponding to newly input data. The inference model 152 is a trained model generated by machine learning using the training dataset 151.

[0028] The inference model 152 is a model that uses, for example, weather-related information as input data and predicts when and where traffic will increase in the future based on the weather-related information. The prediction results from the inference model 152 are notified to the relevant base stations (e.g., base stations 30-1 to 30-3) and are used for wireless resource calculations and policy decisions. This point will be described in detail later.

[0029] An example of the relationship between weather and traffic will be described. It is known that when it rains, an increase in users is observed, leading to an increase in Internet usage. Here, a weather forecast is information indicating what the weather will be like at a specific time and a specific location in the future. In other words, past weather information is information indicating what the weather was like at a specific time and a specific location in the past. By preparing a dataset linking the relationship between past weather information and traffic and allowing it to learn, it is possible to generate an inference model 152 that predicts when and where traffic will increase in the future based on the weather forecast. The server 10 can obtain past weather information by accessing the weather information database 21.

[0030] The training data set 151 includes training data used for machine learning of the inference model 152. The training data included in the training data set 151 corresponds to input data input to the inference model 152 and values ​​that the inference model 152 should output as correct answer data. This input data can also be referred to as explanatory variables, and the correct answer data can also be referred to as objective variables.

[0031] The inference model 152 predicts when and where traffic will increase in the future based on weather information. Therefore, the training data included in the training dataset 151 is data in which "input data" indicating weather information is associated with "correct answer data" indicating when and where traffic will increase in the future when the weather information is applied.

[0032] Such training data is generated by associating input data with correct answer data, and is stored in the storage unit 150 as a training data set 151 .

[0033] The first control unit 120 includes a training data acquisition unit 121 and a learning unit 122 .

[0034] When generating the inference model 152, the training data acquisition unit 121 refers to the storage unit 150 to acquire the training data set 151. The training data acquisition unit 121 outputs the acquired training data set 151 to the learning unit 122.

[0035] The learning unit 122 generates an inference model 152 for predicting when and where traffic will increase in the future based on weather information through machine learning using the training dataset 151 acquired from the training data acquisition unit 121. The inference model 152 is a computational model that uses weather information as an explanatory variable and information indicating when and where traffic will increase in the future as a target variable. The machine learning algorithm is not particularly limited. For example, the learning unit 122 may generate the inference model 152 using a neural network, or may generate the inference model 152 using regression analysis, random forest, or the like.

[0036] The inference model 152 generated by the learning unit 122 is stored in the memory unit 150.

[0037] Although information about weather has been used as an example of input data for the inference model 152, this is not limiting. The input data for the inference model 152 may also be information about trains (train delay information). Here, an example of the relationship between train delays and traffic will be described. When a train stops between stations, it is conceivable that traffic will increase in the area where the train stopped as people try to notify others of the train's stop. Therefore, a dataset linking past train delay information with traffic may be prepared and trained by the learning unit 122. This enables the learning unit 122 to generate an inference model 152 that predicts when and where traffic will increase in the future based on current train delay information.

[0038] Furthermore, the input data of the inference model 152 may be information about an event. Here, an example of the relationship between an event and traffic will be described. For an event that attracts many users, such as a fireworks display, cherry blossom viewing, festival, or live music concert, it is conceivable that users will concentrate at the location where the event is being held, and traffic will increase. Therefore, a dataset linking the relationship between past event information and traffic may be prepared and trained by the learning unit 122. This enables the learning unit 122 to generate an inference model 152 that predicts when and where traffic will increase in the future based on the event information.

[0039] From the above, the input data of the inference model 152 may be at least one of information related to weather, information related to trains, and information related to events. Note that the input data of the inference model 152 may include all of the information related to weather, information related to trains, and information related to events, or may include any combination of the information. In the following, unless otherwise specified, the input data of the inference model 152 will be described as information related to weather (weather forecast information).

[0040] The second control unit 130 includes an information acquisition unit 131 and a prediction unit 132 .

[0041] When predicting when and where traffic will increase in the future, the information acquisition unit 131 accesses the device 20-1 to acquire weather forecast information. The information acquisition unit 131 outputs the acquired weather forecast information to the prediction unit 132.

[0042] The prediction unit 132 inputs the weather forecast information acquired by the information acquisition unit 131 into the inference model 152 to predict when and where traffic will increase in the future. Let's assume that the acquired weather forecast predicts heavy rain in the area (cells 31-1 to 31-3) covered by base stations 30-1 to 30-3 between 6:00 AM and 6:00 PM tomorrow. In this case, the prediction unit 132 predicts that traffic will increase in the area covered by base stations 30-1 to 30-3 between 6:00 AM and 6:00 PM tomorrow. The communication unit 110 notifies the base stations 30-1 to 30-3 located in the area where traffic is predicted to increase. That is, the communication unit 110 transmits information indicating the prediction result by the prediction unit 132 to each of the base stations 30-1 to 30-3.

[0043] The first control unit 120 and the second control unit 130 may be configured as a RAN Intelligent Controller (RIC), which is a controller that manages and controls software on nodes and devices such as RUs (radio units), DUs (distributed units), and CUs (aggregate units) that configure the 5G RAN.

[0044] 3 is a diagram showing an example of functional blocks of the base station 30-1 according to this embodiment. Although not shown, the base stations 30-2 and 30-3 have the same functions as the base station 30-1.

[0045] The base station 30-1 includes a communication unit 310, a control unit 320, a memory 330, and a storage unit 340.

[0046] The communication unit 310 communicates with a user terminal UE located inside the cell 31-1, and also communicates with the server 10 and devices 20-1 to 20-3 via the core network 40. In this embodiment, the communication unit 310 receives information indicating the prediction result by the prediction unit 132, which is transmitted from the communication unit 110 of the server 10. This enables the base station 30-1 to know that traffic will increase in the area (cell 31-1) covered by the base station 30-1 between 6:00 and 18:00 tomorrow.

[0047] The memory 330 temporarily stores various programs executed by the control unit 320 and various data referenced by these programs.

[0048] The storage unit 340 stores information that is read out by the control unit 320, information that is written to the control unit 320, information that is referenced, and the like.

[0049] The control unit 320 includes a radio resource calculation unit 321 , a policy determination unit 322 , and a cooperation control unit 323 .

[0050] The radio resource calculation unit 321 calculates the radio resources required in a time period when traffic is predicted to increase, and outputs the calculated radio resources to the policy determination unit 322.

[0051] The policy determination unit 322 determines a policy for reserving the radio resources calculated by the radio resource calculation unit 321. The policy here includes cooperative control between base stations, such as (1) massive multiple input, multiple output (MIMO) beamforming control, (2) SFN cell cancellation, and (3) load balancing between base stations. The policy determination unit 322 may determine one of the policies (1) to (3) above, or may determine to use all three policies, depending on the radio resources to be reserved. The policy determination unit 322 may also determine a policy other than the policies (1) to (3). The policy determination unit 322 outputs the determined policy to the cooperative control unit 323.

[0052] The cooperative control unit 323 executes a policy for securing radio resources in cooperation with the other base stations 30-2 and 30-3 based on the policy determined by the policy determination unit 322. As a function for executing such a policy, the cooperative control unit 323 may include an inter-base station communication control unit 324. The inter-base station communication control unit 324 may have a function for executing the above policies (1) to (3). The above policies (1) to (3) will be described below.

[0053] Regarding the Massive MIMO beamforming control in (1) above, for example, the base stations 30-1 to 30-3 cooperate to set beamforming weights (MRT, ZF, etc.) toward high-traffic points where traffic is predicted to increase, thereby improving spectral efficiency and significantly increasing communication capacity. This can prevent communication delays and interruptions during high traffic periods. Note that, among the multiple base stations 30-1 to 30-3, for example, only the base station 30-1 may execute Massive MIMO beamforming control.

[0054] Regarding the release of the SFN cell in (2) above, for example, the base stations 30-1 to 30-3 may cooperate to release the SFN cell and change the frequency allocation, etc. This makes it possible to secure radio resources even when traffic increases.

[0055] Regarding the load balancing between base stations in (3) above, for example, the base stations 30-1 to 30-3 may cooperate to perform load balancing and distribute traffic and load evenly among the base stations. This prevents excessive load from being placed on a specific base station or frequency bandwidth, and makes it possible to secure wireless resources even when traffic increases.

[0056] The cooperative controls (1) to (3) above may be executed simultaneously or individually.

[0057] As explained above, according to this embodiment, cooperative control is performed by multiple base stations 30-1 to 30-3 based on the traffic situation in a specific time period in the future, and the necessary radio resources are secured. This prevents radio resources from becoming congested even in the event of a sudden increase in traffic due to a sudden change in weather (for example, sudden rain), a train delay, a large-scale event, or other unexpected event, and ultimately prevents a deterioration in communication quality for users.

[0058] (Processing Flow) The processing flow executed by the server 10 and the base stations 30-1 to 30-3 will be described with reference to Fig. 4. Fig. 4 is a sequence chart showing an example of processing executed by the server 10 and the base stations 30-1 to 30-3. Note that because the base stations 30-1 to 30-3 each have the same functions, Fig. 4 shows only the base station 30-1, and omits the base stations 30-2 and 30-3.

[0059] In step S101, the information acquisition unit 131 of the server 10 accesses the device 20-1 to acquire weather forecast information. The information acquisition unit 131 of the server 10 may access the device 20-2 to acquire train delay information, or may access the device 20-3 to acquire event-related information. The information acquisition unit 131 of the server 10 may also access the devices 20-1 to 20-3 to acquire all of the weather forecast information, train delay information, and event-related information.

[0060] The processing proceeds to step S102, where the prediction unit 132 of the server 10 predicts when and where traffic will increase in the future by inputting the weather forecast information acquired in the processing of step S101 to the inference model 152. The prediction unit 132 of the server 10 may predict when and where traffic will increase in the future by inputting train delay information to the inference model 152. The prediction unit 132 of the server 10 may also predict when and where traffic will increase in the future by inputting information about an event to the inference model 152.

[0061] The process proceeds to step S103, where the communication unit 110 of the server 10 transmits information indicating the prediction result predicted in step S102 to each of the base stations 30-1 to 30-3. Note that the communication unit 110 of the server 10 may transmit the information indicating the prediction result predicted in step S102 only to, for example, the base station 30-1.

[0062] In step S104, the communication unit 310 of each of the base stations 30-1 to 30-3 receives information indicating the prediction result by the prediction unit 132, which is transmitted from the communication unit 110 of the server 10. Note that only the base station 30-1 may receive the information indicating the prediction result by the prediction unit 132, which is transmitted from the communication unit 110 of the server 10.

[0063] The process proceeds to step S105, where the radio resource calculation unit 321 of each of the base stations 30-1 to 30-3 calculates the radio resources required for the time period when traffic is predicted to increase. Note that only the base station 30-1 may calculate the radio resources required for the time period when traffic is predicted to increase.

[0064] The process proceeds to step S106, where the policy determination unit 322 of each of the base stations 30-1 to 30-3 determines a policy for reserving the wireless resources calculated in the process of step S105. Note that only the base station 30-1 may determine the policy for reserving the wireless resources calculated in the process of step S105.

[0065] The process proceeds to step S107, where the cooperative control unit 323 of each of the base stations 30-1 to 30-3 executes the policy for securing radio resources in cooperation with each base station in accordance with the policy determined in step S106.

[0066] As described above, the wireless communication system 1 (control system) according to aspect 1 of the present invention includes a server 10 (control device) and a plurality of base stations 30-1 to 30-3 that can communicate with the server 10. The server 10 predicts future traffic conditions in a specific time period and a specific area based on predetermined information. Furthermore, the server 10 transmits information indicating the prediction result to at least one base station 30-1 (first base station) among the plurality of base stations 30-1 to 30-3 located in an area where traffic is predicted to increase during the specific time period. Based on the information indicating the prediction result, the base station 30-1 calculates the radio resources required for the predicted increase in traffic. Furthermore, the base station 30-1 determines a policy for securing radio resources. The base station 30-1 then executes the policy in cooperation with the other base stations 30-2 to 30-3.

[0067] Furthermore, the server 10 (control device) according to aspect 2 of the present invention may transmit information indicating the prediction results to each of the plurality of base stations 30-1 to 30-3 located in an area where traffic is predicted to increase during a specific time period in the above-described aspect 1. Each of the plurality of base stations 30-1 to 30-3 may calculate the radio resources required for the predicted increase in traffic based on the information indicating the prediction results. Furthermore, each of the plurality of base stations 30-1 to 30-3 may determine a policy for reserving radio resources. Then, each of the plurality of base stations 30-1 to 30-3 may execute the policy in cooperation with other base stations.

[0068] Furthermore, the predetermined information according to aspect 3 of the present invention may include at least one of information about the weather, information about trains, and information about events in the above-mentioned aspects 1 or 2.

[0069] Furthermore, the policy according to aspect 4 of the present invention may include at least one of massive MIMO beamforming control, SFN cell release, and load balancing in any of aspects 1 to 3 above.

[0070] Furthermore, a method (control method) executed by the server 10 (control device) according to aspect 5 of the present invention includes a prediction step (S102) of predicting future traffic conditions in a specific time period and a specific area based on predetermined information, and a transmission step (S103) of transmitting information indicating the prediction result to at least one base station 30-1 (first base station) among multiple base stations 30-1 to 30-3 located in an area where traffic is predicted to increase during the specific time period. The method (control method) executed by the base station 30-1 also includes a calculation step (S105) of calculating the radio resources required for the predicted increased traffic based on the information indicating the prediction result, a determination step (S106) of determining a policy for securing radio resources, and an execution step (S107) of executing the policy in cooperation with other base stations 30-2 to 30-3. Note that the processing flow in the sequence chart shown in FIG. 4 is merely an example, and steps may be deleted or new steps may be added without departing from the spirit of the present invention.

[0071] (Examples of Hardware Configuration and Software Implementation) The control blocks of the server 10, the devices 20-1 to 20-3, the base stations 30-1 to 30-3, and the user terminal UE (particularly the first control unit 120, the second control unit 130, and the various units included in the control unit 320) may be implemented by logic circuits (hardware) formed on an integrated circuit (IC chip) or the like, or may be implemented by software using a CPU (Central Processing Unit). In the latter case, the server 10, the devices 20-1 to 20-3, the base stations 30-1 to 30-3, and the user terminal UE may be configured using a computer (electronic calculator). For ease of explanation, the server 10, the devices 20-1 to 20-3, the base stations 30-1 to 30-3, and the user terminal UE will be collectively referred to as the "server 10, etc."

[0072] FIG. 5 is a block diagram illustrating the configuration of a computer 910 that can be used as the server 10 or the like. The computer 910 includes an arithmetic unit 912, a main memory device 913, an auxiliary memory device 914, and an input / output interface 915, all connected to one another via a bus 911. The arithmetic unit 912, the main memory device 913, and the auxiliary memory device 914 may each be, for example, a CPU, a RAM (random access memory), a solid-state drive, or a hard disk drive. The input / output interface 915 is connected to an input device 920 through which a user inputs various information to the computer 910, and an output device 930 through which the computer 910 outputs various information to the user. The input device 920 and the output device 930 may be built into the computer 910 or may be connected (externally) to the computer 910. For example, the input device 920 may be a button, a keyboard, a mouse, a touch sensor, or the like, and the output device 930 may be a lamp, a display, a printer, a speaker, or the like. It is also possible to apply a device having the functions of both the input device 920 and the output device 930, such as a touch panel in which a touch sensor and a display are integrated. The communication interface 916 is an interface that allows the computer 910 to communicate with external devices.

[0073] The auxiliary storage device 914 stores an information processing program for causing the computer 910 to operate as the server 10, etc. The arithmetic device 912 then deploys the information processing program stored in the auxiliary storage device 914 onto the main storage device 913 and executes instructions contained in the information processing program, thereby causing the computer 910 to function as each unit of the server 10, etc. Note that the recording medium used by the auxiliary storage device 914 to record information such as the information processing program may be any computer-readable "non-transitory tangible medium," and may be, for example, a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, etc.

[0074] Alternatively, the computer 910 may be configured to function using a program stored on a recording medium external to the computer 910 or a program supplied to the computer 910 via any transmission medium (such as a communication network or broadcast waves).The present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

[0075] The present invention is not limited to the above-described embodiment, and various modifications are possible within the scope of the claims.

[0076] 1 Wireless communication system 10 Server 110 Communication unit 132 Prediction unit 30-1, 30-2, 30-3 Base station 321 Wireless resource calculation unit 322 Policy determination unit 323 Cooperation control unit

Claims

1. A control system including a control device and a plurality of base stations capable of communicating with the control device, wherein the control device predicts future traffic conditions in a specific time period and a specific area based on predetermined information, and transmits information indicating the prediction result to at least one first base station among the plurality of base stations located in an area where traffic is predicted to increase during the specific time period, and the first base station calculates the radio resources required for the traffic predicted to increase based on the information indicating the prediction result, determines a policy for securing the radio resources, and executes the policy in cooperation with other base stations.

2. The control system of claim 1, wherein the control device transmits information indicating the prediction result to each of the plurality of base stations located in an area where traffic is predicted to increase during the specific time period, and each of the plurality of base stations calculates the radio resources required for the traffic predicted to increase based on the information indicating the prediction result, determines a policy for securing the radio resources, and executes the policy in cooperation with other base stations.

3. The control system according to claim 1 or 2, wherein the predetermined information includes at least one of information about weather, information about trains, and information about events.

4. The control system according to claim 1 or 2, wherein the policy includes at least one of massive MIMO beamforming control, SFN cell release, and load balancing.

5. A control method executed by a control device and at least one first base station among a plurality of base stations capable of communicating with the control device, the method executed by the control device comprising: a prediction step of predicting a future traffic situation in a specific time period and a specific area based on predetermined information; and a transmission step of transmitting information indicating the prediction result to at least one first base station among the plurality of base stations located in an area where traffic is predicted to increase during the specific time period; and the method executed by the first base station comprising: a calculation step of calculating radio resources required for the traffic predicted to increase based on the information indicating the prediction result; a decision step of deciding a policy for securing the radio resources; and an execution step of executing the policy in cooperation with other base stations.

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