COMMUNICATION MANAGEMENT DEVICE, COMMUNICATION MANAGEMENT METHOD, AND COMMUNICATION MANAGEMENT SYSTEM

Abstract

The present invention aims to manage, with a simpler configuration, radio resources used in MIMO communication for each of a plurality of communication terminals that have common requirements regarding wireless communication. [Solution] The communication terminal device includes a learning unit 13 configured to use a machine learning model to learn the relationship between a request for wireless communication and allocation information of wireless resources for responding to the request for wireless communication, using as training data allocation information of wireless resources in a MIMO transmission method to be assigned to a plurality of communication terminals 2 to which requests for wireless communication are commonly applied, and a setting unit 15 configured to set communication management information including the learned machine learning model constructed by the learning unit 13 to the plurality of communication terminals 2.

Description

[Technical field]

[0001] The present invention relates to a communication management device, a communication management method, and a communication management system, and in particular to a technology for managing radio resources. [Background technology]

[0002] Conventionally, there have been known techniques for managing radio resources used in MIMO (Multiple Input Multiple Output) communication. For example, Patent Document 1 discloses radio resource management for MIMO communication in which a communication terminal notifies a base station of its communication capabilities such as the number of antennas and frequency bands for MIMO communication, and its service request, and the base station assigns an appropriate number of antennas, frequency bands, and component carriers to the communication terminal.

[0003] In the technology described in Patent Document 1, management regarding appropriate allocation of radio resources in response to service requests is performed individually between each communication terminal and a base station, and as a result, the management of radio resources for multiple communication terminals as a whole becomes complicated. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2015-065656 A Summary of the Invention [Problem to be solved by the invention]

[0005] In conventional techniques, it has been difficult to manage, with a simpler configuration, radio resources used in MIMO communication for each of a plurality of communication terminals that share common requirements regarding wireless communication.

[0006] The present invention has been made to solve the above-mentioned problems, and aims to manage, with a simpler configuration, radio resources used in MIMO communication for each of multiple communication terminals that have common wireless communication requirements. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, the communication management device of the present invention includes a learning unit configured to use a machine learning model to learn the relationship between a request for wireless communication and allocation information of wireless resources for responding to the request for wireless communication, using as training data allocation information of wireless resources in a MIMO transmission method that is assigned to a plurality of communication terminals to which requests for wireless communication are commonly applied, and a setting unit configured to set communication management information including the learned machine learning model constructed by the learning unit to the plurality of communication terminals.

[0008] In addition, in the communication management device according to the present invention, the radio resource allocation information may include the number of transmitting and receiving antennas and a frequency band used in the MIMO transmission method, and the frequency band may be one or a plurality of frequency bands.

[0009] In order to achieve the above object, in the communication management device according to the present invention, the request related to wireless communication may be a request that is applied depending on a type of application executed on the plurality of communication terminals.

[0010] In order to solve the above-mentioned problems, the communication management method of the present invention includes a learning step of using a machine learning model to learn the relationship between a request for wireless communication and allocation information of wireless resources for responding to the request for wireless communication, using as training data allocation information of wireless resources in a MIMO transmission method that is assigned to a plurality of communication terminals to which requests for wireless communication are commonly applied, and a setting step of setting communication management information including the learned machine learning model constructed in the learning step to the plurality of communication terminals.

[0011] In addition, in the communication management method according to the present invention, the radio resource allocation information may include the number of transmitting and receiving antennas and a frequency band used in the MIMO transmission method, and the frequency band may be one or a plurality of frequency bands.

[0012] In the communication management method according to the present invention, the request regarding wireless communication may be a request that is applied depending on a type of an application executed in the plurality of communication terminals.

[0013] In addition, the communication management method of the present invention may include a first acquisition step in which each of the multiple communication terminals acquires a request regarding the wireless communication applied to the own terminal, a second acquisition step in which the trained machine learning model included in the communication management information set in the setting step, a calculation step in which the request regarding wireless communication applied to the own terminal acquired in the first acquisition step is provided as an unknown input to the trained machine learning model, and the trained machine learning model is calculated to output allocation information of the wireless resources to be allocated to the own terminal, and a communication management step in which communication is performed using the MIMO transmission method based on the allocation information of the wireless resources output in the calculation step.

[0014] In order to solve the above-mentioned problems, the communication management system of the present invention is a communication management system comprising the above-mentioned communication management device and the multiple communication terminals, each of the multiple communication terminals comprising a first acquisition unit configured to acquire a request regarding the wireless communication applied to the own terminal, a second acquisition unit configured to acquire the trained machine learning model included in the communication management information set by the communication management device, a calculation unit configured to provide the request regarding the wireless communication applied to the own terminal acquired by the first acquisition unit as an unknown input to the trained machine learning model, perform calculations on the trained machine learning model, and output allocation information of the wireless resources to be allocated to the own terminal, and a communication management unit configured to perform communication using the MIMO transmission method based on the wireless resource allocation information output by the calculation unit. Effect of the Invention

[0015] According to the present invention, the relationship between the request for wireless communication and the allocation information of the wireless resource for responding to the request for wireless communication is learned using a machine learning model, with the allocation information of the wireless resource in the MIMO transmission method, which is allocated to a plurality of communication terminals to which the request for wireless communication is commonly applied, as training data. Therefore, with a simpler configuration, it is possible to manage the wireless resource used in the MIMO communication for each of the plurality of communication terminals to which the request for wireless communication is common. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a block diagram showing a configuration of a communication management system including a communication management device and a communication terminal according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing a configuration of a communication terminal according to the present embodiment. [Diagram 3] FIG. 3 is a diagram for explaining an overview of the communication management system according to the present embodiment. [Figure 4]FIG. 4 is a diagram for explaining the configuration of the management table of the communication management device according to the present embodiment. [Diagram 5] FIG. 5 is a diagram for explaining the configuration of the learning unit included in the communication management device according to the present embodiment. [Figure 6] FIG. 6 is a block diagram showing a hardware configuration of the communication management device according to the present embodiment. [Figure 7] FIG. 7 is a block diagram showing a hardware configuration of a communication terminal according to the present embodiment. [Figure 8] FIG. 8 is a sequence showing the operation of the communication management system according to the present embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will now be described in detail with reference to FIGS.

[0018] [Communication management system configuration] 1 is a block diagram showing a configuration of a communication management system including a communication management device 1 and a communication terminal 2 according to an embodiment of the present invention. The communication management system according to the present embodiment includes the communication management device 1, a communication terminal 2, a base station 3, and a core network 4 that are compatible with a 5G wireless communication system. The communication management system manages wireless resources of a MIMO transmission method for each of multiple communication terminals 2 to which requirements related to wireless communication are commonly applied.

[0019] The communication management device 1 and the core network 4 are connected via a network NW such as a LAN, a WAN, the Internet, etc. Furthermore, the base station 3 and the core network 4 are connected via a backhaul link.

[0020] The communication terminal 2 is realized by a mobile communication terminal such as a smartphone, a tablet computer, or a laptop computer, or an IoT terminal using a 5G mobile communication network such as a smart meter, a wearable device, or an industrial sensor. In this embodiment, the communication terminal 2 includes a SIM, and further includes a unique IP address and can connect to the Internet. In this embodiment, the communication terminal 2 includes a plurality of antennas 208, and performs MIMO communication with the base station 3.

[0021] There are a plurality of communication terminals 2, and requirements regarding wireless communication are applied to each communication terminal 2. The requirements regarding wireless communication include requirements that are applied according to the type of application executed on the communication terminal 2. For example, a smart meter or the like requires low power consumption, but does not need to be broadband, and requires low latency only when necessary. On the other hand, streaming media such as video distribution requires broadband, low latency, and stable high throughput. Assigning the optimal number of antennas and frequency bands for MIMO communication to each communication terminal 2 according to the requirements of each application or service leads to efficient use of wireless resources.

[0022] In this embodiment, the communication terminals 2 are grouped into groups of a plurality of communication terminals 2 having common requirements regarding wireless communication, and optimal wireless resources are managed. As shown in FIG. 1, n communication terminals 2 having IP addresses IP01 to IP0n are grouped into group 1. These communication terminals 2 are a group of terminals having common requirements such as bandwidth and delay required by applications and services. In the example of FIG. 1, m groups (m is an integer equal to or greater than 1) having common requirements regarding wireless communication are set. The functional blocks and hardware configuration of the communication terminal 2 will be described in detail later.

[0023] The base station 3 is composed of a wireless base station compatible with the 5G system, and relays communications between the communication terminal 2 located within its range and the core network 4. The base station 3 can be realized, for example, by a computer including a processor, a main memory device, a communication interface, an auxiliary memory device, and an input / output I / O device connected via a bus, and a program that controls these hardware resources.

[0024] The base station 3 further includes a plurality of antennas, and performs wireless communication with the communication terminal 2 by MIMO communication. In this embodiment, the MIMO communication is realized based on the number of antennas and frequency bands for MIMO communication allocated to the communication terminal 2. The base station 3 performs more detailed resource management of frequency and time according to the channel state, and cooperates with the communication terminal 2 to realize MIMO communication in which multiple data streams are transmitted and received simultaneously.

[0025] The core network 4 manages authentication and connection when the communication terminal 2 connects to a mobile communication network. The core network 4 also controls the initiation, maintenance, and termination of a data session for connecting the communication terminal 2 to a data network such as the Internet via a user plane function (UPF) (not shown) using assigned wireless resources and performing data communication.

[0026] FIG. 3 is a diagram for explaining an overview of a communication management system according to this embodiment. FIG. 3 shows a two-dimensional plane in which radio resources are defined by a time axis and a frequency axis, and a spatial axis (number of antennas). As shown in FIG. 3, the radio resources are further divided into resource blocks each consisting of a time slot and a subcarrier. For example, in the case of a 2×2 MIMO system having two transmitting antennas and two receiving antennas, spatial streams s are arranged along the spatial axis in FIG. 3. 1 , s 2 For simplicity of explanation, in FIG. 3, it is assumed that the number of transmitting and receiving antennas is equal to the number of spatial streams, and the number of spatial streams s 1 , s 2 represents two antennas in a 2x2 MIMO configuration.

[0027] The communication management system assigns to the communication terminal 2 a number of antennas N (N is a positive integer of 1 or more) and one or more frequency bands suitable for the requirements regarding wireless communication applied to the communication terminal 2. For example, in a 4×4 MIMO configuration, the number of antennas N=4 and the frequency band f 1 and f 3 When the frequency band f is assigned, the communication terminal 2 and the base station 3 each use four antennas to transmit the frequency band f 1 and f 3 It transmits and receives four data streams in parallel.

[0028] [Function block of communication management device] As shown in FIG. 1, the communication management device 1 includes a third acquisition unit 10, a group management unit 11, a first storage unit 12, a learning unit 13, a second storage unit 14, and a setting unit 15.

[0029] The third acquisition unit 10 acquires requests related to wireless communication as grouping criteria for grouping communication terminals 2 that use the same number of antennas or frequency band. The third acquisition unit 10 can acquire the types of applications executed by each of the multiple communication terminals 2 as the grouping criteria. The third acquisition unit 10 acquires the grouping criteria by accepting an input of the grouping criteria by an administrator, for example.

[0030] The group management unit 11 creates grouping information by assigning a group ID to the information of the request related to wireless communication acquired by the third acquisition unit 10, and further groups a plurality of communication terminals 2. The group management unit 11 can create grouping information by assigning a group ID for each type of application, for example.

[0031] The group management unit 11 acquires information on requests for wireless communication and the IP addresses of the communication terminals 2 from each of the communication terminals 2 to be managed via the core network 4. Then, the group management unit 11 issues a group ID to each of the IP addresses of the multiple communication terminals 2 based on the grouping information.

[0032] 4 shows the configuration of the management table 110 managed by the group management unit 11. First, the group management unit 11 stores information of "group ID" assigned to "application type" in the management table 110 as grouping information. Then, for example, in response to the start of "application 1" by the communication terminal 2, information indicating a request for wireless communication "application 1" is generated in the dedicated application and transmitted to the communication management device 1 together with the IP address "IP01" of the communication terminal 2. In this case, the group management unit 11 issues a group ID "1" to the IP address "IP01" of the communication terminal 2 based on the information indicating "application 1" received from the communication terminal 2.

[0033] The first storage unit 12 stores a management table 110.

[0034] The learning unit 13 uses, as training data, wireless resource allocation information in the MIMO transmission method, which is allocated to multiple communication terminals 2 to which wireless communication requests are commonly applied, to learn the relationship between the wireless communication requests and the wireless resource allocation information for responding to the wireless communication requests, using a machine learning model. The wireless resource allocation information is the number of antennas and frequency bands for MIMO communication.

[0035] The learning unit 13 uses a set of teacher data in which a correct answer label is attached to each of a plurality of group IDs to learn the number of antennas and frequency bands required to meet the wireless communication requirements of each group ID through a machine learning model.

[0036] FIG. 5 shows a neural network structure adopted as an example of a machine learning model for learning by the learning unit 13. The neural network includes an input layer x, a hidden layer h, and an output layer y. The input node of the input layer x is given the value of the group ID issued to the communication terminal 2 by the group management unit 11. Depending on the application executed by the communication terminal 2, a different group ID is given to each communication terminal 2, and the number of antennas and frequency bands for common MIMO communication are assigned to each group ID. The number of antennas and frequency bands assigned to each group ID are the optimal number of antennas and frequency bands for meeting the requirements of the application linked to the group ID. Therefore, a correlation can be found between the group ID and the number of antennas and frequency bands assigned to multiple communication terminals 2 having a common group ID.

[0037] Each output node y of the output layer y 1 ~y N ,o 1 ~o M is the neural network's predicted value for the input group ID value. 1 ~y N outputs the predicted value of the number of antennas. 1 ~y N The indexes 1 to N of the output node o represent the number of antennas. 1 ~o M The output node o outputs the predicted value of the frequency band. 1 ~o M The indices 1 to M correspond to the frequency band f 1 ~f M Specifically, the output node y 1 ~y N As a predicted value of the number of transmitting and receiving antennas, when the number of transmitting and receiving antennas is N=2, (y 1 ,y 2 ,y 3 ,···,y N ) = (0,1,0,···,0) is the predicted value.

[0038] As described above, in this embodiment, one or more frequency bands can be assigned to communication terminals 2 that share a common group ID. 1 ~o M In some cases, multiple predicted output values ​​may take the value "1". For example, in the frequency band f 1 and f 3 If assigned, (o 1 ,o 2 ,o 3 ,···,o M ) = (1, 0, 1, . . . , 0) is the predicted value.

[0039] The learning unit 13 introduces an objective function E shown in the following formula (1) to learn parameters of the neural network model so that the predicted values ​​of the number of antennas and frequency band from the neural network model for the group ID of the communication terminal 2 become the values ​​of the correct label of the training data. The correct label of the training data is the optimal value of the number of antennas and frequency band assigned to the group ID.

[0040]

number

[0041] In the above formula (1), y 1 ,y 2 ,···,y N ,o 1 ,o 2 ,···,o M indicates the predicted output value of each output node. Also, Y 1 ,Y 2 ,···,Y N ,O 1 ,O 2 ,···,O M is the correct label of the training data, and is prepared separately in advance. The learning unit 13 adjusts the weight parameters of the neural network so that the objective function E in the above formula (1) is minimized, that is, becomes 0. The learning unit 13 can optimize the objective function E using the backpropagation method or the like.

[0042] The second memory unit 14 stores the trained machine learning model constructed by the training unit 13.

[0043] The setting unit 15 sets communication management information including the trained machine learning model constructed by the learning unit 13 in the communication terminal 2. Specifically, the setting unit 15 can transmit the communication management information to the communication terminal 2 to be managed via the network NW and set it.

[0044] [Communication terminal function block] Next, the configuration of the communication terminal 2 will be described with reference to the block diagram of Fig. 2. As shown in Fig. 2, the communication terminal 2 includes a third storage unit 20, a first acquisition unit 21, a second acquisition unit 22, a calculation unit 23, and a communication management unit 24. Each of the multiple communication terminals 2 has the same configuration.

[0045] The third storage unit 20 stores the communication management information set by the setting unit 15 of the communication management device 1 .

[0046] The first acquisition unit 21 acquires a request related to wireless communication applied to the own terminal. Specifically, the first acquisition unit 21 can acquire the group ID of the own terminal issued by the group management unit 11 of the communication management device 1 from the communication management device 1.

[0047] The second acquisition unit 22 acquires the trained machine learning model included in the communication management information set by the communication management device 1. Specifically, the second acquisition unit 22 reads out the trained machine learning model stored in the third storage unit 20.

[0048] The calculation unit 23 provides the request related to wireless communication applied to the own terminal acquired by the first acquisition unit 21 as an unknown input to the trained machine learning model, performs calculations on the trained machine learning model, and outputs allocation information of wireless resources to be assigned to the own terminal. More specifically, the calculation unit 23 provides the group ID of the own terminal acquired by the first acquisition unit 21 to the trained machine learning model, performs calculations, and outputs the optimal number of antennas and frequency band.

[0049] The communication management unit 24 performs communication by the MIMO transmission method based on the wireless resource allocation information output by the calculation unit 23. More specifically, the communication management unit 24 acquires channel state information based on the number of antennas and frequency bands allocated to the terminal, and transmits it to the base station 3. The base station 3 optimizes the allocation of wireless resources according to the channel state information. The communication management unit 24 uses antennas 208 of the allocated number of antennas, and establishes MIMO communication with the base station 3.

[0050] [Hardware configuration of communication management device] Next, an example of a hardware configuration for realizing the communication management device 1 having the above-mentioned functions will be described with reference to FIG.

[0051] 6, the communication management device 1 can be realized by, for example, a computer including a processor 102, a main memory device 103, a communication interface 104, an auxiliary memory device 105, and an input / output I / O 106, which are connected via a bus 101, and a program that controls these hardware resources. Furthermore, the communication management device 1 can include a display device 107 connected via the bus 101.

[0052] The processor 102 is realized by a CPU, a GPU, an FPGA, an ASIC, or the like.

[0053] Programs for the processor 102 to perform various controls and calculations are stored in advance in the main memory device 103. The processor 102 and the main memory device 103 realize the various functions of the communication management device 1, such as the third acquisition unit 10, the group management unit 11, the learning unit 13, and the setting unit 15 shown in FIG.

[0054] The communication interface 104 is an interface circuit for connecting the communication management device 1 to various external electronic devices via a network.

[0055] The auxiliary storage device 105 is composed of a readable / writable storage medium and a drive for reading and writing various information such as programs and data from and to the storage medium. A hard disk or a semiconductor memory such as a flash memory can be used as the storage medium for the auxiliary storage device 105.

[0056] The auxiliary storage device 105 has a program storage area for storing the communication management program executed by the communication management device 1. It also has a program storage area for storing the machine learning program executed by the communication management device 1. The auxiliary storage device 105 also has an area for storing information on wireless resources. The first storage unit 12 and the second storage unit 14 described in FIG. 1 are realized by the auxiliary storage device 105. Furthermore, it may have, for example, a backup area for backing up the above-mentioned data, programs, etc.

[0057] The input / output I / O 106 is an input / output device that inputs signals from an external device and outputs signals to an external device.

[0058] The display device 107 is configured with an organic EL display, a liquid crystal display, etc. The display device 107 can display management information of a plurality of communication terminals 2 that are grouped.

[0059] [Hardware configuration of communication terminal] Next, an example of a hardware configuration for realizing the communication terminal 2 having the above-mentioned functions will be described with reference to FIG.

[0060] 7, the communication terminal 2 can be realized by a computer including a processor 202, a main memory device 203, a communication interface 204, an auxiliary memory device 205, and an input / output I / O 206, which are connected via a bus 201, and a program that controls these hardware resources. Furthermore, the communication terminal 2 can include a display device 207 and an antenna 208, which are connected via the bus 201.

[0061] The main memory device 203 stores in advance programs for the processor 202 to perform various controls and calculations. The processor 202 and the main memory device 203 realize the functions of the communication terminal 2, such as the first acquisition unit 21, the second acquisition unit 22, the calculation unit 23, and the communication management unit 24 shown in FIG.

[0062] The auxiliary storage device 205 has a program storage area for storing an arithmetic program executed by the communication terminal 2. It also has a program storage area for storing a communication management program executed by the communication terminal 2. The auxiliary storage device 205 realizes the third storage unit 20 described in Fig. 2. Furthermore, it may have, for example, a backup area for backing up the above-mentioned data, programs, and the like.

[0063] [Operation of the communication management system] Next, the operation of the communication management system having the above-mentioned configuration will be described with reference to the sequence of FIG.

[0064] First, the third acquisition unit 10 acquires grouping criteria for allocating a common number of transmitting and receiving antennas and a common frequency band to the communication terminal 2 (step S1). The third acquisition unit 10 can acquire the type of application executed by the communication terminal 2 as the grouping criteria. Furthermore, the group management unit 11 assigns a group ID to each type of application and stores it in the management table 110.

[0065] Next, the learning unit 13 prepares training data (step S2). The training data is data to which the number of antennas and frequency bands are assigned as correct answer labels for each group ID. The learning unit 13 can receive training data that has been designed in advance externally from the communication interface 104.

[0066] Next, the learning unit 13 uses the teacher data obtained in step S2 to learn the relationship between the group ID indicating a request for wireless communication and the number of antennas and frequency band allocation information for responding to the request for wireless communication, using a machine learning model (step S3). In step S3, the learning unit 13 uses the teacher data for each of the multiple group IDs to learn the number of antennas and frequency bands for responding to the request for wireless communication for each group ID, using the machine learning model.

[0067] The learning unit 13 employs, for example, a model of the neural network structure shown in Fig. 5 as a machine learning model, and adjusts weight parameters of the neural network so that the objective function E expressed by the above formula (1) is minimized, that is, becomes 0. The learning unit 13 can optimize the objective function by a gradient method using an error backpropagation method or the like. The trained machine learning model constructed by the learning unit 13 is stored in the first storage unit 12 (step S4).

[0068] Thereafter, the group management unit 11 instructs the managed communication terminals 2 to transmit the type of the launched application and the IP address of the communication terminal 2 by push notification (step S5). For example, the push notification can be transmitted using the launch of an application on the communication terminal 2 as a trigger.

[0069] Next, in response to the instruction in step S5, the communication terminal 2 notifies the communication management device 1 of its own IP address (IP01) and the type of the launched application (application 1) (step S6). The communication terminal 2 performs the process of step S6 within the dedicated application for communication management. Thereafter, the group management unit 11 of the communication management device 1 refers to the grouping information in the management table 110, and issues a group ID to the IP address of the communication terminal 2 based on the received type of application (step S7). In the example of FIG. 8, the group ID "1" is issued.

[0070] Next, the setting unit 15 of the communication management device 1 sets communication management information including the trained machine learning model constructed in the learning process of step S3 to the communication terminal 2 (step S8). In step S8, the communication management information is transmitted to the communication terminal 2 via the network NW.

[0071] Thereafter, the third storage unit 20 of the communication terminal 2 stores the trained machine learning model included in the communication management information (step S9). Next, the first acquisition unit 21 of the communication terminal 2 loads the trained machine learning model from the third storage unit 20 to acquire the trained machine learning model (step S10). Next, the second acquisition unit 22 acquires the group ID of the terminal itself (step S11). Specifically, the group ID received from the communication management device 1 in step S7 is acquired.

[0072] Next, the calculation unit 23 provides the group ID of the own terminal acquired in step S11 as an unknown input to the trained machine learning model, performs calculations on the trained machine learning model, and outputs allocation information of the number of antennas and frequency bands for MIMO communication to be assigned to the own terminal (step S12).

[0073] Thereafter, the communication management unit 24 performs MIMO communication based on the allocation information of the number of antennas and frequency bands output in step S12 (step S13). In step S13, the communication management unit 24 acquires channel state information based on the number of antennas and frequency bands allocated to its own terminal, and transmits it to the base station 3. The base station 3 optimizes the allocation of wireless resources according to the channel state information. Then, the communication management unit 24 uses antennas 208 of the allocated number of antennas to establish MIMO communication with the base station 3.

[0074] Similarly, the processes from step S9 to step S13 are performed for each of the other communication terminals 2 (IP02 to IP0n) to be managed, whose group ID is "1." Similarly, the processes from step S9 to step S13 are performed for each of the other communication terminals 2 having different group IDs.

[0075] As described above, the communication management device 1 according to the present embodiment uses, as training data, information on the number of antennas and frequency bands in the MIMO transmission method to be assigned to multiple communication terminals 2 to which requests related to wireless communication are commonly applied, and learns the relationship between requests related to wireless communication and information on the number of antennas and frequency bands to respond to the requests related to wireless communication using a machine learning model. Therefore, with a simpler configuration, it is possible to manage wireless resources used in MIMO communication for each of multiple communication terminals 2 that share requests related to wireless communication.

[0076] Moreover, according to the communication management system according to the present embodiment, the communication management device 1 sets communication management information including the learned machine learning model in the communication terminal 2 via the network NW. Therefore, it is possible to centrally control and manage the MIMO communication of the multiple communication terminals 2.

[0077] In the embodiment described above, a neural network model is exemplified as the machine learning model. However, the machine learning model may be a model other than the neural network, such as logistic regression, random forest, decision tree, or deep learning in which the neural network is multi-layered.

[0078] In addition, in the above-described embodiment, a communication management system that complies with 5G has been exemplified, but the communication management system may also be one that complies with LTE or 6G.

[0079] The above describes the embodiments of the communication management device, communication management method, and communication management system of the present invention. However, the present invention is not limited to the described embodiments, and various modifications that a person skilled in the art can imagine can be made within the scope of the invention described in the claims. [Explanation of symbols]

[0080] 1...communication management device, 10...third acquisition unit, 11...group management unit, 12...first memory unit, 13...learning unit, 14...second memory unit, 15...setting unit, 2...communication terminal, 3...base station, 4...core network, 101, 201...bus, 102, 202...processor, 103, 203...main memory unit, 104, 204...communication interface, 105, 205...auxiliary memory unit, 106, 206...input / output I / O, 107, 207...display unit, 208...antenna, NW...network.

Claims

1. a learning unit configured to use, as teacher data, allocation information of radio resources in a MIMO transmission method, which is allocated to each communication terminal group to which a request related to wireless communication is commonly applied, and to learn, using a machine learning model, a relationship between the request related to wireless communication and allocation information of radio resources for responding to the request related to wireless communication; A setting unit configured to set communication management information including the trained machine learning model constructed by the learning unit to a plurality of communication terminals to be managed, the plurality of communication terminals being a group of communication terminals; A communication management device comprising:

2. 2. The communication management device according to claim 1, The wireless resource allocation information includes the number of transmitting and receiving antennas and frequency bands used in the MIMO transmission method, The frequency band may be one or more frequency bands. A communication management device comprising:

3. 2. The communication management device according to claim 1, The request regarding wireless communication is applied depending on the type of application executed in the communication terminal group. A communication management device comprising:

4. a learning step of learning, using a machine learning model, a relationship between a request for wireless communication and allocation information of wireless resources for responding to the request for wireless communication, by using, as training data, allocation information of wireless resources in a MIMO transmission method, which is allocated to each communication terminal group to which a request for wireless communication is commonly applied; a setting step of setting communication management information including the trained machine learning model constructed in the learning step in a plurality of communication terminals to be managed, the plurality of communication terminals being a group of communication terminals; A communication management method comprising:

5. 5. The communication management method according to claim 4, The wireless resource allocation information includes the number of transmitting and receiving antennas and frequency bands used in the MIMO transmission method, The frequency band may be one or more frequency bands. A communication management method comprising:

6. 5. The communication management method according to claim 4, The request regarding wireless communication is applied depending on the type of application executed in the communication terminal group. A communication management method comprising:

7. 5. The communication management method according to claim 4, Furthermore, each of the plurality of communication terminals to be managed A first acquisition step of acquiring a request related to the wireless communication applied to the terminal; A second acquisition step of acquiring the trained machine learning model included in the communication management information set in the setting step; a calculation step of providing the request related to wireless communication applied to the own terminal acquired in the first acquisition step as an unknown input to the trained machine learning model, performing a calculation of the trained machine learning model, and outputting allocation information of the wireless resource to be assigned to the own terminal; a communication management step of performing communication by the MIMO transmission method based on the wireless resource allocation information output in the calculation step; A communication management method comprising:

8. A communication management device according to any one of claims 1 to 3, A plurality of communication terminals to be managed; A communication management system comprising: Each of the plurality of communication terminals to be managed A first acquisition unit configured to acquire a request related to the wireless communication applied to the terminal; A second acquisition unit configured to acquire the trained machine learning model included in the communication management information set by the communication management device; a calculation unit configured to provide the request related to the wireless communication applied to the terminal acquired by the first acquisition unit as an unknown input to the trained machine learning model, perform a calculation of the trained machine learning model, and output allocation information of the wireless resource to be assigned to the terminal; a communication management unit configured to perform communication by the MIMO transmission method based on the wireless resource allocation information output by the calculation unit; A communication management system comprising:

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