JP7629939B2 - COMMUNICATION DEVICE, USER EQUIPMENT, COMMUNICATION SYSTEM, AND COMMUNICATION METHOD - Google Patents

Description

The present invention relates to a communication device, a user device, a communication system and a communication method.

The 3rd Generation Partnership Project (3GPP) has established the specifications for Long Term Evolution (LTE), and is currently establishing the specifications for LTE-Advanced (hereinafter, LTE-Advanced is also referred to as LTE) with the aim of further increasing the speed of LTE. 3GPP is also considering the specifications for a successor system to LTE, known as 5G New Radio (NR) or Next Generation (NG).

The 5G system is stipulated to collect network data (network information) and provide services based on the analysis results (Non-Patent Document 1).

It is also specified that the 5G system will support communication between moving objects and other entities, including vehicle-to-vehicle communication, known as V2X (Vehicle-to-Everything) (Non-Patent Document 2).

3GPP TS 23.288 v16.5.0 (2020-09), Architecture enhancements for 5G System (5GS) to support network data analytics services 3GPP TS 23.287 v16.4.0 (2020-09), Architecture enhancements for 5G System (5GS) to support Vehicle-to-Everything (V2X) services Interdisciplinary Research Group on Intelligence Networking Homepage, URL: https://www.ieice.org/cs/rising/jpn/index.html

In recent years, the application of machine learning to networks (NW) has begun (see non-patent document 3).

As mentioned above, 3GPP specifies the collection and analysis of network information, but does not specify the application of machine learning.

When a program with a relatively large processing load is executed on a terminal, it leads to delays due to the processing burden, etc., while when it is executed in a network core, it leads to delays in communication response, etc., resulting in a trade-off. In recent years, therefore, it has been considered to apply edge computing technology, which involves the distribution of processing and calculation volume, to install mobile edge cloud (MEC) servers near the boundary between the network and the mobile object to execute processing. However, when applied directly to mobile objects that can move while communicating ad hoc, there are problems such as the fact that communication is not always conducted with the same party in the same area, leaving room for improvement.

The present invention has been made in consideration of the above-mentioned circumstances, and aims to provide a method of applying machine learning to provide suitable services or functions, etc. in mobile objects capable of user-to-user communication such as V2X.

One aspect of the present invention is a communication device (server 100) comprising a receiver (receiver 120) that collects first information from a mobile user equipment (UE200) capable of user-to-user communication and collects second information from a network, and a control unit (control unit 150) that obtains a learning parameter value by combining the first information and the second information.

Moreover, one aspect of the present invention includes a transmitting unit (radio transmitting unit 210) that transmits first information regarding a mobile user equipment (UE200) capable of user-to-user communication to a network device, and a receiving unit (radio receiving unit 220) that receives from the network device (server 100) a learned function learned using learning parameter values that combine the collected first information and second information regarding the user equipment collected from the network, or a result of execution of the learned function.

Another aspect of the present invention is a communication system comprising a mobile user equipment (UE200) capable of user-to-user communication, and a network device (server 100), wherein the user equipment (UE200) comprises a transmitting unit that transmits first information relating to the user equipment (UE200) to the network device (server 100), and the network device (server 100) comprises a receiving unit (receiving unit 120) that collects the first information from the user equipment (UE200) and collects second information from the network, and a control unit (control unit 150) that obtains a learning parameter value by combining the first information and the second information.

In addition, a communication method according to one aspect of the present invention includes a step of collecting first information from a mobile user equipment (UE200) capable of user-to-user communication and collecting second information from a network, and a step of obtaining a learning parameter value by combining the first information and the second information.

FIG. 1 is a diagram showing an overall schematic configuration of a wireless communication system 10 according to the present embodiment. FIG. 2 is a functional block diagram of the UE 200. FIG. 3 is a diagram illustrating an example of a functional block configuration of the MEC server 100-2. FIG. 4 is a diagram showing the positional relationship of various server devices 100-1, 2, and 3 in a V2X and three-layer network configuration. FIG. 5 is a diagram showing the advantages and disadvantages of the VAC, MEC, and RCC servers 100-1, 100-2, and 100-3. FIG. 6 is a diagram showing an example of the operation of supervised learning executed in the MEC server 100-2. FIG. 7 is a diagram showing an example of a decision tree for determining a target variable Y from an explanatory variable X. FIG. 8 is a diagram showing an overall flow including processing of the UE 200 on the mobile station side and processing of the MEC server 100-2 at the network edge. FIG. 9 is a diagram illustrating an example of a hardware configuration of the UE 200 and/or the MEC server 100-2.

Hereinafter, the embodiments will be described with reference to the drawings. Note that the same or similar symbols are used for the same functions and configurations, and the description thereof will be omitted as appropriate.

(1) Overall Schematic Configuration of Wireless Communication System Fig. 1 is an overall schematic configuration diagram of a wireless communication system 10 according to this embodiment. The wireless communication system 10 is a wireless communication system conforming to 5G New Radio (NR), and includes a Next Generation-Radio Access Network 20 (hereinafter, NG-RAN 20) including a 5G Mobile System, and a terminal 200 (hereinafter, UE 200).

NG-RAN 20 includes various server devices 100. In this embodiment, NG-RAN 20 includes a VAC (Vehicular Ad hoc Cloud) server 100-1, a MEC (Mobile Edge Cloud) server 100-2 installed in a wireless base station, etc., and an RCC (Remote Central Cloud) server 100-3. Note that the specific configuration of the wireless communication system 10, including the number of various server devices 100 and UEs, is not limited to the illustrated example. For example, in the following embodiment, a description may be given on the premise of a three-layer network in consideration of the configuration of the 5GS (5G system), but the server locations and the number of servers are not limited to this, and may be implemented with N servers (N=1 or more) in M layers (M=1 or more).

Note that NG-RAN 20 may actually be composed of multiple NG-RAN Nodes, specifically including a gNB (or ng-eNB) and connected to a 5G-compliant core network (5GC, not shown). Note that 5G Mobile System, NG-RAN 20, or 5GC may simply be expressed as a "network." Also, RCC server 100-3 may constitute part of 5GC.

The VAC server 100-1 is a cloud server realized by ad-hoc connections (e.g., V2X) between multiple mobile UEs 200.

The MEC server 100-2 is a radio base station conforming to 5G and performs radio communication with the UE 200 conforming to 5G. Here, the MEC server 100-2 may function as the "communication device" of the present application. In addition, the MEC server 100-2 and the UE 200 can support Massive MIMO (Multiple-Input Multiple-Output), which generates a more directional beam BM by controlling radio signals transmitted from multiple antenna elements, Carrier Aggregation (CA), which uses a bundle of multiple component carriers (CCs), Dual Connectivity (DC), which simultaneously communicates between the UE and each of two NG-RAN Nodes, and Integrated Access and Backhaul (IAB), which integrates the radio backhaul between radio communication nodes such as gNBs and the radio access to the UE.

The RCC server 100-3 is a central cloud server located remotely from the UE 200. As described above, in this embodiment, for the sake of explanation, an example is described in which the servers are configured in three hierarchical levels, the VAC server 100-1, the MEC server 100-2, and the RCC server 100-3. However, the present invention is not limited to this example and can be realized in various network configurations. For example, machine learning may be performed to select the optimal hierarchical level and/or the optimal server out of the N servers by applying the method to N servers in M hierarchical levels.

The wireless communication system 10 supports multiple frequency ranges (FR).

As an example, in this embodiment, the following four-step procedure is basically performed. That is, first, the UE 200 transmits first information (mobile information or user data, etc.) related to the user equipment (the own UE 200 and/or the other UE 200) to the MEC server 100-2 (step 1). The MEC server 100-2 combines the first information collected from the UE 200 with second information related to the network (network information or network data, etc.) collected from the network to obtain learning parameter values (step 2). Then, the MEC server 100-2 performs machine learning using the acquired learning parameter values to obtain a learned function (step 3). Finally, the UE 200 transmits a request to the MEC server 100-2 and receives the learned function or the execution result (calculation result) of the learned function (step 4). This allows a mobile entity such as the UE 200 to obtain a learning result according to its location. As described above, in this embodiment, as an example, an example in which the MEC server 100-2 of the servers 100 has a learning function (ML (Machine Learning)-Agent function) will be described; however, this example is not limiting, and instead of or in addition to this, the present invention may be implemented in which other servers 100 such as the VAC server 100-1 or the RCC server 100-3 or any other device has a learning function (ML-Agent function).

(2) Functional Block Configuration of the Wireless Communication System The functional block configuration of the wireless communication system 10 will now be described.

(2.1) UE200
First, the configuration of the UE 200 will be described. FIG. 2 is a functional block diagram of the UE 200. As shown in FIG. 2, the UE 200 includes a radio transmission unit 210, a radio reception unit 220, and a control unit 250. Here, the UE 200 is not limited to a moving body such as an automobile, and may be called an entity including infrastructure. Therefore, the connection between the UEs 200 may be called V2V (Vehicle-to-Vehicle), V2I (Vehicle-to-Infrastructure), V2X (Vehicle-to-Everything), or the like.

The radio transmission unit 210 and the radio reception unit 220 of the UE 200 transmit and receive radio signals conforming to NR. The UE 200 can support Massive MIMO, CA that uses a bundle of multiple CCs, DC that simultaneously communicates between the UE and each of two NG-RAN nodes, and ad-hoc communication between the UE 200.

In this embodiment, the wireless transmission unit 210 is a transmission unit that transmits first information related to the UE 200 to the MEC server 100-2. The first information is information related to the UE 200 itself and/or other UEs 200, and may be referred to as mobile information or user data. As a specific example of the first information, as described in the embodiment described later, the size of data, resources, time (speed), processing load, etc. required by the service or function are assumed, but are not limited to these, and may be any data related to the UE 200. That is, even if information seems unnecessary from a human perspective at first glance, it may be useful information for machine learning or artificial intelligence, so all such information may be included as the first information. As an example, for a service or function for avoiding traffic jams, information related to a mobile body such as vehicle speed, information related to weather such as wiper operation information, etc. are assumed as information types of the first information, but are not limited to these. Also, as an example, for a service or function for image recognition such as automatic driving, image data during driving, etc. are assumed as information types of the first information, but are not limited to these. Moreover, the wireless receiving unit 220 is a receiving unit that receives, from the MEC server 100-2, a learned function learned using the learning parameter values, or an execution result of the learned function, etc. The wireless transmitting unit 210 may transmit an uplink signal (UL signal) conforming to NR to a gNB, the server 100, etc., and the wireless receiving unit 220 may receive a downlink signal (DL signal) conforming to NR from the gNB, the server 100, etc.

Channels include control channels and data channels. Control channels include PDCCH (Physical Downlink Control Channel), PUCCH (Physical Uplink Control Channel), PRACH (Physical Random Access Channel), and PBCH (Physical Broadcast Channel). Data channels include PDSCH (Physical Downlink Shared Channel), PUSCH (Physical Uplink Shared Channel), and so on.

The control unit 250 is a control unit that controls each functional block that constitutes the UE 200. In particular, in this embodiment, the control unit 250 performs control to provide a service or function. As an example, the service or function may be a method (application, etc.) for finding the optimal resource in the shortest time, navigation such as traffic congestion avoidance, or an automatic driving algorithm including image recognition. The control unit 250 may perform control to provide a more appropriate service or function using the learned function received from the MEC server 100-2 or the execution result of the learned function.

(2.2) MEC server 100-2
Next, the functional block configuration of the MEC server 100-2 will be described. Note that the server device 100 other than the MEC server 100-2 may have the same configuration. FIG. 3 is a diagram showing an example of the functional block configuration of the MEC server 100-2. As shown in FIG. 3, the MEC server 100-2 includes a transmitting unit 110, a receiving unit 120, a NW IF unit 130, and a control unit 150. Note that in this embodiment, an example in which the "communication device" of the present invention is installed as a mobile edge cloud (MEC) server in a base station capable of communicating with a mobile body will be described, but the present invention is not limited to this, and may be installed closer to the center of the network, or closer to the user device.

In this embodiment, the transmitting unit 110 and the receiving unit 120 communicate directly or indirectly with the UE 200, etc. For example, the receiving unit 120 receives first information from the UE 200. The receiving unit 120 may also receive a request from the UE 200 (a request to transmit a learned function or a calculation result of the learned function, etc.).

The transmitting unit 110 may transmit a radio signal conforming to the 5G specifications. For example, when the transmitting unit 110 receives the above-mentioned request from the UE 200, the transmitting unit 110 may transmit to the UE 200 a learned function learned using a learning parameter value that combines the first information collected from the UE 200 and the second information collected from the network, or an execution result (calculation result, etc.) of the learned function.

The NW IF unit 130 provides a communication interface that realizes a connection with the network side such as NGC (e.g., 5G Core Network System entity or 5G System Control Entities). For example, the NW IF unit 130 may include interfaces such as N6, X2, Xn, N2, and N3. In this embodiment, the NW IF unit 130 provides a communication interface for collecting second information about the network from the network. As a specific example of the second information, as described in the embodiment described later, remaining resources, computing power, etc. are assumed, but are not limited to these, and may be any data related to the network. In other words, even if information seems unnecessary at first glance from a human perspective, it may be useful information for machine learning or artificial intelligence, so all such information may be included as the second information. As an example, as a service or function for traffic congestion avoidance or image recognition, information about the network in the direction of movement of the moving object (e.g., resources and computing power of the server at the hangover destination) is assumed as an information type of the second information, but is not limited to this.

The control unit 150 controls each functional block that constitutes the MEC server 100-2. In particular, in this embodiment, the control unit 150 controls learning.

Specifically, the control unit 270 obtains a learning parameter value that combines the first information and the second information. Furthermore, the control unit 270 may use the learning parameter value to perform learning for a service or function provided by the UE 200. For example, the control unit 270 may perform machine learning using the learning parameter value to obtain a learned function.

Here, the learning may be supervised learning, unsupervised learning, or reinforcement learning. Examples of learning algorithms that may be used include support vector machines (SVM), decision tree learning, neural networks (NN), genetic programming (GP), Bayesian networks, etc.

The learning target may be, for example, any service or function provided by UE200. In the following embodiment, for the sake of explanation, an example of optimizing a method for finding the optimal resource in the shortest time as the learning target is described, but the present invention is not limited to this example and can be used for various services or functions such as autonomous driving and traffic congestion avoidance. For example, when the learning target is image recognition for autonomous driving, UE200 can improve the accuracy of object recognition according to the characteristics of the area.

(3) Operation of Wireless Communication System Next, a description will be given of the operation of the wireless communication system 10. Specifically, a description will be given of an example of the operation of optimizing, by learning, a method for finding resources in the shortest time in a V2X and three-layer server network configuration.

(3.1) Overview of Operation Figure 4 is a diagram showing the positional relationship of various server devices 100-1, 2, and 3 in a V2X and three-layer network configuration. As shown in Figure 4, the MEC server 100-2 is usually placed near a network edge such as a RAN. On the other hand, the VAC server 100-1 is placed close to the UE 200, and the RCC server 100-3 is placed remotely from the UE 200. Here, Figure 5 is a diagram showing the characteristics of each of the VAC, MEC, and RCC servers 100-1, 2, and 3, including advantages and disadvantages.

As shown in Figure 5, in terms of resources and storage capacity, VAC is small, MEC is medium, and RCC is abundant. On the other hand, in terms of delay and reaction speed, VAC is very short, MEC is short, and RCC is slow. Also, in terms of the amount of signaling required, VAC is small, MEC is medium, and RCC is large.

Therefore, as shown in the bottom of Figure 4, three-tier server devices each have their own advantages and disadvantages, and there is a trade-off between them. For example, VAC has low transmission delay but limited resources. On the other hand, RCC has high transmission delay but abundant resources.

Therefore, UE200 can select an appropriate server device 100-1, 2, 3 depending on the content of the service or function to be provided (e.g., the required speed or resources), thereby leading to improvement of the service or function.

In this embodiment, the MEC server 100-2 combines the first information (mobile information) collected from the mobile side and the second information (network information) collected from the network side to obtain parameter values for learning, and performs machine learning to optimize the method of finding resources in the shortest time. By using the learning results, the UE 200 can select a low-latency server 100 according to the improvement of services and functions, that is, in this example, the required speed and load of the service or function. More specifically, the UE 200 can select the optimal destination (offload destination) from among the servers 100-1, 2, and 3 capable of distributed processing and distributed calculation, taking into account the amount of calculation, delay, load, etc.

(3.2) Operation Example Next, an operation example of the MEC server 100-2 that executes the above-mentioned machine learning will be described. Here, Fig. 6 is a diagram showing an operation example of supervised learning executed in the MEC server 100-2. Note that, among the attributes of X in Fig. 6, x represents the size of data, u represents resources, t represents time, L represents the amount of signaling, rq represents required, rm represents remain, lo represents total, and of represents offload.

6, the MEC server 100-2 executes machine learning using a support vector machine (SVM) with learning parameter values that combine first information (mobile body information) collected from the mobile body side and second information (network information) collected from the network side as training data, and obtains a trained SVM as a trained function (step A). More specifically, the MEC server 100-2 creates a trained SVM based on the attribute contents of the X vector and decision tree judgment with learning parameter values X (explanatory variables) that combine the first information and the second information as training data. Note that the learning method or learning function used by the MEC server 100-2 is not limited to SVM, and may use decision tree learning, neural network (NN), genetic programming (GP), Bayesian network, etc., and may perform unsupervised learning and reinforcement learning in addition to supervised learning.

Then, the MEC server 100-2 inputs test data X (explanatory variables) separate from the training data into the trained SVM and calculates the objective variable Y (step B).

Then, the MEC server 100-2 judges the effectiveness of the machine learning based on the evaluation result such as whether the obtained objective variable Y is appropriate or not (Step C). As an example, the MEC server 100-2 may adopt the SVM after learning if the obtained objective variable Y for the test data X (explanatory variable) is close to the ideal value, and may return the SVM to the state before the learning if it is below the reference value.

Here, FIG. 7 is a diagram showing an example of a decision tree for determining a target variable Y from an explanatory variable X. As described above, in this example, a decision tree for selecting the optimal server 100-1, 2, 3 from the state of mobile object information (first information) and network information (second information) as an example of a learning subject is described.

As shown in Figure 7, when the initial priority value P = 1, 2, or 3 is set as attribute 1 (initial priority) of the explanatory variable X vector, if P = 1, the VAC server 100-1 is selected, if P = 2, the MEC server 100-2 is selected, and if P = 3, the RCC server 100-3 is selected (step D). Note that if P = 1 (VAC server 100-1), the process proceeds to step E, and if P = 2 (MEC server 100-2), the process proceeds to step F.

Then, if attribute 2 (required resources) of the explanatory variable X vector is smaller than the reference value (Rem) for VAC resources, VAC server 100-1 is selected (step E).

Otherwise, for offloading, if attribute 2 (required resources) of explanatory variable X vector is less than the reference value (Rem) for MEC resources, MEC server 100-2 is selected (step F).

Otherwise, evaluate the degree of delay due to the near side or the far side. That is, if the MEC's time offload for attribute 3 (requested time) of the explanatory variable X vector is smaller than the near side, the RCC server 100-3 is selected (step G).

Otherwise, evaluate the amount of signaling that would result from selecting that server. That is, if the MEC load/offload for attribute 4 (amount requested) of the explanatory variable X vector is less than that of the neighboring side, then RCC server 100-3 is selected; otherwise, MEC server 100-2 is selected (step H).

In the above-mentioned operational example, an example of selecting one of the three-tiered servers 100-1, 2, 3 was described, but this is not limited to this, and it is also possible to select to use a combination of multiple servers (e.g., RCC server 100-3 and MEC server 100-2), as in cloud computing.

(3.2.1) Operation example 1
In this operation example 1, an overall flow including the processing of the UE 200 on the mobile side and the processing of the MEC server 100-2 at the network edge will be described. Here, Fig. 8 is a diagram showing an overall flow including the processing of the UE 200 on the mobile side and the processing of the MEC server 100-2 at the network edge.

As shown in FIG. 8, first, the UE 200 transmits the statistical data x _i as first information (mobile entity information) to the MEC server 100-2, which is a network agent (step 1a).

Then, the MEC server 100-2 combines the first information x _i collected from the mobile unit side and the second information collected from the network side to generate explanatory variable X vector elements X ₁ , X ₂ , . . . , X _M (step 1).

Then, the MEC server 100-2 executes learning using the explanatory variable X vector elements X ₁ , X ₂ , . . . , X _M to obtain a decision function f(X) (step 2).

Then, the MEC server 100-2 updates the function f(X) with the new explanatory variables (step 3).

Furthermore, if new first information or second information is obtained from the mobile device side and/or the network side (step 4, Yes), the above-mentioned processing is repeated.

On the other hand, on the mobile side, when UE 200 attempts to start a service (or function), it sends a request for the learning result (in this example, the learned function f(X)) to MEC server 100-2 (step A).

Then, when UE 200 obtains the learned function f(X) from MEC server 100-2 (step B), it selects server devices 100-1, 2, and 3 based on this learned function f(X) (step C).

If the selection result of the server device 100 satisfies a predetermined criterion (step D, Yes), the process ends. On the other hand, if the selection result does not satisfy the predetermined criterion (for example, if the delay or load is too large for the service or function), the UE 200 obtains a new learned function f(X) and repeats the above process.

The evaluation result of UE200 may be fed back to MEC server 100-2 during step 1a, etc. This allows MEC server 100-2 to obtain more appropriate learning results according to the environment, performance, load, etc. of UE200.

Through the above operations, UE200 can select the optimal destination (offload destination) from among servers 100-1, 2, 3 capable of distributed processing and distributed calculation, taking into account the amount of calculation, delay, load, etc.

(4) Actions and Effects According to the above-described embodiment, the following actions and effects can be obtained. Specifically, a communication device (any device having a learning function (ML-Agent function) among the servers 100 including the VAC server 100-1, the MEC server 100-2, and the RCC server 100-3) includes a receiver (receiver 120) that collects first information from a mobile user equipment (UE 200) capable of user-to-user communication (such as V2X) and collects second information from a network, and a controller (controller 150) that obtains a learning parameter value by combining the first information and the second information.

This allows the network information (second information) on the network side and the mobile information (first information) on the mobile side to be integrated to obtain learning parameter values. Therefore, appropriate learning results according to the network environment, etc., and the mobile side's required resources, time, etc. can be provided to UE200, etc. More specifically, a mobile object, etc. that may move while communicating ad hoc does not necessarily communicate with the same communication partner in the same area, but even in such a case, according to this embodiment, by combining the mobile object information and the network side information that are characteristic of that area, learning can be performed to provide a service or function that is suitable for the object, communication environment, etc. that are characteristic of that area.

In addition, the control unit (control unit 150) of the communication device (MEC server 100-2) uses the learning parameter values to perform learning for the service or function provided by UE 200. Therefore, appropriate learning results can be reflected in the service or function according to the resources, time, etc. required for the service or function provided by UE 200.

In addition, since a service or function is a method for finding the optimal resource in the shortest time, or a method for selecting the optimal destination from among devices capable of distributed processing or distributed calculation, taking into account the amount of calculation, delay, or load, the communication device (MEC server 100-2) can provide a method for finding the optimal destination or resource depending on the resources, time, amount of delay, amount of calculation, etc. required for the service or function provided by UE 200.

In addition, the communication device (MEC server 100-2, etc.) transmits the learned function, which is the learning result, or the execution result of the learned function to UE 200. This allows UE 200 to provide an appropriate service or function according to the required resources, time, etc.

(5) Other Embodiments The present invention has been described above with reference to examples. However, the present invention is not limited to these descriptions, and it will be obvious to those skilled in the art that various modifications and improvements are possible.

For example, unlicensed spectrum may be referred to by different names, such as license-exempt or licensed-assisted access (LAA).

The block diagrams (Figures 2 and 3) used to explain the above-mentioned embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and directly or indirectly connected (e.g., using wires, wirelessly, etc.) and these multiple devices. The functional blocks may be realized by combining the one device or the multiple devices with software.

Functions include, but are not limited to, judgement, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, election, establishment, comparison, assumption, expectation, regard, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the transmission function is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on the method of realization for each.

Furthermore, the above-mentioned UE 200 and/or MEC server 100-2 may function as a computer that performs processing of the wireless communication method of the present disclosure. FIG. 9 is a diagram showing an example of a hardware configuration of the UE 200 and/or MEC server 100-2. As shown in FIG. 9, the UE 200 and/or MEC server 100-2 may be configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, and a bus 1007.

In the following description, the term "apparatus" may be interpreted as a circuit, device, unit, etc. The hardware configuration of the apparatus may be configured to include one or more of the apparatuses shown in the figure, or may be configured to exclude some of the apparatuses.

The functional blocks of UE 200 and MEC server 100-2 (see Figures 2 and 3) are realized by any hardware element of the computer device or a combination of the hardware elements.

In addition, each function in UE 200 and MEC server 100-2 is realized by loading a specific software (program) onto hardware such as processor 1001 and memory 1002, so that processor 1001 performs calculations, controls communication by communication device 1004, and controls at least one of reading and writing of data in memory 1002 and storage 1003.

The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control unit, an arithmetic unit, registers, etc.

Furthermore, the processor 1001 reads out programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to these. As the programs, programs that cause a computer to execute at least a part of the operations described in the above-mentioned embodiments are used. Furthermore, the above-mentioned various processes may be executed by one processor 1001, or may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunications line.

The memory 1002 is a computer-readable recording medium and may be composed of at least one of, for example, a Read Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), a Random Access Memory (RAM), etc. The memory 1002 may be called a register, a cache, a main memory (main storage device), etc. The memory 1002 can store a program (program code), software module, etc. capable of executing a method according to one embodiment of the present disclosure.

Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a Compact Disc ROM (CD-ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, and the like. Storage 1003 may be referred to as an auxiliary storage device. The above-mentioned recording medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

The communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc.

The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize, for example, at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD).

The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one configuration (e.g., a touch panel).

In addition, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

Furthermore, the device may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

In addition, the notification of information is not limited to the aspects/embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI), Uplink Control Information (UCI), higher layer signaling (e.g., RRC signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB), System Information Block (SIB))), other signals, or a combination of these. In addition, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, etc.

Each aspect/embodiment described in the present disclosure may be applied to at least one of Long Term Evolution (LTE), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), Future Radio Access (FRA), New Radio (NR), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), and other suitable systems, and next-generation systems that are extended based on these. In addition, multiple systems may be combined (for example, a combination of at least one of LTE and LTE-A and 5G, etc.).

The processing steps, sequences, flow charts, etc. of each aspect/embodiment described in this disclosure may be reordered unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order and are not limited to the particular order presented.

In the present disclosure, a particular operation performed by a base station may be performed by its upper node in some cases. In a network consisting of one or more network nodes having a base station, it is clear that various operations performed for communication with a terminal may be performed by at least one of the base station and other network nodes other than the base station (e.g., MME or S-GW, etc., but are not limited to these). Although the above example illustrates a case where there is one other network node other than the base station, it may also be a combination of multiple other network nodes (e.g., MME and S-GW).

Information, signals (information, etc.) may be output from a higher layer (or a lower layer) to a lower layer (or a higher layer). They may be input and output via multiple network nodes.

The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or appended. The output information may be deleted. The input information may be transmitted to another device.

The determination may be based on a value represented by a single bit (0 or 1), a Boolean (true or false) value, or a numerical comparison (e.g., with a predetermined value).

Each aspect/embodiment described in this disclosure may be used alone, in combination, or switched depending on the execution. In addition, notification of specific information (e.g., notification that "X is the case") is not limited to being done explicitly, but may be done implicitly (e.g., not notifying the specific information).

Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and/or wireless technologies (such as infrared, microwave, etc.), then these wired and/or wireless technologies are included within the definition of a transmission medium.

The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

Note that the terms described in this disclosure and the terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and the symbol may be a signal (signaling). Also, the signal may be a message. Also, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

As used in this disclosure, the terms "system" and "network" are used interchangeably.

In addition, the information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values from a predetermined value, or may be expressed using other corresponding information. For example, radio resources may be indicated by an index.

The names used for the above-mentioned parameters are not limiting in any respect. Moreover, the formulas etc. using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not limiting in any respect.

In this disclosure, terms such as "base station (BS)", "wireless base station", "fixed station", "NodeB", "eNodeB (eNB)", "gNodeB (gNB)", "access point", "transmission point", "reception point", "transmission/reception point", "cell", "sector", "cell group", "carrier", and "component carrier" may be used interchangeably. A base station may also be referred to by terms such as a macrocell, small cell, femtocell, and picocell.

A base station can accommodate one or more (e.g., three) cells (also called sectors). When a base station accommodates multiple cells, the overall coverage area of the base station can be divided into multiple smaller areas, and each smaller area can also be provided with communication services by a base station subsystem (e.g., a small indoor base station (Remote Radio Head: RRH).

The term "cell" or "sector" refers to part or all of the coverage area of a base station and/or a base station subsystem that provides communication services within that coverage.

In this disclosure, terms such as "Mobile Station (MS)", "user terminal", "User Equipment (UE)", "terminal", etc. may be used interchangeably.

A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a communication device, etc. At least one of the base station and the mobile station may be a device mounted on a moving body, the moving body itself, etc. The moving body may be a vehicle (e.g., a car, an airplane, etc.), an unmanned moving body (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). At least one of the base station and the mobile station may be a device that does not necessarily move during communication operation. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

In addition, the base station in the present disclosure may be read as a mobile station (user terminal, the same applies below). For example, each aspect/embodiment of the present disclosure may be applied to a configuration in which communication between a base station and a mobile station is replaced with communication between multiple mobile stations (which may be called, for example, Device-to-Device (D2D), Vehicle-to-Everything (V2X), etc.). In this case, the mobile station may be configured to have the functions that a base station has. Furthermore, terms such as "uplink" and "downlink" may be read as terms corresponding to communication between terminals (for example, "side"). For example, the uplink channel, downlink channel, etc. may be read as a side channel.

Similarly, a mobile station in the present disclosure may be interpreted as a base station, in which case the base station may have the functions of a mobile station.
A radio frame may be composed of one or more frames in the time domain, where each of the one or more frames in the time domain may be referred to as a subframe.
A subframe may further be composed of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

Numerology may be a communication parameter that applies to at least one of the transmission and reception of a signal or channel. Numerology may indicate at least one of, for example, Subcarrier Spacing (SCS), bandwidth, symbol length, cyclic prefix length, Transmission Time Interval (TTI), number of symbols per TTI, radio frame structure, a particular filtering operation performed by the transceiver in the frequency domain, a particular windowing operation performed by the transceiver in the time domain, etc.

A slot may consist of one or more symbols in the time domain (such as Orthogonal Frequency Division Multiplexing (OFDM) symbols, Single Carrier Frequency Division Multiple Access (SC-FDMA) symbols, etc.). A slot may be a numerology-based unit of time.

A slot may include multiple minislots. Each minislot may consist of one or multiple symbols in the time domain. A minislot may also be called a subslot. A minislot may consist of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (or PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (or PUSCH) mapping type B.

Radio frame, subframe, slot, minislot, and symbol all represent time units for transmitting signals. Radio frame, subframe, slot, minislot, and symbol may each be referred to by a different name.

For example, one subframe may be called a transmission time interval (TTI), multiple consecutive subframes may be called a TTI, or one slot or one minislot may be called a TTI. In other words, at least one of the subframe and the TTI may be a subframe (1 ms) in existing LTE, a period shorter than 1 ms (e.g., 1-13 symbols), or a period longer than 1 ms. Note that the unit expressing the TTI may be called a slot, minislot, etc., instead of a subframe.

Here, TTI refers to, for example, the smallest time unit for scheduling in wireless communication. For example, in an LTE system, a base station schedules each user terminal by allocating radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) in TTI units. Note that the definition of TTI is not limited to this.

The TTI may be a transmission time unit for a channel-coded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the time interval (e.g., the number of symbols) in which a transport block, a code block, a code word, etc. is actually mapped may be shorter than the TTI.

When one slot or one minislot is called a TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the minimum time unit of scheduling. In addition, the number of slots (minislots) constituting the minimum time unit of scheduling may be controlled.

A TTI having a time length of 1 ms may be called a normal TTI (TTI in LTE Rel. 8-12), normal TTI, long TTI, normal subframe, normal subframe, long subframe, slot, etc. A TTI shorter than a normal TTI may be called a shortened TTI, short TTI, partial or fractional TTI, shortened subframe, short subframe, minislot, subslot, slot, etc.

Note that a long TTI (e.g., a normal TTI, a subframe, etc.) may be interpreted as a TTI having a time length exceeding 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length less than the TTI length of a long TTI and equal to or greater than 1 ms.

A resource block (RB) is a resource allocation unit in the time domain and frequency domain, and may include one or more consecutive subcarriers in the frequency domain. The number of subcarriers included in an RB may be the same regardless of the numerology, for example, 12. The number of subcarriers included in an RB may be determined based on the numerology.

In addition, the time domain of an RB may include one or more symbols and may be one slot, one minislot, one subframe, or one TTI in length. One TTI, one subframe, etc. may each be composed of one or more resource blocks.

In addition, one or more RBs may be referred to as a physical resource block (PRB), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, an RB pair, etc.

A resource block may also be composed of one or more resource elements (REs). For example, one RE may be a radio resource area of one subcarrier and one symbol.

A Bandwidth Part (BWP), which may also be referred to as a partial bandwidth, may represent a subset of contiguous common resource blocks (RBs) for a given numerology on a given carrier, where the common RBs may be identified by the index of the RBs relative to a common reference point of the carrier. PRBs may be defined in a given BWP and numbered within the BWP.

A BWP may include a BWP for the UL (UL BWP) and a BWP for the DL (DL BWP). One or more BWPs may be configured for a UE within one carrier.

At least one of the configured BWPs may be active, and the UE may not expect to transmit or receive a given signal/channel outside the active BWP. Note that the terms "cell", "carrier", etc. in this disclosure may be read as "BWP".

The above-mentioned structures of radio frames, subframes, slots, minislots, and symbols are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, as well as the number of symbols in a TTI, the symbol length, and the cyclic prefix (CP) length can be changed in various ways.

The terms "connected" and "coupled", or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access". As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and light (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

The reference signal may also be abbreviated as Reference Signal (RS) or may be called a pilot depending on the applicable standard.

As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

The "means" in the configuration of each of the above devices may be replaced with "part," "circuit," "device," etc.

Any reference to elements using designations such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed therein or that the first element must precede the second element in some way.

When used in this disclosure, the terms "include," "including," and variations thereof are intended to be inclusive, as is the term "comprising." Additionally, the term "or," as used in this disclosure, is not intended to be an exclusive or.

In this disclosure, where articles have been added by translation, such as a, an, and the in English, this disclosure may include that the nouns following these articles are in the plural form.

As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. In addition, "judgment" and "decision" can include considering resolving, selecting, choosing, establishing, comparing, etc., to be a "judgment" or "decision." In other words, "judgment" and "decision" can include considering some action to be a "judgment" or "decision." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

In this disclosure, the term "A and B are different" may mean "A and B are different from each other." In addition, the term may mean "A and B are each different from C." Terms such as "separate" and "combined" may also be interpreted in the same way as "different."

Although the present disclosure has been described in detail above, it is 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 spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

10. Wireless communication systems
20 NG-RAN
100 Server device
110 Transmitter
120 Receiving unit
130 NW IF Section
150 Control section
200UE
210 Radio transmitter
220 Radio receiving unit
250 Control section
1001 Processor
1002 Memory
1003 Storage
1004 Communication equipment
1005 Input Device
1006 Output device
1007 Bus

Claims (6)

a receiver for collecting first information about a mobile user device from a mobile user device capable of user-to-user communication and for collecting second information about the network from a network;
a control unit that performs machine learning using a support vector machine using a learning parameter value obtained by combining the first information and the second information as training data, and creates a trained support vector machine as a trained function ;
Equipped with
The first information includes at least one of a vehicle speed of an automobile, which is a moving body in which the user device is mounted, and operation information of a wiper of the automobile;
the second information includes information on a network in a moving direction of the mobile object;
The control unit inputs explanatory variables other than the teacher data into a trained support vector machine, calculates a response variable, and determines the effectiveness of machine learning based on an evaluation result of whether the response variable is appropriate . The communication device according to claim 1, wherein the control unit uses the learning parameter values to perform learning for a service or function provided by the user device. The communication device according to claim 2, wherein the service or function is a method for finding the optimal resource in the shortest time, or a method for selecting the optimal destination from among devices capable of distributed processing or distributed calculation, taking into account the amount of calculation, delay, or load. The communication device according to claim 1 , further comprising a transmission unit configured to transmit a trained function, which is a result of training, or a result of execution of the trained function, to the user device. A communication system including a mobile user device capable of user-to-user communication and a communication device,
The user device includes:
a transmitter configured to transmit first information relating to the user device to the communication device;
The communication device includes:
a receiver for collecting the first information from the user device and for collecting second information about the network from a network;
a control unit that performs machine learning using a support vector machine using a learning parameter value obtained by combining the first information and the second information as training data, and creates a trained support vector machine as a trained function ;
Equipped with
The first information includes at least one of a vehicle speed of an automobile, which is a moving body in which the user device is mounted, and operation information of a wiper of the automobile;
the second information includes information on a network in a moving direction of the mobile object;
The control unit inputs explanatory variables other than the teacher data into a trained support vector machine, calculates a response variable, and determines the effectiveness of machine learning based on an evaluation result of whether the response variable is appropriate . collecting first information from a mobile user device capable of user-to-user communication about the user device and collecting second information from a network about the network;
A step of performing machine learning using a support vector machine by using a learning parameter value obtained by combining the first information and the second information as training data, and creating a trained support vector machine as a trained function ;
Including,
The first information includes at least one of a vehicle speed of an automobile, which is a moving body in which the user device is mounted, and operation information of a wiper of the automobile;
the second information includes information on a network in a moving direction of the mobile object;
In the creating step, explanatory variables other than the teacher data are input to the trained support vector machine to calculate a response variable, and the effectiveness of the machine learning is judged based on an evaluation result of whether the response variable is appropriate.
Communication methods.

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